Photovoltaic power generation assembly fault intelligent diagnosis system based on sensor technology

Abstract

The invention belongs to the field of photovoltaic power generation, relates to a fault diagnosis technology, and aims to solve the problem that the reliability of detection results of all photovoltaic arrays cannot be ensured in the prior art, in particular to an intelligent fault diagnosis system for a photovoltaic power generation module based on a sensor technology. Comprising a data statistics module, a fault monitoring module, a fault diagnosis module and a troubleshooting module. The data statistics module is used for carrying out statistics and analysis on power generation parameters of the photovoltaic array: marking the photovoltaic array as a statistical object, generating an analysis period and obtaining the power generation parameters of the statistical object in the analysis period at the end moment of the analysis period; the power generation data of the photovoltaic array in different illumination environments can be collected in the normal state of the photovoltaic array, the power generation parameters are formed, the power generation parameters serve as the running state judgment standard of the photovoltaic array, and when fault monitoring is carried out, the fault monitoring accuracy is improved. And fault monitoring is carried out by taking the power generation parameters of the same photovoltaic array in the similar illumination environment as a reference.

Description

Technical Field

[0001] The present invention belongs to the field of photovoltaic power generation and relates to fault diagnosis technology, in particular to an intelligent fault diagnosis system for photovoltaic power generation components based on sensor technology. Background Art

[0002] Photovoltaic modules are the core and most important part of solar power generation systems. Their function is to convert solar energy into electrical energy, either by storing it in batteries or by driving loads.

[0003] Fault diagnosis refers to the process of detecting and classifying faults in photovoltaic systems. In principle, existing fault diagnosis methods usually consist of two parts: a detection part, which detects abnormal operation of the photovoltaic system; and a classification part, which classifies the detected faults into various fault categories or causes. Fault detection technology uses different diagnostic methods to identify faults in grid-connected photovoltaic systems, while fault classification technology uses different fault classification methods to distinguish the specific types of faults occurring in photovoltaic systems.

[0004] The invention patent with announcement number CN109194286B discloses a fault diagnosis method and device for photovoltaic modules. The fault diagnosis method determines the fault diagnosis result of the photovoltaic module based on IV curve scanning data and a preset diagnostic model; it avoids the problem of low diagnostic reliability caused by poor quality of collected data due to low irradiance in the prior art; however, due to differences in the installation and maintenance status of different photovoltaic modules, the reliability of the detection results of all photovoltaic arrays cannot be guaranteed when a unified standard is used for fault detection and diagnosis.

[0005] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0006] The purpose of the present invention is to provide a photovoltaic power generation component fault intelligent diagnosis system based on sensor technology, which is used to solve the problem that the existing technology cannot guarantee the reliability of the detection results of all photovoltaic arrays;

[0007] The technical problem to be solved by the present invention is: how to provide a photovoltaic power generation component fault intelligent diagnosis system based on sensor technology that can ensure the reliability of detection results of all photovoltaic arrays.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] The photovoltaic power generation component fault intelligent diagnosis system based on sensor technology includes a data statistics module, a fault monitoring module, a fault diagnosis module and a fault troubleshooting module; the data statistics module, the fault monitoring module, the fault diagnosis module and the fault troubleshooting module are sequentially connected in communication;

[0010] The data statistics module is used to count and analyze the power generation parameters of the photovoltaic array: mark the photovoltaic array as a statistical object, generate an analysis cycle and obtain the power generation parameters of the statistical object within the analysis cycle at the end of the analysis cycle;

[0011] The fault monitoring module is used to perform fault monitoring and analysis on the photovoltaic array: when the number of executions of the analysis cycle reaches L1, a monitoring cycle is generated and the monitoring parameters of the statistical object are obtained at the end of the monitoring cycle, the monitoring parameters of the statistical object are compared with L1 power generation parameters one by one and the compliance coefficient FH of the power generation parameter is obtained, the power generation parameter with the smallest compliance coefficient FH value is marked as the reference parameter, and the reference parameter is sent to the fault diagnosis module;

[0012] The fault diagnosis module is used to perform fault diagnosis and analysis on the photovoltaic array, generate a fault troubleshooting signal when the statistical object operates abnormally, and send the fault troubleshooting signal to the fault troubleshooting module;

[0013] The fault detection module is used to perform fault detection and analysis on the photovoltaic array.

