CN118232834A

CN118232834A - Photovoltaic module fault diagnosis method, device, equipment and storage medium

Info

Publication number: CN118232834A
Application number: CN202410162585.0A
Authority: CN
Inventors: 林孟涵; 李震; 庄庆康
Original assignee: Wenzhou Technician Institute
Current assignee: Wenzhou Technician Institute
Filing date: 2024-02-05
Publication date: 2024-06-21

Abstract

The embodiment of the disclosure provides a photovoltaic module fault diagnosis method, device, equipment and storage medium. Comprising the following steps: acquiring data of a component to be tested of the photovoltaic component; acquiring sample assembly data of a photovoltaic assembly, and constructing a fault diagnosis model according to the sample assembly data, wherein the fault diagnosis model comprises a corresponding relation between the sample assembly data and a fault type; and determining a fault diagnosis result of the photovoltaic module according to the module data to be tested and the fault diagnosis model. Based on the data driving principle, the existing sample data is utilized for modeling and deducing, so that the accuracy and efficiency of fault diagnosis can be improved. With the accumulation of more sample data and the continuous optimization of the model, the effect of fault diagnosis is further improved. By carrying out feature extraction and similarity comparison on the data, the accuracy of fault diagnosis can be improved.

Description

Photovoltaic module fault diagnosis method, device, equipment and storage medium

Technical Field

The invention relates to the field of new energy, in particular to a photovoltaic module fault diagnosis method, device, equipment and storage medium.

Background

With the increasing global energy demand and the increasing awareness of environmental protection, solar energy is becoming more and more interesting as a clean and renewable energy source. The solar panel is a core component for converting solar energy into electric energy, and the performance and the service life of the solar panel directly influence the efficiency and the reliability of a solar power generation system.

However, various faults, such as aging, cracking, short-circuiting, open-circuiting, etc., of the solar panel may occur during the use process, and these faults may cause a decrease in the output power of the panel, affecting the power generation efficiency. Therefore, performing fault diagnosis on the solar panel is one of important means for ensuring the normal operation of the solar power generation system.

At present, the commonly used solar panel fault diagnosis methods mainly comprise electrical detection, optical detection, thermal detection and the like. Among them, electrical inspection is the most commonly used method, which tests a solar panel using an electrical instrument and then judges the type of failure of the panel by analyzing the test data. However, the electrical detection method has problems of low detection accuracy, low detection speed, high detection cost, and the like.

Disclosure of Invention

The invention aims to solve the problems of low detection precision, low detection speed and high detection cost in the prior art, and provides a method, a device, equipment and a storage medium for diagnosing faults of a photovoltaic module, which can construct a fault diagnosis model through iterative training of a neural network to realize fault diagnosis of the photovoltaic module.

In a first aspect, an embodiment of the present disclosure provides a method for diagnosing a fault of a photovoltaic module, including:

acquiring data of a component to be tested of the photovoltaic component;

acquiring sample assembly data of a photovoltaic assembly, and constructing a fault diagnosis model according to the sample assembly data, wherein the fault diagnosis model comprises a corresponding relation between the sample assembly data and a fault type;

And determining a fault diagnosis result of the photovoltaic module according to the module data to be tested and the fault diagnosis model.

Optionally, obtaining the data of the component to be tested of the photovoltaic component includes: collecting circuit related data and environment related data of the photovoltaic module according to the appointed time, wherein the circuit related data comprises a current value, a voltage value and a reverse voltage value, and the environment related data comprises irradiance, a humidity value and a temperature value; extracting characteristics of the circuit related data and the environment related data to obtain component characteristic data; and storing the component characteristic data into a designated address to generate component data to be tested.

Optionally, obtaining sample assembly data of the photovoltaic assembly includes: acquiring historical circuit data and historical environment data of a photovoltaic module; performing feature extraction on the historical circuit data and the historical environment data to obtain historical feature data; acquiring a fault type marked by a user based on the historical characteristic data, and generating a data set according to the corresponding relation between the historical characteristic data and the fault type, wherein the fault type comprises short circuit, open circuit, electric breakdown, thermal breakdown and shadow shielding; dividing the data set into a training set and a testing set according to a specified proportion; the training set and the test set are used as sample component data.

