Photovoltaic power station risk prediction method, device, equipment, medium and program product

Abstract

The present invention discloses a method, device, equipment, medium and program product for predicting risks of photovoltaic power stations. The method comprises: acquiring multidimensional data of the photovoltaic power station collected by an information collection system, processing the multidimensional data, acquiring event data, feature data and fault result data, constructing a mapping matrix based on the mapping relationship between the event data, the feature data and the fault result data, analyzing the fault logic path of the photovoltaic power station according to the mapping matrix, constructing a risk prediction model based on the analysis result, inputting the real-time monitoring data of the photovoltaic power station into the risk prediction model, and obtaining the risk prediction result of the photovoltaic power station, thereby effectively improving the accuracy of the risk prediction of the photovoltaic power station, realizing the accurate prediction of potential risks, ensuring the timeliness of risk identification, and improving the safety of the photovoltaic power station.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a photovoltaic power station risk prediction method, device, equipment, medium and program product. Background Art

[0002] With the advancement of science and technology, new energy systems, represented by photovoltaics, are placing greater emphasis on clean, low-carbon, safe, and efficient energy consumption and power storage. With the increasing global demand for renewable energy, photovoltaic power generation, as a clean, environmentally friendly, and sustainable form of energy, has become a vital component of global energy supply.

[0003] During the long-term operation of photovoltaic power plants, they face numerous potential risks due to equipment failures, changing environmental factors, and maintenance challenges. Equipment failure risks primarily arise from damage or aging of core components such as photovoltaic modules, inverters, battery energy storage systems, and transformers. These failures not only impact power plant efficiency but, in severe cases, can cause system downtime. Therefore, risk prediction for photovoltaic power plants is crucial. Current risk monitoring for photovoltaic power plants typically relies on single or localized data sources, resulting in limited prediction accuracy and coverage, making it impossible to accurately detect potential risks in photovoltaic power plants. Summary of the Invention

[0004] The main purpose of the present invention is to provide a photovoltaic power station risk prediction method, device, equipment, medium and program product, aiming to solve the technical problem that the existing technology relies on a single data source in photovoltaic power station risk prediction, resulting in limited prediction accuracy and coverage, and unable to accurately detect the potential risks of photovoltaic power stations.

[0005] To achieve the above object, the present invention provides a photovoltaic power station risk prediction method, which includes the following steps:

[0006] Acquire multidimensional data of the photovoltaic power station collected by an information collection system, wherein the information collection system includes a space-based collection module, an air-based collection module, and a ground-based collection module, and the multidimensional data includes environmental data, equipment status data, and equipment operation data of the photovoltaic power station;

[0007] Processing the multidimensional data to obtain event data, feature data, and fault result data, wherein the event data includes multiple fault events of the photovoltaic power station, the feature data includes feature data corresponding to each fault event, the feature data is composed of one or more faulty photovoltaic components corresponding to the fault event, and the fault result data includes a fault event result corresponding to each fault event;

[0008] Constructing a mapping matrix based on the mapping relationship between the event data, the feature data, and the fault result data;

[0009] Analyzing the fault logic path of the photovoltaic power station according to the mapping matrix, and building a risk prediction model based on the analysis results;

[0010] The real-time monitoring data of the photovoltaic power station collected by the information collection system is input into the risk prediction model to obtain a risk prediction result of the photovoltaic power station, wherein the risk prediction result includes the risk level of each area in the photovoltaic power station.

[0011] Optionally, analyzing the fault logic path of the photovoltaic power station according to the mapping matrix and constructing a risk prediction model based on the analysis result includes:

[0012] Analyzing the fault logic path of the photovoltaic power station according to the mapping matrix, and constructing a fault logic path model based on the analysis result, wherein the fault logic path model includes multiple fault logic paths;

[0013] Performing path significance analysis on each fault logic path in the fault logic path model;

[0014] Construct an initial path significance model based on the significance analysis results:

[0015] ;

[0016] in, It is a photovoltaic module The reliability parameters, Indicates that it contains photovoltaic modules The minimum path set of Include Path, Represents a binary random vector, if the photovoltaic module Work, then Equal to 1, otherwise is equal to 0, Indicates the fault logic path, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power plants;

[0017] The initial path significance model is optimized based on the logical relationship between each photovoltaic module and each fault event to obtain a risk prediction model.

[0018] Optionally, the optimizing the initial path significance model based on the logical relationship between each photovoltaic module and each fault event to obtain a risk prediction model includes:

[0019] Acquire a logical relationship between each photovoltaic module and each fault event based on the fault logic path;

[0020] Determining the relative positions of the photovoltaic modules and the logical connection relationships between the photovoltaic modules and the fault events according to the logical relationships;

[0021] Constructing a reliability block diagram based on the relative positions and the logical connection relationship;

[0022] The initial path significance model is optimized according to the reliability block diagram to obtain a candidate significance model:

[0023] ;

[0024] in, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power stations, It is a photovoltaic module The reliability parameters, It represents the logic generation structure function constructed based on the logical connection relationship between the PV modules and the fault events and the relative position of the PV modules in the logical connection relationship. Indicates the photovoltaic power station in the photovoltaic components The probability of normal operation under the action of

[0025] A survival analysis is performed on the photovoltaic power station based on the photovoltaic components corresponding to each fault event, and the candidate significance model is optimized based on the survival analysis result to obtain a risk prediction model.

[0026] Optionally, performing a survival analysis on the photovoltaic power station based on the photovoltaic components corresponding to each fault event, and optimizing the candidate significance model based on the survival analysis result to obtain a risk prediction model, includes:

[0027] Based on the photovoltaic components corresponding to each fault event, a survival analysis is performed on the photovoltaic power station to construct a survival signature:

[0028] ;

[0029] The survival function of the photovoltaic power station is constructed according to the survival signature:

[0030] ;

[0031] Optimizing the candidate significance model based on the survival function to obtain a risk prediction model;

[0032] in, Indicates that PV panels and The state vector the number of represents the set of all possible state vectors in a photovoltaic power station, Indicates the Type of photovoltaic modules in time The number of working states at the time.

