A method for evaluating the operating performance and efficiency of distributed photovoltaic power stations

Abstract

The present invention discloses a distributed photovoltaic power station operating performance and efficiency evaluation method, which relates to the field of photovoltaic power generation technology, including: collecting data and preprocessing, building a distributed photovoltaic power station efficiency prediction model based on the preprocessed data; predicting future power generation and equipment failures based on the distributed photovoltaic power station efficiency prediction model; and evaluating the current power station operating performance and efficiency based on the predicted future power generation and equipment failures. In the efficiency prediction model, the present invention accurately predicts future power generation, and predicts potential equipment failures by analyzing equipment operation data. The overall operating efficiency of the power station is improved, and at the same time, through equipment failure prediction, the risk of downtime is reduced, the service life of the equipment is extended, and the comprehensive benefits of the photovoltaic power station are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a method for evaluating the operating performance and efficiency of a distributed photovoltaic power station. Background Art

[0002] With the rapid growth of global energy demand and the improvement of environmental awareness, solar photovoltaic technology has been widely used, especially in the promotion of distributed photovoltaic power stations. The core advantage of distributed photovoltaic power stations lies in their on-site conversion and utilization of energy, which reduces transmission losses and improves the utilization rate of electric energy. This distributed system can not only meet the power demand of local areas, but also realize power dispatching through smart grids. However, the operation performance evaluation and efficiency prediction of distributed photovoltaic power stations have always been technical difficulties. The existing distributed photovoltaic power station operation monitoring system mainly relies on the real-time monitoring of the operating status and environmental parameters of the equipment, but the prediction ability of future power generation and equipment failure is relatively limited. In addition, distributed photovoltaic power stations are usually installed outdoors and are greatly affected by factors such as climatic conditions, component aging and equipment failure, which makes the evaluation of their operation performance and efficiency require more complex algorithms and more comprehensive data processing methods.

[0003] Current technologies have certain deficiencies in predicting the future power generation and equipment failures of distributed photovoltaic power stations. Most existing evaluation methods rely only on historical data and simple linear models, and fail to fully consider the dynamic changes of environmental factors and the nonlinear effects of equipment operating parameters. In addition, many systems lack the ability to accurately predict equipment failures and are unable to detect potential problems with equipment in a timely manner, resulting in a decrease in the operating efficiency of power stations. At the same time, due to the lack of a unified prediction model and standardized data processing procedures, the evaluation results of existing systems are often not accurate and stable enough. Therefore, how to use big data and machine learning technology to comprehensively predict the future power generation and equipment failures of distributed photovoltaic power stations, and based on this, evaluate the operating performance of the power station, has become a problem that needs to be solved urgently. Summary of the invention

[0004] In view of the above-mentioned problems that the existing distributed photovoltaic power station operation performance evaluation technology has low prediction accuracy and delayed equipment failure prediction, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: by constructing a distributed photovoltaic power station efficiency prediction model to predict future power generation and equipment failures, the problems of low prediction accuracy and delayed equipment failure prediction in the prior art are solved.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for evaluating the operating performance and efficiency of a distributed photovoltaic power station, comprising: collecting and preprocessing data, and constructing a distributed photovoltaic power station efficiency prediction model based on the preprocessed data; predicting future power generation and equipment failures based on the distributed photovoltaic power station efficiency prediction model; and evaluating the current power station operating performance and efficiency based on the predicted future power generation and equipment failures.

[0007] As a preferred scheme of the distributed photovoltaic power station operation performance and efficiency evaluation method described in the present invention, the collected data includes ambient temperature, humidity, irradiance, wind speed and wind direction data; equipment parameters, equipment operation data, historical power generation and seasonal changes of the power station and historical fault information.

[0008] As a preferred solution of the distributed photovoltaic power station operation performance and efficiency evaluation method described in the present invention, the distributed photovoltaic power station efficiency prediction model includes predicting future power generation based on equipment parameters, historical power generation of the power station, ambient temperature, humidity, wind speed and wind direction data; and predicting equipment failures based on equipment operation data and historical fault information.