Optionally, constructing the fault diagnosis model includes: setting up an initial network structure of a neural network structure, and acquiring target iteration times input by a user; performing iterative training on the initial network structure according to the training set, and determining the current iteration times; when the current iteration times are consistent with the target iteration times, outputting a corresponding network structure as an initial diagnosis model; generating a fault diagnosis model according to the initial diagnosis model and the test set.

Optionally, generating the fault diagnosis model according to the initial diagnosis model and the test set includes: inputting each historical characteristic data in the test set into an initial diagnosis model to obtain an output test fault type, and determining an actual fault type corresponding to each historical characteristic data; determining the model accuracy according to the test fault type and the actual fault type; judging whether the model accuracy is greater than a preset threshold value, if so, directly taking the initial diagnosis model as a fault diagnosis model; otherwise, acquiring adjustment parameters based on the model accuracy, and adjusting the initial diagnosis model according to the adjustment parameters to generate a fault diagnosis model.

Optionally, determining the fault diagnosis result of the photovoltaic module according to the data of the to-be-tested module and the fault diagnosis model includes: determining the similarity of the component data to be tested and the sample component data based on the fault diagnosis model; taking the sample assembly data with the maximum similarity as target sample data; obtaining a fault type of target sample data as a target diagnosis fault, and obtaining a target solution according to the target diagnosis fault; and taking the target diagnosis fault and the target solution as fault diagnosis results.

Optionally, obtaining the target solution according to the target diagnostic fault includes: acquiring a preset fault solution list, wherein the fault solution list comprises solutions corresponding to each diagnosis fault; and matching the target diagnosis faults through the fault solution list to obtain target solutions matched with the target diagnosis faults.

In a second aspect, embodiments of the present disclosure further provide a photovoltaic module fault diagnosis apparatus, including:

The module to be tested is used for acquiring module to be tested data of the photovoltaic module;

The fault diagnosis model construction module is used for acquiring sample assembly data of the photovoltaic assembly and constructing a fault diagnosis model according to the sample assembly data, wherein the fault diagnosis model comprises a corresponding relation between the sample assembly data and a fault type;

The fault diagnosis result generation module is used for determining a fault diagnosis result of the photovoltaic module according to the module data to be tested and the fault diagnosis model.

In a third aspect, embodiments of the present disclosure further provide an electronic device, including:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

When the memory stores a computer program executable by the at least one processor, the computer program is executed by the at least one processor to enable the at least one processor to perform a photovoltaic module fault diagnosis method as in any embodiment of the present disclosure.

In a fourth aspect, the disclosed embodiments provide a computer storage medium having a computer program stored thereon, which when executed by a processor, implements a photovoltaic module failure diagnosis method as in any of the embodiments of the disclosure.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Therefore, the invention has the following beneficial effects:

1. based on the data driving principle, the existing sample data is utilized for modeling and deducing, so that the accuracy and efficiency of fault diagnosis can be improved.

2. With the accumulation of more sample data and the continuous optimization of the model, the effect of fault diagnosis is further improved.

3. By carrying out feature extraction and similarity comparison on the data, the accuracy of fault diagnosis can be improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a photovoltaic module fault diagnosis method according to a first embodiment of the present invention;

fig. 2 is a flowchart of another method for diagnosing faults of a photovoltaic module according to an embodiment of the present invention;

Fig. 3 is a schematic structural diagram of a photovoltaic module fault diagnosis device according to a second embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a method for diagnosing a failure of a photovoltaic module according to an embodiment of the present invention, where the embodiment is applicable to detecting a failure of a photovoltaic module. The method can be performed by the photovoltaic module fault diagnosis device provided by the embodiment of the disclosure, and the device can be realized in a software and/or hardware mode and can be generally integrated in computer equipment. The method of the embodiment of the disclosure specifically comprises the following steps:

S110: and acquiring data of the component to be tested of the photovoltaic component.

