Patent Description:
The background description provided herein is for the purpose of generally presenting the context of the invention. Work of the presently named inventor(s), to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present invention.

Refrigeration systems are an essential part of many commercial building and dwellings. For example, food retailers may rely on refrigeration systems to ensure the quality and safety of food products. Many other businesses may have products or materials that must be refrigerated or maintained at a lowered temperature. HVAC systems allow people to remain comfortable where they shop, work or live.

Refrigeration systems, however, can require a significant amount of energy to operate. The cost for energy required to operate refrigeration systems can be significant. As such, it may be beneficial for refrigeration system users to closely monitor the performance and energy consumption of the refrigeration systems to maximize efficiency and reduce operational costs. Refrigeration system users may lack the expertise to accurately analyze system performance and energy consumption data to effectively manage energy consumption costs for the refrigeration system.

This section provides a general summary of the invention, and is not a comprehensive disclosure of its full scope or all of its features.

A system useful for understanding the invention is provided and includes a system controller for a refrigeration or HVAC system having a compressor rack with at least one compressor and a condensing unit with at least one condenser fan. The system controller monitors and controls operation of the refrigeration or HVAC system. The system also includes a rack controller in communication with the system controller, the rack controller monitoring and controlling operation of the compressor rack and determining compressor rack power consumption data. The system also includes a condensing unit controller in communication with the system controller. The condensing unit controller monitors and controls operation of the condensing unit and determining condensing unit power consumption data. The system controller receives the compressor rack power consumption data and the condensing unit power consumption data, determines a total power consumption of the refrigeration or HVAC system based on the compressor rack power consumption data and the condensing unit power consumption data, determines at least one of a predicted power consumption and a benchmark power consumption for the refrigeration system, compares the total power consumption with at least one of the predicted power consumption and the benchmark power consumption, and generates an alert based on the comparison.

In other features, the system controller can receive performance coefficients for the refrigeration or HVAC system and determine the predicted power consumption based on the performance coefficients and on operational data for the refrigeration or HVAC system.

In other features, the system controller can monitor power consumption data of the refrigeration or HVAC system over an initialization period and determined the benchmark power consumption based on the monitored power consumption data for the initialization period.

A method useful for understanding the invention is provided that includes monitoring and controlling, with a system controller, operation of a refrigeration or HVAC system having a compressor rack with at least one compressor and a condensing unit with at least one condenser fan. The method also includes monitoring and controller, with a rack controller in communication with the system controller, operation of the compressor. The method also includes determining, with the rack controller, compressor rack power consumption data for the compressor rack. The method also includes monitoring and controller, with a condensing unit controller in communication with the system controller, operation of the condensing unit. The method also includes determining, with the condensing unit controller, power consumption data for the condensing unit. The method also includes receiving, with the system controller, the compressor rack power consumption data and the condensing unit power consumption data. The method also includes determining, with the system controller, a total power consumption of the refrigeration or HVAC system based on the compressor rack power consumption data and the condensing unit power consumption data. The method also includes determining, with the system controller, at least one of a predicted power consumption and a benchmark power consumption for the refrigeration system. The method also includes comparing, with the system controller, the total power consumption with at least one of the predicted power consumption and the benchmark power consumption. The method also includes generating, with the system controller, an alert based on the comparison.

In other features, the method can include receiving, with the system controller, performance coefficients for the refrigeration or HVAC system.

In other features, the method can include determining, with the system controller, the predicted power consumption based on the performance coefficients and on operational data for the refrigeration or HVAC system.

Another system useful for understanding the invention is provided and includes a controller for a refrigeration or HVAC system having a compressor rack with at least one compressor and a condensing unit with at least one condenser fan, the system controller monitoring and controlling operation of the refrigeration or HVAC system. The controller determines compressor rack power consumption data corresponding to a power consumption of the compressor rack, determines condensing unit power consumption data corresponding to a power consumption of the condensing unit, determines a total power consumption of the refrigeration or HVAC system based on the compressor rack power consumption data and the condensing unit power consumption data, determines at least one of a predicted power consumption and a benchmark power consumption for the refrigeration system, compares the total power consumption with at least one of the predicted power consumption and the benchmark power consumption, and generates an alert based on the comparison.

In other features, the controller receives performance coefficients for the refrigeration or HVAC system and determines the predicted power consumption based on the performance coefficients and on operational data for the refrigeration or HVAC system.

In other features, the controller monitors power consumption data of the refrigeration or HVAC system over an initialization period and determines the benchmark power consumption based on the monitored power consumption data for the initialization period.

Another method useful for understanding the invention is provided and includes monitoring and controlling, with a controller, operation of a refrigeration or HVAC system having a compressor rack with at least one compressor and a condensing unit with at least one condenser fan. The method also includes monitoring and controller, with the system controller, operation of the compressor. The method also includes determining, with the controller, compressor rack power consumption data for the compressor rack. The method also includes monitoring and controller, with the system controller, operation of the condensing unit. The method also includes determining, with the controller, power consumption data for the condensing unit. The method also includes determining, with the controller, a total power consumption of the refrigeration or HVAC system based on the compressor rack power consumption data and the condensing unit power consumption data. The method also includes determining, with the controller, at least one of a predicted power consumption and a benchmark power consumption for the refrigeration system. The method also includes comparing, with the controller, the total power consumption with at least one of the predicted power consumption and the benchmark power consumption. The method also includes generating, with the controller, an alert based on the comparison.

In other features, the method also includes receiving, with the controller, performance coefficients for the refrigeration or HVAC system and determining, with the controller, the predicted power consumption based on the performance coefficients and on operational data for the refrigeration or HVAC system.

In other features, the method also includes monitoring, with the controller, power consumption data of the refrigeration or HVAC system over an initialization period and determining, with the controller, the benchmark power consumption based on the monitored power consumption data for the initialization period.

In other features, the method can include monitoring, with the system controller, power consumption data of the refrigeration or HVAC system over an initialization period and determining, with the system controller, the benchmark power consumption based on the monitored power consumption data for the initialization period.

Another system useful for understanding the invention is provided and includes a system controller for a refrigeration or HVAC system having a compressor rack with at least one compressor and a condensing unit with at least one condenser fan, the system controller monitoring and controlling operation of the refrigeration or HVAC system. The system also includes a rack controller in communication with the system controller, the rack controller monitoring and controlling operation of the compressor rack and determining compressor rack power consumption data. The system also includes a condensing unit controller in communication with the system controller, the condensing unit controller monitoring and controlling operation of the condensing unit and determining condensing unit power consumption data. The system controller receives the compressor rack power consumption data and the condensing unit power consumption data, determines a total power consumption of the refrigeration or HVAC system based on the compressor rack power consumption data and the condensing unit power consumption data, and modifies operation of at least one of the compressor rack and the condensing unit to minimize the total power consumption of the refrigeration or HVAC system.

Another method useful for understanding the invention is provided and includes monitoring and controlling, with a system controller, a refrigeration or HVAC system having a compressor rack with at least one compressor and a condensing unit with at least one condenser fan. The method also includes monitoring and controller, with a rack controller in communication with the system controller, operation of the compressor rack. The method also includes determining, with the rack controller, compressor rack power consumption data. The method also includes monitoring and controller, with a condensing unit controller in communication with the system controller, operation of the condensing unit. The method also includes determining, with the condensing unit controller, condensing unit power consumption data. The method also includes receiving, with the system controller, the compressor rack power consumption data and the condensing unit power consumption data. The method also includes determining, with the system controller, a total power consumption of the refrigeration or HVAC system based on the compressor rack power consumption data and the condensing unit power consumption data. The method also includes modifying, with the system controller, operation of at least one of the compressor rack and the condensing unit to minimize the total power consumption of the refrigeration or HVAC system.

In accordance with the present invention, a system is provided and includes a system controller for a refrigeration or HVAC system having a compressor rack with a plurality of compressors and a condensing unit with a plurality of condenser fans, the system controller monitoring and controlling operation of the refrigeration or HVAC system. The system also includes a rack controller in communication with the system controller, the rack controller monitoring and controlling operation of the compressor rack. The system also includes a condensing unit controller in communication with the system controller, the condensing unit controller monitoring and controlling operation of the condensing unit. The system controller determines a startup power demand for each compressor of the plurality of compressors and each condenser fan of the plurality of condenser fans and determines a startup sequence and timing for the plurality of compressors and the plurality of condenser fans to limit peak power demand during a startup operation to be below a predetermined power threshold.