[0014] Furthermore, the process of obtaining the power generation parameters of the statistical object includes: dividing the analysis period into several analysis time periods, and obtaining the light intensity data, temperature data and power generation data of the statistical object at the end of each analysis time period. The process of obtaining the light intensity data includes: randomly setting several collection points in the statistical object, obtaining the maximum value of the light radiation intensity value of the collection point in the analysis period and marking it as the light intensity value of the collection point, and summing and averaging the light intensity values ​​of all the collection points to obtain the light intensity data; the process of obtaining the temperature data includes: obtaining the maximum value of the temperature value of the collection point in the analysis period and marking it as the temperature display value, and summing and averaging the temperature display values ​​of all the collection points to obtain the temperature data; the power generation data is the power generation of the statistical object in the analysis period; after the analysis period ends, the light intensity analysis curve, the temperature analysis curve, and the power generation analysis curve are drawn according to the time-light intensity data, the time-temperature data, and the time-power generation data respectively; the light intensity analysis curve, the temperature analysis curve, and the power generation analysis curve constitute the power generation parameters of the statistical object in the analysis period.

[0015] Furthermore, the process of acquiring the monitoring parameters of the statistical object includes: dividing the monitoring period into a number of monitoring time periods, the number and duration of the monitoring time periods in the monitoring period are respectively equal to the number and duration of the analysis time periods in the analysis period, and drawing the light intensity monitoring curve, temperature monitoring curve and power generation monitoring curve of the statistical object at the end of the monitoring period. The light intensity monitoring curve, temperature monitoring curve and power generation monitoring curve of the statistical object constitute the monitoring parameters of the statistical object.

[0016] Furthermore, the process of obtaining the compliance coefficient FH of the power generation parameters includes: obtaining the light intensity compliance value GF and the temperature compliance value WF of the power generation parameters of the statistical object relative to the monitoring parameters and performing numerical calculations to obtain the compliance coefficient FH; the process of obtaining the light intensity compliance value GF includes: translating the light intensity monitoring curve in the monitoring parameters to the coordinate system of the light intensity analysis curve in the power generation parameters, connecting the light intensity monitoring curve with the end point of the light intensity analysis curve, and then marking the sum of the area values ​​of all closed figures formed by the light intensity monitoring curve and the light intensity analysis curve as the light intensity analysis value, drawing a perpendicular line from the end point of the light intensity analysis curve to the X-axis, marking the area value of the closed figure formed by the light intensity analysis curve, the perpendicular line, the X-axis, and the Y-axis as the light intensity reference value, and marking the ratio of the light intensity analysis value to the light intensity reference value as the light intensity compliance value GF of the power generation parameters; the process of obtaining the temperature compliance value WF is the same as the process of obtaining the light intensity compliance value GF.

[0017] Furthermore, the specific process of the fault diagnosis module performing fault diagnosis analysis on the photovoltaic array includes: marking the power generation analysis curve in the benchmark parameters as the power generation benchmark curve, translating the power generation monitoring curve to the coordinate system of the power generation benchmark curve, connecting the power generation monitoring curve with the end point of the power generation benchmark curve, marking the sum of the area values ​​of all closed figures formed by the power generation monitoring curve and the power generation benchmark curve as the power generation analysis value, drawing a perpendicular line from the end point of the power generation benchmark curve to the X-axis, marking the area value of the closed figure formed by the power generation benchmark curve, the perpendicular line, the X-axis, and the Y-axis as the power generation benchmark value, marking the ratio of the power generation analysis value to the power generation benchmark value as the fault coefficient of the statistical object within the monitoring period, and judging whether the operating status of the statistical object meets the requirements through the fault coefficient.

[0018] Furthermore, the specific process of determining whether the operating status of the statistical object meets the requirements includes: comparing the fault coefficient with the preset fault threshold: if the fault coefficient is less than the fault threshold, it is determined that the operating status of the statistical object during the monitoring period meets the requirements; if the fault coefficient is greater than or equal to the fault threshold, it is determined that the operating status of the statistical object during the monitoring period does not meet the requirements, a fault troubleshooting signal is generated and the fault troubleshooting signal is sent to the fault troubleshooting module.