[0009] As a preferred embodiment of the distributed photovoltaic power station operation performance and efficiency evaluation method described in the present invention, the prediction of future power generation includes preprocessing the equipment parameters, ambient temperature, humidity, irradiance, wind speed and wind direction data; based on the preprocessed equipment parameters, ambient temperature, humidity, irradiance, wind speed and wind direction data, calculating the predicted power generation at time t ; Introduce historical power generation and seasonal changes to obtain optimized power generation forecast values ; Introduce equipment operation data to obtain the final optimized power generation forecast result .

[0010] As a preferred solution of the distributed photovoltaic power station operation performance and efficiency evaluation method described in the present invention, the predicted power generation at time t is The calculation formula is as follows:

[0011] ,

[0012] represents the predicted power generation at time t, represents the time-varying regulation function, represents the i-th influencing factor, represents the weight, Indicates the historical power generation at the previous moment, , represents the weight, represents the dynamic error term; the optimized power generation forecast value The calculation formula is as follows:

[0013] ,

[0014] in, represents the predicted value of power generation after optimization, represents the predicted power generation at time t, represents the weight coefficient of future power generation forecast, represents the seasonal change gradient at future times, represents the adjustment parameters, Represents the historical error term; the final optimized power generation forecast result The calculation formula is as follows:

[0015] ,

[0016] in, represents the final optimized power generation forecast result, represents the predicted value of power generation after optimization, represents the weight coefficient of the future power generation forecast result, Indicates the equipment operation data. Indicates the adjustment parameter.

[0017] As a preferred solution of the distributed photovoltaic power station operation performance and efficiency evaluation method described in the present invention, the prediction of equipment failure includes preprocessing equipment operation data and historical fault information, and collecting the cumulative operation time change rate and equipment three-dimensional data of the equipment at the future moment; based on the preprocessed equipment operation data and historical fault information, calculating the equipment failure probability at time t ; Introduce the cumulative running time of the equipment at the future moment to obtain the optimized equipment failure probability ; Introduce equipment 3D data to obtain the final equipment failure probability .

[0018] As a preferred solution of the distributed photovoltaic power station operation performance and efficiency evaluation method described in the present invention, the equipment failure probability at time t is The formula is as follows:

[0019] ,

[0020] in, represents the probability of equipment failure at time t, represents the activation function, represents the equipment aging adjustment function, Indicates the current device status data. Indicates the device status data at the last moment. Indicates the cumulative running time of the device. , , All represent weight coefficients, b represents bias term; the probability of equipment failure after optimization The calculation formula is as follows:

[0021] ,

[0022] in, represents the optimized equipment failure probability, represents the probability of equipment failure at time t, represents the future equipment failure prediction weight coefficient, ∇C(t+Δt) represents the rate of change of the equipment's cumulative operating time at the future moment, represents the adjustment parameters, represents the historical error term; the final probability of equipment failure The calculation formula is as follows:

[0023] ,

[0024] in, represents the final equipment failure probability, represents the optimized equipment failure probability, Represents the weight coefficient of the future 3D state data of the device, Represents the future three-dimensional state data of the equipment, Represents the adjustment coefficient.

[0025] Another object of the present invention is to provide a distributed photovoltaic power station operating performance and efficiency evaluation system, including: a model building module, used to collect and preprocess data, and build a distributed photovoltaic power station efficiency prediction model based on the preprocessed data; a prediction module, used to predict future power generation and equipment failures based on the distributed photovoltaic power station efficiency prediction model; an evaluation module, used to evaluate the current power station operating performance and efficiency based on the predicted future power generation and equipment failures.

[0026] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the above-mentioned distributed photovoltaic power station operation performance and efficiency evaluation method when executing the computer program.

[0027] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned distributed photovoltaic power station operation performance and efficiency evaluation method are implemented.