In accordance with the present invention, a method is provided and includes monitoring and controlling, with a system controller, a refrigeration or HVAC system having a compressor rack with a plurality of compressors and a condensing unit with a plurality of condenser fans. The method also includes monitoring and controller, with a rack controller in communication with the system controller, operation of the compressor rack. The method also includes monitoring and controller, with a condensing unit controller in communication with the system controller, operation of the condensing unit. The method also includes determining, with the system controller, a startup power demand for each compressor of the plurality of compressors and each condenser fan of the plurality of condenser fans. The method also includes determining, with the system controller, a startup sequence and timing for the plurality of compressors and the plurality of condenser fans to limit peak power demand during a startup operation to be below a predetermined power threshold.

Another system useful for understanding the invention is provided and includes a system controller for a refrigeration or HVAC system having a compressor rack with a plurality of compressors and a condensing unit with a plurality of condenser fans, the system controller monitoring and controlling operation of the refrigeration or HVAC system. The system also includes a rack controller in communication with the system controller, the rack controller monitoring and controlling operation of the compressor rack. The system also includes a condensing unit controller in communication with the system controller, the condensing unit controller monitoring and controlling operation of the condensing unit. The system controller receives a signal corresponding to limiting power consumption and selects at least one compressor from the plurality of compressors and at least one condenser fan from the plurality of condenser fans to operate to maximize refrigeration capacity while maintaining a total power consumption below a power threshold associated with the signal.

In other features, the signal can be received from a utility as a demand shed signal and wherein the power threshold is associated with the demand shed signal.

In other features, the signal can be received from an onsite power generation device and wherein the power threshold corresponds to an amount of power generated by the onsite power generation device.

In other features, the signal can be received from an onsite power generation device and wherein the power threshold corresponds to a predicted amount of power to be generated by the onsite power generation device.

Another method useful for understanding the invention is provided and includes monitoring and controlling, with a system controller, a refrigeration or HVAC system having a compressor rack with a plurality of compressors and a condensing unit with a plurality of condenser fans. The method also includes monitoring and controller, with a rack controller in communication with the system controller, operation of the compressor rack. The method also includes monitoring and controller, with a condensing unit controller in communication with the system controller, operation of the condensing unit. The method also includes receiving, with the system controller, a signal corresponding to limiting power consumption. The method also includes selecting, with the system controller, at least one compressor from the plurality of compressors and at least one condenser fan from the plurality of condenser fans to operate to maximize refrigeration capacity while maintaining a total power consumption below a power threshold associated with the signal.

In other features, the signal can be received from an on-site power generation device and wherein the power threshold corresponds to a predicted amount of power to be generated by the on-site power generation device.

Another system useful for understanding the invention is provided and includes a system controller for a refrigeration or HVAC system having a compressor rack with at least one compressor and a condensing unit with at least one condenser fan, the system controller monitoring and controlling operation of the refrigeration or HVAC system. The system also includes a rack controller in communication with the system controller, the rack controller monitoring and controlling operation of the compressor rack and determining compressor rack power consumption data. The system also includes a condensing unit controller in communication with the system controller, the condensing unit controller monitoring and controlling operation of the condensing unit and determining condensing unit power consumption data. The system controller receives the compressor rack power consumption data and the condensing unit power consumption data, receives forecast weather data for a future time period, determines a predicted total power consumption of the refrigeration or HVAC system based on the forecast weather data, compares the predicted total power consumption of the refrigeration or HVAC system with a predetermined power threshold, and generates an alert when the predicted total power consumption is greater than the predetermined power threshold.

In other features, the system controller modifies operation of the refrigeration system prior to the future time period to reduce power consumption of the refrigeration system during the future time period.

Another method useful for understanding the invention is provided and includes monitoring and controlling, with a system controller, operation of a refrigeration or HVAC system having a compressor rack with at least one compressor and a condensing unit with at least one condenser fan. The method also includes monitoring and controller, with a rack controller in communication with the system controller, operation of the compressor. The method also includes determining, with the rack controller, compressor rack power consumption data for the compressor rack. The method also includes monitoring and controller, with a condensing unit controller in communication with the system controller, operation of the condensing unit. The method also includes determining, with the condensing unit controller, power consumption data for the condensing unit. The method also includes receiving, with the system controller, the compressor rack power consumption data and the condensing unit power consumption data. The method also includes receiving, with the system controller, forecast weather data for a future time period. The method also includes determining, with the system controller, a predicted total power consumption of the refrigeration or HVAC system based on the forecast weather data. The method also includes comparing, with the system controller, the predicted total power consumption of the refrigeration or HVAC system with a predetermined power threshold. The method also includes generating, with the system controller, an alert when the predicted total power consumption is greater than the predetermined power threshold.

In other features, the method can also include modifying, with the system controller, operation of the refrigeration system prior to the future time period to reduce power consumption of the refrigeration system during the future time period.

Another system useful for understanding the invention is provided and includes a monitoring device for a refrigeration or HVAC system having a compressor rack with at least one compressor and a condensing unit with at least one condenser fan, the monitoring device monitoring and controlling operation of the refrigeration or HVAC system. The system also includes a rack controller in communication with the monitoring device, the rack controller monitoring and controlling operation of the compressor rack. The system also includes a condensing unit controller in communication with the monitoring device, the condensing unit controller monitoring and controlling operation of the condensing unit. The monitoring device monitors operational data, including at least one of a suction pressure, a discharge pressure, a suction temperature, a discharge temperature, a liquid temperature, and power consumption data for the HVAC system, and determines at least one of a coefficient of performance, a capacity, a power input, an isentropic efficiency percentage, and a mass flow rate based on the monitored operational data.

Another method useful for understanding the invention is provided and includes monitoring and controlling, with a monitoring device, operation of a refrigeration or HVAC system having a compressor rack with at least one compressor and a condensing unit with at least one condenser fan. The method also includes monitoring and controller, with a rack controller in communication with the monitoring device, operation of the compressor rack. The method also includes monitoring and controlling, with a condensing unit controller in communication with the monitoring device, operation of the condensing unit. The method also includes monitoring, with the monitoring device, operational data, including at least one of a suction pressure, a discharge pressure, a suction temperature, a discharge temperature, a liquid temperature, and power consumption data for the HVAC system. The method also includes determining, with the monitoring device, at least one of a coefficient of performance, a capacity, a power input, an isentropic efficiency percentage, and a mass flow rate based on the monitored operational data.

Another system useful for understanding the invention is provided and includes a controller for a refrigeration or HVAC system having a compressor rack with at least one compressor. The controller includes a monitoring module configured to monitor power consumption of a compressor in the compressor rack based on data received from a power meter associated with the compressor, a supply voltage for the compressor, or amperage of the compressor. The system further includes a tracking module configured to track performance of the compressor based on the power consumption of the compressor.

In other features, the monitoring module further includes a voltage determining module, a power factor module, and a power consumption module. The voltage determining module is configured to determine the supply voltage for the compressor based on power supplied to the compressor rack and a number of compressors in the compressor rack. The power factor module is configured to adjust a power factor for the compressor based on the supply voltage and a voltage rating of the compressor. The power consumption module is configured to determine the power consumption of the compressor based on the adjusted power factor, the supply voltage for the compressor, and the amperage of the compressor.

In other features, the monitoring module further includes a power consumption module and an error correction module. The power consumption module is configured to estimate the power consumption of each compressor in the compressor rack based on the amperage of the compressor, a voltage rating of the compressor, and a power factor rating of the compressor. The error correction module is configured to determine an error correction factor to apply to the estimated power consumption of each compressor such that a sum of power consumption values of each compressor and other loads of the refrigeration or HVAC system equals a measured aggregate power consumption of the compressor rack.

