Techniques for tuning solar power system performance models

Abstract

Techniques for solar power system performance model tuning are disclosed. In at least one illustrative embodiment, a method of tuning a system performance model for an electrical device may include estimating an expected output of an inverter of the electrical device using the system performance model; obtaining a measured output of the inverter; determining whether a difference between the expected inverter output and the measured inverter output is within a predefined range; and in response to determining that the difference is within the predefined range, tuning the system performance model using the difference. The electrical device may additionally include at least one solar panel array, and the inverter may be electrically coupled to the at least one solar panel array.

Description

[0001] CROSS REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and priority to U.S. Provisional Patent Application Nos. 62 / 694,340 and 62 / 694,434, filed on July 5, 2018, the entire disclosures of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates generally to power systems and, more particularly, to techniques for connecting the electrical output of solar photovoltaic (PV) elements to a residential electrical grid. Background Art

[0004] Smart inverters used in solar power generation can be programmed to provide ancillary services including voltage and frequency regulation, power factor correction, and reactive power control. These advanced functionalities can help stabilize the power system and smooth out rapid fluctuations in supply and demand caused by intermittent solar irradiation and flexible loads. High penetration of distributed energy resources (DER) including solar photovoltaic components (PV) can be enforced worldwide for grid services.

[0005] Equipment-level power generation forecasting models have been developed to predict equipment output for investment planning and long-term forecasting based on historical measurements. However, unlike conventional power generation (e.g., coal, gas, nuclear, or hydroelectric power), the input required by solar photovoltaic (PV) units (i.e., solar panel arrays) (i.e., solar irradiation) is intermittent and weather-dependent. Existing forecasting models may not take into account the various uncertainties in weather parameters and the performance of the operating power equipment or model degradation caused by the aging of the physical system over time. Summary of the invention

[0006] According to one aspect of the present disclosure, a method of tuning a system performance model for an electrical device may include estimating an expected output of an inverter of the electrical device using the system performance model; obtaining a measured output of the inverter; determining whether a difference between the expected inverter output and the measured inverter output is within a predefined range; and in response to determining that the difference is within the predefined range, tuning the system performance model using the difference. The electrical device may additionally include at least one solar panel array, and the inverter may be electrically coupled to the at least one solar panel array.

[0007] In some embodiments, the system performance model for the power plant may include (i) an array performance model characterizing the at least one solar panel array and (ii) an inverter performance model characterizing the inverter.

[0008] In some embodiments, estimating the expected output of the inverter using the system performance model may include estimating the expected output of the inverter using the inverter performance model.

[0009] In some embodiments, the method may further include estimating an expected output of the at least one solar panel array using the array performance model.

[0010] In some embodiments, estimating the expected output of the inverter may include estimating the expected output of the inverter using the expected output of the at least one solar panel array as an input to the inverter performance model.

[0011] In some embodiments, the step of estimating the expected output of the at least one solar panel array and the expected output of the inverter is performed in response to determining that the inverter is not operating in a maximum power point tracking (MPPT) mode.

[0012] In some embodiments, the method may further include obtaining a measured output of the at least one solar panel array.

[0013] In some embodiments, estimating the expected output of the inverter may include estimating the expected output of the inverter using the measured output of the at least one solar panel array as an input to the inverter performance model.

[0014] In some embodiments, the steps of obtaining the measured output of the at least one solar panel array and estimating the expected output of the inverter may be performed in response to determining that the inverter is operating in a maximum power point tracking (MPPT) mode.

[0015] In some embodiments, the measured output of the at least one solar panel array may be obtained in real time.

[0016] In some embodiments, the measured output of the at least one solar panel array may be obtained without taking the at least one solar panel array offline.

[0017] In some embodiments, the measured output of the inverter may be obtained in real time.

[0018] In some embodiments, the measured output of the inverter may be obtained without taking the inverter offline.