[0028] The beneficial effects of the present invention are as follows: First, by collecting and preprocessing the voltage, current, power, frequency, ambient temperature, humidity and other data of the inverter, the accuracy and consistency of the input data are improved, thereby ensuring the accuracy of the subsequent prediction model. In the distributed photovoltaic power station efficiency prediction model, the future power generation is accurately predicted by combining the equipment parameters, historical power generation and fault data, and potential equipment failures are predicted by analyzing the equipment operation data. The introduction of factors such as historical power generation and seasonal changes makes the power generation prediction model more dynamic and flexible. By gradually optimizing the prediction results, the system can accurately evaluate the current operating performance of the photovoltaic power station, reducing the errors caused by the lack of historical data or equipment status data in traditional methods. This optimization calculation step improves the overall operating efficiency of the power station. At the same time, through the prediction of equipment failures, the risk of downtime is reduced and the service life of the equipment is extended, thereby greatly improving the comprehensive benefits of the photovoltaic power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0030] Figure 1 This is a schematic diagram of the structure of a terminal in the first embodiment of the present invention;

[0031] Figure 2 It is a schematic diagram of a flow chart in the first embodiment of the present invention;

[0032] Figure 3 FIG. 4 is a schematic diagram of the structure of a computer device in a third embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0035] Example 1

[0036] Reference Figure 1 , which is an embodiment of the present invention, provides a method for evaluating the operating performance and efficiency of a distributed photovoltaic power station.

[0037] First, the distributed photovoltaic power station operation performance and efficiency evaluation method provided in this application can be applied to Figure 1 In the terminal shown. Figure 1 As shown, the terminal may include one or two ( Figure 1 Only one is shown in the figure) processor and memory for storing data, wherein the processor may include but is not limited to a processing system such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device and an input and output device for communication functions. It can be understood by ordinary technicians in this field that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0038] The memory can be used to store computer programs, such as the computer program corresponding to the distributed photovoltaic power station operation performance and efficiency evaluation method in this embodiment. The processor executes various functional applications and data processing by running the computer program stored in the memory, that is, implementing the above method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0039] The transmission device is used to receive or send data via a network. The above-mentioned network includes a wireless network provided by the communication provider of the terminal. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0040] like Figure 2 As shown, the embodiment of the present invention provides a method for evaluating the operating performance and efficiency of a distributed photovoltaic power station. Figure 1The terminal in is used as an example to illustrate, including the following steps:

[0041] S1: Collect and preprocess data, and build a distributed photovoltaic power station efficiency prediction model based on the preprocessed data.

[0042] Specifically, the collected data include ambient temperature, humidity, irradiance, wind speed and direction data; equipment parameters, equipment operation data, historical power generation and seasonal changes of the power station, and historical fault information.

[0043] Furthermore, the ambient temperature affects the efficiency of solar cells, the irradiance determines the available solar energy resources, and the wind speed and direction affect the heat dissipation and cleanliness of the panels.

[0044] Furthermore, equipment parameters and operating data reflect the working status and efficiency of each component of the PV system, helping to identify performance degradation or potential failures. The power plant's historical power generation and seasonal variation data are used to establish a baseline to evaluate current performance and identify long-term trends and cyclical patterns.

[0045] S2: Predict future power generation and equipment failures based on the distributed photovoltaic power station efficiency prediction model.

[0046] Specifically, the distributed photovoltaic power station efficiency prediction model includes predicting future power generation based on equipment parameters, historical power generation and seasonal changes of the power station, ambient temperature, humidity, irradiance, wind speed and direction data, and equipment operation data; and predicting equipment failures based on equipment operation data and historical failure information.

[0047] Furthermore, the prediction of future power generation includes preprocessing equipment parameters, ambient temperature, humidity, irradiance, wind speed and wind direction data; and calculating the predicted power generation at time t based on the preprocessed equipment parameters, ambient temperature, humidity, irradiance, wind speed and wind direction data. .

[0048] Predicted power generation at time t The calculation formula is as follows:

[0049] ,

[0050] represents the predicted power generation at time t, represents the time-varying regulation function, represents the i-th influencing factor, represents the weight, Indicates the historical power generation at the previous moment, , represents the weight, represents the dynamic error term.

[0051] Furthermore, historical power generation and seasonal changes are introduced to obtain the optimized power generation forecast value .

