Fault diagnosis method of photovoltaic string, electronic equipment and storage medium

Abstract

The invention relates to the technical field of photovoltaic fault diagnosis, and discloses a fault diagnosis method of a photovoltaic string, electronic equipment and a storage medium. The fault diagnosis method for the photovoltaic string comprises the following steps: acquiring IV data of the photovoltaic string; preprocessing the IV data, converting the preprocessed IV data into an IV curve image, and making a data set; training the data set to obtain a photovoltaic fault type identification model, and combining the photovoltaic fault type identification model with a photovoltaic fault degree discrimination method to obtain a photovoltaic fault diagnosis model; and performing fault diagnosis on the IV curve image according to the photovoltaic fault diagnosis model to obtain a fault diagnosis result of the photovoltaic string. According to the invention, the method can achieve the recognition of the fault type and the discrimination of the fault degree, provides a guidance direction for guiding the operation and maintenance of the photovoltaic string, and precisely and appropriately provides different early warning prompts and fault processing suggestions for a user according to different fault types and different fault severity degrees.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of photovoltaic fault diagnosis, and in particular to a photovoltaic string fault diagnosis method, electronic equipment, and storage medium. Background Art

[0002] With the development and maturity of the photovoltaic industry, efficient identification and handling of photovoltaic string faults has become a key strategy to increase power station output and reduce investment risks, while also guiding the development of operation and maintenance. Failure of photovoltaic strings significantly affects power generation efficiency and sometimes even causes a series of safety issues. The operating status of photovoltaic strings directly affects the power generation efficiency of photovoltaic power generation systems. By analyzing the current and voltage data (IV data) generated during the photovoltaic string power generation process, fault diagnosis of photovoltaic strings can be achieved.

[0003] However, when existing PV string fault diagnosis methods use IV data for fault diagnosis, they often only focus on fault classification and ignore the different severity of faults. For example, different degrees of shading have different effects on the power generation efficiency of PV strings. Obviously, the urgency of handling these faults and the early warning prompts and handling suggestions given to users should be different. However, the existing technology lacks a PV string fault diagnosis method that integrates fault severity discrimination, and the urgency of handling these faults and the early warning prompts and handling suggestions given to users are not accurate and appropriate enough. Summary of the invention

[0004] The present invention provides a photovoltaic string fault diagnosis method, electronic device and storage medium, which solve the problem that the existing methods are not accurate and appropriate in terms of the urgency of fault processing and the early warning prompts and processing suggestions given to users. The present invention can realize the identification of fault types and the determination of fault severity, and provide users with different early warning prompts and fault processing suggestions accurately and appropriately.

[0005] According to one aspect of the present invention, a photovoltaic string fault diagnosis method is provided, and the photovoltaic string fault diagnosis method comprises:

[0006] Get IV data of PV strings;

[0007] Preprocessing the IV data, converting the preprocessed IV data into an IV curve image and preparing a data set;

[0008] The data set is trained to obtain a photovoltaic fault type recognition model, and the photovoltaic fault type recognition model is combined with a photovoltaic fault degree discrimination method to obtain a photovoltaic fault diagnosis model;

[0009] Fault diagnosis is performed on the IV curve image according to the photovoltaic fault diagnosis model to obtain a fault diagnosis result of the photovoltaic string.

[0010] Optionally, obtaining IV data of a photovoltaic string includes:

[0011] Acquire historical fault IV data of the photovoltaic string under different fault types and different fault degrees;

[0012] The IV data of the photovoltaic string when in normal use is obtained.

[0013] Optionally, preprocessing the IV data, converting the preprocessed IV data into an IV curve image and preparing a data set includes:

[0014] Cleaning the IV data to remove erroneous data and obtain IV data that meets the conditions;

[0015] The IV data meeting the conditions are drawn into an IV curve, and then normalized or resized or the IV curve coordinate axis is not displayed to produce an IV curve image;

[0016] The IV curve images are classified according to different fault types and fault degrees and a data set is prepared.

[0017] Optionally, the photovoltaic fault degree determination method includes:

[0018] Calculating a distance value between an actual maximum power point on the IV curve and a virtual maximum power point on the IV curve image, wherein the distance value is used as a threshold for determining a photovoltaic fault degree;

[0019] The photovoltaic fault degree is judged according to the photovoltaic fault degree judgment threshold.

