Patent Publication Number: US-2010114390-A1

Title: Load operation profiling and notification in a monitored electrical power distribution system

Description:
FIELD OF THE INVENTION 
     The present invention relates to load operation profiling and notification in monitored electrical power distribution systems. 
     BACKGROUND OF THE INVENTION 
     One power monitoring system function is to provide notification when a monitored load operates outside expected norms. One common approach is to configure at least one set point to monitor a measurement representing load operation; when measurement values exceed a preset bound, a notification is generated. The set point approach can be used to detect extreme measurement values that are outside the typical operating range of a load, but cannot be used to detect unexpected measurement values that occur within the operating range of the load. 
     SUMMARY OF THE INVENTION 
     In accordance with one embodiment, a method of monitoring the operation of a load in an electrical power distribution system comprises selecting a parameter representing operation of the load, determining an expected characteristic of the parameter during normal operation of the load, and comparing measured values of the parameter with the expected characteristic to detect potential abnormal operation of the load. 
     In one implementation, the expected characteristic of the parameter is a statistical summary or model of multiple measured values of the parameter during normal operation of the load. For example, the statistical summary or model may comprise amplitudes for different harmonic frequencies from a Fourier analysis of measured values for the load, or a standard deviation from the mean of a set of measured values of the parameter. 
     In one particular embodiment, the expected characteristic defines expected bounds for variations in the parameter as a function of a second parameter during normal operation of the load. For example, the expected characteristic may be a statistical summary or model of multiple measured values of a first parameter versus a second parameter that is a driver of the normal operation of the load. 
     The foregoing and additional aspects of the present invention will be apparent to those of ordinary skill in the art in view of the detailed description of various embodiments, which is made with reference to the drawings, a brief description of which is provided next. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention may best be understood by reference to the following description taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  is a block diagram of a monitored electrical power distribution system having multiple monitors and multiple loads. 
         FIG. 2  is a plot of variations in a parameter representing operation of a load or a portion of a load as a function of time, along with predetermined profile bounds for the parameter. 
         FIG. 3  is a plot of energy consumption of a load or power circuit as a function of the hour of day. 
         FIG. 4  is a plot of energy consumption of a fan load as a function of whether the fan is on or off. 
         FIG. 5  is a plot of energy consumption of a load or power circuit as a function of temperature, and including a best-fit line for the plotted data. 
         FIG. 6  is a graphic illustration of the results of a Fourier analysis of a set of energy consumption data grouped by the status of a fan load. 
         FIG. 7  is a plot of energy consumption of a packaged rooftop unit as a function of sub-loads within the rooftop unit. 
         FIG. 8  is a plot of energy consumption of a packaged rooftop unit as a function of temperature. 
         FIG. 9  is a plot of energy consumption of a power transformer as a function of harmonic frequency for a first portion of a day. 
         FIG. 10  is a plot of energy consumption of a power transformer as a function of harmonic frequency for a second portion of a day. 
     
    
    
     DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENTS 
     Although the invention will be described in connection with certain preferred embodiments, it will be understood that the invention is not limited to those particular embodiments. On the contrary, the invention is intended to cover all alternatives, modifications, and equivalent arrangements as may be included within the spirit and scope of the invention as defined by the appended claims. 
     Turning now to the drawings and referring first to  FIG. 1 , a monitoring system for an electrical power distribution system includes a pair of monitors M 1  and M 2  that perform measurements of one or more parameters (such as kW or amps) related to attached loads. The monitor M 1  is connected to a load  11  that includes two separate load modules LM 1  and LM 2 , such as fans and heating or cooling coils within HVAC equipment, and the monitor M 2  is connected to a load  12 . The monitors M 1  and M 2  store the measured information and communicate it to a server  13  via a communications network  14 . The server  13  also stores this information and performs load operation profiling, analysis and notification functions. A user  15  may use a personal computer  16  connected to the communications network  14  to perform various functions such as analyzing parameter measurements from the monitors M 1  and M 2 , configuring profiles used in analyzing the measurements made by the monitoring system, and configuring, sending and receiving notifications. 
       FIG. 2  illustrates a “load operation profile” that describes an expected range of values of a parameter selected to represent the operation of a load or multiple loads on a circuit. A “profile” defines one or more bounds of the expected operation (parameter trend characterization, harmonic characterization, delta change in parameter after event, etc.) of a load in terms of one or more parameters. Profiles can be developed using a number of different, complementary techniques (including regression analysis of a load parameter vs. some “driver” parameter, and profiling of harmonic spectra). This profiling can also be combined with voltage disturbance curves for monitored equipment to assess whether a particular load is offline following a detected disturbance, as described in detail in copending U.S. patent application Ser. No. 12/252,047, entitled “System for Detecting Load Loss Following an Electrical Power Disturbance,” filed Oct. 15, 2008. 
     In  FIG. 2 , the area between a pair of upper and lower profile bounds  20  and  21  represent the range of expected values for the amplitude of measured values of a parameter P 1  as a function of time t. The parameter P 1  is a parameter representing operation of a load or multiple loads on a power circuit. Actual measured values of the parameter P 1  are represented by line  22 . When measurements of the parameter P 1  fall outside the bounds  20  and  21 , as illustrated by the portion of the line  22  between points  23  and  24  in  FIG. 2 , a notification of potential abnormal operation can be generated and sent to preselected recipients or addresses. Thus, the actual values of the magnitude of the parameter P 1  can be continually evaluated against the profile bounds  20  and  21  to assess the likelihood of abnormal operation of at least a portion of the monitored load or power circuit. A notification may also be sent when the parameter measurements return to values within the profile bounds, as occurs at point  24  in  FIG. 2 . 
     The operation of a load can be profiled and tracked using the following steps:
         1. Parameter P 1  selected to represent load operation, and parameter P 2  (representing some “driver” of load operation) is selected.   2. Collect parameter P 1  and parameter P 2  values for the operating range of the load.   3. Select and perform analysis of collected parameter P 1  and P 2  values.   4. Observe analysis results and either: (a) run steps 2 and 3 again; or (b) set load operation profile bounds for notification.   5. Configure notification recipients and conditions.   6. System gathers parameter P 1  values and evaluates against configured bounds, generating notifications when parameter P 1  values exceed set bounds and notification conditions are met.       

