System and method for forecasting power output of a wind farm

The present disclosure is directed to a system and method for forecasting a farm-level power output of a wind farm having a plurality of wind turbines. The method includes collecting actual operational data and/or site information for the wind farm. The method also includes predicting operational data for the wind farm for a future time period. Further, the method includes generating a model-based power output forecast based on the actual operational data, the predicted operational data, and/or the site information. In addition, the method includes measuring real-time operational data from the wind farm and adjusting the power output forecast based on the measured real-time operational data. Thus, the method also includes forecasting the farm-level power output of the wind farm based on the adjusted power output forecast.

FIELD OF THE INVENTION

Embodiments of the present invention relate generally to wind farms, and more particularly, to systems and methods for forecasting power output of a wind farm or an individual wind turbine.

BACKGROUND OF THE INVENTION

Wind power is considered one of the cleanest, most environmentally friendly energy sources presently available, and wind turbines have gained increased attention in this regard. A modern wind turbine typically includes a tower, a generator, a gearbox, a nacelle, and a rotor having one or more rotor blades. The rotor blades transform wind energy into a mechanical rotational torque that drives one or more generators via the rotor. The generators are sometimes, but not always, rotationally coupled to the rotor through the gearbox. The gearbox steps up the inherently low rotational speed of the rotor for the generator to efficiently convert the rotational mechanical energy to electrical energy, which is fed into a utility grid via at least one electrical connection. Such configurations may also include power converters that are used to convert a frequency of generated electric power to a frequency substantially similar to a utility grid frequency.

A plurality of wind turbines are commonly used in conjunction with one another to generate electricity and are commonly referred to as a “wind farm.” Wind turbines on a wind farm typically include their own meteorological monitors that perform, for example, temperature, wind speed, wind direction, barometric pressure, and/or air density measurements. In addition, a separate meteorological mast or tower (“met mast”) having higher quality meteorological instruments that can provide more accurate measurements at one point in the farm is commonly provided. The correlation of meteorological data with power output allows the empirical determination of a “power curve” for the individual wind turbines.

Unfortunately, such renewable energy systems can be intermittent in nature, for example, due to changing wind speed, cloud coverage, and/or sun blockage from the photovoltaic panels (in solar power systems). As such, it is difficult to accurately predict or forecast the amount of power that can be generated in the future for such systems. Without accurate power predictions, operators cannot effectively bid into the day-ahead energy markets.

Thus, an improved system and method for more accurately forecasting power output of a wind farm and/or a wind turbine would be advantageous.

SUMMARY OF THE INVENTION

Aspects and advantages of embodiments of the invention will be set forth in part in the following description, or may be obvious from the description, or may be learned through practice of embodiments of the invention.

In one aspect, the present disclosure is directed to a method for forecasting a farm-level power output of a wind farm having a plurality of wind turbines. The method includes collecting actual operational data and/or site information for the wind farm. The method also includes generating a model-based power output forecast based on forecast operational data, the site information, and/or the actual operational data. In addition, the method includes measuring real-time operational data from the wind farm and adjusting the power output forecast based on the measured real-time operational data. Thus, the method also includes forecasting the farm-level power output of the wind farm based on the adjusted power output forecast.

In one embodiment, the actual operational data may include any one of or a combination of the following: power output, torque output, pitch angle, tip speed ratio, yaw angle, temperature, pressure, time of day, month of year, number of wind turbines on-line, wind speed, wind direction, wind shear, wake, wind turbulence, wind acceleration, wind gusts, wind veer, or any other suitable operational data.

In another embodiment, the site information may include any one of or a combination of the following: farm-level power curve, a turbine-level power curve, elevation, wind turbine location, wind farm location, weather conditions, location of nearby wind farms, geographical layout, or any other suitable site condition.

In further embodiments, the method may also include predicting the forecast operational data for the wind farm for a future time period. In yet another embodiment, the step of generating the model-based power output forecast may include using a physics-based model.

In additional embodiments, the method may include adjusting the power output forecast based on the measured real-time operational data via a compensator module. In such embodiments, the compensator module may include a statistical compensator that utilizes at least one of a neural network, regression (linear or non-linear), and/or machine learning to adjust the power output forecast based on the measured real-time operational data. Further, the method may include learning, via the compensator module, one or more deviations between the real-time operational data and the predicted operational data and adjusting the power output forecast based on the learned deviation(s).