Another system useful for understanding the invention is provided and includes a controller for a refrigeration or HVAC system having a compressor rack with at least one compressor. The controller communicates with a performance tracking module configured to track performance of a compressor in the compressor rack. In response to rated performance data for the compressor being unavailable, the performance tracking module is configured to generate baseline data for the compressor and to assess the performance of the compressor by comparing operational data of the compressor to the baseline data for the compressor. In response to the rated performance data for the compressor being available, the performance tracking module is configured to assess the performance of the compressor by comparing the operational data of the compressor to the rated performance data for the compressor.

In other features, the controller includes the performance tracking module.

In other features, a remote controller includes the performance tracking module.

In other features, the performance tracking module includes a baseline data module and a monitoring module. The baseline data module is configured to generate the baseline data for the compressor based on data received from the compressor immediately following installation of compressor. The monitoring module is configured to assess the performance of the compressor by comparing the baseline data to the operational data of the compressor obtained subsequent to developing the baseline data.

In other features, the performance tracking module includes a regression-based monitoring module configured to perform a regression analysis on the rated performance data and the data obtained from the compressor during operation, and assess the performance of the compressor based on the regression analysis.

In other features, the regression-based monitoring module includes a benchmark generating module and an analyzing module. The benchmark generating module is configured to generate a benchmark polynomial and a benchmark hull. The analyzing module is configured to analyze data obtained from the compressor during operation using the benchmark polynomial and the benchmark hull and to assess the performance of the compressor based on the analysis.

In other features, the system further includes an optimizing module configured to select only statistically significant variables affecting a selected one of the rated performance data and to eliminate statistically insignificant variables, and to optimize the benchmark polynomial using the selected variables.

In other features, the system further includes an outlier detecting module configured to detect outliers in the data obtained from the compressor during operation and to remove outliers with largest deviation.

In other features, the system further includes a comparing module configured to compare the benchmark polynomial and the benchmark hull with historical benchmark polynomial and hull data and to assess the performance of the compressor based on the comparison.

Another method useful for understanding the invention is provided and includes controlling, with a controller, a refrigeration or HVAC system having a compressor rack with at least one compressor. The method further includes monitoring, with a monitoring module, power consumption of a compressor in the compressor rack based on data received from a power meter associated with the compressor, a supply voltage for the compressor, or amperage of the compressor. The method further includes tracking, with a tracking module, performance of the compressor based on the power consumption of the compressor.

In other features, the monitoring the power consumption of the compressor in the compressor rack further includes the following: determining, with a voltage determining module, the supply voltage for the compressor based on power supplied to the compressor rack and a number of compressors in the compressor rack; adjusting, with a power factor module, a power factor for the compressor based on the supply voltage and a voltage rating of the compressor; and determining, with a power consumption module, the power consumption of the compressor based on the adjusted power factor, the supply voltage for the compressor, and the amperage of the compressor.

In other features, the method further includes estimating, with a power consumption module, the power consumption of each compressor in the compressor rack based on the amperage of the compressor, a voltage rating of the compressor, and a power factor rating of the compressor. The method further includes determining, with an error correction module, an error correction factor to apply to the estimated power consumption of each compressor such that a sum of power consumption values of each compressor and other loads of the refrigeration or HVAC system equals a measured aggregate power consumption of the compressor rack.

Another method useful for understanding the invention is provided and includes controlling, with a controller, a refrigeration or HVAC system having a compressor rack with at least one compressor. The method further includes communicating with a performance tracking module configured to track performance of a compressor in the compressor rack. The method further includes, in response to rated performance data for the compressor being unavailable, generating, with the performance tracking module, baseline data for the compressor and assessing the performance of the compressor by comparing operational data of the compressor to the baseline data for the compressor. The method further includes, in response to the rated performance data for the compressor being available, assessing, with the performance tracking module, the performance of the compressor by comparing the operational data of the compressor to the rated performance data for the compressor.

In other features, the method further includes generating, with a baseline data module, the baseline data for the compressor based on data received from the compressor immediately following installation of compressor; and assessing, with a monitoring module, the performance of the compressor by comparing the baseline data to the operational data of the compressor obtained subsequent to developing the baseline data.

In other features, the method further includes performing, with a regression-based monitoring module, a regression analysis on the rated performance data and the data obtained from the compressor during operation; and assessing, with the regression-based monitoring module, the performance of the compressor based on the regression analysis.

In other features, the method further includes generating, with a benchmark generating module, a benchmark polynomial and a benchmark hull; and analyzing, with an analyzing module, data obtained from the compressor during operation using the benchmark polynomial and the benchmark hull and assessing the performance of the compressor based on the analysis.

In other features, the method further includes selecting, with an optimizing module, only statistically significant variables affecting a selected one of the rated performance data and eliminating statistically insignificant variables; and optimizing, with the optimizing module, the benchmark polynomial using the selected variables.

In other features, the method further includes detecting, with an outlier detecting module, outliers in the data obtained from the compressor during operation and removing outliers with largest deviation.

In other features, the method further includes comparing, with a comparing module, the benchmark polynomial and the benchmark hull with historical benchmark polynomial and hull data and assessing the performance of the compressor based on the comparison.

The drawings described herein are for illustrative purposes only of selected embodiments and not all possible implementations, and are not intended to limit the scope of the present invention.

With reference to <FIG>, an exemplary refrigeration system <NUM> is shown and includes a plurality of compressors <NUM> piped together in a compressor rack <NUM> with a common suction manifold <NUM> and a discharge header <NUM>. While <FIG> shows an example refrigeration system <NUM>, the teachings of the present disclosure also apply, for example, to HVAC systems.

Each compressor <NUM> has an associated compressor controller <NUM> that monitors and controls operation of the compressor <NUM>. For example, the compressor controller <NUM> may monitor electric power, voltage, and/or current delivered to the compressor <NUM> with a power sensor, a voltage sensor, and/or a current sensor. Further, the compressor controller <NUM> may also monitor For example, a discharge outlet of each compressor <NUM> can include a respective discharge temperature sensor <NUM>. A discharge pressure sensor can be used in addition to, or in place of, the discharge temperature sensor <NUM>. An input to the suction manifold <NUM> can include both a suction pressure sensor <NUM> and a suction temperature sensor <NUM>. Further, a discharge outlet of the discharge header <NUM> can include an associated discharge pressure sensor <NUM>. A discharge temperature sensor can be used in addition to, or in place of, the discharge pressure sensor <NUM>. As described in further detail below, the various sensors can be implemented for managing and monitoring energy consumption of the compressors <NUM> in the compressor rack <NUM>.

A rack controller <NUM> may monitor and control operation of the compressor rack <NUM> via communication with each of the compressor controllers <NUM>. For example, the rack controller <NUM> may instruct individual compressors <NUM> to turn on or turn off through communication with the compressor controllers <NUM>. Additionally, the rack controller <NUM> may instruct variable capacity compressors to increase or decrease capacity through communication with the compressor controllers <NUM>. In addition, the rack controller <NUM> may receive data indicating the electric power, voltage, and/or current delivered to each of the compressors <NUM> from the compressor controllers <NUM>. Further, the rack controller <NUM> may also receive data indicating the suction or discharge temperatures or pressures of each of the compressors <NUM> from the compressor controllers <NUM>. Additionally or alternatively, the rack controller <NUM> may communicate directly with the suction or discharge temperature or pressure sensors to receive such data. Additionally, the rack controller <NUM> may be in communication with other suction and discharge temperature and pressure sensors, including, for example, discharge pressure sensor <NUM>, suction pressure sensor <NUM>, and suction temperature sensor <NUM>.

Electric power may be delivered to the compressor rack <NUM> from a power supply <NUM> for distribution to the individual compressors <NUM>. A rack power sensor <NUM> may sense the amount of power delivered to the compressor rack <NUM>. A current sensor or a voltage sensor may be used in place of or in addition to the power sensor <NUM>. The rack controller <NUM> may communicate with the rack power sensor <NUM> and monitor the amount of power delivered to the compressor rack <NUM>. Alternatively, the rack power sensor <NUM> may be omitted and the total power delivered to the compressor rack <NUM> may be determined based on the power data for the power delivered to each of the individual compressors <NUM> as determined by the compressor controllers <NUM>.

