Automatic hot spot fault diagnosis method and system for photovoltaic power station module

Abstract

An automatic hot spot fault diagnosis method and system for a photovoltaic power station module, the method comprising: collecting local environmental data, power output data, and module temperature data of a target photovoltaic power station; on the basis of the environmental data and the power output data, obtaining a relationship between environmental factors and power output; acquiring output power of photovoltaic modules in different plots under different dust accumulation conditions, and obtaining a relationship between different dust accumulation conditions and power output; and on the basis of characteristics of power loss caused by module hot spots and the module temperature data, determining a hot spot fault level for a photovoltaic power station module. Solar irradiance data and photovoltaic module temperature data are collected, and detection and recognition algorithms for partial shading issues are combined, so as to achieve automatic diagnosis of photovoltaic module hot spot faults and determination of an optimal photovoltaic module cleaning cycle, thereby effectively improving the power generation efficiency and yield of the photovoltaic power station.

Description

An automatic diagnosis method and system for hot spot faults in photovoltaic power plant modules Technical Field

[0001] This invention relates to the field of hot spot fault diagnosis of photovoltaic modules, and in particular to an automatic method and system for diagnosing hot spot faults in photovoltaic power plant modules. Background Technology

[0002] Domestically and internationally, methods for cleaning dust accumulation on photovoltaic (PV) module surfaces are mainly divided into manual cleaning, semi-automatic cleaning, and fully automatic cleaning. Different power plants vary in terms of installed capacity, terrain, and climate; therefore, the cleaning method for PV modules must be selected based on the actual conditions of the power plant. Dust accumulation on PV modules not only affects their power generation efficiency but also their lifespan, which has attracted significant attention from industry professionals. While PV module dust removal technology can increase the efficiency of PV power plants, it also increases costs. Optimizing the balance between efficiency and cost—that is, scientifically and rationally determining the PV panel dust removal cycle—has become a challenge. Currently, the cleaning frequency of PV modules is mostly fixed or adjusted based on actual operating conditions. Existing cleaning models often overlook the fact that the impact of dust coverage on PV modules and the power generation of PV power plants is not strictly linear, and they do not consider the influence of factors such as rainfall and wind on dust accumulation on modules, making it imperative to further improve the applicability of the cleaning models. Excessively long cleaning cycles lead to increased dust accumulation on photovoltaic (PV) panels, significantly reducing their solar energy reception and causing economic losses to the PV power plant. Conversely, excessively short cleaning cycles result in energy and labor costs exceeding the economic benefits of maintaining clean PV panels. Furthermore, existing research on automated cleaning primarily focuses on ground-mounted power plants and does not comprehensively consider the dust accumulation characteristics in complex mountainous environments. To avoid power generation efficiency losses caused by dust accumulation, effective cleaning solutions are needed, requiring real-time monitoring and prediction of dust accumulation on PV modules.

[0003] To address the aforementioned issues, this invention proposes an automatic diagnosis method for hot spot faults in photovoltaic (PV) power plant modules. This method comprehensively considers the impact of irradiance, temperature, and installation geographical conditions on PV array output and PV power plant power generation. It focuses on the power output attenuation caused by dust accumulation in centralized large-scale PV power plants and the potential induced hot spot effect. The method determines the PV panel cleaning cycle based on the hot spot fault level. This provides a novel approach for real-time detection of dust accumulation on PV modules and for determining PV module cleaning cycles. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the problem to be solved by this invention is that: existing cleaning models often ignore the fact that the effect of dust on photovoltaic modules and on the power generation of photovoltaic power plants is not strictly linear, and do not consider the influence of multiple factors such as rainfall and wind on dust accumulation on modules. Furthermore, existing automatic cleaning research focuses on ground power plants and does not comprehensively consider the dust accumulation characteristics in complex mountainous climate environments.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: an automatic diagnosis method for hot spot faults in photovoltaic power plant modules, comprising: collecting local environmental data, power output data, and module temperature data of the target photovoltaic power plant; obtaining the relationship between environmental factors and power output based on the environmental data and power output data; acquiring the output power of photovoltaic modules in different plots under different dust accumulation conditions, and obtaining the relationship between different dust accumulation conditions and power output; and determining the level of hot spot faults occurring in the photovoltaic power plant modules based on the characteristics of power loss caused by hot spots and the module temperature data.