[0020] Optionally, the distance value is calculated as follows:

[0021]

[0022] in, is the distance between the actual maximum power point and the virtual maximum power point on the IV curve image, V mpp_px is the coordinate of the actual maximum power point on the horizontal axis of the IV curve image, I mpp_px is the coordinate of the actual maximum power point on the vertical axis of the IV curve image, V T_px is the coordinate of the virtual maximum power point on the horizontal axis of the IV curve image, I T_px is the virtual maximum power point P T The coordinate of the vertical axis on the IV curve graph.

[0023] Optionally, the calculation formula of the coordinate of the actual maximum power point on the horizontal axis of the IV curve image is as follows:

[0024]

[0025] Among them, V mpp is the voltage at the actual maximum power point, V min is the minimum value in the voltage array, V max is the maximum value in the voltage array, and H is the height of the IV curve image;

[0026] The calculation formula of the coordinate of the actual maximum power point on the vertical axis of the IV curve image is as follows:

[0027]

[0028] Among them, I mpp is the current at the actual maximum power point, I min is the minimum value in the current array, I max is the maximum value in the current array, W is the width of the IV curve image;

[0029] The calculation formula of the coordinate of the virtual maximum power point on the horizontal axis of the IV curve image is as follows:

[0030]

[0031] in, ;

[0032] The calculation formula of the coordinate of the virtual maximum power point on the vertical axis of the IV curve image is as follows:

[0033]

[0034] in, is the short circuit current.

[0035] Optionally, the step of training the data set to obtain a photovoltaic fault type recognition model includes:

[0036] The photovoltaic fault type recognition model is obtained by training the data set based on a convolutional neural network model.

[0037] Optionally, the fault type includes at least one of: shielding, hot spot, glass breakage, open circuit, short circuit and aging;

[0038] The fault degree includes at least one of a minor fault, a medium fault and a major fault.

[0039] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0040] one or more processors;

[0041] A memory for storing one or more programs;

[0042] When the one or more programs are executed by the one or more processors, the one or more processors implement the photovoltaic string fault diagnosis method as described in any embodiment of the present invention.

[0043] According to another aspect of the present invention, there is further provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the photovoltaic string fault diagnosis method as described in any embodiment of the present invention.

[0044] The technical solution of the embodiment of the present invention provides a fault diagnosis method for photovoltaic strings that integrates fault degree discrimination, by obtaining IV data of the photovoltaic strings, preprocessing the IV data, converting the preprocessed IV data into IV curve images and making a data set; training the data set to obtain a photovoltaic fault type recognition model, combining the photovoltaic fault type recognition model with the photovoltaic fault degree discrimination method to obtain a photovoltaic fault diagnosis model; performing fault diagnosis on the IV curve image according to the photovoltaic fault diagnosis model to obtain the fault diagnosis result of the photovoltaic string, and giving corresponding early warning prompts and processing suggestions; the identification of the fault type and the discrimination of the fault degree can be realized, providing a guiding direction for guiding the operation and maintenance of the photovoltaic strings, and accurately and appropriately giving users different early warning prompts and fault handling suggestions according to different fault types and different fault severity. In summary, the present invention solves the problem that the existing photovoltaic string fault diagnosis method often only focuses on fault classification and ignores the different severity of the fault when using IV data for fault diagnosis, and the urgency of handling these faults and the early warning prompts and processing suggestions given to users are not accurate and appropriate.

[0045] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 is a flow chart of a photovoltaic string fault diagnosis method provided according to an embodiment of the present invention;

[0048] Figure 2 is a flowchart of using a photovoltaic fault diagnosis model provided according to an embodiment of the present invention;

[0049] Figure 3 is a flow chart of another photovoltaic string fault diagnosis method provided according to an embodiment of the present invention;

[0050] Figure 4 is a flow chart of another photovoltaic string fault diagnosis method provided according to an embodiment of the present invention;

[0051] Figure 5 is a schematic diagram of an IV curve and key parameters of a photovoltaic string provided according to an embodiment of the present invention;

[0052] Figure 6 is a schematic diagram of an IV data scanning process provided according to an embodiment of the present invention;

[0053] Figure 7 is a schematic diagram of a fault degree distinguished by D value provided by an embodiment of the present invention;

[0054] Figure 8 is a schematic diagram of an image after processing of some fault types provided by an embodiment of the present invention;

[0055] Fig. 9 It is an overall flow chart from IV data acquisition to building a photovoltaic fault diagnosis model according to an embodiment of the present invention;

[0056] Fig.10 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0058] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0059] Figure 1 is a flowchart of a photovoltaic string fault diagnosis method provided according to an embodiment of the present invention, referring to Figure 1 The embodiment of the present invention provides a fault diagnosis method for a photovoltaic string. The method can be performed by a fault diagnosis device for a photovoltaic string. The diagnosis device can be integrated into an electronic device. The diagnosis device can be implemented by software and / or hardware. The fault diagnosis method for a photovoltaic string includes:

[0060] S110, obtaining IV data of the photovoltaic string.