     The steps above can also be repeated to generate multiple profiles for a load, varying elements such as the parameters, analytical techniques and date range of stored measurements used. 
     Two examples of load profiling analysis approaches are: (a) the best fit of a parameter P 1  vs. parameter P 2 ; and (b) a Fourier transform of parameter P 1  (in terms of amplitude and frequency) vs. parameter P 2 . 
     The best fit approach is illustrated in  FIGS. 3-5 . In  FIG. 3 , the magnitude of a parameter P 1  such as energy consumption is plotted for each increment of a second parameter P 2  such as the hour of day, for a period of three days (three values for each hour). It can be seen that the parameter P 1  values are grouped by parameter P 2  increments, as shown by the groups  31 - 38  in  FIG. 3 , and a statistical summary of the grouped values can be generated. In  FIG. 3 , the parameter P 1  values are groupe by the hour in the day in which they occur, and a statistical summary (such as the mean and standard deviation) for each group of values can be generated and used to establish load profile bounds. A standard deviation measures how widely spread the values in a data set are. If many data points are close to the mean, then the standard deviation is small and, conversely, if many data points are far from the mean, then the standard deviation is large. If all data values are equal, then the standard deviation is zero. A standard deviation is expressed in the same units as the data. 
     In the example shown in  FIG. 4 , parameter P 2  is the state of a fan (on or off) within a monitored load. Here again, the parameter P 1  values can be grouped by parameter P 2  values (fan on or off), as shown by the groups  40  and  41  in  FIG. 4 , and a statistical summary for each group of values can be generated and used to establish load profile bounds. 
     If there is a more continuous relationship between parameter  1  and parameter P 2 , a more traditional regression analysis may be performed, as illustrated in  FIG. 5 . Parameter P 1  in  FIG. 5  is energy consumption, and parameter P 2  is temperature. A best-fit line or curve  50  can be determined and used to develop a load operation profile. This best-fit line  50  may be accompanied by other statistical summary information (such as a confidence interval) which can be used to establish load profile bounds. 
     The Fourier transform approach is illustrated by  FIG. 6  for a Fourier analysis of parameter P 1 , grouped by values of parameter P 2 . In the example in  FIG. 6 , parameter P 1  values (energy consumption) are organized by values of parameter P 2  (the status of a fan), and a Fourier analysis is used to generate amplitude values within different harmonic frequency “bins.” A statistical analysis of amplitude values within each harmonic frequency bin can be used to develop the two illustrated harmonic spectrum profiles  60  and  61  for the two different states of the fan. 
     The load profiling approaches described above generate an “expected” range of values for a parameter selected to represent load operation, typically expressed in statistical terms such as mean, standard deviation and/or confidence interval. Load profile bounds can be based on selected statistical parameter values, and notifications generated when load parameter values exceed these bounds. As an example, if parameter P 1  values are collected over the operating range of a load and are grouped by parameter P 2  values, as described above, standard deviations can be calculated for each parameter P 1  grouping, and load profile bounds set at two standard deviations for each grouping. 
     One or more of the approaches described above can be applied to develop load operation profiles that may be evaluated together to provide a comprehensive view of expected load operation. Two examples are illustrated in  FIGS. 7-10 . 
     In  FIGS. 7 and 8 , a packaged rooftop unit (RTU) example is illustrated by two load operation profiles.  FIG. 7  profiles kW values (parameter P 1 ) vs. the on/off status of RTU load modules (parameter P 2 , e.g., fan, fan plus chiller), reflecting the fact that, when energized, the RTU either (a) turns on a fan, or (b) turns on both the fan and a chiller. The kW values fall within tight groups, as shown by the groups  70  and  71  in  FIG. 7 , and expected load operation bounds for these groups can be described by statistical summary parameters such as mean and standard deviation. If measured kW values fall outside these groups, one or more of the load modules may not be operating as expected. 
       FIG. 8  profiles kWh values (parameter P′ 1 ) vs. ambient temperature (parameter P′ 2 ), with a regression analysis generating two piecewise linear best-fit lines  100  and  101 .  FIG. 8  indicates that the RTU consumes more energy as the ambient temperature increases, with a greater rate of consumption after the “breakpoint”  82  formed by the junction of the two linear best-fit lines  80  and  81 . One or more statistical summary parameters (such as a confidence interval) may be used to establish expected load operation bounds around both linear best-fit lines. 
     In  FIGS. 9 and 10 , a power transformer example is illustrated by two load operation profiles. In this example, a Fourier analysis is applied to total kW measurement values, with the kW values (parameter P 1 ) grouped by two different time-of-day ranges (parameter P 2 ), 6 AM to 10 PM in  FIG. 9  and 10 PM to 6 AM in  FIG. 10 . The kW amplitude values captured at each harmonic frequency over the operating range of the power transformer are grouped by harmonic, as shown by the groups  90 ,  91  and  92  in  FIG. 9  for 6 AM to 10 PM, and by the groups  100 ,  101  and  102  in  FIG. 10  for 10 PM to 6 AM. One or more statistical summary parameters (such as mean and standard deviation) may be used to establish expected bounds for the kW values, by harmonic, for each time-of-day range. If Fourier analysis of measured kW values yields amplitude values that fall outside the bounds for any harmonic frequency, for the applicable time-of-day range, the power transformer may not be operating as expected. Note that this approach can be used to track both amplitude and frequency changes in load operation. 
     Load operation profiles generated using either of the two main approaches outlined above may be further manipulated by a user before being put into use by the system. As an example, a user may observe the kW vs. sub-load profile shown in  FIGS. 7 and 8  and remove data points that occurred during a planned RTU maintenance outage. 
     Notification rules are used to describe conditions under which a notification is sent to one or more recipients. These rules may incorporate a number of factors, including the following:
         Send a notification when parameter measurements exceed established load profile bounds   Send a notification when parameter measurements have returned to values within load profile bounds   Send a notification only when parameter measurements exceed or return within load profile bounds for at least some length of time   Send notifications to different recipients (or recipient groups) based on time of date or day of week   Send a notification only when the bounds of multiple load profiles are exceeded       

     Notification rules may also be used to trigger additional monitoring system actions. As an example, consider a circular buffer continuously gathering 30-second per-phase ampere measurements for an HVAC unit. This buffer uses a fixed amount of memory and may reuse memory in a FIFO (first in, first out) fashion. The buffer may be configured such that, on receipt of a trigger, 10 minutes of pre-trigger measurements and 10 minutes of post-trigger measurements in the buffer are captured and stored for further analysis. 
     Multiple notification rules may be used for one load profile to indicate the severity of a deviation from expected load operation. For example, one notification may be triggered when parameter measurements exceed one standard deviation away from the mean representing the load profile, and another notification may be triggered when measurements exceed two standard deviations away from the mean. 
     While particular embodiments and applications of the present invention have been illustrated and described, it is to be understood that the invention is not limited to the precise construction and compositions disclosed herein and that various modifications, changes, and variations may be apparent from the foregoing descriptions without departing from the spirit and scope of the invention as defined in the appended claims.