[0019] Furthermore, the specific process of the fault troubleshooting module for troubleshooting and analyzing the photovoltaic array includes: using infrared thermal imaging technology, electrical parameter diagnosis technology and visual detection in turn to troubleshoot the fault location of the statistical object. If the fault location is successfully located, the corresponding troubleshooting technology is marked as the matching object of this troubleshooting process; if the fault location fails to be located, it is determined that the statistical object does not have any fault characteristics, and the benchmark parameters are marked as power generation parameters.

[0020] The present invention has the following beneficial effects:

[0021] 1. The data statistics module can be used to count and analyze the power generation parameters of the photovoltaic array. When the photovoltaic array is in normal state, its power generation data under different lighting environments are collected and used to form power generation parameters. The power generation parameters are used as the criterion for judging the operating state of the photovoltaic array. When performing fault monitoring, the power generation parameters of the same photovoltaic array under similar lighting environments are used as the benchmark for fault monitoring to ensure the accuracy of the fault monitoring results.

[0022] 2. The fault monitoring module can be used to monitor and analyze the faults of the photovoltaic array. After the monitoring cycle is completed, the monitoring parameters of the statistical object are counted. The power generation parameters are screened for similarity based on the monitoring parameters to obtain the benchmark parameters. The benchmark parameters are the normal power generation status data of the photovoltaic array under similar lighting environments, providing data support for fault diagnosis;

[0023] 3. The fault diagnosis module can be used to perform fault diagnosis and analysis on the photovoltaic array, extract the power generation reference curve in the reference parameter and compare it with the power generation monitoring curve in the monitoring parameter to obtain the fault coefficient, and use the fault coefficient to provide feedback on the normality of the operation status of the photovoltaic array, so as to provide timely warning when the operation is abnormal;

[0024] 4. The fault troubleshooting module can be used to perform fault troubleshooting analysis on the photovoltaic array. Several conventional fault troubleshooting techniques are used to troubleshoot the faults in turn, and then the troubleshooting means are prioritized. Corresponding troubleshooting means priority sequences are generated for different fault intervals to improve the troubleshooting efficiency of the fault location. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0026] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0027] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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 are within the scope of protection of the present invention.

[0028] like Figure 1As shown, the intelligent fault diagnosis system for photovoltaic power generation components based on sensor technology includes a data statistics module, a fault monitoring module, a fault diagnosis module and a fault troubleshooting module, and the data statistics module, the fault monitoring module, the fault diagnosis module and the fault troubleshooting module are communicatively connected in sequence.

[0029] The data statistics module is used to count and analyze the power generation parameters of the photovoltaic array: the photovoltaic array is marked as a statistical object, and all statistical objects are troubleshooted by manual troubleshooting. After ensuring that all statistical objects are operating normally, an analysis cycle is generated and divided into several analysis time periods. At the end of each analysis time period, the light intensity data, temperature data and power generation data of the statistical object are obtained. The process of obtaining the light intensity data includes: randomly setting several collection points in the statistical object, obtaining the maximum value of the light radiation intensity value of the collection point in the analysis time period and marking it as the light intensity value of the collection point, and summing and averaging the light intensity values ​​of all collection points to obtain the light intensity data; the process of obtaining the temperature data includes: obtaining the maximum value of the temperature value of the collection point in the analysis time period and marking it as Temperature display value, the temperature data is obtained by summing up and averaging the temperature display values ​​of all the collection points; the power generation data is the power generation of the statistical object during the analysis period; after the end of the analysis period, the light intensity analysis curve, temperature analysis curve, and power generation analysis curve are drawn according to the time-light intensity data, time-temperature data, and time-power generation data respectively; the power generation parameters of the statistical object during the analysis period are constituted by the light intensity analysis curve, the temperature analysis curve, and the power generation analysis curve; under the normal state of the photovoltaic array, its power generation data under different lighting environments are collected, and the power generation parameters are constituted based on this. The power generation parameters are used as the criterion for judging the operating status of the photovoltaic array. When performing fault monitoring, the power generation parameters of the same photovoltaic array under similar lighting environments are used as the benchmark for fault monitoring to ensure the accuracy of the fault monitoring results.