The compressor rack <NUM> compresses refrigerant vapor that is delivered to a condensing unit <NUM> having a condenser <NUM> where the refrigerant vapor is liquefied at high pressure. Condenser fans <NUM> may enable improved heat transfer from the condenser <NUM>. The condensing unit <NUM> can include an associated ambient temperature sensor <NUM>, a condenser temperature sensor <NUM>, and/or a condenser discharge pressure sensor <NUM>. Each of the condenser fans <NUM> may include a condenser fan power sensor <NUM> that senses the amount of power delivered to each of the condenser fans <NUM>. A current sensor or a voltage sensor may be used in place of or in addition to the condenser fan power sensor <NUM>.

A condensing unit controller <NUM> may monitor and control operation of the condenser fans <NUM>. For example, the condensing unit controller <NUM> may turn on or turn off individual condenser fans <NUM> and/or increase or decrease capacity of any variable speed condenser fans <NUM>. In addition, the condensing unit controller <NUM> may receive data indicating the electric power delivered to each of the condenser fans <NUM> through communication with the condenser fan power sensors <NUM>. Additionally, the condensing unit controller <NUM> may be in communication with the other condensing unit sensors, including, for example, the ambient temperature sensor <NUM>, the condenser temperature sensor <NUM>, and the condenser discharge pressure sensor <NUM>.

Electric power may be delivered to the condensing unit <NUM> from the power supply <NUM> for distribution to the individual condenser fans <NUM>. A condensing unit power sensor <NUM> may sense the amount of power delivered to the condensing unit <NUM>. A current sensor or a voltage sensor may be used in place of or in addition to the condensing unit power sensor <NUM>. The condensing unit controller <NUM> may communicate with the condensing unit power sensor <NUM> and monitor the amount of power delivered to the condensing unit <NUM>.

The high-pressure liquid refrigerant from the condensing unit <NUM> may be delivered to refrigeration cases <NUM>. For example, refrigeration cases <NUM> may include a group <NUM> of refrigeration cases <NUM>. The refrigeration cases <NUM> may be refrigerated or frozen food cases at a grocery store, for example. Each refrigeration case <NUM> may include an evaporator <NUM> and an expansion valve <NUM> for controlling the superheat of the refrigerant and an evaporator temperature sensor <NUM>. The refrigerant passes through the expansion valve <NUM> where a pressure drop causes the high pressure liquid refrigerant to achieve a lower pressure combination of liquid and vapor. As hot air from the refrigeration case <NUM> moves across the evaporator <NUM>, the low pressure liquid turns into gas. The low pressure gas is then delivered back to the compressor rack <NUM>, where the refrigeration cycle starts again.

A case controller <NUM> may monitor and control operation of the evaporators <NUM> and/or the expansion valves <NUM>. For example, the case controller <NUM> may turn on or turn off evaporator fans of the evaporators <NUM> and/or increase or decrease capacity of any variable speed evaporator fans. The case controller <NUM> may be in communication with the evaporator temperature sensor <NUM> and receive evaporator temperature data.

Electric power may be delivered to the group <NUM> of refrigeration cases <NUM> from the power supply <NUM> for distribution to the individual condenser fans <NUM>. A refrigeration case power sensor <NUM> may sense the amount of power delivered to the group <NUM> of refrigeration cases <NUM>. A current sensor or a voltage sensor may be used in place of or in addition to the refrigeration case power sensor <NUM>. The case controller <NUM> may communicate with the refrigeration case power sensor <NUM> and monitor the amount of power delivered to the group <NUM> of refrigeration cases <NUM>.

As discussed above, while <FIG> shows an example refrigeration system <NUM>, the teachings of the present disclosure also apply, for example, to HVAC systems, including, for example, air conditioning and heat pump systems. In the example of an HVAC system, the evaporators <NUM> would be installed in air handler units instead of in refrigeration cases <NUM>.

A system controller <NUM> monitors and controls operation of the entire refrigeration system <NUM> through communication with each of the rack controller <NUM>, condensing unit controller <NUM>, and the case controller <NUM>. Alternatively, the rack controller <NUM>, condensing unit controller <NUM>, and/or case controller <NUM> could be omitted and the system controller <NUM> could directly control the compressor rack <NUM>, condensing unit <NUM>, and/or group <NUM> of refrigeration cases <NUM>. The system controller <NUM> can receive the operation data of the refrigeration system <NUM>, as sensed by the various sensors, through communication with the rack controller <NUM>, condensing unit controller <NUM>, and/or case controller <NUM>. For example, the system controller can receive data regarding the various temperatures and pressures of the system and regarding electric power, current, and/or voltage delivered to the various system components. Alternatively, some or all of the various sensors may be configured to communicate directly with the system controller. For example, the ambient temperature sensor <NUM> may communicate directly with the system controller <NUM> and provide ambient temperature data.

The system controller <NUM> may coordinate operation of the refrigeration system, for example, by increasing or decreasing capacity of various system components. For example, the system controller <NUM> may instruct the rack controller <NUM> to increase or decrease capacity by activating or deactivating a compressor <NUM> or by increasing or decreasing capacity of a variable capacity compressor <NUM>. The system controller <NUM> may instruct the condensing unit controller <NUM> to increase or decrease condensing unit capacity by activating or deactivating a condenser fan <NUM> or by increasing or decreasing a speed of a variable speed condenser fan <NUM>. The system controller <NUM> may instruct the case controller <NUM> to increase or decrease evaporator capacity by activating or deactivating an evaporator fan of an evaporator <NUM> or by increasing or decreasing a speed of a variable speed evaporator fan. The system controller <NUM> may include a computer-readable medium, such as a volatile or non-volatile memory, to store instructions executable by a processor to carry out the functionality described herein to monitor and control operation of the refrigeration system <NUM>.

The system controller <NUM> may be, for example, an E2 RX refrigeration controller available from Emerson Climate Technologies Retail Solutions, Inc. of Kennesaw, Georgia. If the system is an HVAC system instead of a refrigeration system, the system controller <NUM> may be, for example, an E2 BX HVAC and lighting controller also available from Emerson Climate Technologies Retail Solutions, Inc. of Kennesaw, Georgia. Further, any other type of programmable controller that may be programmed with the functionality described in the present disclosure can also be used.

The system controller <NUM> may be in communication with a communication device <NUM>. The communication device <NUM> may be, for example, a desktop computer, a laptop, a tablet, a smartphone or other computing device with communication/networking capabilities. The communication device <NUM> may communicate with the system controller <NUM> via a local area network at the facility location of the refrigeration system <NUM>. The communication device <NUM> may also communicate with the system controller <NUM> via a wide area network, such as the internet.

The communication device <NUM> may communicate with the system controller <NUM> to receive and view operational data of the refrigeration system <NUM>, including, for example, energy or performance data for the refrigeration system <NUM>.

The system controller <NUM> may also communicate with a remote monitor <NUM> via, for example, a wide area network, such as the internet, or via phone lines, cellular, and/or satellite communication. The remote monitor <NUM> may communicate with multiple system controllers <NUM> associated with multiple refrigeration or HVAC systems. The remote monitor <NUM> may also be accessible to a communication device <NUM>, such as a desktop computer, a laptop, a tablet, a smartphone or other computing device with communication/networking capabilities. The communication device <NUM> may communicate with the remote monitor <NUM> to receive and view operational data for one or more refrigeration or HVAC systems, including, for example, energy or performance data for the refrigeration or HVAC systems.

The system controller <NUM> can monitor the actual power consumption of the refrigeration system <NUM>, including the compressor rack <NUM>, the condensing unit <NUM>, and the refrigeration cases <NUM>, and compare the actual power consumption of the refrigeration system <NUM> with a predicted power consumption or with a benchmark power consumption for the refrigeration system <NUM>.

With reference to <FIG>, a control algorithm <NUM> is shown for comparing actual power consumption with predicted power consumption or benchmark power consumption. The control algorithm <NUM> may be performed, for example, by the system controller <NUM> and starts at <NUM>. At <NUM>, the system controller <NUM> receives actual power consumption data for the refrigeration system <NUM>. For example, as discussed above, the system controller <NUM> can receive power consumption data regarding the compressor rack <NUM>, the condensing unit <NUM>, and the group <NUM> of refrigeration cases <NUM> from the rack controller <NUM>, the condensing unit controller <NUM>, and the case controller <NUM>. At <NUM>, the system controller <NUM> determines predicted or benchmark power consumption for the system based on operational data for the refrigeration system <NUM>. Further details for determining the predicted or benchmark power consumption for the system are discussed below with reference to <FIG>.