[0007] As a preferred embodiment of the automatic diagnosis method for hot spot faults in photovoltaic power station modules according to the present invention, the environmental data is acquired by collecting historical irradiance data, meteorological data, dust detection data, and terrain data received by the target photovoltaic power station through a deployed solar irradiance recorder; the power output data and module temperature data are acquired by acquiring the corresponding power output data based on the historical irradiance data, and monitoring and recording the photovoltaic power station module temperature data through a deployed infrared thermal imager.

[0008] As a preferred embodiment of the automatic diagnosis method for hot spot faults of photovoltaic power station modules according to the present invention, the step of obtaining the relationship between environmental factors and power output based on environmental data and power output data includes obtaining the correlation between local photovoltaic power station power and terrain environment, irradiance, and meteorological changes based on the obtained irradiance and power output time series diagram and meteorological data and power output time series diagram, and obtaining a photovoltaic module dust accumulation status monitoring algorithm for photovoltaic module output power and IV curve.

[0009] As a preferred embodiment of the automatic diagnosis method for hot spot faults of photovoltaic power station modules according to the present invention, the method of obtaining the relationship between different dust accumulation conditions and power output includes: obtaining the output power of photovoltaic modules in different plots under different dust accumulation conditions, obtaining the correlation data between dust thickness and power output, obtaining the trend of module output power change of photovoltaic modules in different plots under different dust accumulation conditions, and correcting the module output loss caused by shading.

[0010] As a preferred embodiment of the automatic diagnosis method for hot spot faults in photovoltaic power station modules according to the present invention, the step of determining the level of hot spot faults in photovoltaic power station modules based on the characteristics of power loss caused by hot spots and module temperature data includes: based on the characteristics of power loss caused by hot spots, coupling analysis of the differences in power output of photovoltaic modules caused by dust accumulation and hot spots to obtain a distinguishing detection and identification algorithm for dust accumulation and local shading problems in mountainous photovoltaic power stations; after removing abnormal power output data caused by shading, coupling module temperature to determine and identify modules with abnormal power output, and comparing and analyzing with irradiance data.

[0011] As a preferred embodiment of the automatic diagnosis method for hot spot faults in photovoltaic power plant modules according to the present invention, the method for determining the level of hot spot faults in photovoltaic power plant modules based on the characteristics of power loss caused by hot spots and module temperature data further includes: solving the balance condition between hot spot benefits and shading losses based on the analysis results; extracting temperature points where the module temperature is higher than a threshold, and integrating the extracted data; diagnosing modules with both integrated area and temperature value higher than a set value as faulty modules; and classifying faulty modules into different fault levels based on the fault area and temperature value obtained from the integration.

[0012] As a preferred embodiment of the automatic diagnosis method for hot spot faults of photovoltaic power station modules according to the present invention, the method of dividing the faults into different levels includes setting at least two levels of fault levels, and determining whether to clean the photovoltaic modules based on the different fault levels.

[0013] Another objective of this invention is to provide an automatic diagnosis system for hot spot faults in photovoltaic power plant modules. This system can automatically diagnose the hot spot fault level of photovoltaic modules and determine whether to clean the modules based on the fault level.

[0014] To address the aforementioned technical problems, this invention provides the following technical solution: a system for automatic diagnosis of hot spot faults in photovoltaic power plant modules, comprising: a data acquisition module, an environmental factor analysis module, a dust accumulation factor analysis module, and a fault level judgment module; the data acquisition module acquires local environmental data, power output data, and module temperature data of the target photovoltaic power plant; the environmental factor analysis module obtains the relationship between environmental factors and power output based on the environmental data and power output data; the dust accumulation factor analysis module obtains the output power of photovoltaic modules in different sites under different dust accumulation conditions, and obtains the relationship between different dust accumulation conditions and power output; the fault level judgment module determines the level of hot spot faults occurring in the photovoltaic power plant modules based on the characteristics of power loss caused by hot spots and the module temperature data.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the automatic diagnosis method for hot spot faults in photovoltaic power plant modules as described above.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the automatic diagnosis method for hot spot faults in photovoltaic power plant modules as described above.

[0017] The beneficial effects of this invention are as follows: By collecting solar irradiance data and photovoltaic module temperature data, and combining them with algorithms for detecting and identifying local shading problems, this invention enables automatic diagnosis of hot spot faults in photovoltaic modules and determination of the optimal cleaning cycle for photovoltaic modules, effectively improving the power generation efficiency and profitability of photovoltaic power plants. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0019] Figure 1 is a flowchart of an automatic diagnosis method for hot spot faults in photovoltaic power station modules in Example 1.

[0020] Figure 2 is a time-series diagram of meteorological data and power output, irradiance and power output for an automatic diagnosis method for hot spot faults in photovoltaic power station modules in Example 1.

