Photovoltaic module fault detection method

Abstract

The invention provides a photovoltaic module fault detection method. The photovoltaic module fault detection method comprises the following steps: acquiring monitoring data of monitoring modules on photovoltaic modules through the monitoring modules correspondingly associated with the photovoltaic modules; carrying out primary monitoring analysis according to the monitoring data and the environmental data, and determining a photovoltaic module with a suspected fault as a to-be-detected target; patrolling and detecting the to-be-detected target through the patrolling device to obtain patrolling and detecting data; and carrying out secondary detection analysis on the patrol detection data and the monitoring data to obtain a fault analysis result. According to the photovoltaic module fault detection method, the photovoltaic module with the fault can be timely and effectively positioned, the effective operation of the photovoltaic module is ensured, and the benefit of a photovoltaic power station is further ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic module detection, and in particular to a photovoltaic module fault detection method. Background Art

[0002] Photovoltaic modules are the smallest indivisible solar cell devices that can independently provide DC power output. Photovoltaic modules mainly include nine core components: cells, interconnects, busbars, tempered glass, EVA, backplane, aluminum alloy, silicone, and junction boxes.

[0003] Photovoltaic power stations usually have a lot of photovoltaic modules, and each photovoltaic module is set up outdoors. Due to the complex outdoor environment, it is easy to induce photovoltaic module failure. When a single failure occurs, it does not feel much overall, but it actually affects the power generation efficiency; therefore, a photovoltaic module fault detection method is urgently needed to locate the faulty photovoltaic module in a timely and effective manner, ensure the effective operation of the photovoltaic module, and then ensure the benefits of the photovoltaic power station. Summary of the invention

[0004] One of the purposes of the present invention is to provide a photovoltaic module fault detection method to timely and effectively locate the faulty photovoltaic module, thereby ensuring the effective operation of the photovoltaic module and further ensuring the benefits of the photovoltaic power station.

[0005] An embodiment of the present invention provides a photovoltaic module fault detection method, comprising:

[0006] Acquiring monitoring data of the photovoltaic components monitored by the monitoring components through the monitoring components corresponding to the photovoltaic components;

[0007] Conduct a monitoring analysis based on the monitoring data and environmental data to identify the PV panels with suspected faults as the targets to be tested;

[0008] Conduct inspection and detection of the target to be inspected through the inspection device to obtain inspection and detection data;

[0009] Conduct secondary detection and analysis on the inspection data and monitoring data to obtain fault analysis results.

[0010] Preferably, the monitoring component includes: a voltage monitoring unit and / or a current monitoring unit.

[0011] Preferably, a monitoring analysis is performed based on the monitoring data and the environmental data to determine the photovoltaic components suspected of failure as the target to be detected, including:

[0012] According to the environmental data, the corresponding monitoring range is determined from the pre-configured monitoring range determination library;

[0013] Based on the monitoring scope, monitoring data and pre-configured judgment rules, the target to be detected is determined.

[0014] Preferably, based on the monitoring range, monitoring data and pre-configured judgment rules, determining the target to be detected includes:

[0015] Determine whether the monitoring data is within the monitoring range; if not, take the photovoltaic module corresponding to the monitoring data as the target to be detected.

[0016] Preferably, determining the target to be detected based on the monitoring range, monitoring data and pre-configured judgment rules also includes:

[0017] A trigger parameter is configured; when the target to be detected is not determined according to the determination rule of whether the monitoring data is within the monitoring range, the trigger parameter is increased by one; when the target to be detected is determined according to the determination rule of whether the monitoring data is within the monitoring range, the trigger parameter is reset;

[0018] When the trigger parameter is greater than or equal to the preset trigger threshold, the risk value of each photovoltaic module is determined based on the monitoring range and monitoring data;

[0019] Arrange the risk values ​​in descending order and use the photovoltaic modules with the first preset number as targets to be detected.

[0020] Preferably, the risk value of each photovoltaic module is determined based on the monitoring scope and monitoring data, including:

[0021] Divide the monitoring scope into risk segments, determine multiple regional scopes and the corresponding risk assessment values ​​for each regional scope;

[0022] Extract the data of the most recent preset number of times in the monitoring data;

[0023] Determine the area range corresponding to each data, and then determine the risk assessment value corresponding to each data;

[0024] Taking a weighted average of the risk assessment values ​​of each data to obtain a first assessment value;

[0025] Determine the conversion efficiency of each PV module based on monitoring data and pre-configured conversion efficiency analysis;

[0026] According to the conversion efficiency, a pre-configured conversion efficiency and a second evaluation value correspondence table is queried to determine the second evaluation value;

[0027] Calculate the difference between each adjacent data and construct a stable analysis vector based on the difference;

[0028] Using the stability analysis vector, querying a pre-configured stability analysis library to determine a third evaluation value;

[0029] A weighted sum of the first evaluation value, the second evaluation value, and the third evaluation value is calculated to obtain a risk value.