At <NUM>, the system controller <NUM> compares the predicted or benchmark power consumption with the actual power consumption for the system. At <NUM>, the system controller <NUM> determines whether the difference between the actual power consumption and the predicted or benchmark power consumption is greater than a predetermined threshold. At <NUM>, when the difference is greater than the predetermined threshold, the system controller <NUM> can generate an alert. For example, the system controller <NUM> may communicate an alert to the communication device <NUM> or to the remote monitor <NUM> for subsequent communication to the communication device <NUM>. At <NUM>, when the difference is not greater than the predetermined threshold, the control algorithm <NUM> proceeds to <NUM>. At <NUM>, the control algorithm <NUM> ends.

In addition to generating alerts based on the difference between the actual power consumption and the benchmark or predicted power consumption, the system controller <NUM> can also determine a trend over time and provide a user, via the communication device <NUM>, with information regarding the trend. For example, the system controller <NUM> may predict a future date, based on the current trend, when the difference will be greater than a predetermined threshold. The difference between the actual power consumption and the benchmark or predicted power consumption can also be used to calculate a system or component health score. Additionally, while the control algorithm <NUM> is described with reference to the power consumption for the entire refrigeration system <NUM>, additionally or alternatively, the system controller <NUM> could perform the control algorithm <NUM> for one or more components of the refrigeration system <NUM>, including one or more of the compressor rack <NUM>, the condensing unit <NUM>, and/or the refrigeration cases <NUM>.

With reference to <FIG>, a control algorithm <NUM> is shown for determining predicted power consumption based on performance coefficients for system components and operational data for the system. The functionality of <FIG>, for example, is encapsulated at <NUM> of <FIG>. The control algorithm <NUM> may be performed by the system controller <NUM> and starts at <NUM>. At <NUM>, the system controller <NUM> receives performance coefficient data for the system components of the refrigeration system <NUM>. The performance coefficients are published by system component manufacturers and can be used to determine expected operational characteristics, including predicted power consumption, for a given system component, given particular operation conditions. For example, the compressor manufacturer may publish performance coefficients for a particular model of compressor. The system controller <NUM> may, for example, access a public database of performance coefficients at a system component manufacturer's website and determine the particular performance coefficients for the system components included in the refrigeration system. The performance coefficients may correspond to a particular model of the system component. Alternatively, the performance coefficients may be determined on a per-component basis at the time of manufacture. In such case, the performance coefficients may correspond to a particular model and serial number for the system component. For example, the system controller <NUM> may query the manufacturer's database with the particular model and serial number for the particular component to retrieve the performance coefficients. Additionally, the performance coefficients may be stored in a non-volatile memory on or with the system component itself. Alternatively, the performance coefficients may be received from a user via the communication device <NUM> or from the remote monitor <NUM> or communication device <NUM>. After receiving the performance coefficients at <NUM>, the system controller <NUM> proceeds to <NUM>.

At <NUM>, the system controller <NUM> receives operational data for the refrigeration system. For example, the operational data may include: discharge temperatures and/or pressures for the compressor rack <NUM>; suction temperatures and/or pressures for the compressor rack <NUM>; condensing temperature; condensing unit discharge temperature and/or pressure; evaporator temperatures and/or pressures; and/or outdoor ambient temperatures; etc. The operational data can be indicative of the load on the refrigeration system <NUM> and can be used, along with the performance coefficients, to determine predicted power consumption for the refrigeration system <NUM> for a particular load.

At <NUM>, the system controller <NUM> calculates the predicted power consumption based on the performance coefficients for the system components and the operational data for the refrigeration system <NUM>. At <NUM>, the control algorithm <NUM> ends.

With reference to <FIG>, a control algorithm <NUM> is shown for determining benchmark power consumption based on system performance during a predetermined time period, such as an initialization period. The functionality of <FIG>, for example, is encapsulated at <NUM> of <FIG>. The control algorithm <NUM> may be performed by the system controller <NUM> and starts at <NUM>. At <NUM>, the system controller <NUM> receives operation data for the system during a predetermined initialization period. For example, the predetermined initialization period may be a time period, such as one or more weeks or months, just after the refrigeration system <NUM> is first installed or first repaired, or after maintenance is performed on the refrigeration system <NUM>. The operational data may include: discharge temperatures and/or pressures for the compressor rack <NUM>; suction temperatures and/or pressures for the compressor rack <NUM>; condensing temperature; condensing unit discharge temperature and/or pressure; evaporator temperatures and/or pressures; and/or outdoor ambient temperatures; etc., as well as power consumption data for the refrigeration system components, such as the compressor rack <NUM>, condensing unit <NUM>, and refrigeration cases <NUM>.

At <NUM>, the system controller <NUM> calculates benchmark power consumption data based on the operational data for the system over the predetermined initialization period. In this way, the benchmark power consumption may be associated, for example, with the power consumed by the system after installation, maintenance, or repair. As discussed above, the actual power consumption can then be compared with the benchmark power consumption to determine whether refrigeration system performance has degraded and to what extent additional power is being consumed by the refrigeration system <NUM> due to deterioration. The control algorithm <NUM> ends at <NUM>.

Systems and methods for calculating projected energy consumption data for a component of a refrigeration system based on ambient temperature data for comparison with actual energy consumption data are described in <CIT>.

Additionally, the monitored operational data can be used to calculate an overall coefficient of performance of the refrigeration system <NUM>. For example, the system controller <NUM> may monitor suction pressure, discharge pressure suction temperature, discharge temperature, a liquid temperature, and power consumption data, and use thermophysical equations stored in the system controller <NUM> and the refrigerant type to determine the coefficient of performance and other performance characteristics of the refrigeration system <NUM>. For example, the system controller <NUM> may determine capacity (kW), power input (kW), isentropic efficiency percentage, suction superheat temperature in degrees Celsius, discharge superheat temperature in degrees, superheat (K), subcooling (K), discharge temperature in degrees, and/or mass flow rate in kg/s.

With reference to <FIG>, a control algorithm <NUM> is shown for optimizing total refrigeration system energy consumption. For example, the system controller <NUM> may modify the operation of individual system components and monitor how the modification affected overall power consumption of the refrigeration system <NUM>. While a particular modification for operation of a particular component may result in an increase in power consumption for that component, it may cause a greater decrease in power consumption of another component, resulting in decreased power consumption of the refrigeration system <NUM> overall. For example, an increase in capacity of the condenser fan operation may result in increased power consumption by the condensing unit <NUM>, but may result in decreased power consumption by the refrigeration cases <NUM> and/or compressor rack <NUM>.

The control algorithm <NUM> may be performed by the system controller <NUM> and starts at <NUM>. At <NUM>, the system controller receives power consumption data for the compressor rack <NUM>, condensing unit <NUM>, and refrigeration cases <NUM>. At <NUM>, the system controller <NUM> modifies operation of at least one of the compressor rack, condensing unit, and/or the refrigeration cases to minimize the total power consumption of the system. For example, the system controller <NUM> may modify setpoints or capacities of the various system components and monitor the resulting effect on total power consumption for the refrigeration system <NUM>. When the modification resulted in decreased total power consumption, the system controller <NUM> may make a similar modification to determine whether the similar modification likewise decreases total power consumption. When the modification does not result in decreased total power consumption, the system controller <NUM> may make the opposite modification and monitor the effect on total power consumption. The control algorithm <NUM> ends at <NUM>.

Systems and methods for modulating a condenser set point to minimize energy consumption are described in <CIT>.

With reference to <FIG>, a control algorithm <NUM> is shown for limiting peak power demand during startup operations. The control algorithm <NUM> may be performed by the system controller <NUM> and starts at <NUM>. At <NUM>, the system controller <NUM> determines the startup power demand for each compressor <NUM> and condenser fan <NUM> in the refrigeration system <NUM>. At startup, each component may receive an inrush of current at startup, resulting in a spike in power demand during the startup. Once the component is operating normally, the power consumed by the component may level off. At <NUM>, the system controller <NUM> may calculate the startup power demand for each compressor <NUM> and condenser fan <NUM> based on known characteristics of the component, such as the manufacturer's nameplate ratings, horsepower, capacity, etc. Alternatively or additionally, the system controller <NUM> may monitor power consumption of the component during startup operations and record the peak power demand.