[0030] The fault monitoring module is used to perform fault monitoring and analysis on the photovoltaic array: when the number of executions of the analysis cycle reaches L1, a monitoring cycle is generated and divided into several monitoring periods. The number and duration of the monitoring periods in the monitoring cycle are equal to the number and duration of the analysis periods in the analysis cycle. At the end of the monitoring cycle, the light intensity monitoring curve, temperature monitoring curve and power generation monitoring curve of the statistical object are drawn. The light intensity monitoring curve, temperature monitoring curve and power generation monitoring curve of the statistical object constitute the monitoring parameters of the statistical object. The monitoring parameters of the statistical object are compared with the L1 power generation parameters one by one: the statistical object is obtained. The light intensity compliance value GF and temperature compliance value WF of the power generation parameter relative to the monitoring parameter, L1 is a numerical constant, and the specific value of L1 is set by the management personnel. The process of obtaining the light intensity compliance value GF includes: translating the light intensity monitoring curve in the monitoring parameter to the coordinate system of the light intensity analysis curve in the power generation parameter, connecting the light intensity monitoring curve with the end point of the light intensity analysis curve, and then marking the sum of the area values ​​of all closed figures formed by the light intensity monitoring curve and the light intensity analysis curve as the light intensity analysis value, drawing a perpendicular line from the end point of the light intensity analysis curve to the X-axis, and marking the area value of the closed figure formed by the light intensity analysis curve, the perpendicular line, the X-axis, and the Y-axis The light intensity reference value is marked as the ratio of the light intensity analysis value to the light intensity reference value as the light intensity compliance value GF of the power generation parameter; the process of obtaining the temperature compliance value WF includes: translating the temperature monitoring curve in the monitoring parameter to the coordinate system of the temperature analysis curve in the power generation parameter, connecting the temperature monitoring curve with the end point of the temperature analysis curve, and then marking the sum of the area values ​​of all closed figures formed by the temperature monitoring curve and the temperature analysis curve as the temperature analysis value, drawing a vertical line from the end point of the temperature analysis curve to the X-axis, marking the area value of the closed figure formed by the temperature analysis curve, the vertical line, the X-axis, and the Y-axis as the temperature reference value, and The ratio of the value to the temperature reference value is marked as the temperature compliance value WF of the power generation parameter; the compliance coefficient FH of the power generation parameter is obtained by the formula FH=k1*GF+k2*WF, wherein k1 and k2 are both proportional coefficients, and k1>k2>1, and the power generation parameter with the smallest compliance coefficient FH is marked as the reference parameter, and the reference parameter is sent to the fault diagnosis module; after the monitoring cycle is completed, the monitoring parameters of the statistical object are counted, and the power generation parameters are screened for similarity according to the monitoring parameters to obtain the reference parameter, which is the normal power generation status data of the photovoltaic array under similar lighting environment, providing data support for fault diagnosis.

[0031] The fault diagnosis module is used to perform fault diagnosis and analysis on the photovoltaic array: mark the power generation analysis curve in the benchmark parameters as the power generation benchmark curve, translate the power generation monitoring curve to the coordinate system of the power generation benchmark curve, connect the power generation monitoring curve with the end point of the power generation benchmark curve, mark the sum of the area values ​​of all closed figures formed by the power generation monitoring curve and the power generation benchmark curve as the power generation analysis value, draw a vertical line from the end point of the power generation benchmark curve to the X-axis, mark the area value of the closed figure formed by the power generation benchmark curve, the vertical line, the X-axis, and the Y-axis as the power generation benchmark value, and mark the ratio of the power generation analysis value to the power generation benchmark value as the statistical object in the monitoring The fault coefficient within the cycle is compared with the preset fault threshold: if the fault coefficient is less than the fault threshold, it is determined that the operating status of the statistical object within the monitoring cycle meets the requirements; if the fault coefficient is greater than or equal to the fault threshold, it is determined that the operating status of the statistical object within the monitoring cycle does not meet the requirements, and a fault troubleshooting signal is generated and sent to the fault troubleshooting module; the power generation reference curve in the benchmark parameters is extracted and compared with the power generation monitoring curve in the monitoring parameters to obtain the fault coefficient, and the normality of the operating status of the photovoltaic array is fed back through the fault coefficient, so as to give a timely warning when the operation is abnormal.