Among them, a photovoltaic module is a device for converting solar energy into electric energy, and is generally composed of a plurality of solar cells. Each solar cell is composed of two layers of semiconductor material that when irradiated with sunlight, excite movement of electrons and holes, thereby generating an electric current. The solar cells are connected in series and in parallel to form a photovoltaic module, and the generated current is collected and output to an external circuit. The performance and efficiency of the photovoltaic module are affected by a variety of factors, and a fault diagnosis of the photovoltaic module is required.

Specifically, circuit-related data and environment-related data of the photovoltaic module are collected according to a specified time interval. These data include current values, voltage values, reverse voltage values, irradiance, humidity values, temperature values, and the like. These data are critical to assessing the performance and operating conditions of the photovoltaic module. To acquire these data, various sensors may be used to measure parameters such as voltage, current, and temperature of the component, or unmanned aerial vehicle or other devices may be used to capture images of the component. The data may be transmitted to a data center for processing by wireless transmission or the like. Next, the controller performs feature extraction on the collected circuit-related data and environment-related data to obtain component feature data. The controller refers to a computer controller for performing photovoltaic fault diagnosis. Feature extraction is a data processing technique that can extract meaningful features from raw data to better understand and analyze the data. In this process, various data analysis methods and algorithms, such as statistical analysis, machine learning, etc., can be used to extract key features of the photovoltaic module. And finally, storing the extracted component characteristic data into a designated address to generate component data to be tested. The specified address may be a local file system, a database, or cloud storage, etc. The component data to be tested can be used for subsequent analysis and processing, such as evaluating the performance of the photovoltaic component, detecting faults, optimizing the system design, and the like.

S120: and obtaining sample assembly data of the photovoltaic assembly, and constructing a fault diagnosis model according to the sample assembly data, wherein the fault diagnosis model comprises a corresponding relation between the sample assembly data and fault types.

Specifically, after obtaining the component data to be tested and the sample component data, we can construct a fault diagnosis model according to the sample component data. The model comprises a corresponding relation between sample assembly data and fault types, and a mapping relation between different fault types and corresponding data features is established by analyzing and processing the sample assembly data.

Fig. 2 is a flowchart of a photovoltaic module fault diagnosis method according to an embodiment of the present invention, and step S120 mainly includes steps S121 to S125 as follows:

s121: sample assembly data of the photovoltaic assembly is obtained.

Specifically, the historical circuit data includes a current value, a voltage value, a reverse voltage value, and the like, and the historical environment data includes irradiance, a humidity value, a temperature value, and the like. After the historical circuit data and the historical environment data are acquired, feature extraction is required for the data to acquire the historical feature data. These historical characteristic data may be used to construct a fault diagnosis model, for example, if the voltage value of a certain component is abnormally low, it may have a short circuit fault. Feature extraction may use a cluster analysis method, and in particular, historical data may be divided into different clusters, each cluster representing a type of fault. By analyzing the characteristics of the clusters, key characteristics of the photovoltaic module can be extracted.

Further, based on the historical feature data, the fault type marked by the user can be obtained. The user labeling refers to labeling and classifying the fault types of the photovoltaic modules according to experience and knowledge of a user. A data set may be generated based on the correspondence of the historical feature data and the fault type. The data set includes historical characteristic data of the photovoltaic module and corresponding fault types for subsequent fault diagnosis and prediction. Fault types include short circuits, open circuits, electrical breakdown, thermal breakdown, shadow masking, and the like. According to the corresponding relation between the historical characteristic data and the fault type, a data set can be generated. In order to improve the accuracy and reliability of fault diagnosis, the data set needs to be divided. The data set is divided into a training set and a testing set according to a specified proportion. The training set is used for training the fault diagnosis model, and the testing set is used for verifying the performance of the model.

S122: setting up an initial network structure of the neural network structure, and acquiring target iteration times input by a user.

Specifically, the controller may first build an initial network structure of the neural network structure, and obtain a target iteration number input by a user. The initial network structure may be a simple neural network, such as a multi-layer perceptron (MLP) or Convolutional Neural Network (CNN).

S123: and carrying out iterative training on the initial network structure according to the training set, and determining the current iteration times.

Specifically, the controller performs iterative training on the initial network structure according to the training set. In each iteration, the network needs to be updated according to the data of the training set to improve the performance of the network. In the training process, the current iteration number needs to be determined, and the network is adjusted according to the current iteration number.