[0061] Specifically, the IV data of the PV strings can be obtained at the power station level and the inverter level. The power station level is to process the PV strings connected to all inverters under the power station, and the inverter level is to process the PV strings under a single or multiple inverters. The logic of obtaining the IV data of the PV strings is as follows: after the gateway receives the IV data scanning task request, it sends an IV data scanning instruction to the inverter to notify it to start scanning. After waiting for the inverter to complete the scan, the IV data scanning address of the inverter is queried through the relevant protocol. According to the obtained IV data scanning address, go to each inverter address to obtain the actual IV data. The obtained IV data is saved and sent to the web page or the local IV diagnostic program front end for display.

[0062] S120 , preprocessing the IV data, converting the preprocessed IV data into an IV curve image and creating a data set.

[0063] Specifically, the acquired IV data is preprocessed, and the data is classified according to different fault types and fault degrees and converted into images to produce a data set. First, the original IV data is preprocessed, including data cleaning, eliminating erroneous data, and obtaining qualified IV data. Then the qualified IV data is drawn into an IV curve and then processed into an IV curve image after normalization, resizing, and not displaying the IV curve coordinate axis. Finally, the above IV curve images are classified according to different fault types and fault degrees to produce a data set, which is used to train a photovoltaic fault type recognition model.

[0064] The dataset for training the photovoltaic fault type recognition model is the processed image of the IV curve. The processing methods include but are not limited to: normalization, fixed image size, and non-display of the IV curve coordinate axis. The purpose is to focus only on the shape of the IV curve without considering the value itself, which is used to deal with the problem of different IV curve values ​​in different inverter models and different usage scenarios.

[0065] S130, training the data set to obtain a photovoltaic fault type recognition model, and combining the photovoltaic fault type recognition model with a photovoltaic fault degree discrimination method to obtain a photovoltaic fault diagnosis model.

[0066] Specifically, a convolutional neural network model can be used to train the data set to obtain a photovoltaic fault type recognition model, which can be replaced with other image processing models or the superposition of multiple models to achieve similar effects. The photovoltaic fault degree discrimination method can use the power analysis method of photovoltaic strings to realize the discrimination of the fault degree. For example, the fault degree can be divided into three levels: slight, medium and severe. The photovoltaic fault type recognition model is obtained by training the data set, and then combined with the photovoltaic fault degree discrimination method to obtain the final photovoltaic fault diagnosis model. Figure 2 is a flowchart of a photovoltaic fault diagnosis model provided according to an embodiment of the present invention, referring to Figure 2 , Figure 2 The whole process of combining the identification of photovoltaic fault types and the determination of photovoltaic fault degrees to obtain the final photovoltaic fault diagnosis model is shown.

[0067] S140 , performing fault diagnosis on the IV curve image according to the photovoltaic fault diagnosis model to obtain a fault diagnosis result of the photovoltaic string.

[0068] Specifically, the fault diagnosis result includes the identification of the fault type and the determination of the fault degree. The IV curve image is diagnosed according to the pre-trained photovoltaic fault type identification model, and the fault type identification result of the photovoltaic string is first obtained. Then, the fault degree of the IV curve image is determined by the photovoltaic fault degree determination method, and finally the fault diagnosis result of the photovoltaic string is obtained. Among them, the fault degree of the photovoltaic string can also be determined first, and then the fault type of the photovoltaic string can be identified.

[0069] According to the fault diagnosis results of photovoltaic strings, corresponding early warning prompts and handling suggestions are given for different fault types and fault degrees, and early warnings of different urgency levels can be given when users use photovoltaic string products. For example, a red warning is given for a fault with a serious fault degree, prompting the user to handle it as soon as possible, and informing the user that the fault degree seriously affects the power generation efficiency of the photovoltaic string, and then giving fault handling suggestions according to the fault type.