[0032] The fault troubleshooting module is used to troubleshoot and analyze the photovoltaic array: infrared thermal imaging technology, electrical parameter diagnosis technology and visual detection are used in turn to troubleshoot the fault location of the statistical object. If the fault location is successfully located, the corresponding troubleshooting technology is marked as the matching object of this troubleshooting process; if the fault location fails to be located, it is determined that the statistical object does not have a fault feature, and the benchmark parameter is marked as the power generation parameter; when the number of successful fault location reaches L2, L2 is a numerical constant, and the specific value of L2 is set by the management personnel. The maximum and minimum values ​​of the fault coefficient corresponding to the troubleshooting process constitute the fault range, and the fault range is divided into Several fault intervals are counted, and the number of times different fault troubleshooting technologies in the fault interval are marked as matching objects is counted, and a troubleshooting means priority sequence of the fault interval is generated in order of the marking times from most to least; when the operating status of the statistical object does not meet the requirements in the subsequent monitoring cycle, the fault interval corresponding to the fault coefficient of the statistical object in the monitoring cycle is retrieved, and then the fault troubleshooting is performed according to the troubleshooting means priority sequence of the fault interval; several conventional fault troubleshooting technologies are used to perform fault troubleshooting in turn, and then the troubleshooting means are prioritized, and corresponding troubleshooting means priority sequences are generated for different fault intervals to improve the troubleshooting efficiency of the fault location.

[0033] The intelligent fault diagnosis system for photovoltaic power generation components based on sensor technology marks the photovoltaic array as a statistical object when working, generates an analysis cycle and counts the power generation parameters of the statistical object at the end of the analysis cycle, generates a monitoring cycle when the execution number of the analysis cycle reaches L1, obtains the monitoring parameters of the statistical object at the end of the monitoring cycle, compares the monitoring parameters with the power generation parameters and obtains the benchmark parameters based on the comparison results, calculates the fault coefficient of the monitoring parameters relative to the benchmark parameters, and determines whether the operating status of the statistical object in the monitoring cycle is abnormal based on the fault coefficient.

[0034] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0035] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; for example: formula FH = k1*GF+k2*WF; technicians in this field collect multiple groups of sample data and set corresponding compliance coefficients for each group of sample data; substitute the set compliance coefficients and the collected sample data into the formula, any two formulas constitute a set of two-variable linear equations, screen the calculated coefficients and take the average, and obtain the values ​​of k1 and k2 as 3.52 and 2.17 respectively;

[0036] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the technical personnel in this field initially setting the corresponding compliance coefficient for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the compliance coefficient is proportional to the value of the light intensity compliance value.

[0037] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0038] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Photovoltaic power generation component fault intelligent diagnosis system based on sensor technology, characterized in that: It includes a data statistics module, a fault monitoring module, a fault diagnosis module and a fault troubleshooting module; the data statistics module, the fault monitoring module, the fault diagnosis module and the fault troubleshooting module are sequentially connected in communication; The data statistics module is used to count and analyze the power generation parameters of the photovoltaic array: mark the photovoltaic array as a statistical object, generate an analysis cycle and obtain the power generation parameters of the statistical object within the analysis cycle at the end of the analysis cycle; The fault monitoring module is used to perform fault monitoring and analysis on the photovoltaic array: when the number of executions of the analysis cycle reaches L1, a monitoring cycle is generated and the monitoring parameters of the statistical object are obtained at the end of the monitoring cycle, the monitoring parameters of the statistical object are compared with L1 power generation parameters one by one and the compliance coefficient FH of the power generation parameter is obtained, the power generation parameter with the smallest compliance coefficient FH value is marked as the reference parameter, and the reference parameter is sent to the fault diagnosis module; The fault diagnosis module is used to perform fault diagnosis and analysis on the photovoltaic array, generate a fault troubleshooting signal when the statistical object operates abnormally, and send the fault troubleshooting signal to the fault troubleshooting module; The fault detection module is used to perform fault detection and analysis on the photovoltaic array.

2. The photovoltaic power generation component fault intelligent diagnosis system based on sensor technology according to claim 1 is characterized in that: The process of obtaining the power generation parameters of the statistical object includes: dividing the analysis period into several analysis time periods, and obtaining the light intensity data, temperature data and power generation data of the statistical object at the end of each analysis time period. The process of obtaining the light intensity data includes: randomly setting several collection points in the statistical object, obtaining the maximum value of the light radiation intensity value of the collection point in the analysis period and marking it as the light intensity value of the collection point, and summing and averaging the light intensity values ​​of all the collection points to obtain the light intensity data; the process of obtaining the temperature data includes: obtaining the maximum value of the temperature value of the collection point in the analysis period and marking it as the temperature display value, and summing and averaging the temperature display values ​​of all the collection points to obtain the temperature data; the power generation data is the power generation of the statistical object in the analysis period; after the end of the analysis period, the light intensity analysis curve, temperature analysis curve, and power generation analysis curve are drawn according to the time-light intensity data, time-temperature data, and time-power generation data respectively; the light intensity analysis curve, temperature analysis curve, and power generation analysis curve constitute the power generation parameters of the statistical object in the analysis period.