S124: and when the current iteration times are consistent with the target iteration times, outputting a corresponding network structure as an initial diagnosis model.

Specifically, when the current iteration number is consistent with the target iteration number, a corresponding network structure may be output as an initial diagnostic model. The initial diagnostic model is a trained neural network that can be used to diagnose faults in new samples.

S125: generating a fault diagnosis model according to the initial diagnosis model and the test set.

Wherein the test set is a sample set containing known fault types that can be used to verify the performance of the initial diagnostic model. When generating the fault diagnosis model, it is necessary to predict the test set according to the initial diagnosis model and compare the prediction result with the actual fault type. If the predicted result is consistent with the real fault type, the initial diagnosis model is good in performance, and the method can be used for carrying out fault diagnosis on a new sample.

It should be noted that, before the historical feature data is input into the initial diagnostic model, the historical feature data may be preprocessed, for example, cleaned, normalized, etc., to ensure the quality and availability of the data.

Specifically, the preprocessed historical feature data may be input into an initial diagnostic model for training to obtain an output test fault type. And determining the accuracy of the model according to the output test fault type and the actual fault type. Model accuracy refers to the degree of consistency between the test fault type and the actual fault type of the model output. If the model accuracy is less than the preset threshold, the performance of the initial diagnostic model is not ideal enough, and the model needs to be adjusted. And obtaining adjustment parameters according to the accuracy of the model, such as adjusting the structure of the neural network, adjusting training parameters and the like, so as to improve the performance of the model.

S130: and determining a fault diagnosis result of the photovoltaic module according to the module data to be tested and the fault diagnosis model.

Specifically, by inputting the component data to be tested into the fault diagnosis model, the similarity between the component data to be tested and each sample component data can be calculated. The similarity may be calculated by some distance measure, such as euclidean distance, cosine similarity, etc.

The sample component data with the greatest similarity can then be selected as the target sample data. The target sample data represents the sample component most similar to the component data to be tested. And then the fault type corresponding to the target sample data can be obtained from the sample assembly data to serve as a target diagnosis fault of the photovoltaic assembly.

Further, according to the type of the target diagnosis fault, a corresponding target solution can be obtained from a predefined fault solution library, and the target solution is a specific suggestion and measure of the target diagnosis fault. Finally, the target diagnosis fault and the target solution are output as a fault diagnosis result. The fault diagnosis results may be provided to maintenance personnel or related decision makers so that they take appropriate action to repair or maintain the photovoltaic module.

Specifically, the fault solution list includes solutions corresponding to various diagnostic faults. The fault solution list is prepared in advance, with possible solutions for different fault types listed. Comparing and matching the target diagnostic fault with each fault type in the fault solution list may be performed, in particular, by a matching algorithm or rule to determine the solution that best matches the target diagnostic fault. Once the type of fault matching the target diagnostic fault is found, the corresponding solution, i.e., the target solution, may be obtained from the fault solution list. Based on a preset fault solution list, the most suitable solution is determined through matching, so that the efficiency and accuracy of fault processing can be improved.

According to the technical scheme, the data of the component to be tested of the photovoltaic component are obtained; acquiring sample assembly data of a photovoltaic assembly, and constructing a fault diagnosis model according to the sample assembly data, wherein the fault diagnosis model comprises a corresponding relation between the sample assembly data and a fault type; and determining a fault diagnosis result of the photovoltaic module according to the module data to be tested and the fault diagnosis model. Based on the data driving principle, the existing sample data is utilized for modeling and deducing, so that the accuracy and efficiency of fault diagnosis can be improved. With the accumulation of more sample data and the continuous optimization of the model, the effect of fault diagnosis is further improved. By carrying out feature extraction and similarity comparison on the data, the accuracy of fault diagnosis can be improved.