[0070] The technical solution of the embodiment of the present invention provides a fault diagnosis method for photovoltaic strings that integrates fault degree discrimination, by obtaining IV data of the photovoltaic strings, preprocessing the IV data, converting the preprocessed IV data into IV curve images and making a data set; training the data set to obtain a photovoltaic fault type recognition model, combining the photovoltaic fault type recognition model with the photovoltaic fault degree discrimination method to obtain a photovoltaic fault diagnosis model; performing fault diagnosis on the IV curve image according to the photovoltaic fault diagnosis model to obtain the fault diagnosis result of the photovoltaic string, and giving corresponding early warning prompts and processing suggestions; the identification of the fault type and the discrimination of the fault degree can be realized, providing a guiding direction for guiding the operation and maintenance of the photovoltaic strings, and accurately and appropriately giving users different early warning prompts and fault handling suggestions according to different fault types and different fault severity. In summary, the present invention solves the problem that the existing photovoltaic string fault diagnosis method often only focuses on fault classification and ignores the different severity of the fault when using IV data for fault diagnosis, and the urgency of handling these faults and the early warning prompts and processing suggestions given to users are not accurate and appropriate.

[0071] On the basis of the above embodiments, the embodiment of the present invention further refines step S110, which is described in detail below, but is not intended to limit the present invention.

[0072] Figure 3 is a flowchart of another photovoltaic string fault diagnosis method provided according to an embodiment of the present invention, referring to Figure 3 , obtaining IV data of PV strings includes:

[0073] S111. Obtain historical fault IV data of photovoltaic strings with different fault types and different fault degrees.

[0074] S112, obtaining IV data of the photovoltaic string when it is in normal use.

[0075] Specifically, obtaining IV data of PV strings is divided into two stages. The first stage obtains historical fault IV data of PV strings under different fault types and fault degrees, which is used for training the PV fault type recognition model and determining the threshold for distinguishing the degree of PV faults. The second stage obtains IV data of PV strings during normal use, and uses the PV fault diagnosis model to diagnose faults for running equipment.

[0076] On the basis of the above embodiments, the embodiment of the present invention further refines step S120, which is described in detail below, but is not intended to limit the present invention.

[0077] Figure 4 is a flowchart of another photovoltaic string fault diagnosis method provided according to an embodiment of the present invention, referring to Figure 4 , preprocessing the IV data, converting the preprocessed IV data into IV curve images and making data sets include:

[0078] S121. Clean the IV data, remove erroneous data, and obtain IV data that meets the requirements.

[0079] S122, drawing the IV data that meets the conditions into an IV curve, and then making an IV curve image through normalization or size adjustment or non-displaying the IV curve coordinate axis.

[0080] S123. Classify the IV curve images according to different fault types and fault degrees and create a data set.

[0081] Optionally, the fault type includes: at least one of: shading, hot spot, glass breakage, open circuit, short circuit and aging; the fault degree includes: at least one of: minor fault, moderate fault and severe fault.

[0082] Specifically, first, the original IV data is preprocessed, including data cleaning, eliminating erroneous data, and obtaining qualified IV data. Then, the qualified IV data is drawn into an IV curve and then normalized, resized, and the IV curve coordinate axis is not displayed to produce an IV curve image. Finally, the above IV curve images are classified according to different fault types and fault degrees to produce a fault data set, which is used to train the photovoltaic fault type recognition model. Among them, the fault types include shading, hot spots, glass breakage, open circuit, short circuit and aging. The degree of fault is divided into three levels: mild, moderate and severe.

[0083] Optionally, the photovoltaic fault degree determination method includes: calculating the distance value between the actual maximum power point on the IV curve and the virtual maximum power point on the IV curve image, using the distance value as a determination threshold for the photovoltaic fault degree; and determining the photovoltaic fault degree according to the determination threshold for the photovoltaic fault degree.

[0084] Optionally, the photovoltaic fault degree determination threshold includes at least one of a slight threshold, a medium threshold and a severe threshold.

[0085] Optionally, the distance value is calculated as follows:

[0086]

[0087] in, is the distance between the actual maximum power point and the virtual maximum power point on the IV curve image, V mpp_px is the coordinate of the actual maximum power point on the horizontal axis of the IV curve image, I mpp_px is the coordinate of the actual maximum power point on the vertical axis of the IV curve image, V T_px is the coordinate of the virtual maximum power point on the horizontal axis of the IV curve image, I T_px is the virtual maximum power point P T The coordinate of the vertical axis on the IV curve graph.