[0052] Furthermore, the optimized power generation forecast value The calculation formula is as follows:

[0053] ,

[0054] in, represents the predicted value of power generation after optimization, represents the predicted power generation at time t, represents the weight coefficient of future power generation forecast, represents the seasonal change gradient at future times, represents the adjustment parameters, represents the historical error term.

[0055] Furthermore, the equipment operation data is introduced to obtain the final optimized power generation forecast result. .

[0056] Furthermore, the final optimized power generation forecast result The calculation formula is as follows:

[0057] ,

[0058] in, represents the final optimized power generation forecast result, represents the predicted value of power generation after optimization, represents the weight coefficient of the future power generation forecast result, Indicates the equipment operation data. Indicates the adjustment parameter.

[0059] Furthermore, the prediction of equipment failure includes preprocessing equipment operation data and historical failure information; calculating the equipment failure probability at time t based on the preprocessed data ; Introduce the cumulative running time of the equipment at the future moment to obtain the optimized equipment failure probability ; Introduce equipment 3D data to obtain the final equipment failure probability .

[0060] Furthermore, the probability of equipment failure at time t is The formula is as follows:

[0061] ,

[0062] in, represents the probability of equipment failure at time t, represents the activation function, represents the equipment aging adjustment function, Indicates the current device status data. Indicates the device status data at the last moment. Indicates the cumulative running time of the device. , , All represent weight coefficients, and b represents the bias term;

[0063] Furthermore, the probability of equipment failure after optimization The calculation formula is as follows:

[0064] ,

[0065] in, represents the optimized equipment failure probability, represents the probability of equipment failure at time t, represents the future equipment failure prediction weight coefficient, ∇C(t+Δt) represents the rate of change of the equipment's cumulative operating time at the future moment, represents the adjustment parameters, represents the historical error term;

[0066] Furthermore, the final equipment failure probability The calculation formula is as follows:

[0067] ,

[0068] in, represents the final equipment failure probability, represents the optimized equipment failure probability, Represents the weight coefficient of the future 3D state data of the device, Represents the future three-dimensional state data of the equipment, Represents the adjustment coefficient.

[0069] S3: Evaluate current plant operation performance and efficiency based on predicted future power generation and equipment failures.

[0070] Specifically, based on the previously predicted final optimized power generation forecast results and the final equipment failure probability , and use the results as a basis for evaluating the current operating performance and efficiency of the power plant.

[0071] First, these data are normalized so that they can be compared with the actual operation data of the current power plant; through comparison, the health status of the current system operation can be obtained.

[0072] Furthermore, the evaluation of the operating performance of the power station mainly involves the actual power generation and and define a performance metric.

[0073] Furthermore, according to the probability of equipment failure Compare with the current equipment status data of the power station to determine whether there is a potential fault in the equipment.

[0074] Furthermore, the results of power generation assessment and equipment health assessment are comprehensively evaluated to obtain the comprehensive operating efficiency of the power plant.

[0075] Ultimately, the various results are used to generate an evaluation report, which may include a detailed analysis of current performance, potential risks, and optimization suggestions.

[0076] Example 2

[0077] Reference Figure 1-2 , is an embodiment of the present invention, and provides a distributed photovoltaic power station operation performance and efficiency evaluation system, including:

[0078] The model building module is used to collect and preprocess data, and build a distributed photovoltaic power station efficiency prediction model based on the preprocessed data;

[0079] A prediction module is used to predict future power generation and equipment failures based on a distributed photovoltaic power station efficiency prediction model;

[0080] Evaluation module, used to evaluate the current power plant operation performance and efficiency based on the predicted future power generation and equipment failures.

[0081] For the specific definition of the distributed photovoltaic power station operation performance and efficiency evaluation system, please refer to the definition of the distributed photovoltaic power station operation performance and efficiency evaluation method above, which will not be repeated here. Each module in the above-mentioned distributed photovoltaic power station operation performance and efficiency evaluation system can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0082] Example 3

[0083] Reference Figure 3 , which is the third embodiment of the present invention. Based on the first two embodiments, the embodiment of the present invention provides a computer device, which may be a server, and its internal structure diagram may be as shown in FIG. Figure 3As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium.