In yet another embodiment, the future time period may include from about twelve (12) hours to about seven (7) days in the future. In additional embodiments, the future time period may include less than 12 hours or more than 7 days.

In another aspect, the present disclosure is directed to a method for forecasting a turbine-level power output of a wind turbine. The method includes collecting actual operational data and site information for the wind turbine. The method also includes generating a model-based power output forecast based on forecast operational data, the site information, and/or the actual operational data. In addition, the method includes measuring real-time operational data from the wind turbine and adjusting the power output forecast based on the measured real-time operational data. Thus, the method also includes forecasting the power output of the wind turbine based on the adjusted power output forecast. It should be understood that the method may further include any of the additional steps and/or features as described herein.

In yet another aspect, the present disclosure is directed to a system for forecasting a farm-level power output of a wind farm having a plurality of wind turbines. The system includes a processor configured to perform one or more operations, including but not limited to collecting actual operational data and site information for the wind farm, generating a model-based power output forecast based on forecast operational data, the site information, and/or the actual operational data, measuring real-time operational data from the wind farm, determining a deviation between the real-time operational data and the predicted operational data, adjusting the power output forecast based on the deviation, and forecasting the farm-level power output of the wind farm based on the adjusted power output forecast. It should be understood that the system may further include any of the additional features as described herein.

For example, in one embodiment, the processor may be further configured to adjust the power output forecast based on the deviation via a compensator module.

These and other features, aspects and advantages of embodiments of the present invention will become better understood with reference the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate the embodiments of the invention and, together with the description, serve to explain the principles of embodiments of the invention.

DETAILED DESCRIPTION

Reference now will be made in detail to embodiments of the invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of embodiments of the invention, not limitation of embodiments of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made in embodiments of the present invention without departing from the scope or spirit of embodiments of the invention. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that embodiments of the present invention covers such modifications and variations as come within the scope of the appended claims and their equivalents.

Generally, the present disclosure is directed to a system and method for providing a power forecast for a wind farm and/or individual wind turbine. The system generally includes two parts, including (1) a model-based power predictor module and (2) a compensator module. First, the power prediction module combines multiple configurations of the wind farm and/or wind turbine and forecast data (such as wind speed, wind direction, air pressure, and temperature) with a model representing the relationship between the atmospheric information and the generated wind turbine/wind farm power. Second, the compensator module adjusts the output of the model-based power module based on learned deviations between the predicted and actual power to enable a more accurate power forecast for the wind turbine or the wind farm. For example, the amount to adjust the forecast power can be learned from representative samples of forecast conditions synchronized with the actual power produced by the wind turbine or the wind farm.

The various embodiments of the system and method of the present disclosure provide numerous advantages not present in the prior art. For example, the present disclosure provides accurate real-time, day-ahead, and multi-day power and energy forecasting of the wind farm or an individual wind turbine. Thus, the present disclosure provides direct value in the energy markets including the real-time and day-ahead markets and enables more efficient maintenance planning.

The wind turbine10may also include a wind turbine controller26centralized within the nacelle16. However, in other embodiments, the controller26may be located within any other component of the wind turbine10or at a location outside the wind turbine. Further, the controller26may be communicatively coupled to any number of the components of the wind turbine10in order to control the operation of such components and/or to implement a control action. As such, the controller26may include a computer or other suitable processing unit. Thus, in several embodiments, the controller26may include suitable computer-readable instructions that, when implemented, configure the controller26to perform various different functions, such as receiving, transmitting and/or executing wind turbine control signals. Accordingly, the controller26may generally be configured to control the various operating modes of the wind turbine10(e.g., start-up or shut-down sequences), de-rate or up-rate the wind turbine10, and/or control various components of the wind turbine10. For example, the controller26may be configured to control the blade pitch or pitch angle of each of the rotor blades22(i.e., an angle that determines a perspective of the rotor blades22with respect to the direction of the wind) to control the power output generated by the wind turbine10by adjusting an angular position of at least one rotor blade22relative to the wind. For instance, the controller26may control the pitch angle of the rotor blades22by rotating the rotor blades22about a pitch axis28, either individually or simultaneously, by transmitting suitable control signals to a pitch drive or pitch adjustment mechanism (not shown) of the wind turbine10.