[0021] Figure 3 is a schematic diagram of the shadow correction algorithm of an automatic diagnosis method for hot spot faults in photovoltaic power station modules in Example 1.

[0022] Figure 4 is a hot spot fault diagnosis logic diagram of an automatic hot spot fault diagnosis method for photovoltaic power station modules in Example 1.

[0023] Figure 5 is a module structure diagram of an automatic diagnosis system for hot spot faults in photovoltaic power station modules in Example 2. Detailed Implementation

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0025] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0026] Example 1, referring to Figures 1 and 2, is the first embodiment of the present invention. This embodiment provides an automatic diagnosis method for hot spot faults in photovoltaic power plant modules, as shown in Figure 1:

[0027] S1. Collect local environmental data, power output data, and module temperature data of the target photovoltaic power station.

[0028] Historical irradiance data, meteorological data, dust detection data, and terrain data of the target photovoltaic power station are collected by deploying solar irradiance recorders.

[0029] Based on historical irradiance data, corresponding power output data is obtained, and the temperature data of photovoltaic power station modules is monitored and recorded by deployed infrared thermal imagers.

[0030] S2. Based on environmental data and power output data, the relationship between environmental factors and power output is obtained.

[0031] As shown in Figure 2, based on the obtained irradiance and power output time series diagram and meteorological data and power output time series diagram, the correlation between the power of the local photovoltaic power station and the terrain environment, irradiance and meteorological changes is obtained, and a photovoltaic module dust accumulation status monitoring algorithm is obtained for the output power of photovoltaic modules and IV curve.

[0032] S3. Obtain the output power of photovoltaic modules on different plots under different dust accumulation conditions, and obtain the relationship between different dust accumulation conditions and power output.

[0033] The output power of photovoltaic modules on different plots under different dust accumulation conditions was obtained, and the correlation data between dust thickness and power output was obtained. The trend of module output power change of photovoltaic modules on different plots under different dust accumulation conditions was obtained, and the module output loss caused by shading was corrected. The specific steps of shading correction are shown in Figure 3.

[0034] S4. Based on the characteristics of power loss caused by hot spots in the modules and the module temperature data, determine the level of hot spot faults in the photovoltaic power station modules.

[0035] As shown in Figure 4, based on the characteristics of power loss caused by hot spots in the components, the difference in power output of photovoltaic modules caused by dust accumulation and hot spots is coupled and analyzed to obtain a detection and identification algorithm for distinguishing between dust accumulation and local shadow problems in mountain photovoltaic power stations.

[0036] After removing abnormal power output data caused by shading, the coupling component temperature determination identifies components with abnormal power output and compares and analyzes them with irradiance data.

[0037] Based on the analysis results, the balance condition between hot spot benefits and shadow loss was solved.

[0038] Extract temperature points where the component temperature exceeds the threshold, and integrate the extracted data. Components whose integrated area and temperature value both exceed the set value are diagnosed as faulty components.

[0039] The faulty components are classified into different fault levels based on the fault area and temperature values ​​obtained from integration.

[0040] The fault level should be set to at least two levels, and the decision to clean the photovoltaic modules should be made based on the different fault levels.

[0041] In this embodiment, the fault level is set to two levels: low and high. When the fault level is judged to be low, the energy consumption and labor costs of cleaning the photovoltaic panel are considered to be greater than the economic benefits of maintaining the cleanliness of the photovoltaic panel, so the photovoltaic module is not cleaned. When the fault level is judged to be high, the photovoltaic module needs to be cleaned because the hot spot effect has a great impact on the photovoltaic power generation efficiency.

[0042] Example 2, referring to Figure 5, is the second embodiment of the present invention, which differs from the first embodiment in that: a system for automatic diagnosis of hot spot faults in photovoltaic power station modules includes a data acquisition module 100, an environmental factor analysis module 200, a dust accumulation factor analysis module 300, and a fault level judgment module 400; the data acquisition module 100 collects local environmental data, power output data, and module temperature data of the target photovoltaic power station; the environmental factor analysis module 200 obtains the relationship between environmental factors and power output based on the environmental data and power output data; the dust accumulation factor analysis module 300 obtains the output power of photovoltaic modules in different plots under different dust accumulation conditions, and obtains the relationship between different dust accumulation conditions and power output; the fault level judgment module 400 judges the level of hot spot faults occurring in the photovoltaic power station modules based on the characteristics of power loss caused by hot spots and module temperature data.