[0019] In some embodiments, the method may further include tuning the system performance model using default parameters prior to obtaining any measured output.

[0020] In some embodiments, the array performance model may be a validated model relating solar irradiance to expected electrical output of the at least one solar panel array.

[0021] In some embodiments, the inverter performance model may relate a direct current (DC) input to the inverter to an expected alternating current (AC) output of the inverter.

[0022] In some embodiments, the system performance model for the electrical device may additionally include a local compensation model characterizing local losses associated with the electrical device.

[0023] In some embodiments, the difference between the expected inverter output and the measured inverter output may be smoothed before being used to tune the system performance model.

[0024] In some embodiments, tuning the system performance model may include comparing the system performance model for the electrical device to another system performance model for another electrical device.

[0025] In some embodiments, tuning the system performance model may compensate for degradation of the system performance model over time and incrementally adjust parameters of the system performance model to represent actual performance characteristics of the electrical device.

[0026] In some embodiments, tuning the system performance model may include updating a key performance indicator (KPI) for the electrical device.

[0027] In some embodiments, the KPI may be at least one of a power performance indicator (PPI), an energy performance indicator (EPI), a baseline power performance indicator (BPPI), and a baseline energy performance indicator (EPI).

[0028] In some embodiments, the method may further include transmitting an alarm signal to a system operator in response to determining that the difference is not within the predefined range.

[0029] In some embodiments, the method may further include performing a diagnostic analysis on the electrical device in response to determining that the difference is not within the predefined range.

[0030] In some embodiments, the method may further include transmitting results of the diagnostic analysis of the electrical device to a system operator.

[0031] According to another aspect of the present disclosure, one or more computer-readable media may store multiple instructions that, when executed by a power controller, cause the power controller to perform any one of the methods described above and throughout the present disclosure, wherein the power controller is operably connected to (i) at least one solar panel array and (ii) an inverter electrically coupled to the at least one solar panel array.

[0032] According to another aspect of the present disclosure, a power controller may be configured to perform any of the methods described above and throughout the present disclosure. In some embodiments, the power controller is operably connected to (i) at least one solar panel array and (ii) an inverter electrically coupled to the at least one solar panel array. In some embodiments, the power controller, at least one solar panel array, and the inverter may be incorporated into an electrical device. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The concepts described in the present disclosure are illustrated by way of example and not by way of limitation in the accompanying drawings. For simplicity and clarity of illustration, the elements illustrated in the drawings are not necessarily drawn to scale. For example, the dimensions of some elements may be exaggerated relative to other elements for clarity. In addition, reference numerals have been repeated among the figures to indicate corresponding or similar elements where deemed appropriate. The detailed description makes specific reference to the accompanying drawings, in which:

[0034] Figure 1 is a simplified block diagram of at least one embodiment of an electrical power system including a solar power device and a system operator;

[0035] Figure 2 It is used to determine Figure 1 A simplified block diagram of at least one embodiment of a system performance model of an output power of an electric power plant;

[0036] Figure 3 is used Figure 2 A simplified block diagram of at least one embodiment of a system performance model for determining a power performance indicator (PPI);

[0037] Figure 4 is used Figure 2 A simplified block diagram of at least one embodiment of determining a baseline power performance indicator (BPPI) using a system performance model;

[0038] Figure 5 Yes Description Figure 1 The use of electrical equipment Figure 2 A simplified block diagram of a loss model for compensating an in-situ compensation model of a system performance model;

[0039] Figure 6 yes Figure 2A simplified block diagram of at least one embodiment of an inverter performance model of a system performance model;

[0040] Figure 7 yes Figure 2 A simplified block diagram of at least one embodiment of an array performance model of a system performance model; and

[0041] Figure 8 and 9 is a simplified flow chart of at least one embodiment of a method for updating a system performance model, which may be performed by Figure 1 The power controller of the power equipment is executed. DETAILED DESCRIPTION

[0042] Although specific embodiments of the concepts of the present disclosure are shown by way of example in the drawings and described in detail herein, the concepts of the present disclosure are susceptible to various modifications and alternative forms. However, it should be understood that it is not intended to limit the concepts of the present disclosure to the specific forms disclosed, but on the contrary, it is intended to cover all modifications, equivalents and alternative forms that are consistent with the present invention and the appended claims.