[0033] Optionally, optimizing the candidate significance model based on the survival function to obtain a risk prediction model includes:

[0034] Obtain the failure time distribution information of each photovoltaic module;

[0035] Generate boundary conditions based on the failure time distribution information, wherein the boundary conditions include an upper limit condition and a lower limit condition:

[0036] ;

[0037] in, is the upper limit condition, is the lower limit condition, and An exponential decay function representing the change in the failure probability of a PV power plant or PV module over time;

[0038] The candidate significance model is optimized according to the boundary conditions to obtain a risk prediction model.

[0039] Optionally, optimizing the candidate significance model according to the boundary conditions to obtain a risk prediction model includes:

[0040] Perform uncertainty analysis on each photovoltaic module to obtain the uncertainty impact information of each photovoltaic module:

[0041] ;

[0042] ;

[0043] The candidate significance model is optimized based on the uncertainty impact information and the boundary conditions to obtain a risk prediction model:

[0044] ;

[0045] in, Represents photovoltaic modules At a specific time The relative uncertainty of the impact of information, Indicates the The relative interval parameters of each photovoltaic module, Indicates that when The possibility of a failure event in the photovoltaic system when a photovoltaic module occurs, Indicates the The possibility of a failure event in the photovoltaic system when the photovoltaic modules are not present. It is a photovoltaic module The reliability parameters, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power plants.

[0046] In addition, to achieve the above-mentioned purpose, the present invention further proposes a photovoltaic power station risk prediction device, the photovoltaic power station risk prediction device comprising:

[0047] A data acquisition module is used to obtain multi-dimensional data of the photovoltaic power station collected by the information acquisition system, wherein the information acquisition system includes a space-based acquisition module, an air-based acquisition module, and a ground-based acquisition module. The multi-dimensional data includes environmental data, equipment status data, and equipment operation data of the photovoltaic power station;

[0048] a data processing module, configured to process the multidimensional data to obtain event data, characteristic data, and fault result data, wherein the event data includes multiple fault events of the photovoltaic power station, the characteristic data includes characteristic data corresponding to each fault event, the characteristic data is composed of one or more faulty photovoltaic components corresponding to the fault event, and the fault result data includes a fault event result corresponding to each fault event;

[0049] A matrix construction module, configured to construct a mapping matrix based on a mapping relationship between the event data, the feature data, and the fault result data;

[0050] a model building module, configured to analyze the fault logic path of the photovoltaic power station according to the mapping matrix and build a risk prediction model based on the analysis results;

[0051] The risk prediction module is used to input the real-time monitoring data of the photovoltaic power station collected by the information collection system into the risk prediction model to obtain the risk prediction result of the photovoltaic power station, and the risk prediction result includes the risk level of each area in the photovoltaic power station.

[0052] In addition, to achieve the above-mentioned purpose, the present application also proposes a photovoltaic power station risk prediction device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the photovoltaic power station risk prediction method as described above.

[0053] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the photovoltaic power station risk prediction method as described above are implemented.

[0054] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the photovoltaic power station risk prediction method as described above.

[0055] The present invention acquires multidimensional data of a photovoltaic power station collected by an information collection system, the information collection system including a space-based collection module, an air-based collection module and a ground-based collection module, the multidimensional data including environmental data, equipment status data and equipment operation data of the photovoltaic power station; processes the multidimensional data to acquire event data, feature data and fault result data, the event data including multiple fault events of the photovoltaic power station, the feature data including feature data corresponding to each fault event, the feature data consisting of one or more faulty photovoltaic components corresponding to the fault event, the fault result data including fault event results corresponding to each fault event; constructs a mapping matrix based on the mapping relationship between the event data, the feature data and the fault result data; and A mapping matrix analyzes the fault logic path of the photovoltaic power station and constructs a risk prediction model based on the analysis results; the real-time monitoring data of the photovoltaic power station collected by the information acquisition system is input into the risk prediction model to obtain the risk prediction result of the photovoltaic power station, and the risk prediction result includes the risk level of each area in the photovoltaic power station; because the present invention greatly improves the accuracy of risk prediction of photovoltaic power stations by analyzing the mapping relationship and logical path between fault events, fault characteristics and fault results, and performs risk prediction on photovoltaic power stations through the risk prediction model, it realizes risk prediction of remote areas, improves the risk prediction range, accurately detects potential risks of photovoltaic power stations, ensures the timeliness of risk identification, and improves the safety of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0058] Figure 1 It is a schematic structural diagram of a photovoltaic power station risk prediction device in a hardware operating environment involved in an embodiment of the present invention;

[0059] Figure 2This is a flow chart of a first embodiment of a photovoltaic power station risk prediction method according to the present invention;

[0060] Figure 3 This is a schematic diagram of the structure of a risk prediction and assessment system according to an embodiment of a risk prediction method for a photovoltaic power station of the present invention;

[0061] Figure 4 This is a flow chart of a second embodiment of a photovoltaic power station risk prediction method according to the present invention;

[0062] Figure 5 Schematic diagram of the reliability block diagram in an embodiment of a risk prediction method for a photovoltaic power station according to the present invention;

[0063] Figure 6 Schematic diagram of ROC curve test in an embodiment of a photovoltaic power station risk prediction method of the present invention;

[0064] Figure 7 Schematic diagram of PR curve testing in an embodiment of a photovoltaic power station risk prediction method of the present invention;

[0065] Figure 8 This is a structural block diagram of the first embodiment of the photovoltaic power station risk prediction device of the present invention.