[0043] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0044] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0045] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0046] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0047] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An automatic diagnosis method for hot spot faults in photovoltaic power plant modules, characterized in that: include, Collect local environmental data, power output data, and module temperature data for the target photovoltaic power station; Based on environmental data and power output data, the relationship between environmental factors and power output is obtained; The output power of photovoltaic modules on different plots of land under different dust accumulation conditions was obtained, and the relationship between different dust accumulation conditions and power output was obtained. Based on the characteristics of power loss caused by hot spots in the modules and the module temperature data, the level of hot spot faults in photovoltaic power plant modules is determined.

2. The method for automatically diagnosing hot spot failure of a photovoltaic power station assembly according to claim 1, characterized in that: The environmental data acquisition methods include collecting historical irradiance data, meteorological data, dust detection data, and terrain data of the target photovoltaic power station through deployed solar irradiance recorders; The methods for acquiring the power output data and component temperature data include obtaining the corresponding power output data based on historical irradiance data, and monitoring and recording the photovoltaic power station component temperature data through deployed infrared thermal imagers.

3. The method for automatically diagnosing hot spot failure of a photovoltaic power station assembly according to claim 2, characterized in that: The process of obtaining the relationship between environmental factors and power output based on environmental data and power output data includes: determining the correlation between local photovoltaic power station power and topographic environment, irradiance, and meteorological changes based on the obtained irradiance and power output time series diagrams and meteorological data and power output time series diagrams; and obtaining a photovoltaic module dust accumulation status monitoring algorithm for photovoltaic module output power and IV curve.

4. The method for automatically diagnosing hot spot failure of a photovoltaic power station assembly according to claim 3, characterized in that: The process of obtaining the relationship between different dust accumulation conditions and power output includes acquiring the output power of photovoltaic modules on different plots under different dust accumulation conditions, obtaining the correlation data between dust thickness and power output, obtaining the trend of module output power change of photovoltaic modules on different plots under different dust accumulation conditions, and correcting the module output loss caused by shading.

5. The method for automatically diagnosing hot spot failure of a photovoltaic power station assembly according to claim 4, characterized in that: The method for determining the level of hot spot faults in photovoltaic power station modules based on the characteristics of power loss caused by hot spots and module temperature data includes, based on the characteristics of power loss caused by hot spots, coupling analysis of the differences in photovoltaic module power output caused by dust accumulation and hot spots, and obtaining a distinguishing detection and identification algorithm for dust accumulation and local shadow problems in mountainous photovoltaic power stations. After removing abnormal power output data caused by shading, the coupling component temperature determination identifies components with abnormal power output and compares and analyzes them with irradiance data.

6. The method for automatically diagnosing hot spot failure of a photovoltaic power station assembly according to claim 5, characterized in that: The method of determining the level of hot spot faults in photovoltaic power plant modules based on the characteristics of power loss caused by hot spots and module temperature data also includes solving the balance condition between hot spot benefits and shading losses based on the analysis results. Extract temperature points where the component temperature is higher than the threshold, and integrate the extracted data. Components whose integrated area and temperature value are both higher than the set value are diagnosed as faulty components. The faulty components are classified into different fault levels based on the fault area and temperature values ​​obtained from integration.

7. The method of claim 6, wherein the method further comprises: determining the location of the hot spot fault based on the temperature distribution of the photovoltaic power station component. The classification into different fault levels includes setting at least two fault levels, and determining whether to clean the photovoltaic modules based on the different fault levels.

8. A system for automatically diagnosing a hot spot fault of a photovoltaic power plant assembly using the method according to any one of claims 1 to 7, characterized in that: It includes a data acquisition module (100), an environmental factor analysis module (200), a dust accumulation factor analysis module (300), and a fault level judgment module (400); The data acquisition module (100) acquires local environmental data, power output data, and component temperature data of the target photovoltaic power station; The environmental factor analysis module (200) obtains the relationship between environmental factors and power output based on environmental data and power output data; The dust accumulation factor analysis module (300) obtains the output power of photovoltaic modules on different plots under different dust accumulation conditions, and obtains the relationship between different dust accumulation conditions and power output; The fault level judgment module (400) determines the fault level of the photovoltaic power station module based on the characteristics of power loss caused by hot spots in the module and the module temperature data. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: When the processor executes the computer program, it implements the steps of the automatic diagnosis method for hot spot faults of photovoltaic power plant modules according to any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: When the computer program is executed by the processor, it implements the steps of the automatic diagnosis method for hot spot faults of photovoltaic power plant modules according to any one of claims 1 to 7.

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