[0084] The database of the computer device is used to store action detection data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in any of the above-mentioned sparse tensor operation acceleration method embodiments are implemented.

[0085] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0086] In one embodiment, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in any of the above-mentioned sparse tensor operation acceleration method embodiments are implemented.

[0087] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0088] Example 4

[0089] As an embodiment of the present invention, a method for evaluating the operating performance and efficiency of a distributed photovoltaic power station is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0090] Specifically, a photovoltaic power station was selected as the test object, and the voltage, current, power and frequency data of its inverter were collected; the temperature and irradiance data of the components were collected; the ambient temperature, humidity, wind speed and wind direction data; equipment parameters, equipment operation data, historical power generation and historical fault information of the power station were collected. The data collection cycle was set to one month, and the data recording frequency was once an hour. The collected raw data was cleaned and standardized to remove outliers and noise. The data was smoothed according to the time series to reduce the impact of errors on the model.

[0091] Furthermore, the experimental data are as follows:

[0092] Table 1: Comparison of power generation forecast data

[0093] ,

[0094] Table 2: Comparison of equipment failure prediction data

[0095] ,

[0096] Table 3: Operational performance indicators

[0097] ,

[0098] Furthermore, by comparing the data in the table, it can be seen that the distributed photovoltaic power station efficiency prediction model based on the invention content has significant advantages over the traditional model. The specific analysis is as follows: As can be seen from Table 1, the error of the final predicted power generation (248 kWh, 253 kWh, 259 kWh, 246 kWh, 268 kWh) is significantly reduced compared with the actual power generation (250 kWh, 255 kWh, 260 kWh, 245 kWh, 270 kWh). The error of the optimized predicted power generation has dropped from an average of -0.8% of the initial prediction to -0.4% of the final prediction. This shows that the optimized prediction that introduces historical power generation, seasonal changes and equipment operation data can more accurately reflect the actual power generation and effectively improve the accuracy of power generation prediction.

[0099] Furthermore, it can be seen from Table 2 that the final failure probability (5.2%, 4.5%, 6.0%, 4.6%, 5.7%) has a smaller error than the actual failure probability (5%, 4%, 6%, 4.5%, 5.5%). The error of the optimized failure probability is about 0.5 percentage points less than the initial failure probability, indicating that the optimized fault prediction model combining the cumulative running time change rate of the equipment and the three-dimensional data has a significant improvement in accuracy.

[0100] Furthermore, it can be seen from Table 3 that by comparing the actual power generation with the final predicted power generation, the power generation error is reduced and the performance evaluation score is generally high. The power generation error is reduced to -0.7% on average, and the accuracy of equipment failure prediction is improved, so that the overall operating performance is better evaluated. The performance evaluation score reflects the effect of the model in real applications and shows the significant advantages of the invented model in improving the accuracy and reliability of power plant performance evaluation.

[0101] In summary, the above data analysis verifies the innovation and advantages of the distributed photovoltaic power station efficiency prediction model in power generation prediction and equipment failure prediction. The model can not only significantly improve the prediction accuracy, but also more accurately evaluate the operating efficiency of the power station, thereby achieving more efficient power station management and optimization.