[0066] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0067] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0068] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a photovoltaic power station risk prediction device in the hardware operating environment involved in the embodiment of the present invention.

[0069] like Figure 1As shown, the photovoltaic power plant risk prediction device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display and an input unit, such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also be a storage device independent of the processor 1001.

[0070] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the photovoltaic power station risk prediction device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0071] like Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a photovoltaic power station risk prediction program.

[0072] exist Figure 1 In the photovoltaic power station risk prediction device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the photovoltaic power station risk prediction device of the present invention can be set in the photovoltaic power station risk prediction device, and the photovoltaic power station risk prediction device calls the photovoltaic power station risk prediction program stored in the memory 1005 through the processor 1001, and executes the photovoltaic power station risk prediction method provided by the embodiment of the present invention.

[0073] The embodiment of the present invention provides a photovoltaic power station risk prediction method, referring to Figure 2 , Figure 2 Schematic diagram of the first embodiment of the photovoltaic power station risk prediction method of the present invention.

[0074] In this embodiment, the photovoltaic power station risk prediction method includes the following steps:

[0075] Step S10: Acquire multi-dimensional data of the photovoltaic power station collected by the information collection system.

[0076] It should be understood that the execution entity of this embodiment may be a computing service device with data processing, network communication, and program execution capabilities, such as a computer, or a terminal electronic device capable of implementing the aforementioned functions. This embodiment and the following embodiments will be described below using a photovoltaic power plant risk prediction device (hereinafter referred to as the prediction device) as an example.

[0077] It should be noted that the information collection system includes a space-based collection module, an airborne collection module, and a ground-based collection module. The multidimensional data includes environmental data, equipment status data, and equipment operation data of the photovoltaic power station. For example, the space-based collection module can be a satellite module, the airborne module can be a drone, and the ground-based module can be a ground sensor.

[0078] In some embodiments, the prediction device can leverage satellite remote sensing, drone inspections, and ground-based monitoring equipment to comprehensively capture environmental information, equipment status, and real-time operational data from the PV power plant. This builds a multi-source, heterogeneous data collection system to provide data support for comprehensive risk assessments of PV power plants.

[0079] In some embodiments, the prediction device can obtain environmental information about the PV power plant area through sensors carried by satellites. Satellite remote sensing data can provide macro-level environmental monitoring of the PV power plant and help identify potential external risks. By regularly acquiring this data, the external environment of the power plant can be continuously monitored, providing data support for subsequent risk prediction and maintenance decisions.

[0080] In some embodiments, drone inspections and ground-based monitoring equipment are used to monitor the internal equipment and site conditions of a PV power plant in real time. Drone inspections utilize aerial photography and thermal imaging technology to obtain high-definition images of PV panels, detect hot spots, and identify key issues such as surface cracks. Ground-based monitoring equipment directly collects electrical and environmental parameters from the power plant, providing detailed data on the plant's operational status.

[0081] In some embodiments, the prediction device can build a unified information processing center based on the data collection of the sky and ground collaboration, and form a comprehensive integrated risk prediction and assessment system through the integration of multi-source data, including the sky and ground data collection module, information processing center and operation and maintenance module. Figure 3 As shown, Figure 3 This is a schematic diagram of the risk prediction and assessment system structure. The system combines multi-dimensional data obtained by multi-dimensional data sensing devices (including space-based sensing devices, air-based sensing devices, and ground-based sensing devices) to provide comprehensive photovoltaic power station operation status monitoring and fault warning capabilities.

[0082] Step S20: Process the multi-dimensional data to obtain event data, feature data and fault result data.

[0083] It should be noted that the event data includes multiple fault events of the photovoltaic power station, the characteristic data includes characteristic data corresponding to each fault event, the characteristic data is composed of one or more faulty photovoltaic components corresponding to the fault event, and the fault result data includes fault event results corresponding to each fault event.

[0084] It should be noted that a fault event may include one or more characteristics, such as ambient temperature, radiation temperature, PV module power, etc. Each characteristic may be composed of one or more PV modules. For example, the radiation temperature characteristic may be the radiation temperature of one or more PV modules.

[0085] In some embodiments, the prediction device may pre-process multidimensional data through data cleaning, abnormal data identification and elimination, missing data filling and other processing methods to improve data quality, reduce the interference of abnormal data and invalid data, and ensure the accuracy of feature analysis.

[0086] Step S30: constructing a mapping matrix based on the mapping relationship between the event data, the feature data, and the fault result data.

[0087] It can be understood that the prediction device maps event data, feature data and fault result data to a unified space for processing through mapping and feature processing.

[0088] In some embodiments, is the set of fault event records of the photovoltaic power station (i.e., event data), where Represents the entire One event in each record. Contains multiple features, such as ambient temperature, radiation temperature, photovoltaic module power, etc. For each fault event, a set of features is extracted ,in, Representing a total of One of the characteristics, Represents the characteristics of fault events obtained by various sensors or remote sensing equipment.

[0089] Each feature Multiple photovoltaic panels Composition, of which express All One of the photovoltaic modules. In order to form a pattern , select a photovoltaic module As a conditional variable , which belongs to the variable set , indicating a fault event One of all the relevant condition factors in the fault event The consequences of being selected as the target PV module , also included in the target set .

[0090] After the data mapping space is constructed, the collected raw data is processed for missing values, outlier detection and repair to ensure the integrity and reliability of the data. These inputs will be standardized into a unified mapping space. The input mapping space implemented can be represented by the matrix To represent, refer to the following mapping matrix:

[0091] ;

[0092] Among them, each row of the mapping matrix represents a fault event Record, each column Indicates a fault event A feature PV panels, but The consequences of the event. Through the construction of this matrix, the risks of photovoltaic power plants can be further analyzed and modeled.

[0093] Step S40: analyzing the fault logic path of the photovoltaic power station according to the mapping matrix, and constructing a risk prediction model based on the analysis result.