[0043] References in this specification to "one embodiment," "an embodiment," "an illustrative embodiment," etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment may include the particular feature, structure, or characteristic. Furthermore, such phrases do not necessarily refer to the same embodiment. In addition, when a particular feature, structure, or characteristic is described in conjunction with an embodiment, it should be understood that it is within the knowledge of a person skilled in the art to implement such feature, structure, or characteristic in conjunction with other embodiments, whether or not explicitly described.

[0044] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on a transient or non-transitory computer-readable storage medium, which may be read and executed by one or more processors. A computer-readable storage medium may be implemented as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a computing device (e.g., volatile or non-volatile memory, a media disk, or other media device).

[0045] In the figures, some structural or method features may be shown in a specific arrangement and / or order. However, it should be understood that such specific arrangement and / or order may not be required. On the contrary, in some embodiments, such features may be arranged in a different manner and / or order than shown in the illustrative figures. In addition, the inclusion of a structural or method feature in a particular figure is not intended to imply that such feature is required in all embodiments, and in some embodiments, may not be included or may be combined with other features.

[0046] Reference now Figure 1 , a power system 100 is used for field-based power system performance model calibration, maintenance, and tuning based on real-time field measurements to maintain target performance of power devices 102, wherein the power system 100 includes power devices 102 communicatively coupled to a system operator 104 via a network 106. The power devices 102 can be large power devices or aggregated small residential or commercial photovoltaic (PV) resources (e.g., solar panel arrays 130).

[0047] Solar modeling is used for predictive maintenance and performance assessment of the solar power system 100 over its lifetime. In an illustrative embodiment, as further described below, the system performance model 200 is periodically or continuously updated based on real-time field measurements to compensate for model degradation that may occur over the lifetime of the solar power device 102. Such system performance models can be used to enable operational planning and market participation for the grid-connected PV system 100. For example, the system performance model 200 can be used to characterize and track the real-time and long-term performance of the power device 102, initiate preventive and predictive maintenance, and / or determine the future value of the PV power generation project, which value can be expressed as predicted power output. As further described below, the power controller 120 is configured to perform power system performance model tuning and maintenance based on real-time field measurements without taking the system out of service for calibration testing using machine learning techniques and / or reinforcement learning techniques.

[0048] The interaction between the power device 102 and the system operator 104 may be direct (e.g., for a solar farm) or indirect through a third-party aggregator (e.g., for a residential solar panel). The third-party aggregator may utilize a distributed energy management system (DERMS) available from ABB, Inc. A DERMS is a system for managing and coordinating the performance of a large number of individual solar generators and other distributed resources. In the absence of a system operator or a link to a system operator, the DERMS receives site capability data and provides coordinated set points for grid support.

[0049] The power plant 102 includes a power controller 120 operably connected to one or more solar panel arrays 130 via one or more inverters 140. For example, in the illustrative embodiment, the inverter 140 is implemented as a direct current (DC) to alternating current (AC) (DAC) converter 140. The power controller 120 is further communicatively coupled to the system operator 104. The DAC converter 140 is used for solar power generation and can be programmed to provide auxiliary services such as voltage and frequency regulation, power factor correction, and reactive power control. Such DAC converters 140 can help stabilize the power system 100 and compensate for rapid fluctuations in supply and demand caused by intermittent renewable resources.