Referring now toFIG. 2, a block diagram of one embodiment of suitable components that may be included within the controller26is illustrated in accordance with aspects of the present disclosure. As shown, the controller26may include one or more processor(s)58and associated memory device(s)60configured to perform a variety of computer-implemented functions (e.g., performing the methods, steps, calculations and the like disclosed herein). As used herein, the term “processor” refers not only to integrated circuits referred to in the art as being included in a computer, but also refers to a controller, a microcontroller, a microcomputer, a programmable logic controller (PLC), an application specific integrated circuit, application-specific processors, digital signal processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), and/or any other programmable circuits. Further, the memory device(s)60may generally include memory element(s) including, but are not limited to, computer readable medium (e.g., random access memory (RAM)), computer readable non-volatile medium (e.g., a flash memory), one or more hard disk drives, a floppy disk, a compact disc-read only memory (CD-ROM), compact disk-read/write (CD-R/W) drives, a magneto-optical disk (MOD), a digital versatile disc (DVD), flash drives, optical drives, solid-state storage devices, and/or other suitable memory elements.

Additionally, the controller26may also include a communications module62to facilitate communications between the controller26and the various components of the wind turbine10. For instance, the communications module62may include a sensor interface64(e.g., one or more analog-to-digital converters) to permit the signals transmitted by one or more sensors65,66,68to be converted into signals that can be understood and processed by the controller26. Furthermore, it should be appreciated that the sensors65,66,68may be communicatively coupled to the communications module62using any suitable means. For example, as shown inFIG. 2, the sensors65,66,68are coupled to the sensor interface64via a wired connection. However, in alternative embodiments, the sensors65,66,68may be coupled to the sensor interface64via a wireless connection, such as by using any suitable wireless communications protocol known in the art. For example, the communications module62may include the Internet, a local area network (LAN), wireless local area networks (WLAN), wide area networks (WAN) such as Worldwide Interoperability for Microwave Access (WiMax) networks, satellite networks, cellular networks, sensor networks, ad hoc networks, and/or short-range networks. As such, the processor58may be configured to receive one or more signals from the sensors65,66,68.

The sensors65,66,68may be any suitable sensors configured to measure any operational data of the wind turbine10and/or wind parameters of the wind farm200. For example, the sensors65,66,68may include blade sensors for measuring a pitch angle of one of the rotor blades22or for measuring a loading acting on one of the rotor blades22; generator sensors for monitoring the generator (e.g. torque, rotational speed, acceleration and/or the power output); and/or various wind sensors for measuring various wind parameters (e.g. wind speed, wind direction, etc.). Further, the sensors65,66,68may be located near the ground of the wind turbine10, on the nacelle16, on a meteorological mast of the wind turbine10, or any other location in the wind farm.

It should also be understood that any other number or type of sensors may be employed and at any location. For example, the sensors may be accelerometers, pressure sensors, strain gauges, angle of attack sensors, vibration sensors, MIMU sensors, camera systems, fiber optic systems, anemometers, wind vanes, Sonic Detection and Ranging (SODAR) sensors, infra lasers, Light Detecting and Ranging (LIDAR) sensors, radiometers, pitot tubes, rawinsondes, other optical sensors, and/or any other suitable sensors. It should be appreciated that, as used herein, the term “monitor” and variations thereof indicates that the various sensors of the wind turbine10may be configured to provide a direct measurement of the parameters being monitored or an indirect measurement of such parameters. Thus, the sensors65,66,68may, for example, be used to generate signals relating to the parameter being monitored, which can then be utilized by the controller26to determine the actual condition.