At <NUM>, the system controller <NUM> may determine a sequence and timing for starting components of the system, including the compressors <NUM> and condenser fans <NUM> to limit the total peak power demand during startup operations. For example, the system controller <NUM> may stagger the initiation of startup operations for the components over time. Additionally, the system controller <NUM> may opt to start a component with a high peak power demand at the same time as a component with a low peak power demand. The system controller <NUM> and/or the remote monitor <NUM> may calculate and report energy savings resulting from limiting the peak startup power demand and/or tie the results to a utility data model. The control algorithm ends at <NUM>.

With reference to <FIG>, a control algorithm <NUM> is shown for providing demand shed functionality. The control algorithm <NUM> may be performed by the system controller <NUM> and starts at <NUM>. At <NUM>, the system controller <NUM> may receive a demand shed signal from a utility company. For example, at certain times the utility company may require utility users to reduce their overall power consumption to limit the total power being demanded from the utility.

At <NUM>, the system controller <NUM> can determine a set of components that will maximize refrigeration capacity while meeting the demand shed requirement under the current operating conditions. For example, based on having monitored power consumption and capacity data for each component of the system, along with operational data indicative of system load, the system controller <NUM> can determine which subsets of compressors and condenser fans can operate together with a total power consumption that is less than the power demand shed requirement. From those possible subsets of compressor and condenser fan combinations, the system controller <NUM> can determine the particular combination that will maximize total refrigeration capacity, given the current operating conditions.

In addition, if onsite power generation is available, such as solar or wind power generation, the system controller <NUM> may receive an energy limiting signal from the onsite power generation device, such as a photovoltaic array. The system controller <NUM> can the coordinate the selection of components for operation to limit the current power demand to be below the power being generated by the onsite power generation device or below a predicted power to be generated by the onsite power generation device.

In addition, at <NUM> the system controller <NUM> can also modify existing defrost schedules and/or other operations, such as scheduled precooling operations, based on the onsite generation capacity and/or the demand shed signal.

With reference to <FIG>, a control algorithm <NUM> is shown for predicting energy required for a future time period and modifying system operation. The control algorithm <NUM> may be performed by the system controller <NUM> and starts at <NUM>. At <NUM>, the system controller <NUM> receives weather or temperature forecast data for a future time period. The system controller <NUM> may access a weather database or weather service website and/or receive weather forecast and temperature data from the remote monitor <NUM>, the communication device, or the communication device <NUM>. At <NUM>, the system controller <NUM> estimates the predicted energy consumption for the system based on the indicated weather or temperature forecast data. For example, based on the forecast, the system controller <NUM> can predict the anticipated load on the refrigeration system <NUM> as well as the anticipated power consumption for the refrigeration system.

At <NUM>, the system controller <NUM> determines whether the predicted energy consumption is greater than a predetermined threshold. At <NUM>, when the predicted energy consumption is greater than the predetermined threshold, the system controller <NUM> proceeds to <NUM> and can send an alert to a user or operator of the refrigeration system <NUM> via the communication device <NUM>, remote monitor <NUM>, and/or communication device <NUM>. Additionally, the system controller <NUM> can modify operation of the system components and schedules. For example, the system controller <NUM> may reschedule previously scheduled defrost operations. Additionally, the system controller <NUM> may implement precooling prior to the future time period. For example, the system controller <NUM> may increase capacity of the refrigeration system <NUM> prior to the future time period to decrease the temperature in particular refrigeration cases <NUM> prior to the future time period. In this way, the load on the refrigeration system <NUM> during the future time period may be decreased as compared with normal operation.

Additionally, the system controller <NUM> may receive real time pricing information and/or smart grid initiatives to determine a predicted energy cost for the future time period. Similarly, the system controller <NUM> may modify operation of the system components and schedules based on the predicted energy cost and/or smart grid initiatives.

At <NUM>, when the predicted energy consumption is not greater than the predetermined threshold, the system controller <NUM> proceeds to <NUM>. At <NUM>, the control algorithm <NUM> ends.

The various aspects of the present disclosure described above are now described in further detail below. The disclosure below is organized as follows. <FIG>, and <FIG> illustrate power monitoring of individual compressors <NUM> in the compressor rack <NUM> shown in <FIG>. <FIG> illustrate systems and methods for tracking performance of individual compressors <NUM>. <FIG> and <FIG> illustrate a system and method for regression-based monitoring of compressor performance.

With reference to <FIG>, an example of a system <NUM> for monitoring power consumption of individual compressors <NUM> in the compressor rack <NUM> of <FIG> is shown. In <FIG>, the system <NUM> is implemented in the system controller <NUM> shown in <FIG>. The system controller <NUM> includes a power monitoring module <NUM> and a performance tracking module <NUM>. The power monitoring module <NUM> monitors the power consumption of individual compressors <NUM> in the compressor rack <NUM>. The performance tracking module <NUM> tracks the performance of the individual compressors <NUM> based on the power consumption monitored by the power monitoring module <NUM>. The performance tracking module <NUM> also diagnoses the health of the individual compressors <NUM> based on the power consumption monitored by the power monitoring module <NUM> and the performance tracked by the performance tracking module <NUM>. Accordingly, the power monitoring and performance tracking can be used for both energy management and maintenance and diagnostics of the refrigeration system <NUM>.

In <FIG>, an example of the power monitoring module <NUM> is shown. The power monitoring module <NUM> includes a power consumption module <NUM>, a voltage determining module <NUM>, a power factor module <NUM>, and an error correction module <NUM>. The power consumption module <NUM> determines the power consumption of each compressor <NUM> in different ways depending on the type of data available. For example, if each compressor <NUM> has a power meter associated with it, the power consumption module <NUM> determines the power consumption of each compressor <NUM> directly from the power consumption data received from the power meter associated with the respective compressor <NUM>. If, however, a power meter is not available for each compressor <NUM>, the power consumption module <NUM> determines the power consumption of each compressor <NUM> in one of two ways.

In a first way, the voltage determining module <NUM> determines a supply voltage available for each compressor <NUM> based on the power supplied to the compressor rack <NUM> by the power supply <NUM> (shown in <FIG>) and a number of compressors <NUM> in the compressor rack <NUM>. The power factor module <NUM> adjusts a power factor for a particular compressor <NUM> based on the supply voltage for the particular compressor <NUM> determined by the voltage determining module <NUM>. The power factor for the particular compressor <NUM> changes due to changes in operating conditions (e.g., load) of the particular compressor <NUM> and changes in the supply voltage for the particular compressor <NUM>. The power factor module <NUM> adjusts the power factor for the particular compressor <NUM> to compensate for differences between the actual supply voltage for the particular compressor <NUM> (e.g., 240V or 220V) and a voltage rating of the particular compressor <NUM> (e.g., 230V).

The power factor module <NUM> adjusts the power factor for the particular compressor <NUM> using the formula (or other PF correction formula applicable to the compressor) PF = Voltsrating*PFrating*(Ampsnominal-rating/Ampsactual)/Voltsactual, where Voltsrating denotes the voltage rating of the particular compressor <NUM>, PFrating denotes a power factor rating of the particular compressor <NUM>, Ampsnominal-rating denotes an amperage or a current rating of the particular compressor <NUM>, Ampsactual denotes an actual current consumption of the particular compressor <NUM>, and Voltsactual denotes the actual supply voltage for the particular compressor <NUM> determined by the voltage determining module <NUM>.

The power consumption module <NUM> determines the power consumption of the particular compressor <NUM> based on the adjusted or corrected power factor determined by the power factor module <NUM>. The power consumption module <NUM> determines the power consumption of the particular <NUM>-phase (for example) compressor <NUM> using the formula Power = Volts*PF*amps*<NUM>^. <NUM>, where Volts denotes the actual supply voltage for the particular compressor <NUM> determined by the voltage determining module <NUM>, PF denotes the adjusted or corrected power factor determined by the power factor module <NUM>, and amps denotes the actual amperage of the particular compressor <NUM>.