[0050] In an illustrative embodiment, the power device 102 may operate in a maximum power point tracking (MPPT) mode or a non-MPPT mode. In the MPPT mode, the power device 102 is configured to maintain a maximum power point (MPP) at which the highest effectiveness of power transfer from the solar panel array 130 is achieved. In other words, the power device 102 in the MPPT mode is adapted to output its maximum power extraction (i.e., peak power) under all conditions. For example, in a grid-tied PV system, the MPPT in the grid-tied PV system may attempt to operate the inverter 140 at its maximum power point. In other words, the inverter 140 is set to provide maximum active power output at a unity or constant power factor of the MPPT.

[0051] In non-MPPT mode, the power device 102 is set to produce output power that is curtailed by a fraction (e.g., 10%) of its rated or estimated capacity (i.e., available peak power, or APP) to provide margin for on-demand regulation. As discussed above, in a PV system, APP fluctuates based on numerous variables and attributes (e.g., the amount of solar irradiation drop on the solar panel array 130, the solar panel temperature, and / or the inverter efficiency). Thus, in order to maintain or set a target regulation range and curtailment performance, the APP is estimated and transmitted to the system operator 104 for market participation. Thus, reliable automatic generation control (AGC) and ancillary services require an accurate estimate of the APP. In this case, the controller 120 is configured to follow a set point signal received periodically from the system operator 104. The set point is adjusted based on an estimate of the APP in each dispatch cycle to maintain the target regulation margin. In an illustrative embodiment, the APP is estimated using a system performance model 200 to provide the accuracy of the APP estimate based on real-time measured variables and attributes. The APP is used to optimize the efficiency of power delivery from the power device 102.

[0052] The power controller 120 may be implemented as any type of device capable of performing the functions described herein. In an illustrative embodiment, the power controller 120 is configured to periodically or continuously update or tune the system performance model 200 based on real-time measurements. To this end, the power controller 120 includes an error processor 122. Power system performance model tuning may include modifying the set of independent variables used for prediction and / or changing the model execution parameters to find the best model descriptor that captures the observed data behavior and performance. In some embodiments, the default parameters of the system performance model may be obtained from the manufacturer's specification sheet, and the accuracy of the model may be further optimized using measured values ​​from field measurements of instrument health in the operating system. The power controller 120 may initially train the system performance model using the default parameters. Over time, the power controller 120 is configured to adjust the model parameters to reflect natural device aging. It should be understood that in some embodiments, the power controller 120 may be implemented as part of the DAC converter 140 or the system operator 104. In other embodiments, the power controller 120 may be implemented as a server device (e.g., a cloud server or an edge gateway device) coupled to the network 106 in a communicative manner. Additionally, the power controller 120 is configured to dynamically control the performance of the DAC converter 140 based on the system performance model.

[0053] The error processor 122 may be implemented as any type of processor capable of performing the functions described herein. For example, the error processor 122 may be implemented as a single-core or multi-core processor, a digital signal processor, a microcontroller, or other processor or processing / control circuit. In an illustrative embodiment, the error processor 122 is configured to process error signals (e.g., error signals generated when the measured output is different from the expected output of the power device 102) to adjust the system performance model. For example, by tuning the model and tracking performance issues related to the inverter 140 and the array 130, the system performance model is configured to maintain the precision and accuracy required for peak power estimation.

[0054] In some embodiments, the power plant 102 may include a storage device 150 configured to store excess energy generated by the solar panel array 120. For example, the storage device 150 may be implemented as a battery that is charged during the day and can be used during the night to provide power to loads connected to the power plant 102. It should be appreciated that in such embodiments, the storage capacity can be fully utilized when setting up the PV plant for grid service.

[0055] As described above, the system operator 104 and the power equipment 102 illustratively communicate via the network 106, which can be implemented as any type of wired or wireless communication network, including, for example, a global network (e.g., the Internet), a local area network (LAN) or a wide area network (WAN), a cellular network (e.g., Global System for Mobile Communications (GSM), 4G, 3G, Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX)), etc.), a digital subscriber line (DSL) network, a cable network (e.g., a coaxial network, a fiber network, etc.), or any combination thereof.