3. The photovoltaic power generation component fault intelligent diagnosis system based on sensor technology according to claim 2 is characterized in that: The process of obtaining the monitoring parameters of the statistical object includes: dividing the monitoring cycle into several monitoring time periods, the number and duration of the monitoring time periods in the monitoring cycle are equal to the number and duration of the analysis time periods in the analysis cycle, and drawing the light intensity monitoring curve, temperature monitoring curve and power generation monitoring curve of the statistical object at the end of the monitoring cycle. The light intensity monitoring curve, temperature monitoring curve and power generation monitoring curve of the statistical object constitute the monitoring parameters of the statistical object.

4. The photovoltaic power generation component fault intelligent diagnosis system based on sensor technology according to claim 3 is characterized in that: The process of obtaining the compliance coefficient FH of the power generation parameters includes: obtaining the light intensity compliance value GF and the temperature compliance value WF of the power generation parameters of the statistical object relative to the monitoring parameters and performing numerical calculation to obtain the compliance coefficient FH; the process of obtaining the light intensity compliance value GF includes: translating the light intensity monitoring curve in the monitoring parameters to the coordinate system of the light intensity analysis curve in the power generation parameters, connecting the light intensity monitoring curve with the end point of the light intensity analysis curve, and then marking the sum of the area values ​​of all closed figures formed by the light intensity monitoring curve and the light intensity analysis curve as the light intensity analysis value, drawing a perpendicular line from the end point of the light intensity analysis curve to the X-axis, marking the area value of the closed figure formed by the light intensity analysis curve, the perpendicular line, the X-axis, and the Y-axis as the light intensity reference value, and marking the ratio of the light intensity analysis value to the light intensity reference value as the light intensity compliance value GF of the power generation parameters; the process of obtaining the temperature compliance value WF is the same as the process of obtaining the light intensity compliance value GF.

5. The photovoltaic power generation component fault intelligent diagnosis system based on sensor technology according to claim 4 is characterized in that: The specific process of the fault diagnosis module performing fault diagnosis and analysis on the photovoltaic array includes: marking the power generation analysis curve in the benchmark parameters as the power generation benchmark curve, translating the power generation monitoring curve to the coordinate system of the power generation benchmark curve, connecting the power generation monitoring curve with the end point of the power generation benchmark curve, marking the sum of the area values ​​of all closed figures formed by the power generation monitoring curve and the power generation benchmark curve as the power generation analysis value, drawing a vertical line from the end point of the power generation benchmark curve to the X-axis, marking the area value of the closed figure formed by the power generation benchmark curve, the vertical line, the X-axis, and the Y-axis as the power generation benchmark value, marking the ratio of the power generation analysis value to the power generation benchmark value as the fault coefficient of the statistical object within the monitoring period, and judging whether the operating status of the statistical object meets the requirements through the fault coefficient.

6. The photovoltaic power generation component fault intelligent diagnosis system based on sensor technology according to claim 5 is characterized in that: The specific process of determining whether the operating status of the statistical object meets the requirements includes: comparing the fault coefficient with the preset fault threshold: if the fault coefficient is less than the fault threshold, it is determined that the operating status of the statistical object during the monitoring period meets the requirements; if the fault coefficient is greater than or equal to the fault threshold, it is determined that the operating status of the statistical object during the monitoring period does not meet the requirements, a fault troubleshooting signal is generated and the fault troubleshooting signal is sent to the fault troubleshooting module.

7. The photovoltaic power generation component fault intelligent diagnosis system based on sensor technology according to claim 6 is characterized in that: The specific process of the fault troubleshooting module for troubleshooting and analyzing the photovoltaic array includes: using infrared thermal imaging technology, electrical parameter diagnosis technology and visual detection to troubleshoot the fault location of the statistical object in turn. If the fault location is successfully located, the corresponding troubleshooting technology is marked as the matching object of this troubleshooting process; if the fault location fails to be located, it is determined that the statistical object does not have any fault characteristics, and the benchmark parameters are marked as power generation parameters.

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