[0094] It should be noted that this embodiment introduces the concept of path and describes photovoltaic power station faults using a logical path model. Each path represents a logical link that may cause a fault.

[0095] It can be understood that this embodiment analyzes the logical paths that cause photovoltaic power station failures based on the mapping matrix, determines the characteristics contained in each path that causes the failure, thereby determining the contribution of each characteristic to the failure event, and constructs a risk prediction model based on the analysis results.

[0096] In some embodiments, the risk prediction model may be an improved path-based significance metric impact weight assessment model (IPBS), and the prediction device achieves accurate risk prediction of various components and assemblies in a photovoltaic power station by using the improved path-based significance metric impact weight assessment model.

[0097] Step S50: inputting the real-time monitoring data of the photovoltaic power station collected by the information collection system into the risk prediction model to obtain a risk prediction result of the photovoltaic power station.

[0098] It should be noted that the risk prediction result includes the risk level of each area in the photovoltaic power station.

[0099] In some embodiments, the prediction device can determine the risk level of each area in the photovoltaic power station based on the risk prediction results, and generate a risk heat map based on the risk level. The color depth of the risk heat map indicates the high and low risk levels. The darker the color, the higher the safety risk, and the lighter the color, the lower the safety risk.

[0100] This embodiment acquires multi-dimensional data of the photovoltaic power station collected by an information collection system, the information collection system including a space-based collection module, an air-based collection module and a ground-based collection module, the multi-dimensional data including environmental data, equipment status data and equipment operation data of the photovoltaic power station; processes the multi-dimensional data to acquire event data, feature data and fault result data, the event data including multiple fault events of the photovoltaic power station, the feature data including feature data corresponding to each fault event, the feature data consisting of one or more faulty photovoltaic components corresponding to the fault event, the fault result data including fault event results corresponding to each fault event; constructs a mapping matrix based on the mapping relationship between the event data, the feature data and the fault result data; and The mapping matrix analyzes the fault logic path of the photovoltaic power station and constructs a risk prediction model based on the analysis results; the real-time monitoring data of the photovoltaic power station collected by the information collection system is input into the risk prediction model to obtain the risk prediction result of the photovoltaic power station, and the risk prediction result includes the risk level of each area in the photovoltaic power station; since this embodiment greatly improves the accuracy of risk prediction of photovoltaic power stations by analyzing the mapping relationship and logical path between fault events, fault characteristics and fault results, and performs risk prediction on photovoltaic power stations through the risk prediction model, it realizes risk prediction of remote areas, improves the risk prediction range, accurately detects potential risks of photovoltaic power stations, ensures the timeliness of risk identification, and improves the safety of photovoltaic power stations.

[0101] refer to Figure 4 , Figure 4 2 is a flow chart of a second embodiment of a photovoltaic power station risk prediction method according to the present invention.

[0102] Based on the first embodiment above, in this embodiment, step S40 further includes:

[0103] Step S41: analyzing the fault logic path of the photovoltaic power station according to the mapping matrix, and constructing a fault logic path model based on the analysis result.

[0104] It should be noted that the fault logic path model includes multiple fault logic paths, and each fault logic path represents a logic link that may cause a fault in the photovoltaic power station.

[0105] Step S42: performing path significance analysis on each fault logic path in the fault logic path model.

[0106] It is understandable that this embodiment calculates the failure probability contribution of the minimum path set containing a certain photovoltaic module by analyzing the path significance. The path significance model is used to analyze the direct impact of photovoltaic modules on the reliability of photovoltaic power stations. Assuming that the photovoltaic power station can work normally, there is at least one path containing photovoltaic modules. The minimum logical path also works normally.

[0107] Step S43: constructing an initial path significance model based on the significance analysis results.

[0108] It should be noted that the initial path significance model refers to the following formula:

[0109] ;

[0110] in, It is a photovoltaic module The reliability parameters, Indicates that it contains photovoltaic modules The minimum path set of Include Path, Represents a binary random vector, if the photovoltaic module Work, then Equal to 1, otherwise is equal to 0, Indicates the fault logic path, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power plants.

[0111] Step S44: Optimizing the initial path significance model based on the logical relationship between each photovoltaic module and each fault event to obtain a risk prediction model.

[0112] It is understandable that this embodiment includes photovoltaic components by calculating The minimum path set The contribution to the occurrence of photovoltaic power station failures quantifies the impact of abnormalities of each photovoltaic module on the function of the photovoltaic power station, thereby quantifying the contribution to the failure of the photovoltaic module.

[0113] Furthermore, in order to improve the performance of the risk prediction model, the above step S44 may include:

[0114] Step S441: obtaining a logical relationship between each photovoltaic module and each fault event based on the fault logic path;

[0115] Step S442: determining the relative position of each photovoltaic module and the logical connection relationship between each photovoltaic module and each fault event according to the logical relationship;

[0116] Step S443: constructing a reliability block diagram based on the relative positions and the logical connection relationship;

[0117] Step S444: optimizing the initial path significance model according to the reliability block graph to obtain a candidate significance model;

[0118] Step S445: performing a survival analysis on the photovoltaic power station based on the photovoltaic components corresponding to each fault event, and optimizing the candidate significance model based on the survival analysis result to obtain a risk prediction model.

[0119] It should be noted that the candidate saliency model refers to the following formula:

[0120] ;

[0121] in, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power stations, It is a photovoltaic module The reliability parameters, It represents the logic generation structure function constructed based on the logical connection relationship between the PV modules and the fault events and the relative position of the PV modules in the logical connection relationship. Indicates the photovoltaic power station in the photovoltaic components The probability of normal operation under the action.

[0122] It should be noted that photovoltaic modules The significance of Whether the PV modules are directly or inversely proportional depends on their series or parallel logic connection. In a parallel connection, even the conduction of a minimum logic path can cause a fault event, while in a series connection, all PV modules must be turned on to cause a fault event.