Example two

Fig. 3 is a schematic structural diagram of a photovoltaic module fault diagnosis device according to a second embodiment of the present invention. The apparatus may be implemented in software and/or hardware and may generally be integrated in an electronic device for performing the method. As shown in fig. 3, the apparatus includes: the module to be tested data acquisition module 210 is configured to acquire module to be tested data of the photovoltaic module;

the fault diagnosis model construction module 220 is configured to obtain sample assembly data of the photovoltaic assembly, and construct a fault diagnosis model according to the sample assembly data, where the fault diagnosis model includes a correspondence between the sample assembly data and a fault type;

The fault diagnosis result generating module 230 is configured to determine a fault diagnosis result of the photovoltaic module according to the component data to be tested and the fault diagnosis model.

Optionally, the module 210 for acquiring data of the component to be tested is specifically configured to: collecting circuit related data and environment related data of the photovoltaic module according to the appointed time, wherein the circuit related data comprises a current value, a voltage value and a reverse voltage value, and the environment related data comprises irradiance, a humidity value and a temperature value; extracting characteristics of the circuit related data and the environment related data to obtain component characteristic data; and storing the component characteristic data into a designated address to generate component data to be tested.

Optionally, the fault diagnosis model construction module 220 specifically includes: a sample component data acquisition unit configured to: acquiring historical circuit data and historical environment data of a photovoltaic module; performing feature extraction on the historical circuit data and the historical environment data to obtain historical feature data; acquiring a fault type marked by a user based on the historical characteristic data, and generating a data set according to the corresponding relation between the historical characteristic data and the fault type, wherein the fault type comprises short circuit, open circuit, electric breakdown, thermal breakdown and shadow shielding; dividing the data set into a training set and a testing set according to a specified proportion; the training set and the test set are used as sample component data.

Optionally, the fault diagnosis model construction module 220 specifically includes: an initial network structure building unit for: setting up an initial network structure of a neural network structure, and acquiring target iteration times input by a user; an iterative training unit, configured to: performing iterative training on the initial network structure according to the training set, and determining the current iteration times; an initial diagnostic model generation unit configured to: when the current iteration times are consistent with the target iteration times, outputting a corresponding network structure as an initial diagnosis model; a fault diagnosis model generation unit configured to: generating a fault diagnosis model according to the initial diagnosis model and the test set.

Optionally, the fault diagnosis model generating unit is specifically configured to: inputting each historical characteristic data in the test set into an initial diagnosis model to obtain an output test fault type, and determining an actual fault type corresponding to each historical characteristic data; determining the model accuracy according to the test fault type and the actual fault type; judging whether the model accuracy is greater than a preset threshold value, if so, directly taking the initial diagnosis model as a fault diagnosis model; otherwise, acquiring adjustment parameters based on the model accuracy, and adjusting the initial diagnosis model according to the adjustment parameters to generate a fault diagnosis model.

Optionally, the fault diagnosis result generating module 230 specifically includes: a similarity determination unit configured to: determining the similarity of the component data to be tested and the sample component data based on the fault diagnosis model; a target sample data determination unit configured to: taking the sample assembly data with the maximum similarity as target sample data; a target solution acquisition unit configured to: obtaining a fault type of target sample data as a target diagnosis fault, and obtaining a target solution according to the target diagnosis fault; a fault diagnosis result generation unit configured to: and taking the target diagnosis fault and the target solution as fault diagnosis results.

Optionally, the target solution obtaining unit is specifically configured to: acquiring a preset fault solution list, wherein the fault solution list comprises solutions corresponding to each diagnosis fault; and matching the target diagnosis faults through the fault solution list to obtain target solutions matched with the target diagnosis faults.

The photovoltaic module fault diagnosis device provided by the embodiment of the invention can execute the photovoltaic module fault diagnosis method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example III

Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a photovoltaic module failure diagnosis method. Namely: acquiring data of a component to be tested of the photovoltaic component; acquiring sample assembly data of a photovoltaic assembly, and constructing a fault diagnosis model according to the sample assembly data, wherein the fault diagnosis model comprises a corresponding relation between the sample assembly data and a fault type; and determining a fault diagnosis result of the photovoltaic module according to the module data to be tested and the fault diagnosis model.