[0056] In an illustrative embodiment, the power controller 120 is configured to measure and analyze the power production of a particular grid-connected photovoltaic (PV) system 100 using a power system performance model 200 to assess the quality of PV system performance. The power system performance model 200 may be used for operations planning and calculation of various key performance indicators (KPIs) that characterize and track the performance of the power equipment 102. For example, power and energy performance indicators (e.g., power performance indicators (PPIs), energy performance indicators (EPIs), baseline power performance indicators (BPPIs), and baseline energy performance indicators (EPIs)) are among the KPIs that may be applied to predictive maintenance, commissioning, and / or financial assessment of solar sites. In some embodiments, the modeling uncertainty may vary. In an illustrative embodiment, the power controller 120 is configured to assess the quality of PV system performance using a system performance model to provide performance tracking and grid support applications.

[0057] Reference now Figure 2 , a system performance model 200 for evaluating the quality of power equipment performance is shown. The system performance model 200 includes an array performance model 202, an inverter performance model 204, and an in-situ compensation model 206. In an illustrative embodiment, system performance model maintenance and tuning can be achieved by decoupling array performance from inverter performance, utilizing real-time measurements, and including a feedback loop and an error processor 122 that is configured to distinguish modeling errors from excursions generated by performance problems. As discussed further below, only eligible modeling errors are used for model tuning.

[0058] Reference now Figure 3, the power controller 120 may perform a method 300 for determining a performance power indicator (PPI) based on fixed inputs, historical variable inputs, and measured outputs using the system performance model 200. The method 300 begins at block 302, where the power controller 120 obtains a predicted system output based on fixed inputs and historical variable inputs using the system performance model 200. In block 304, the power controller 120 obtains the measured system output. Subsequently, in block 306, the power controller 120 determines whether the difference between the measured system output and the expected system output is within a predefined range. If the power controller 120 determines in block 308 that the difference is within the predefined range, the method 300 proceeds to block 310 to set or update the performance power indicator (PPI). However, if the power controller 120 determines that the difference is not within the predefined range, the method 300 loops back to block 302.

[0059] Reference now Figure 4 , the power controller 120 may perform a method 400 for determining a baseline performance power indicator (BPPI) using the system performance model 200. The method 400 begins at block 402, where the power controller 120 obtains a predicted system output based on fixed inputs and measured variable inputs using the system performance model 200. In block 404, the power controller 120 obtains the measured system output. Subsequently, in block 406, the power controller 120 determines whether the difference between the measured system output and the expected system output is within a predefined range. If the power controller 120 determines in block 408 that the difference is within the predefined range, the method 400 proceeds to block 410 to set or update the baseline performance power indicator (BPPI). However, if the power controller 120 determines that the difference is not within the predefined range, the method 400 loops back to block 402.

[0060] Return to reference Figure 2 By decoupling array performance from inverter performance in model 200, the effects of meteorological parameters and array condition estimation errors can be isolated and allow for the characterization and traceability of a number of sources of uncertainty in the estimation process. When in MPPT mode, these variables are incorporated into the estimation method that employs real-time measurements of the DC input of the DAC inverter 140. However, as discussed above, when the inverter follows the command set point received from the system operator 104, it may not be at the maximum power point (i.e., non-MPPT mode). In this case, the real-time input sent to the inverter production model 204 is the estimated output value generated by the array production model 202.

[0061] It should be appreciated that the output of DAC converter 140 (i.e., AC power) may not be a system output with respect to power exported to the grid. Therefore, local compensation model 206 is configured to determine and compensate for losses and local consumption that may be included within engineering tolerances. For example, when AC power is transmitted across distances before reaching a residential meter, AC losses in residential scale equipment may be significant. Figure 5 As described in , there are additional loss elements and possible local loads and storage devices that can be included in the local compensation model 206. When the DAC converter 140 is in MPPT mode, these losses can be directly estimated in near real time and factored into the peak power estimation by applying non-negative least squares. In some embodiments, the local compensation model 206 can be implemented as a multiplication or addition factor in its simplest form.