[0123] It can be understood that this embodiment constructs a reliability block diagram, referring to Figure 5 , Figure 5 Schematic diagram of the reliability block diagram in one embodiment, based on which the reliability block diagram can represent the logic generation structure function of the fault event of all features and their relative positions and logical connections of photovoltaic components. .

[0124] Furthermore, in order to improve model performance and prediction accuracy, the above step S445 may include:

[0125] Step S4451: performing survival analysis on the photovoltaic power station based on the photovoltaic components corresponding to each fault event to construct a survival signature;

[0126] Step S4452: constructing a survival function of the photovoltaic power station according to the survival signature;

[0127] Step S4453: Optimize the candidate significance model based on the survival function to obtain a risk prediction model.

[0128] It should be noted that the survival function is used to quantify the probability of survival or reliability of a PV plant at a given moment, while the survival signature is used to evaluate the survival probability of a PV plant with multiple types and interchangeable PV modules. The survival signature describes the probability that a PV plant will operate normally under multiple PV module characteristics. The survival signature formula is as follows:

[0129] ;

[0130] Through the survival signature, the overall survival function of the photovoltaic power station can be expressed as:

[0131] ;

[0132] in, Indicates the Type of photovoltaic modules in time The number of working states at this time can be calculated by the following formula:

[0133] ;

[0134] in, Indicates that PV panels and The state vector the number of represents the set of all possible state vectors in a photovoltaic power station, Indicates the Type of photovoltaic modules in time The number of working states at the time.

[0135] It should be noted that the survival signature provides an estimate of the survival probability of the entire PV power station by summing the status of all PV modules, which can efficiently evaluate the reliability of multi-type PV module systems.

[0136] Furthermore, in order to solve the problem of insufficient samples and data volume and uncertainty in fault analysis, the above step S4453 may include:

[0137] Step S44531: Obtaining failure time distribution information of each photovoltaic module;

[0138] Step S44532: generating boundary conditions based on the failure time distribution information;

[0139] Step S44533: Optimize the candidate significance model according to the boundary conditions to obtain a risk prediction model.

[0140] It should be noted that, assuming is a set of non-decreasing cumulative distribution functions (CDFs) that satisfy .Then, will be a possibility box if they are used to describe the boundaries of a probability distribution that is known to be imprecise. The lower and upper bounds of the failure time distribution in a feature can be expressed as and , and can be determined by the numerical range of all distributions within the defined parameter interval. Its lower limit and upper limit are respectively expressed as the boundary conditions include upper limit conditions and lower limit conditions:

[0141] ;

[0142] in, is the upper limit condition, is the lower limit condition, and An exponential decay function represents the change in the failure probability of a PV power station or PV module over time. The functions of the upper and lower limit conditions above represent the upper and lower limits of the survival function, respectively. These functions can be used to evaluate the impact of uncertainty on system reliability and provide boundary conditions for further risk assessment.

[0143] It should be noted that due to insufficient data volume, insufficient samples, and incomplete input data, there is uncertainty in the probability of photovoltaic module failure. To address this problem, this embodiment can introduce a possibility box model to handle these uncertainties.

[0144] Furthermore, in order to further consider the impact of the uncertainty of photovoltaic modules on the prediction of fault events and improve the prediction performance, the above step S44533 may include:

[0145] Conduct uncertainty analysis on each photovoltaic module to obtain the uncertainty impact information of each photovoltaic module;

[0146] The candidate significance model is optimized based on the uncertainty impact information and the boundary conditions to obtain a risk prediction model.

[0147] It should be noted that the uncertainty impact information is obtained based on the following formula:

[0148] ;

[0149] ;

[0150] This embodiment further considers the impact of the uncertainty of photovoltaic modules on the prediction of fault events, combines the candidate significance model with the possibility framework to quantify the inaccuracy of photovoltaic modules, and ultimately derives the contribution of photovoltaic modules to photovoltaic power station failures, thereby obtaining a risk prediction model. The risk prediction model refers to the following formula:

[0151] ;

[0152] in, Represents photovoltaic modules At a specific time The relative uncertainty of the impact of information, Indicates the The relative interval parameters of each photovoltaic module, Indicates that when The possibility of a failure event in the photovoltaic system when a photovoltaic module occurs, Indicates the The possibility of a failure event in the photovoltaic system when the photovoltaic modules are not present. It is a photovoltaic module The reliability parameters, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power plants.

[0153] In some embodiments, to verify the accuracy of model predictions, the prediction device may use ROC (Receiver Operating Characteristic Curve) and PR (Precision-Recall Curve) curves to test the prediction results. The ROC curve evaluates the performance of a classifier by showing the relationship between the model's false positive rate (FPR) and true positive rate (TPR, also known as recall rate) at different classification thresholds. An ideal model should be able to maximize the true positive rate while maintaining a low false positive rate, so that the ROC curve is as close to the upper left corner as possible. AUC represents the area under the ROC curve, and its value ranges from 0 to 1. The closer the AUC is to 1, the better the model performance. The PR curve focuses on the relationship between precision (Precision) and recall (Recall, also known as true positive rate) and is particularly suitable for dealing with data imbalance. In this case, an imbalance in the class distribution may cause the ROC curve to produce a more optimistic result. An ideal model should maintain a high recall rate while maintaining high precision, and the PR curve should be as close to the upper right corner as possible.