In a second way, the error correction module <NUM> determines an error correction factor in the event that the supply voltage for the particular compressor <NUM> is unknown but the total power consumption of the compressor rack <NUM> is known (e.g., from the rack power sensor <NUM> shown in <FIG>). The power consumption of each individual compressor <NUM> is calculated based on the actual amperage, rated voltage, and rated power factor of each compressor <NUM>. The correction factor is applied to the individual power consumption values of each compressor <NUM> such that the sum of the power consumption values of the individual compressors (plus fans and other loads) equals the measured total power consumption of the compressor rack <NUM>.

With reference to <FIG>, an example of a control algorithm <NUM> for monitoring power consumption of individual compressors <NUM> in the compressor rack <NUM> is shown. For example, the control algorithm <NUM> may be performed by the system controller <NUM> shown in <FIG>. The control algorithm <NUM> starts at <NUM>. At <NUM>, the system controller <NUM> determines whether power consumption data for a particular compressor <NUM> is available from a power meter is associated with the particular compressor <NUM>. If power consumption data is available from a power meter, the system controller <NUM> uses the power consumption data from the power meter to determine the power consumption of the particular compressor <NUM> at <NUM>.

If, however, power consumption data is unavailable from a power meter, at <NUM>, the system controller <NUM> determines whether a supply voltage for the particular compressor <NUM> is available. For example, the system controller <NUM> may determine the supply voltage for a particular compressor <NUM> based on the power supplied by the power supply <NUM> to the compressor rack <NUM> and the number of compressors <NUM> in the compressor rack <NUM> (see <FIG>).

If the system controller <NUM> can determine the supply voltage for the particular compressor <NUM>, at <NUM>, the system controller <NUM> adjusts or corrects a power factor for the particular compressor <NUM> based on the supply voltage to compensate for difference between the actual supply voltage for the particular compressor <NUM> and a voltage rating of the particular compressor <NUM>. For example, the system controller <NUM> adjusts or corrects the power factor for the particular compressor <NUM> using the formula disclosed above in the description of the power factor module <NUM> with reference to <FIG>. At <NUM>, the system controller <NUM> determines the power consumption of the particular compressor <NUM> based on the adjusted or corrected power factor and actual supply voltage and amperage of the particular compressor <NUM>. For example, the system controller <NUM> determines the power consumption of the particular compressor <NUM> using the formula disclosed above in the description of the power consumption module <NUM> with reference to <FIG>.

If the supply voltage for the particular compressor <NUM> is unavailable, at <NUM>, the system controller <NUM> estimates the power consumption of the particular compressor <NUM> using the amperage of the particular compressor <NUM> and the voltage rating and the rated power factor of the particular compressor <NUM>. If a power meter (e.g., the rack power sensor <NUM> shown in <FIG>) measures an aggregate power consumption of the compressor rack <NUM>, an error correction factor is applied such that sum of power consumption of individual compressors (plus fans and other loads) equals aggregate power consumption.

At <NUM>, the system controller <NUM> uses the power consumption determined as described above to track the performance and diagnose the health of the particular compressor <NUM>. The system controller <NUM> determines the power consumption of each of the compressors <NUM> and tracks the performance and diagnoses the health of each of the compressors <NUM> as described above. The control algorithm <NUM> ends at <NUM>.

With reference to <FIG>, an example of a system <NUM> for tracking performance of the compressors <NUM> in the compressor rack <NUM> of <FIG> is shown. The system <NUM> can be generally implemented in the system controller <NUM> shown in <FIG> and can be specifically implemented in the performance tracking module <NUM> shown in <FIG>. The performance tracking module <NUM> determines whether the performance of the compressors <NUM> conforms to the manufacturer's rated performance. The performance tracking module <NUM> includes a baseline data module <NUM>, a performance monitoring module <NUM>, and a regression-based monitoring module (regression module) <NUM>. The operation of these modules is explained below in brief with reference to <FIG>.

Briefly, if rated performance data for the compressor <NUM> is unavailable, the performance tracking module <NUM> generates baseline data for the compressor <NUM> and assesses the performance and diagnoses the health of the compressor <NUM> by comparing operational data of the compressor <NUM> to the baseline data for the compressor <NUM>. If, however, the rated performance data for the compressor <NUM> is available, the performance tracking module <NUM> assesses the performance and diagnoses the health of the compressor <NUM> by comparing the operational data of the compressor <NUM> to the rated performance data for the compressor <NUM>.

The baseline data module <NUM> generates the baseline data for the compressor <NUM> based on data received from the compressor <NUM> immediately following installation of compressor <NUM>. The performance monitoring module <NUM> assesses the performance and diagnoses the health of the compressor <NUM> by comparing the baseline data to the operational data of the compressor <NUM> obtained subsequent to developing the baseline data for the compressor <NUM>.

The regression-based monitoring module <NUM> performs a regression analysis on the rated performance data and the data obtained from the compressor <NUM> during operation and assesses the performance and diagnoses the health of the compressor <NUM> based on the regression analysis.

With reference to <FIG>, an example of a control algorithm <NUM> for tracking performance of the compressors <NUM> and the compressor rack <NUM> of <FIG> is shown. For example, the control algorithm <NUM> may be performed generally by the system controller <NUM> shown in <FIG> and specifically by the performance tracking module <NUM> shown in <FIG>. The control algorithm <NUM> is explained below in brief. A detailed description of the modules of <FIG> and the control algorithm <NUM> follows thereafter.

The control algorithm <NUM> starts at <NUM>. At <NUM>, the performance tracking module <NUM> determines whether rated performance data for the compressors <NUM> is available. If the rated performance data for the compressors <NUM> is unavailable, the baseline data module <NUM> generates baseline data for each compressor <NUM> at startup following installation at <NUM>. At <NUM>, the performance monitoring module <NUM> uses the baseline data generated by the baseline data module <NUM> as reference and compares data obtained during operation with the baseline data to monitor and assess the performance and to diagnose the health of the compressor <NUM>.

If, however, the rated performance data for the compressors <NUM> is available, at <NUM>, the performance tracking module <NUM> determines whether other methods including but not limited to regression-based analysis is used to monitor and assess the performance and diagnose the health of the compressor <NUM>. If regression-based analysis is used, at <NUM>, the regression module <NUM> uses statistically based procedures to compare ratings and baseline data to monitored data in order to assess compressor and system behavior and health. The control algorithm <NUM> ends at <NUM>.

With reference to <FIG>, an example of the regression-based monitoring module <NUM> is shown in further detail. The regression-based monitoring module <NUM> can monitor performance of compressor, condenser, evaporator, or any other system component for which performance data is available. Therefore, while the operation of the regression-based monitoring module <NUM> is described below with reference to the compressor <NUM> for example only, the teachings of the present disclosure can also be applied to monitor the performance and diagnose health of other system components.

The regression-based monitoring module <NUM> includes a benchmark generating module <NUM>, an analyzing module <NUM>, an optimizing module <NUM>, an outlier detecting module <NUM>, and a comparing module <NUM>. The operation of these modules is described below in detail with reference to <FIG>.

Briefly, the regression-based monitoring module <NUM> performs a regression analysis on the rated performance data and the data obtained from the compressor <NUM> during operation, and assesses the performance and diagnoses the health of the compressor <NUM> based on the regression analysis as follows. The benchmark generating module <NUM> generates a benchmark polynomial and a benchmark hull. The analyzing module <NUM> analyzes data obtained from the compressor <NUM> during operation using the benchmark polynomial and the benchmark hull and assesses the performance and diagnoses the health of the compressor <NUM> based on the analysis.

The optimizing module <NUM> selects only statistically significant variables affecting a selected one of the rated performance data (e.g., power consumption of the compressor <NUM>) and eliminates statistically insignificant variables that do not significantly affect the selected one of the rated performance data (e.g., power consumption of the compressor <NUM>). The optimizing module <NUM> optimizes the benchmark polynomial using the selected variables.