[0062] Reference now Figure 6 The inverter performance model 204 relates the DC input of the inverter to its expected AC output in a nominal configuration to account for inverter effectiveness. Inverter effectiveness is defined as the ratio of AC output power to DC input power and varies with AC output power, DC voltage, and / or inverter temperature, as shown in equation (1).

[0063] P AC (t) = f(P DC (t), V DC (t), T(t), params) (1)

[0064] It should be noted that the expected output of the inverter 140 determined using the inverter performance model 204 is based on the measured (in MPPT mode) or estimated (in non-MPPT mode) input (V DC and P DC or I DC ) of the inverter output. In other words, unlike the forecast output based on historical measurements, the estimated output is based on real-time measurements or estimates of the input variables. If a high accuracy forecast value is available, then the output is a forecast output that is still valuable for production scheduling for day-ahead or longer-term market participation. It should be appreciated that in illustrative embodiments, historical measurements can be used to train machine learning models for model tuning and maintenance.

[0065] The expected power supplied by the inverter performance model 204 in MPPT mode at time t is the available peak power (APP), in which case the expected power should closely match the measured output power of the inverter 140. On the other hand, the expected power calculated from the inverter production model 204 at time t in commanded or non-MPPT mode is the available peak power output by the inverter in that case. As described above, the inverter may not operate the array in commanded or non-MPPT mode near the MPP point, and the maximum DC power from the array is unknown. However, direct measurement of the maximum power can only be done when the inverter is disconnected from the panel, which is not practical online. Thus, the maximum APP is estimated from the current-voltage (IV) curve.

[0066] Reference now Figure 7 , the array performance model 202 can be any validated model that relates solar irradiance to expected power output. Acting as a "digital twin", the model can be expressed in analytical form (stateless model) or derived from machine inclination training using regression, neural network or other techniques. In general, the model can follow the following general mathematical relationship.

[0067] (I 模块 ,V 模块 )=f(E e ,T,params) (2)

[0068] P 模块 =V 模块 *I 模块 (3)

[0069] Reference now Figure 8 and 9 In use, the power controller 120 may perform a method 800 for tuning the system performance model 200 to compensate for model degradation over time and incrementally adjust model parameters to represent actual performance characteristics of the site. As discussed above, the power controller 120 may initially train the system performance model 200 based on default parameters. Over time, the power controller 120 is configured to adjust the model parameters to reflect natural equipment aging. Thus, in some embodiments, the method 800 may begin at block 802, where the power controller 120 performs an initial tuning of the system performance model 200 (i.e., the array performance model 202, the inverter performance model 204, the local compensation model 206) based on default parameters.

[0070] In block 804, the power controller 120 obtains the expected array output (e.g., V DC and P DC). To this end, in some embodiments, as illustrated in block 806, the power controller 120 may estimate the expected array output using the array performance model 202. For example, if the inverter 140 is in a non-MPPT mode, then based on the set point of the command received from the system operator 104 (e.g., the expected array output), the output of the inverter 140 is curtailed to provide the necessary headroom from the reliability service. In such cases, the power controller 120 estimates the expected array output using the array performance model 202. In other embodiments, as illustrated in block 808, the power controller 120 may obtain a measured array output. For example, if the inverter 140 is in an MPPT mode, then the inverter 140 is adapted to produce a maximum output. In this case, the actual array output may be used as the expected array output.

[0071] At block 810, the power controller 120 estimates the expected inverter output using the inverter performance model 204. To this end, in some embodiments, as illustrated in block 812, the power controller 120 may estimate the expected inverter output based on the estimated array output (from block 806). For example, when the inverter 140 is operating in a non-MPPT mode, blocks 806 and 812 may be utilized. In other embodiments, as illustrated in block 814, the power controller 120 may estimate the expected inverter output based on the obtained measured array output (from block 808). For example, when the inverter 140 is operating in an MPPT mode, blocks 808 and 814 may be utilized.