[0088] Optionally, the calculation formula of the coordinate of the actual maximum power point on the horizontal axis of the IV curve image is as follows:

[0089]

[0090] Among them, V mpp is the voltage at the actual maximum power point, V min is the minimum value in the voltage array, V max is the maximum value in the voltage array, and H is the height of the IV curve image;

[0091] The calculation formula for the coordinate of the actual maximum power point on the vertical axis of the IV curve image is as follows:

[0092]

[0093] Among them, I mpp is the current at the actual maximum power point, I min is the minimum value in the current array, I max is the maximum value in the current array, W is the width of the IV curve image;

[0094] The calculation formula for the coordinate of the virtual maximum power point on the horizontal axis of the IV curve image is as follows:

[0095]

[0096] in, ;

[0097] The calculation formula of the coordinate of the virtual maximum power point on the vertical axis of the IV curve image is as follows:

[0098]

[0099] in, is the short circuit current.

[0100] The method of classifying the degree of photovoltaic faults is introduced as follows: Figure 5 Schematic diagram of an IV curve and key parameters of a photovoltaic string provided according to an embodiment of the present invention, the IV curve and key parameters of a photovoltaic string are shown in FIG. Figure 5 As shown:

[0101] Figure 5 The open circuit voltage V oc It is the maximum voltage obtained from the PV string, which occurs at zero current. sc It is the current through the photovoltaic string when the voltage across the battery is zero. The actual maximum power point P mpp It is defined as the point on the IV curve where the PV string has the maximum output power. The current corresponding to the actual maximum power point is the actual maximum power point current I mpp , the voltage corresponding to the actual maximum power point is the actual maximum power point voltage V mpp .P T Represents the virtual maximum power point, which is the product of the open circuit voltage and the short circuit current. When the IV curve is normalized and fixed to a uniform size, P mpp and P T The distance value of the corresponding point on the image is used as the threshold for dividing the fault degree. The formula for converting the distance coordinate of the IV curve parameter is as follows:

[0102]

[0103]

[0104]

[0105]

[0106] Where H is the height of the IV curve image, and W is the width of the IV curve image (the unit can be the size of the image or pixels, etc., a fixed size is sufficient). min , V min are the minimum values ​​in the current and voltage arrays respectively. max , V max are the maximum values ​​in the current and voltage arrays respectively.mpp_px and I mpp_px P mpp The coordinates of the point on the IV curve graph on the horizontal and vertical axes, V T_px and I T_px P T The coordinates of the point on the horizontal and vertical axes of the IV curve image. Then the actual maximum power point P can be obtained. mpp and virtual maximum power P T The distance value on the image is D, and the formula is as follows:

[0107]

[0108] Using the value of D as the threshold for judging the degree of photovoltaic faults, a large number of IV data of photovoltaic strings with different fault types and degrees were analyzed in the early stage to determine the slight threshold. , medium threshold and critical thresholds .

[0109] The specific analysis process is: the collected historical fault IV data of different fault types and fault degrees are classified according to the fault simulation degree (blocking area, coverage area, connected broken glass and resistance size, etc.), and the classified IV data are analyzed after obtaining the D value, including but not limited to analyzing the average value, maximum and minimum values ​​of the D value, and obtaining the three-level discrimination threshold that meets the conditions. For example, the three-level discrimination threshold can be a micro threshold , medium threshold and critical thresholds Then, the historical fault IV data with different fault types and fault degrees are used for verification, and the discrimination threshold of the photovoltaic fault degree is finally determined after it is correct.

[0110] The method for determining the degree of photovoltaic faults uses the distance between the virtual maximum power point and the actual maximum power point on the IV curve image as the threshold for determining the degree of photovoltaic faults. The distance value unit can be pixels or inches, etc., and the slight threshold is obtained using , medium threshold and critical thresholds The three-level threshold is used to judge the fault degree. Finally, the final photovoltaic fault diagnosis model is obtained by combining the identification of photovoltaic fault type and the judgment of photovoltaic fault degree.

[0111] Optionally, training the data set to obtain the photovoltaic fault type recognition model includes: training the data set based on a convolutional neural network model to obtain the photovoltaic fault type recognition model.

[0112] Specifically, the prepared data set is used to train the Convolutional Neural Network (CNN) model, which includes an input layer, four groups of convolutional layers, a flattening layer, a fully connected layer, and an output layer. After comparison and verification, the best model to be used for fault type identification is saved, and finally a photovoltaic fault type identification model is obtained.

[0113] Optionally, the fault diagnosis result of the photovoltaic string is obtained by performing fault diagnosis on the IV curve image according to the photovoltaic fault diagnosis model, including:

[0114] Perform fault diagnosis on the IV curve image according to the photovoltaic fault type identification model to obtain the fault type identification result of the photovoltaic string;

[0115] The fault degree of the IV curve image is judged according to the photovoltaic fault degree judgment method, and finally the fault diagnosis result of the photovoltaic string is obtained.