The outlier detecting module <NUM> detects outliers in the data obtained from the compressor <NUM> during operation and removes outliers with largest deviation. The comparing module <NUM> compares the benchmark polynomial and the benchmark hull with historical benchmark polynomial and hull data and assesses the performance and diagnoses the health of the compressor <NUM> based on the comparison.

In general, the regression-based monitoring module <NUM> performs the following functions: data collecting and evaluation at regular intervals (e.g., multiple times a day), periodically (e.g., weekly or monthly) benchmarking and evaluation of data outside hull (explained below), and long-term evaluation (e.g., quarterly, semiannually, or yearly). The benchmarking function further includes creating a model, checking the model for validity, eliminating outliers, simplifying the model by eliminating irrelevant variables, and calculating Hull. These functions are explained below in detail.

With reference to <FIG>, an example of a control algorithm <NUM> for regression-based performance monitoring of individual compressors <NUM> in the compressor rack <NUM> is shown. For example, the control algorithm <NUM> may be performed generally by the system controller <NUM> shown in <FIG>, specifically by the performance tracking module <NUM> shown in <FIG>, and more specifically by the regression-based monitoring module <NUM> shown in <FIG>. The control algorithm <NUM> starts at <NUM>.

At <NUM>, the regression-based monitoring module <NUM> collects system or compressor sensor data multiple times a day (e.g., every second, minute, hour). For example, the data may be for power consumption, mass flow rate, or any other parameter of any system component relevant for determining system performance and diagnosing system health trends.

At <NUM>, the benchmark generating module <NUM> processes the data having rating curves and within acceptable tolerance of the rating curves. If the data is not within the acceptable tolerance of the rating curves an error or warning is generated. The data within the acceptable tolerance is stored and processed for generating benchmark polynomial and benchmark hull. Hull is a region of data points inside of which a regression formula such as a polynomial can be used for prediction. The benchmark generating module <NUM> generates a model and checks the validity of the model using statistical methods.

At <NUM>, the optimizing module <NUM> selects only statistically significant variables that affect the selected performance parameter (e.g., power consumption of the compressor <NUM>) and eliminates statistically irrelevant variables to simplify the benchmark polynomial being generated. Additionally, the outlier detecting module <NUM> detects any outliers in the data, determines whether the outliers are not noise, and removes the outliers with the largest deviation to further simplify the benchmark polynomial being generated. The outlier removal also improves the accuracy of the model. The outliers are stored in a database and are evaluated over the long-term to determine whether the outliers were caused in fact by a system problem. The optimizing module <NUM> optimizes the benchmark polynomial based on the selected variables and the eliminated outliers. The optimizing module <NUM> also calculates benchmark hull along with the benchmark polynomial for data evaluation.

At <NUM>, the analyzing module <NUM> analyzes the system data being collected at regular intervals using the benchmark polynomial, the benchmark hull, and the rating curves, and detects errors based on the analysis. For example, the analyzing module <NUM> compares the data to the benchmark polynomial and determines whether the data is within one or more (e.g., ± <NUM>) standard deviations of the benchmark polynomial. The analyzing module <NUM> also determines whether the data is outside the benchmark hull. Further, the analyzing module <NUM> determines whether the data is within an acceptable tolerance of the rating curves for the data. If the data is within the acceptable tolerance of the rating curves for the data, the data is stored and used for generating future benchmark polynomial and benchmark hull. If the data is not within the acceptable tolerance of the rating curves for the data, an error or warning regarding compressor performance and health is issued.

At <NUM>, the comparing module <NUM> periodically (e.g., quarterly, semiannually, or yearly) compares the benchmarks to detect long-term trends, determines whether the long-term trends show any deterioration of the equipment, and issues an error or warning if the long-term trends show any deterioration of the equipment.

In summary, the systems and methods described above can perform energy management functions for refrigeration systems. Specifically, the systems and methods can track performance of individual compressors by comparing actual versus predicted parameters (e.g., power consumption). The systems and methods can optimize power consumption of the refrigeration system <NUM> by coordinating power consumption of the compressor rack <NUM> and other components of the refrigeration system <NUM> such as the condenser <NUM>, for example. The systems and methods can limit peak power by using a smart startup algorithm. The systems and methods can provide demand shed capabilities. The systems and methods can predict energy required in view of future operating conditions.

The foregoing description is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. The broad teachings of the disclosure can be implemented in a variety of forms. Therefore, while this disclosure includes particular examples, thetreescope of the invention should not be so limited since other modifications will become apparent upon a study of the drawings, the specification, and the following claims. The invention is defined in broadest terms in the appended independent claims.

Spatial and functional relationships between elements (for example, between modules, circuit elements, semiconductor layers, etc.) are described using various terms, including "connected," "engaged," "coupled," "adjacent," "next to," "on top of," "above," "below," and "disposed. " Unless explicitly described as being "direct," when a relationship between first and second elements is described in the above disclosure, that relationship can be a direct relationship where no other intervening elements are present between the first and second elements, but can also be an indirect relationship where one or more intervening elements are present (either spatially or functionally) between the first and second elements. As used herein, the phrase at least one of A, B, and C should be construed to mean a logical (A OR B OR C), using a non-exclusive logical OR, and should not be construed to mean "at least one of A, at least one of B, and at least one of C.

In the figures, the direction of an arrow, as indicated by the arrowhead, generally demonstrates the flow of information (such as data or instructions) that is of interest to the illustration. For example, when element A and element B exchange a variety of information but information transmitted from element A to element B is relevant to the illustration, the arrow may point from element A to element B. This unidirectional arrow does not imply that no other information is transmitted from element B to element A. Further, for information sent from element A to element B, element B may send requests for, or receipt acknowledgements of, the information to element A.

In this application, including the definitions below, the term "module" or the term "controller" may be replaced with the term "circuit. " The term "module" may refer to, be part of, or include: an Application Specific Integrated Circuit (ASIC); a digital, analog, or mixed analog/digital discrete circuit; a digital, analog, or mixed analog/digital integrated circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system-on-chip.

The term memory circuit is a subset of the term computer-readable medium. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave); the term computer-readable medium may therefore be considered tangible and non-transitory. Non-limiting examples of a non-transitory, tangible computer-readable medium are nonvolatile memory circuits (such as a flash memory circuit, an erasable programmable read-only memory circuit, or a mask read-only memory circuit), volatile memory circuits (such as a static random access memory circuit or a dynamic random access memory circuit), magnetic storage media (such as an analog or digital magnetic tape or a hard disk drive), and optical storage media (such as a CD, a DVD, or a Blu-ray Disc).

The apparatuses and methods described in this application may be partially or fully implemented by a special purpose computer created by configuring a general purpose computer to execute one or more particular functions embodied in computer programs. The functional blocks, flowchart components, and other elements described above serve as software specifications, which can be translated into the computer programs by the routine work of a skilled technician or programmer.

The computer programs include processor-executable instructions that are stored on at least one non-transitory, tangible computer-readable medium. The computer programs may also include or rely on stored data. The computer programs may encompass a basic input/output system (BIOS) that interacts with hardware of the special purpose computer, device drivers that interact with particular devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, etc..

The computer programs may include: (i) descriptive text to be parsed, such as HTML (hypertext markup language) or XML (extensible markup language), (ii) assembly code, (iii) object code generated from source code by a compiler, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. As examples only, source code may be written using syntax from languages including C, C++, C#, Objective C, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®, HTML5, Ada, ASP (active server pages), PHP, Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, and Python®.

Claim 1:
A system comprising:
a system controller (<NUM>) for a refrigeration or HVAC system (<NUM>) having a compressor rack (<NUM>) with a plurality of compressors (<NUM>) and a condensing unit (<NUM>) with a plurality of condenser fans (<NUM>), the system controller configured to monitor and control operation of the refrigeration or HVAC system;
a rack controller (<NUM>) in communication with the system controller, the rack controller configured to monitor and control operation of the compressor rack; and
a condensing unit controller (<NUM>) in communication with the system controller, the condensing unit controller configured to monitor and control operation of the condensing unit;
characterized in that the system controller is configured to:
determine a startup power demand for each compressor of the plurality of compressors and each condenser fan of the plurality of condenser fans, and
determine a startup sequence and timing for the plurality of compressors and the plurality of condenser fans to limit peak power demand during a startup operation to be below a predetermined power threshold.