[0072] exist Fig. 9 In block 816 shown in FIG. 8 , the power controller 120 obtains the measured inverter output. Subsequently, in block 818 , the power controller 120 compares the expected inverter output to the measured inverter output and determines the difference between the inverter output values.

[0073] In box 820, the power controller 120 determines whether the difference between the expected inverter output and the measured inverter output is within a predefined range. In box 820, the power controller 120 determines whether the difference between the expected inverter output and the measured inverter output is within a predefined range. If the power controller 120 determines that the difference is within the predefined range, the power controller 120 determines that the system performance model 200 is outdated and the method 800 proceeds to box 822 to perform system performance model tuning. In other words, if the difference is within an acceptable range, an error signal is generated and processed by the error processor 122 of the power controller 120 to eliminate the original error signal (e.g., moving average) and mitigate the effects of noise, jitter and other artifacts. It should be understood that the error processor 122 can check the operating mode, operating constraints, internal protection, grid stability and power quality, diagnosis, time synchronization, sensor integrity, network security and / or system description based on the error signal and supplementary data, so as to adjust the system performance model 200 for model tuning and performance tracking and diagnosis. By doing so, error variance and / or bias are reduced so that systematic changes in inverter performance are reflected in the updated system performance model 200. In some embodiments, the power controller 120 may cross-compare the system performance model with other inverters from the same site or the same category to update the system performance model 200. The method 800 then loops back to block 804 to continue tuning the system performance model 200 based on real-time field measurements.

[0074] However, in some embodiments, if the power controller 120 determines in block 820 that the difference is not within the predefined range, the power controller 120 may proceed to block 824. In block 824, the power controller 120 may transmit an alarm signal to the system operator 104. In other words, if the difference or error is not within an acceptable range, the error processor 122 may determine that the error is due to a factor (e.g., weather) that the inverter 140 cannot control or influence. In this case, the error signal is not processed as an eligible input for adjusting the system performance model 200. Additionally, in some embodiments, the power controller 120 may activate real-time diagnostic analysis and may transmit diagnostic results to the system operator 104. The method 800 then loops back to block 804 to continue tuning the system performance model 200 based on real-time field measurements.

[0075] While the disclosure has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered exemplary and not restrictive in nature, and it is to be understood that only illustrative embodiments have been shown and described and that all changes and modifications that come within the spirit of the disclosure are desired to be protected.

[0076] There are a number of advantages of the present disclosure resulting from the various features of the methods, apparatuses, and systems described herein. It should be noted that alternative embodiments of the methods, apparatuses, and systems of the present invention may not include all of the described features, but still benefit from at least some of the advantages of such features. One of ordinary skill in the art may readily design and have one or more of the features of the present disclosure and their own implementation of the methods, apparatuses, and systems that fall within the spirit and scope of the present disclosure as defined by the appended claims.

Claims

1. A method of tuning a system performance model for an electrical device, the electrical device comprising at least one solar panel array, an inverter electrically coupled to the at least one solar panel array, and a power controller including an error processor, the method comprising: Training the system performance model using default parameters; estimating an expected output of the inverter using the system performance model; obtaining a measured output of the inverter; calculating, by the power controller, a difference between the expected output of the inverter and the measured output of the inverter; determining, by the error handler, whether the difference represents a modeling error or a performance problem; and In response to determining that the difference represents the modeling error, the system performance model is tuned using the difference for subsequent use of the system performance model to estimate the expected output of the inverter.

2. The method of claim 1, wherein the system performance model for the power device comprises (i) an array performance model characterizing the at least one solar panel array and (ii) an inverter performance model characterizing the inverter. 3 . The method of claim 2 , wherein estimating the expected output of the inverter using the system performance model comprises estimating the expected output of the inverter using the inverter performance model.