[0116] Specifically, according to the diagnosis results of the photovoltaic fault diagnosis model, corresponding early warning prompts and processing suggestions are given for different fault types and fault degrees.

[0117] The logic of obtaining the IV data of the photovoltaic string in step S110 is as follows:

[0118] First, to measure the instrument accuracy, the accuracy of the current and voltage measuring instruments of the photovoltaic inverter string or array should be ≤1.0%. Then, after the gateway receives the IV data scanning task request, it temporarily stops communicating with other commands of the inverter, and then sends the IV data scanning command to the inverter to notify it to start scanning. Wait for the inverter scan to complete. If the inverter scan fails, the reason for the failure will be queried through the relevant protocol. If the scan is successful, the IV scanning address of the inverter will be queried through the relevant protocol. According to the obtained IV scanning address, go to each inverter address to obtain the actual IV data. Send the obtained IV data to the web page or the local IV diagnostic program front end for display. Figure 6 is a schematic diagram of an IV data scanning process provided according to an embodiment of the present invention. Figure 6 shown.

[0119] The IV data of PV strings can be obtained at the power station level and the inverter level. The power station level is to process the PV strings connected to all inverters in the power station, and the inverter level is to process the PV strings under a single or multiple inverters.

[0120] The introduction of obtaining IV data of PV strings at the power station level is as follows: Communication between IV diagnostic program and Ethernet card: Use network port and optical fiber to send IV scan instructions to the Ethernet card end through a specific port number. When the Ethernet card receives the IV scan instruction sent from the specific port number, it immediately sends the shutdown instruction to the data collector through the serial port, and starts to send instructions to the inverter to start IV scanning and collect IV data of the inverter, and upload it to the background of the IV diagnostic program in real time. After all the transmissions are completed, the power-on instruction is immediately sent to the data collector through the serial port to resume normal real-time data collection and upload.

[0121] The following is an introduction to the acquisition of IV data of PV strings at the inverter level: The communication between the diagnostic program and the Ethernet card is achieved by clicking the "IV curve" function module in the cloud. The server immediately sends an IV data scan command to the local Ethernet card through the specified protocol. When the Ethernet card receives the IV scan command sent by the cloud, it immediately interrupts the real-time data communication with the inverter, and starts to send instructions to the inverter to start IV scanning and collect the IV data of the inverter, and upload it to the server background in real time. After all the data is sent, the real-time data communication with the inverter is immediately restored, and normal real-time data collection and upload are performed.

[0122] Step S120 includes the process of preprocessing the data, cleaning the collected IV data of the photovoltaic strings and removing erroneous data. The screening rules are as follows: data with negative voltage or current, current greater than 5% of the short-circuit current (sudden change in irradiance), abnormal curve slope, voltage fallback or data with the same voltage, etc. The fault types included include shading, hot spots, glass breakage, open circuit, short circuit, and aging. The degree of fault is divided into three levels: mild, moderate and severe. Among them, the corresponding relationship between fault type and fault degree is shown in Table 1:

[0123] Table 1 Correspondence between fault type and fault severity

[0124]

[0125] When making a data set, the IV time series data is converted into an IV curve image of fixed size, normalized, and without displaying the IV data coordinate axis. Here, the coordinate axis is erased and the normalization process is performed to control the IV data between 0 and 1. This process can deal with the numerical differences in different environments and different types of strings. The IV curve image is scaled to a fixed size in order to find the range of power point changes in the image as a threshold for judging the degree of fault. According to the photovoltaic fault degree judgment method, the pixel distance value between the actual maximum power point and the virtual maximum power point can be used to judge the degree of fault. This judgment only focuses on the shape change of the IV curve, and is not affected by the size of the power value itself. This judgment method is suitable for different environments and different types of strings.

[0126] According to the proposed photovoltaic fault degree discrimination method, the D value is used as the discrimination threshold. In the early stage, the D value of a large number of fault IV data was analyzed to determine the slight threshold. , medium threshold and critical thresholds . Figure 7 : is a schematic diagram of a fault degree distinguished by D value provided according to an embodiment of the present invention. The schematic diagram of the fault degree corresponding to different D values ​​is as follows Figure 7 As shown (taking the occlusion type as an example).