[0154] like Figure 6 、 7 As shown, Figure 6 This is a schematic diagram of the ROC curve test. Figure 7 The figure is a schematic diagram of the PR curve test. The larger the area under the ROC curve (AUC), the better the prediction accuracy of the risk prediction model. The risk prediction model is compared with the currently commonly used RNN and LSTM neural networks. Among them, the AUC of the risk prediction model is 0.9172, and the AUCs of the RNN and LSTM neural networks are 0.8846 and 0.8613 respectively. It can be seen that the risk prediction model has a high accuracy in predicting the risks of photovoltaic power stations under multi-source biased data environments. With respect to the PR curve, the risk prediction model (IPBS) used in this embodiment is significantly better than the RNN and LSTM neural network models in performance.

[0155] This embodiment analyzes the fault logic paths of the photovoltaic power station according to the mapping matrix, constructs a fault logic path model based on the analysis results, performs path significance analysis on each fault logic path in the fault logic path model, constructs an initial path significance model based on the significance analysis results, and optimizes the initial path significance model based on the logical relationship between each photovoltaic component and each fault event to obtain a risk prediction model. Since this embodiment performs logical path analysis on the faults of the photovoltaic power station, it quantifies the contribution of the features and components that cause the faults in each path, analyzes the influence of each element on the photovoltaic power station fault, and thus improves the accuracy of risk prediction.

[0156] In addition, an embodiment of the present invention also proposes a computer-readable storage medium, which stores a photovoltaic power station risk prediction program. When the photovoltaic power station risk prediction program is executed by a processor, the steps of the photovoltaic power station risk prediction method described above are implemented.

[0157] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0158] The computer-readable storage medium may be included in the photovoltaic power station risk prediction device; or may exist independently without being assembled into the photovoltaic power station risk prediction device.

[0159] In addition, an embodiment of the present invention further provides a computer program product, including a photovoltaic power station risk prediction program, which implements the steps of the photovoltaic power station risk prediction method described above when executed by a processor.

[0160] The specific implementation of the computer program product of the present invention is basically the same as the embodiments of the above-mentioned photovoltaic power station risk prediction method, and will not be repeated here.

[0161] Reference Figure 8 , Figure 8 This is a structural block diagram of the first embodiment of the photovoltaic power station risk prediction device of the present invention.

[0162] like Figure 8 As shown, the photovoltaic power station risk prediction device proposed in the embodiment of the present invention includes:

[0163] A data acquisition module 10 is configured to acquire multi-dimensional data of the photovoltaic power station collected by an information acquisition system, wherein the information acquisition system includes a space-based acquisition module, an air-based acquisition module, and a ground-based acquisition module. The multi-dimensional data includes environmental data, equipment status data, and equipment operation data of the photovoltaic power station;

[0164] a data processing module 20 configured to process the multidimensional data to obtain event data, characteristic data, and fault result data, wherein the event data includes multiple fault events of the photovoltaic power station; the characteristic data includes characteristic data corresponding to each fault event, the characteristic data consisting of one or more faulty photovoltaic modules corresponding to the fault event; and the fault result data includes a fault event result corresponding to each fault event;

[0165] A matrix construction module 30 is configured to construct a mapping matrix based on a mapping relationship between the event data, the feature data, and the fault result data;

[0166] A model building module 40 is configured to analyze the fault logic path of the photovoltaic power station according to the mapping matrix and to build a risk prediction model based on the analysis results;

[0167] The risk prediction module 50 is used to input the real-time monitoring data of the photovoltaic power station collected by the information collection system into the risk prediction model to obtain the risk prediction result of the photovoltaic power station, and the risk prediction result includes the risk level of each area in the photovoltaic power station.

[0168] Furthermore, the model building module 40 is further configured to analyze the fault logic path of the photovoltaic power station according to the mapping matrix, and to build a fault logic path model based on the analysis result, wherein the fault logic path model includes multiple fault logic paths;

[0169] Performing path significance analysis on each fault logic path in the fault logic path model;

[0170] Construct an initial path significance model based on the significance analysis results:

[0171] ;

[0172] in, It is a photovoltaic module The reliability parameters, Indicates that it contains photovoltaic modules The minimum path set of Include Path, Represents a binary random vector, if the photovoltaic module Work, then Equal to 1, otherwise is equal to 0, Indicates the fault logic path, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power plants;

[0173] The initial path significance model is optimized based on the logical relationship between each photovoltaic module and each fault event to obtain a risk prediction model.

[0174] Furthermore, the model building module 40 is further configured to optimize the initial path significance model based on the logical relationship between each photovoltaic module and each fault event to obtain a risk prediction model, including:

[0175] Acquire a logical relationship between each photovoltaic module and each fault event based on the fault logic path;

[0176] Determining the relative positions of the photovoltaic modules and the logical connection relationships between the photovoltaic modules and the fault events according to the logical relationships;

[0177] Constructing a reliability block diagram based on the relative positions and the logical connection relationship;

[0178] The initial path significance model is optimized according to the reliability block diagram to obtain a candidate significance model:

[0179] ;

[0180] in, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power stations, It is a photovoltaic module The reliability parameters, It represents the logic generation structure function constructed based on the logical connection relationship between the PV modules and the fault events and the relative position of the PV modules in the logical connection relationship. Indicates the photovoltaic power station in the photovoltaic components The probability of normal operation under the action of

[0181] A survival analysis is performed on the photovoltaic power station based on the photovoltaic components corresponding to each fault event, and the candidate significance model is optimized based on the survival analysis result to obtain a risk prediction model.

[0182] Furthermore, the model building module 40 is further configured to perform a survival analysis on the photovoltaic power station based on the photovoltaic components corresponding to each fault event, and optimize the candidate significance model based on the survival analysis result to obtain a risk prediction model, including:

[0183] Based on the photovoltaic components corresponding to each fault event, a survival analysis is performed on the photovoltaic power station to construct a survival signature:

[0184] ;

[0185] The survival function of the photovoltaic power station is constructed according to the survival signature:

[0186] ;

[0187] Optimizing the candidate significance model based on the survival function to obtain a risk prediction model;

[0188] in, Indicates that PV panels and The state vector the number of represents the set of all possible state vectors in a photovoltaic power station, Indicates the Type of photovoltaic modules in time The number of working states at the time.