Referring now toFIG. 3, a wind farm200that is controlled according to the system and method of the present disclosure is illustrated. As shown, the wind farm200may include a plurality of wind turbines202, including the wind turbine10described above, and a farm controller220. For example, as shown in the illustrated embodiment, the wind farm200includes twelve wind turbines, including wind turbine10. However, in other embodiments, the wind farm200may include any other number of wind turbines, such as less than twelve wind turbines or greater than twelve wind turbines. In one embodiment, the controller26of the wind turbine10may be communicatively coupled to the farm controller220through a wired connection, such as by connecting the controller26through suitable communicative links222(e.g., a suitable cable). Alternatively, the controller26may be communicatively coupled to the farm controller220through a wireless connection, such as by using any suitable wireless communications protocol known in the art. In addition, the farm controller220may be generally configured similar to the controllers26for each of the individual wind turbines202within the wind farm200. Further, the farm controller220may be configured with any of the components described with respect to the turbine controller26ofFIG. 2.

In additional embodiments, one or more of the wind turbines202in the wind farm200may include a plurality of sensors for monitoring various operational data of the individual wind turbines202and/or one or more wind parameters of the wind farm200. For example, as shown, each of the wind turbines202includes a wind sensor216, such as an anemometer or any other suitable device, configured for measuring wind speeds or any other wind parameter. For example, in one embodiment, the wind parameters include information regarding at least one of or a combination of the following: a wind gust, a wind speed, a wind direction, a wind acceleration, a wind turbulence, a wind shear, a wind veer, a wake, SCADA information, or similar.

As is generally understood, wind speeds may vary significantly across a wind farm200. Thus, the wind sensor(s)216may allow for the local wind speed at each wind turbine202to be monitored. In addition, the wind turbine202may also include one or more additional sensors218. For instance, the sensors218may be configured to monitor electrical properties of the output of the generator of each wind turbine202, such as current sensors, voltage sensors, temperature sensors, or power sensors that monitor power output directly based on current and voltage measurements. Alternatively, the sensors218may include any other sensors that may be utilized to monitor the power output of a wind turbine202. It should also be understood that the wind turbines202in the wind farm200may include any other suitable sensor known in the art for measuring and/or monitoring wind parameters and/or wind turbine operational data.

Referring now toFIGS. 4 and 5, a system150and method100for forecasting a farm-level power output of a wind farm, such as the wind farm200ofFIG. 3, is illustrated. More specifically,FIG. 4illustrates a flow diagram of one embodiment of the method100for forecasting a farm-level power output of the wind farm200andFIG. 5illustrates a schematic diagram of a processor152of the system150for forecasting a farm-level power output of the wind farm200. It should also be understood that the same method may be applied to an individual wind turbine (such as the wind turbine10ofFIG. 1) in additional to the overall wind farm200. In one embodiment, the farm controller220(or the individual wind turbine controllers26) may be configured to perform any of the steps of the method100as described herein. Further, in additional embodiments, the method100of the present disclosure may be performed manually via a separate computer not associated with the wind farm200.

Thus, as shown at102, the method100includes collecting actual operational data154and/or site information158for the wind farm200. More specifically, as shown inFIG. 5, the actual operational data154as described herein may include information regarding at least one of or a combination of the following: power output, torque output, pitch angle, tip speed ratio, yaw angle, temperature, pressure, time of day, month of year, number of wind turbines on-line, wind speed, wind direction, wind shear, wake, wind turbulence, wind acceleration, wind gusts, wind veer, or any other suitable operational data. Similarly, as shown, the site information158may include any one of or a combination of the following: farm-level power curve, a turbine-level power curve, elevation, wind turbine location, wind farm location, weather conditions, location of nearby wind farms, geographical layout, or any other suitable site condition. Further, in particular embodiments, the operational data154may be part of the Supervisory Control and Data Acquisition (SCADA) system for remote monitoring and control of the wind farm200that operates with coded signals over communication channels. In additional embodiments, the controller(s)26,220may be configured to further analyze (i.e. filter, average, and/or adjust) the operational data as described herein.

Still particularly toFIG. 4, as shown at104, the method100may also optionally include predicting the forecast operational data for the wind farm200for a future time period. As shown inFIG. 5, it should be understood that the predicted/forecast operational data156may include any of the actual operational data described herein. Further, in certain embodiments, the future time period may include from about twelve (12) hours to about seven (7) days in the future as hourly predictions of forecast wind energy can enable a wind farm operator to bid into the day-ahead energy markets. Thus, energy forecasts a day or more in the future can enable a wind farm operation to plan maintenance scheduling when the forecast wind and associated power generation is low so as to minimize the amount of lost revenue during wind turbine and/or wind farm maintenance. In alternative embodiments, the future time period may include less than 12 hours or more than 7 days.