4. The method of claim 3, further comprising estimating an expected output of the at least one solar panel array using the array performance model.

5. The method of claim 4, wherein estimating the expected output of the inverter comprises estimating the expected output of the inverter using the expected output of the at least one solar panel array as an input to the inverter performance model.

6. The method of claim 5, wherein the step of estimating the expected output of the at least one solar panel array and the expected output of the inverter is performed in response to determining that the inverter is not operating in a maximum power point tracking (MPPT) mode.

7. The method of claim 3, further comprising obtaining a measured output of the at least one solar panel array.

8. The method of claim 7, wherein estimating the expected output of the inverter comprises estimating the expected output of the inverter using the measured output of the at least one solar panel array as an input to the inverter performance model.

9. The method of claim 8, wherein the steps of obtaining the measured output of the at least one solar panel array and estimating the expected output of the inverter are performed in response to determining that the inverter is operating in a maximum power point tracking (MPPT) mode.

10. The method of any of claims 7-9, wherein the measured output of the at least one solar panel array is obtained in real time.

11. The method of any of claims 7-9, wherein the measured output of the at least one solar panel array is obtained without taking the at least one solar panel array offline.

12. The method of any of claims 1-9, wherein the measured output of the inverter is obtained in real time.

13. The method of any of claims 1-9, wherein the measured output of the inverter is obtained without taking the inverter offline.

14. The method of any of claims 1-9, wherein the measured output of the inverter is obtained in real time without taking the inverter offline.

15. The method of any of claims 1-9, further comprising tuning the system performance model using default parameters prior to obtaining any measured output.

16. The method of any of claims 2-4, wherein the array performance model is a validated model relating solar irradiance to expected electrical output of the at least one solar panel array.

17. The method of any of claims 2-9, wherein the inverter performance model relates a direct current (DC) input of the inverter to an expected alternating current (AC) output of the inverter.

18. The method of any of claims 1-9, wherein the system performance model for the power plant additionally includes a local compensation model characterizing local losses associated with the power plant.

19. The method of any of claims 1-9, wherein the difference between the expected output of the inverter and the measured output of the inverter is smoothed before being used to tune the system performance model.

20. The method of any of claims 1-9, wherein tuning the system performance model comprises comparing the system performance model for the electrical device with another system performance model for another electrical device.

21. The method of any one of claims 1-9, wherein tuning the system performance model compensates for degradation of the system performance model over time and incrementally adjusts parameters of the system performance model to represent actual performance characteristics of the electrical equipment.

22. The method of any one of claims 1-9, wherein tuning the system performance model comprises updating a key performance indicator (KPI) for the electrical equipment.

23. The method of claim 22, wherein the KPI is at least one of a power performance index (PPI), an energy performance index (EPI), a baseline power performance index (BPPI), and a baseline energy performance index (EPI).

24. The method of any of claims 1-9, further comprising transmitting an alarm signal to a system operator in response to determining that the difference is not within a predefined range.

25. The method of any of claims 1-9, further comprising performing a diagnostic analysis on the electrical device in response to determining that the difference is not within a predefined range.

26. The method of claim 25, further comprising transmitting results of the diagnostic analysis of the electrical device to a system operator.

27. A computer-readable medium storing a plurality of instructions which, when executed by a power controller, cause the power controller to perform a method according to any of the preceding claims, the power controller being operably connected to (i) at least one solar panel array and (ii) an inverter electrically coupled to the at least one solar panel array.

28. A power controller operably connected to (i) at least one solar panel array and (ii) an inverter electrically coupled to the at least one solar panel array, the power controller being configured to perform the method of any one of claims 1-26.

29. An electric power device, comprising: at least one solar panel array; an inverter electrically coupled to the at least one solar panel array; and A power controller operably connected to the at least one solar panel array and the inverter, the power controller being configured to perform the method according to any one of claims 1-26.

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