[0127] When using CNN to train the photovoltaic fault type recognition model in step S130, first, read the IV curve image in the data set and uniformly adjust it to a size of 300x300 pixels. Then the IV curve image is normalized (the pixel value is divided by 255, and the pixel value of the image is usually expressed in an integer range of 0 to 255, because a pixel value represents the color intensity or grayscale level of a point in the image. In the RGB color model, the value range of each color channel (red, green, and blue) is 0 to 255, which represents the intensity or brightness of the channel). The normalized image data is very useful for subsequent image processing and machine learning tasks because it can reduce the dynamic range of the data and avoid the loss of precision caused by numerical overflow or too small values ​​during the calculation process. In addition, the normalized data is usually easier to compare and operate in the algorithm, especially when tasks such as calculating distance, similarity, or feature extraction are required. After normalization, the image data is added to the feature set X list, and the label information is added to the y_label list, completing the construction of the feature set and label set. Then store a mapping table y_label_map to record the correspondence between the fault label and its one-hot encoding. A function called counting_data is used to count the total number of images in the dataset and the number of images with different labels. Then use LabelEncoder to integer encode the label, and then use to_categorical to convert it to one-hot encoding so that the model can handle multi-classification problems. Use the train_test_split function to divide the dataset into training and test sets, with a ratio of 90% training set and 10% test set. Then, use Keras's Sequential model to build a CNN architecture. Specifically include the following:

[0128] (1) Input Layer: Specify the size of the input IV curve image as 300x300 pixels. Figure 8 FIG. 1 is a schematic diagram of an image after processing of some fault types according to an embodiment of the present invention, wherein the IV fault image after normalization in some data sets and without displaying the coordinate axis is as follows: Figure 8 shown.

[0129] (2) Convolutional layer (Conv2D), there are 4 groups of convolutional layers, each followed by a maximum pooling layer (MaxPooling2D). The first convolutional layer uses 32 3x3 convolution kernels, and the activation function is ReLU. It is followed by a 2x2 maximum pooling layer, which is used to reduce the spatial resolution of the feature map while reducing the number of parameters and the amount of computation. The second convolutional layer uses 64 3x3 convolution kernels, and the activation function is ReLU. The maximum pooling layer is the same as the first group. The third and fourth convolutional layers both use 128 3x3 convolution kernels, and the activation function is ReLU. The maximum pooling layer is also the same as the first group.

[0130] (3) A flattening layer converts the multi-dimensional feature map into a one-dimensional array so that it can be input into the fully connected layer.

[0131] (4) A fully connected layer (Dense) with 512 neuron nodes and ReLU activation function. This layer is usually used to extract high-level features.

[0132] (5) The output layer (Dense) is also a fully connected layer. Its number of neurons is the same as the number of categories (determined by the size of the y_label_map dictionary). The activation function is softmax, which is used for multi-classification tasks.

[0133] After defining the above model structure, categorical_crossentropy is used as the loss function when training the model. This is a commonly used loss function in multi-classification problems and is suitable for the case where the label is one-hot encoded. It calculates the cross entropy loss between the predicted probability distribution and the true label distribution. Adam is used as the optimizer. Adam is an optimization algorithm with adaptive learning rate. It combines gradient descent and momentum methods and usually performs well in deep learning. Accuracy is used as an evaluation metric to compile the model. It is a direct measure of the accuracy of the model and indicates the proportion of samples correctly predicted by the model. Call the fit function to train the model, specifying 35 training rounds and a batch size of 128. At the same time, 10% of the data is used for verification during the training process. The fit function is the main function used in Keras for training models. It accepts training data, labels, training rounds (epochs), batch size (batch size), and verification data as parameters. After training, the model will be named and saved according to the current date and configuration parameters, and the loss and accuracy curves during the training process will be plotted and displayed using the show_history function. Finally, the trained model is evaluated using the test set, the test accuracy is output, and the label mapping is saved to a file for subsequent use.

[0134] Through the verification and testing process, a final photovoltaic fault diagnosis model is finally obtained. The model can classify IV curve images and identify different fault types. Since the current data of the string circuit break is almost close to 0, the image processing curve method is not applicable. Here, after the IV data is preprocessed, the open circuit threshold is set to detect this type. Other fault types are identified by the fault type recognition model. Combining the above process and methods, Fig. 9 This is an overall flow chart from IV data acquisition to building a photovoltaic fault diagnosis model provided by an embodiment of the present invention. Fig. 9 It includes the overall process of photovoltaic fault diagnosis model structure from IV data acquisition to model diagnosis.

[0135] Fig.10 A schematic diagram of the structure of an electronic device 1 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0136] like Fig.10 As shown, the electronic device 1 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory ROM12, a random access memory RAM13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory ROM12 or the computer program loaded from the storage unit 18 to the random access memory RAM13. In the RAM 13, various programs and data required for the operation of the electronic device 1 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. The input / output I / O interface 15 is also connected to the bus 14.