[0102] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the operating performance and efficiency of a distributed photovoltaic power station, characterized in that: include: Collect and preprocess data, and build a distributed photovoltaic power station efficiency prediction model based on the preprocessed data; Predict future power generation and equipment failures based on the distributed photovoltaic power station efficiency prediction model; Evaluate current plant operating performance and efficiency based on predicted future power generation and equipment failures; The predicted future power generation includes: Preprocess equipment parameters, ambient temperature, humidity, irradiance, wind speed and wind direction data; Based on the preprocessed equipment parameters, ambient temperature, humidity, irradiance, wind speed and wind direction data, calculate the predicted power generation P(t) at time t; Introducing historical power generation and seasonal changes, we can obtain the optimized power generation forecast value P′(t); Introduce equipment operation data to obtain the final optimized power generation forecast result P″(t); The calculation formula of the predicted power generation P(t) at time t is as follows: P(t) represents the predicted power generation at time t, φ(t) represents the time-varying regulation function, and X i (t) represents the i-th influencing factor, w i represents the weight, H(t-1) represents the historical power generation at the previous moment, α and γ represent the weights, and ∈(t) represents the dynamic error term; The calculation formula for optimizing the predicted power generation P′(t) is as follows: Among them, P′(t) represents the optimized power generation forecast value, P(t) represents the predicted power generation at time t, γ′ represents the weight coefficient of future power generation forecast, represents the seasonal change gradient at future moments, λ′ represents the adjustment parameter, and ∈'(t) represents the historical error term; The calculation formula of the final power generation prediction result P″(t) is as follows: Among them, P″(t) represents the final optimized power generation forecast result, P′(t) represents the optimized power generation forecast value, θ′ represents the weight coefficient of the future power generation forecast result, F(t) represents the equipment operation data, and η′ represents the adjustment parameter; The predicted equipment failure includes: Pre-process equipment operation data and historical fault information, and collect the cumulative equipment operation time change rate and equipment three-dimensional data at future moments; Based on the preprocessed equipment operation data and historical fault information, calculate the equipment failure probability G(t) at time t; Introducing the cumulative running time of the equipment at future moments, we can obtain the optimized equipment failure probability G′(t); Introduce the three-dimensional data of the equipment to obtain the final equipment failure probability G″(t); The formula for calculating the initial fault data G(t) is as follows: G(t)=σ(Ω(t)·[v·Y(t)+u·Z(t-1)+χ·C(t)]+b) Among them, G(t) represents the probability of equipment failure at time t, σ represents the activation function, Ω(t) represents the equipment aging adjustment function, Y(t) represents the current equipment status data, Z(t-1) represents the equipment status data at the previous moment, C(t) represents the cumulative running time of the equipment, v, u, χ all represent weight coefficients, and b represents the bias term; The calculation formula for optimizing fault data is as follows: Among them, G′(t) represents the optimized equipment failure probability, G(t) represents the equipment failure probability at time t, χ′ represents the future equipment failure prediction weight coefficient, represents the rate of change of the equipment's cumulative operating time at the future moment, λ′ represents the adjustment parameter, and ∈'(t) represents the historical error term; The calculation formula for the final equipment failure result is as follows: Among them, G″(t) represents the final equipment failure probability, G′(t) represents the optimized equipment failure probability, ξ′ represents the weight coefficient of the future three-dimensional state data of the equipment, K(t) represents the future three-dimensional state data of the equipment, and η′ represents the adjustment coefficient.

2. The distributed photovoltaic power station operation performance and efficiency evaluation method according to claim 1, characterized in that: The data collected include: Ambient temperature, humidity, irradiance, wind speed and direction data; Equipment parameters, equipment operation data, historical power generation and seasonal changes of the power station, and historical fault information.

3. The method for evaluating the operating performance and efficiency of a distributed photovoltaic power station according to claim 2, characterized in that: The distributed photovoltaic power station efficiency prediction model includes: Predict future power generation based on equipment parameters, historical power generation and seasonal variations of the power plant, ambient temperature, humidity, irradiance, wind speed and direction data, and equipment operation data; Predict equipment failures based on equipment operation data and historical failure information.

4. A system using the distributed photovoltaic power station operation performance and efficiency evaluation method as described in any one of claims 1 to 3, characterized in that: include: The model building module is used to collect and preprocess data, and build a distributed photovoltaic power station efficiency prediction model based on the preprocessed data; A prediction module is used to predict future power generation and equipment failures based on a distributed photovoltaic power station efficiency prediction model; Evaluation module, used to evaluate the current power plant operation performance and efficiency based on the predicted future power generation and equipment failures.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for evaluating the operating performance and efficiency of a distributed photovoltaic power station according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating the operating performance and efficiency of a distributed photovoltaic power station according to any one of claims 1 to 3 are implemented.

Images