Further, as shown at106ofFIG. 4, the method100also includes generating a model-based power output forecast162via a model160based on one or more of the actual operational data154, the predicted/forecast operational data156, and/or the site information158. For example, as shown inFIG. 5, the model-based power output forecast162may generated by a physics-based model160. As is generally understood, physics-based models are used to generate and visualize constrained shapes, motions of rigid and non-rigid objects, and/or object interactions with the surrounding environment for the purposes of animation modeling. Thus, controller(s)26,220are configured to combine the actual operational data154and/or the predicted operational data156with a physics-based model representing the relationship between atmospheric information and the generated power output of the wind farm200and/or an individual wind turbine.

As shown at108, the method100also includes measuring real-time operational data166of the wind farm200. In certain embodiments, the real-time operational data166may be generated via one or more of the sensors (e.g. via sensors65,66,68,216,218, or any other suitable sensor). In addition, the real-time operational data166may be determined via a computer model within the one of the controllers26,220. More specifically, as shown inFIG. 5, the real-time actual operational data166as described herein may include power output, torque output, pitch angle, tip speed ratio, yaw angle, temperature, pressure, time of day, month of year, number of wind turbines on-line, wind speed, wind direction, wind shear, wake, wind turbulence, wind acceleration, wind gusts, wind veer, or any other suitable operational data. In addition, the real-time operational data166may include any of the actual operational data154and/or site information158as described herein.

As shown at110, the method100further includes adjusting or correcting the power output forecast162generated by the model160based on, at least, the measured real-time operational data166. For example, as shown inFIG. 5, a compensator module168may be configured to adjust the power output forecast162as a function of the measured real-time operational data166and/or the previous predicted operational data156. In such embodiments, the compensator module156may include a statistical compensator that utilizes at least one of a neural network, regression (e.g. linear or non-linear), or machine learning to adjust the power output forecast162based on the measured real-time operational data166and/or the previous predicted operational data156.

Thus, as shown at112, the method100includes forecasting the farm-level power output172of the wind farm200(or a turbine-level power output of an individual wind turbine) based on the adjusted power output forecast162. In additional embodiments, the method100may include learning, via the compensator module156, one or more deviations between the real-time operational data166and the predicted operational data156and forecasting the power output172of the wind farm200based on the learned deviations.

Referring now toFIG. 6, a schematic diagram of another embodiment of a system250for forecasting a farm-level power output of the wind farm200is illustrated. As shown, the system250includes a processor252configured to perform one or more operations. For example, as shown, the processor252is configured to collect actual operational data254and site information256for the wind farm200(or an individual wind turbine). Further, as shown, the processor252is configured to optionally predict operational data256for the wind farm200for a future time period. The actual operational data254, the predicted operational data256, and/or the site information258may include any of the operational data and/or site information as described herein. As such, the processor252may also be configured to generate a model-based power output forecast262via model260based on the actual operational data254, the predicted or predicted operational data256, and/or the site information258. Further, the processor252may include a compensator module266that is configured to measure real-time operational data264from the wind farm200and determine a deviation268between the real-time operational data264and the predicted operational data256. As shown at270, the processor is further configured to adjust the power output forecast262based on the deviation268and forecast the farm-level power output272of the wind farm200(or the turbine-level power output of an individual wind turbine) based on the adjusted power output forecast.

Exemplary embodiments of a wind farm, a controller for a wind farm, and a method for controlling a wind farm are described above in detail. The method, wind farm, and controller are not limited to the specific embodiments described herein, but rather, components of the wind turbines and/or the controller and/or steps of the method may be utilized independently and separately from other components and/or steps described herein. For example, the controller and method may also be used in combination with other power systems and methods, and are not limited to practice with only the wind turbine controller as described herein. Rather, the exemplary embodiment can be implemented and utilized in connection with many other wind turbine or power system applications.