[0137] A number of components in the electronic device 1 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, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 1 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0138] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, for example, a fault diagnosis method for a photovoltaic string.

[0139] In some embodiments, the fault diagnosis method of the photovoltaic string can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1 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 the fault diagnosis method of the photovoltaic string described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the fault diagnosis method of the photovoltaic string in any other appropriate manner (for example, by means of firmware).

[0140] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0141] Computer programs for implementing the 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, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0142] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0143] To provide interaction with a user, the systems and techniques described herein may 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 the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the 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, voice input, or tactile input).

[0144] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0145] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0146] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0147] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A photovoltaic string fault diagnosis method, characterized in that: include: Get IV data of PV strings; Preprocessing the IV data, converting the preprocessed IV data into an IV curve image and preparing a data set; The data set is trained to obtain a photovoltaic fault type recognition model, and the photovoltaic fault type recognition model is combined with a photovoltaic fault degree discrimination method to obtain a photovoltaic fault diagnosis model; Fault diagnosis is performed on the IV curve image according to the photovoltaic fault diagnosis model to obtain a fault diagnosis result of the photovoltaic string.

2. The photovoltaic string fault diagnosis method according to claim 1, characterized in that: The step of obtaining the IV data of the photovoltaic string includes: Acquire historical fault IV data of the photovoltaic string under different fault types and different fault degrees; The IV data of the photovoltaic string when in normal use is obtained.

3. The photovoltaic string fault diagnosis method according to claim 1, characterized in that: The preprocessing of the IV data, converting the preprocessed IV data into an IV curve image and preparing a data set comprises: Cleaning the IV data to remove erroneous data and obtain IV data that meets the conditions; The IV data meeting the conditions are drawn into an IV curve, and then normalized or resized or the IV curve coordinate axis is not displayed to produce an IV curve image; The IV curve images are classified according to different fault types and fault degrees and a data set is prepared.

4. The photovoltaic string fault diagnosis method according to claim 1, characterized in that: The photovoltaic fault degree determination method comprises: Calculating a distance value between an actual maximum power point on the IV curve and a virtual maximum power point on the IV curve image, wherein the distance value is used as a threshold for determining a photovoltaic fault degree; The photovoltaic fault degree is judged according to the photovoltaic fault degree judgment threshold.

5. The photovoltaic string fault diagnosis method according to claim 4, characterized in that: The distance value is calculated as follows: ； in, is the distance between the actual maximum power point and the virtual maximum power point on the IV curve image, V mpp_px is the coordinate of the actual maximum power point on the horizontal axis of the IV curve image, I mpp_px is the coordinate of the actual maximum power point on the vertical axis of the IV curve image, V T_px is the coordinate of the virtual maximum power point on the horizontal axis of the IV curve image, I T_px is the virtual maximum power point P T The coordinate of the vertical axis on the IV curve graph.

6. The photovoltaic string fault diagnosis method according to claim 5, characterized in that: The calculation formula of the coordinate of the actual maximum power point on the horizontal axis of the IV curve image is as follows: ； Among them, V mpp is the voltage at the actual maximum power point, V min is the minimum value in the voltage array, V max is the maximum value in the voltage array, and H is the height of the IV curve image; The calculation formula of the coordinate of the actual maximum power point on the vertical axis of the IV curve image is as follows: ； Among them, I mpp is the current at the actual maximum power point, I min is the minimum value in the current array, I max is the maximum value in the current array, W is the width of the IV curve image; The calculation formula of the coordinate of the virtual maximum power point on the horizontal axis of the IV curve image is as follows: ； in, ; The calculation formula of the coordinate of the virtual maximum power point on the vertical axis of the IV curve image is as follows: ； in, is the short circuit current.

7. The photovoltaic string fault diagnosis method according to claim 1, characterized in that: The step of training the data set to obtain a photovoltaic fault type identification model comprises: The photovoltaic fault type recognition model is obtained by training the data set based on a convolutional neural network model.

8. The photovoltaic string fault diagnosis method according to claim 2, characterized in that: The fault type includes: at least one of: shielding, hot spot, glass breakage, open circuit, short circuit and aging; The fault degree includes at least one of a minor fault, a medium fault and a major fault.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the photovoltaic string fault diagnosis method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the fault diagnosis method for a photovoltaic string as described in any one of claims 1 to 8 is implemented.

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