[0189] Furthermore, the model building module 40 is further configured to optimize the candidate significance model based on the survival function to obtain a risk prediction model, including:

[0190] Obtain the failure time distribution information of each photovoltaic module;

[0191] Generate boundary conditions based on the failure time distribution information, wherein the boundary conditions include an upper limit condition and a lower limit condition:

[0192] ;

[0193] in, is the upper limit condition, is the lower limit condition, and An exponential decay function representing the change in the failure probability of a PV power plant or PV module over time;

[0194] The candidate significance model is optimized according to the boundary conditions to obtain a risk prediction model.

[0195] Furthermore, the model building module 40 is further configured to optimize the candidate significance model according to the boundary conditions to obtain a risk prediction model, including:

[0196] Perform uncertainty analysis on each photovoltaic module to obtain the uncertainty impact information of each photovoltaic module:

[0197] ;

[0198] ;

[0199] The candidate significance model is optimized based on the uncertainty impact information and the boundary conditions to obtain a risk prediction model:

[0200] ;

[0201] in, Represents photovoltaic modules At a specific time The relative uncertainty of the impact of information, Indicates the The relative interval parameters of each photovoltaic module, Indicates that when The possibility of a failure event in the photovoltaic system when a photovoltaic module occurs, Indicates the The possibility of a failure event in the photovoltaic system when the photovoltaic modules are not present. It is a photovoltaic module The reliability parameters, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power plants.

[0202] This embodiment acquires multi-dimensional data of the photovoltaic power station collected by an information collection system, the information collection system including a space-based collection module, an air-based collection module and a ground-based collection module, the multi-dimensional data including environmental data, equipment status data and equipment operation data of the photovoltaic power station; processes the multi-dimensional data to acquire event data, feature data and fault result data, the event data including multiple fault events of the photovoltaic power station, the feature data including feature data corresponding to each fault event, the feature data consisting of one or more faulty photovoltaic components corresponding to the fault event, the fault result data including fault event results corresponding to each fault event; constructs a mapping matrix based on the mapping relationship between the event data, the feature data and the fault result data; and The mapping matrix analyzes the fault logic path of the photovoltaic power station and constructs a risk prediction model based on the analysis results; the real-time monitoring data of the photovoltaic power station collected by the information collection system is input into the risk prediction model to obtain the risk prediction result of the photovoltaic power station, and the risk prediction result includes the risk level of each area in the photovoltaic power station; since this embodiment greatly improves the accuracy of risk prediction of photovoltaic power stations by analyzing the mapping relationship and logical path between fault events, fault characteristics and fault results, and performs risk prediction on photovoltaic power stations through the risk prediction model, it realizes risk prediction of remote areas, improves the risk prediction range, accurately detects potential risks of photovoltaic power stations, ensures the timeliness of risk identification, and improves the safety of photovoltaic power stations.

[0203] The photovoltaic power station risk prediction device provided in this application utilizes the photovoltaic power station risk prediction method described in the aforementioned embodiments to address the technical challenges of photovoltaic power station risk prediction. Compared to the prior art, the photovoltaic power station risk prediction device provided in this application offers the same beneficial effects as the photovoltaic power station risk prediction method described in the aforementioned embodiments. Other technical features of the photovoltaic power station risk prediction device are the same as those disclosed in the aforementioned embodiments and are not further detailed here.

[0204] It should be understood that the above is only an example and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any limitation on this.

[0205] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the embodiment scheme, and no limitation is made here.

[0206] In addition, for technical details not fully described in this embodiment, reference can be made to the photovoltaic power station risk prediction method provided in any embodiment of the present invention, and will not be repeated here.

[0207] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0208] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0209] Through the above description of the embodiments, those skilled in the art will clearly understand that the above-mentioned embodiments and methods can be implemented by means of software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, a magnetic disk, or an optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0210] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A photovoltaic power station risk prediction method, characterized in that: The photovoltaic power station risk prediction method includes: Acquire multidimensional data of the photovoltaic power station collected by an information collection system, wherein the information collection system includes a space-based collection module, an air-based collection module, and a ground-based collection module, and the multidimensional data includes environmental data, equipment status data, and equipment operation data of the photovoltaic power station; Processing the multidimensional data to obtain event data, feature data, and fault result data, wherein the event data includes multiple fault events of the photovoltaic power station, the feature data includes feature data corresponding to each fault event, the feature data is composed of one or more faulty photovoltaic components corresponding to the fault event, and the fault result data includes a fault event result corresponding to each fault event; Constructing a mapping matrix based on the mapping relationship between the event data, the feature data, and the fault result data; Analyzing the fault logic path of the photovoltaic power station according to the mapping matrix, and building a risk prediction model based on the analysis results; inputting the real-time monitoring data of the photovoltaic power station collected by the information collection system into the risk prediction model to obtain a risk prediction result of the photovoltaic power station, wherein the risk prediction result includes a risk level of each area in the photovoltaic power station; Analyzing the fault logic path of the photovoltaic power station according to the mapping matrix and constructing a risk prediction model based on the analysis results includes: Analyzing the fault logic path of the photovoltaic power station according to the mapping matrix, and constructing a fault logic path model based on the analysis result, wherein the fault logic path model includes multiple fault logic paths; Performing path significance analysis on each fault logic path in the fault logic path model; Construct an initial path significance model based on the significance analysis results: ； in, It is a photovoltaic module The reliability parameters, Indicates that it contains photovoltaic modules The minimum path set of Include Path, Represents a binary random vector, if the photovoltaic module Work, then Equal to 1, otherwise is equal to 0, Indicates the fault logic path, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power plants; The initial path significance model is optimized based on the logical relationship between each photovoltaic module and each fault event to obtain a risk prediction model.