In some embodiments, a photovoltaic module fault diagnosis method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of one of the above-described photovoltaic module failure diagnosis methods may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform a photovoltaic module fault diagnosis method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A photovoltaic module failure diagnosis method, comprising:

acquiring data of a component to be tested of the photovoltaic component;

Obtaining sample assembly data of a photovoltaic assembly, and constructing a fault diagnosis model according to the sample assembly data, wherein the fault diagnosis model comprises a corresponding relation between the sample assembly data and a fault type;

and determining a fault diagnosis result of the photovoltaic module according to the data of the to-be-detected module and the fault diagnosis model.

2. The method for diagnosing a failure of a photovoltaic module according to claim 1, wherein the step of obtaining the data of the component to be tested of the photovoltaic module includes:

Collecting circuit related data and environment related data of the photovoltaic module according to the appointed time, wherein the circuit related data comprises a current value, a voltage value and a reverse voltage value, and the environment related data comprises irradiance, a humidity value and a temperature value;

performing feature extraction on the circuit-related data and the environment-related data to obtain component feature data;

And storing the component characteristic data into a designated address to generate the component data to be tested.

3. The method for diagnosing a failure of a photovoltaic module according to claim 1, wherein said obtaining sample module data of the photovoltaic module comprises:

acquiring historical circuit data and historical environment data of a photovoltaic module;

performing feature extraction on the historical circuit data and the historical environment data to obtain historical feature data;

Acquiring a fault type marked by a user based on the historical characteristic data, and generating a data set according to the corresponding relation between the historical characteristic data and the fault type, wherein the fault type comprises short circuit, open circuit, electric breakdown, thermal breakdown and shadow shielding;

dividing the data set into a training set and a testing set according to a specified proportion;

The training set and the test set are used as the sample component data.

4. A method of diagnosing a photovoltaic module as recited in claim 3, wherein said constructing a fault diagnosis model comprises:

setting up an initial network structure of a neural network structure, and acquiring target iteration times input by a user;

performing iterative training on the initial network structure according to the training set, and determining the current iteration times;

When the current iteration times are consistent with the target iteration times, outputting a corresponding network structure as an initial diagnosis model;

Generating the fault diagnosis model according to the initial diagnosis model and the test set.

5. The method of claim 4, wherein generating the fault diagnosis model from the initial diagnosis model and the test set comprises:

Inputting each historical characteristic data in the test set into the initial diagnosis model to obtain an output test fault type, and determining an actual fault type corresponding to each historical characteristic data;

determining model accuracy according to the test fault type and the actual fault type;

judging whether the model accuracy is greater than a preset threshold value, if so, directly taking the initial diagnosis model as the fault diagnosis model;

otherwise, acquiring an adjustment parameter based on the model accuracy, and adjusting the initial diagnosis model according to the adjustment parameter to generate the fault diagnosis model.

6. The method for diagnosing a failure of a photovoltaic module according to claim 1, wherein said determining a failure diagnosis result of the photovoltaic module according to the to-be-tested module data and the failure diagnosis model includes:

determining the similarity of the component data to be tested and the sample component data based on the fault diagnosis model;

taking the sample assembly data with the maximum similarity as target sample data;

obtaining the fault type of the target sample data as a target diagnosis fault, and obtaining a target solution according to the target diagnosis fault;

And taking the target diagnosis fault and the target solution as the fault diagnosis result.

7. The method according to claim 6, wherein the obtaining a target solution according to the target diagnosis fault comprises:

acquiring a preset fault solution list, wherein the fault solution list comprises solutions corresponding to each diagnosis fault;

And matching the target diagnosis faults through the fault solution list so as to acquire target solutions matched with the target diagnosis faults.

8. A photovoltaic module failure diagnosis apparatus, characterized by comprising:

the fault diagnosis model construction module is used for acquiring sample assembly data of the photovoltaic assembly and constructing a fault diagnosis model according to the sample assembly data, wherein the fault diagnosis model comprises a corresponding relation between the sample assembly data and fault types;

and the fault diagnosis result generation module is used for determining a fault diagnosis result of the photovoltaic module according to the data of the to-be-detected module and the fault diagnosis model.

9. An electronic device, the electronic device comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of claims 1-7.

10. A computer storage medium storing computer instructions for causing a processor to perform the method of claims 1-7 when executed.