2. The photovoltaic power station risk prediction method according to claim 1, characterized in that: The initial path significance model is optimized based on the logical relationship between each photovoltaic module and each fault event to obtain a risk prediction model, including: Acquire a logical relationship between each photovoltaic module and each fault event based on the fault logic path; Determining the relative positions of the photovoltaic modules and the logical connection relationships between the photovoltaic modules and the fault events according to the logical relationships; Constructing a reliability block diagram based on the relative positions and the logical connection relationship; The initial path significance model is optimized according to the reliability block diagram to obtain a candidate significance model: ； in, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power stations, It is a photovoltaic module The reliability parameters, It represents the logic generation structure function constructed based on the logical connection relationship between the PV modules and the fault events and the relative position of the PV modules in the logical connection relationship. Indicates the photovoltaic power station in the photovoltaic components The probability of normal operation under the action of A survival analysis is performed on the photovoltaic power station based on the photovoltaic components corresponding to each fault event, and the candidate significance model is optimized based on the survival analysis result to obtain a risk prediction model.

3. The photovoltaic power station risk prediction method according to claim 2, characterized in that: The method of performing survival analysis on the photovoltaic power station based on the photovoltaic components corresponding to each fault event and optimizing the candidate significance model based on the survival analysis result to obtain a risk prediction model includes: Based on the photovoltaic components corresponding to each fault event, a survival analysis is performed on the photovoltaic power station to construct a survival signature: ； The survival function of the photovoltaic power station is constructed according to the survival signature: ； Optimizing the candidate significance model based on the survival function to obtain a risk prediction model; in, Indicates that PV panels and The state vector the number of represents the set of all possible state vectors in a photovoltaic power station, Indicates the Type of photovoltaic modules in time The number of working states at the time.

4. The photovoltaic power station risk prediction method according to claim 3, characterized in that: Optimizing the candidate significance model based on the survival function to obtain a risk prediction model includes: Obtain the failure time distribution information of each photovoltaic module; Generate boundary conditions based on the failure time distribution information, wherein the boundary conditions include an upper limit condition and a lower limit condition: ； in, is the upper limit condition, is the lower limit condition, and An exponential decay function representing the change in the failure probability of a PV power plant or PV module over time; The candidate significance model is optimized according to the boundary conditions to obtain a risk prediction model.

5. The photovoltaic power station risk prediction method according to claim 4, characterized in that: Optimizing the candidate significance model according to the boundary conditions to obtain a risk prediction model includes: Perform uncertainty analysis on each photovoltaic module to obtain the uncertainty impact information of each photovoltaic module: ； ； The candidate significance model is optimized based on the uncertainty impact information and the boundary conditions to obtain a risk prediction model: ； in, Represents photovoltaic modules At a specific time The relative uncertainty of the impact of information, Indicates the The relative interval parameters of each photovoltaic module, Indicates that when The possibility of a failure event in the photovoltaic system when a photovoltaic module occurs, Indicates the The possibility of a failure event in the photovoltaic system when the photovoltaic modules are not present. It is a photovoltaic module The reliability parameters, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power plants.

6. A photovoltaic power station risk prediction device, characterized in that: The photovoltaic power station risk prediction device includes: A data acquisition module is used to obtain multi-dimensional data of the photovoltaic power station collected by the information acquisition system, wherein the information acquisition system includes a space-based acquisition module, an air-based acquisition module, and a ground-based acquisition module. The multi-dimensional data includes environmental data, equipment status data, and equipment operation data of the photovoltaic power station; a data processing module, configured to process the multidimensional data to obtain event data, characteristic data, and fault result data, wherein the event data includes multiple fault events of the photovoltaic power station, the characteristic data includes characteristic data corresponding to each fault event, the characteristic data is composed of one or more faulty photovoltaic components corresponding to the fault event, and the fault result data includes a fault event result corresponding to each fault event; A matrix construction module, configured to construct a mapping matrix based on a mapping relationship between the event data, the feature data, and the fault result data; a model building module, configured to analyze the fault logic path of the photovoltaic power station according to the mapping matrix and build a risk prediction model based on the analysis results; a risk prediction module, configured to input the real-time monitoring data of the photovoltaic power station collected by the information collection system into the risk prediction model to obtain a risk prediction result of the photovoltaic power station, wherein the risk prediction result includes a risk level of each area in the photovoltaic power station; The model building module is further configured to analyze the fault logic path of the photovoltaic power station according to the mapping matrix, and construct a fault logic path model based on the analysis results, wherein the fault logic path model includes multiple fault logic paths; perform path significance analysis on each fault logic path in the fault logic path model; and construct an initial path significance model based on the significance analysis results: ； in, It is a photovoltaic module The reliability parameters, Indicates that it contains photovoltaic modules The minimum path set of Include Path, Represents a binary random vector, if the photovoltaic module Work, then Equal to 1, otherwise is equal to 0, Indicates the fault logic path, Indicates that in photovoltaic modules Reliability Vector The reliability of photovoltaic power plants; The initial path significance model is optimized based on the logical relationship between each photovoltaic module and each fault event to obtain a risk prediction model.

7. A photovoltaic power station risk prediction device, characterized in that: The photovoltaic power station risk prediction device includes: a memory, a processor, and a photovoltaic power station risk prediction program stored in the memory and executable on the processor, wherein the photovoltaic power station risk prediction program is configured to implement the photovoltaic power station risk prediction method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a photovoltaic power station risk prediction program, and when the photovoltaic power station risk prediction program is executed by the processor, the photovoltaic power station risk prediction method according to any one of claims 1 to 5 is implemented.

9. A computer program product, characterized in that The computer program product includes a photovoltaic power station risk prediction program, which implements the steps of the photovoltaic power station risk prediction method according to any one of claims 1 to 5 when executed by a processor.

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