[0030] Preferably, the inspection device comprises: a drone, a first image acquisition device and a second image acquisition device carried on the drone;

[0031] The first image acquisition device is responsible for acquiring infrared images, and the second image acquisition device is responsible for acquiring visible light images.

[0032] Preferably, a secondary detection and analysis is performed on the inspection data and the monitoring data to obtain a fault analysis result, including:

[0033] Extracting features of the infrared image, the visible light image and the monitoring data respectively to obtain a first feature parameter, a second feature parameter and a third feature parameter;

[0034] Arranging the first characteristic parameter, the second characteristic parameter and the third characteristic parameter in order to form a fault analysis parameter set;

[0035] The pre-configured secondary detection analysis library is indexed with the fault analysis parameter set to obtain the fault analysis result.

[0036] Preferably, the photovoltaic module fault detection method further includes:

[0037] Obtaining usage data of the failed photovoltaic module, performance data of each component, and location data of the installation location as analysis data;

[0038] Build component evaluation database based on analysis data;

[0039] Processing usage data of the photovoltaic module, performance data of each component, and location data of the installation location to obtain an evaluation parameter set;

[0040] Determine the evaluation result of the photovoltaic module according to the evaluation parameter set and the module evaluation database;

[0041] When the evaluation results meet the preset secondary detection and analysis conditions, secondary detection and analysis are carried out.

[0042] Preferably, the photovoltaic module fault detection method further includes:

[0043] Conduct risk assessment on the operation data of PV modules based on the pre-configured operation assessment database;

[0044] When the assessed operational risk meets the conditions for secondary detection and analysis, secondary detection and analysis shall be carried out.

[0045] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0046] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 is a schematic diagram of a photovoltaic module fault detection method according to an embodiment of the present invention;

[0049] Figure 2 is a schematic diagram of another photovoltaic module fault detection method according to an embodiment of the present invention;

[0050] Figure 3 Schematic diagram of another photovoltaic module fault detection method in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0052] The embodiment of the present invention provides a photovoltaic module fault detection method, such as Figure 1 As shown, including:

[0053] Step 1: obtaining monitoring data of the photovoltaic components monitored by the monitoring components through the monitoring components corresponding to each photovoltaic component;

[0054] Among them, the monitoring component includes: a voltage monitoring unit and / or a current monitoring unit. The monitoring component mainly monitors the operation of the photovoltaic module, and the operation of the photovoltaic module is mainly the output after the light energy is converted, that is, the output current and / or the output voltage; the output current and the output voltage are monitored respectively by the voltage monitoring unit and / or the current monitoring unit; further, for photovoltaic modules that can track the sun, angle data such as the rotation angle and the pitch angle can be detected by configuring an angle sensor;

[0055] Step 2: Conduct a monitoring analysis based on the monitoring data and environmental data to determine the PV modules suspected of failure as the target to be detected;

[0056] Environmental data includes data on external factors related to photovoltaic power generation, such as temperature, humidity, and sunshine intensity. Based on known external data, combined with the status of photovoltaic modules (the position of components receiving light energy), the approximate range of power generation can be predicted and analyzed. The actual power generation can be calculated by monitoring the current and voltage. In this way, suspected faults can be determined based on the range of actual power generation and predicted power generation, and photovoltaic modules with suspected faults can be extracted as targets to be detected for secondary detection and analysis.

[0057] Step 3: Perform patrol inspection on the target to be inspected through the patrol device to obtain patrol inspection data;

[0058] The inspection device includes: a drone, a first image acquisition device and a second image acquisition device mounted on the drone; wherein the first image acquisition device is responsible for acquiring infrared images, and the second image acquisition device is responsible for acquiring visible light images. The infrared image and visible light image of the target to be inspected are directly acquired by the drone inspection method; the data acquisition of the secondary inspection and analysis is realized quickly, and the efficiency of the secondary analysis of suspected faults is improved; the inspection and detection data are essentially the infrared image and visible light image taken by the inspection device of the target to be inspected;

[0059] Step 4: Perform secondary detection and analysis on the inspection data and monitoring data to obtain fault analysis results.

[0060] After the inspection data is obtained, it can be analyzed jointly with the monitoring data, which is the secondary inspection analysis; wherein, the inspection data and the monitoring data are subjected to secondary inspection analysis to obtain the fault analysis results, including: extracting features from the infrared image, the visible light image and the monitoring data respectively to obtain the first feature parameter, the second feature parameter and the third feature parameter; arranging the first feature parameter, the second feature parameter and the third feature parameter in order to form a fault analysis parameter set; indexing the pre-configured secondary inspection analysis library with the fault analysis parameter set to obtain the fault analysis results; wherein, the feature extraction of the infrared image is to partition the infrared image into preset grids, and determine the maximum, minimum and average temperature of each partition. value; therefore, the first characteristic parameter includes parameters representing the maximum, minimum and average values ​​of the temperature of each partition; the feature extraction of visible light images is similar to that of infrared images; the partitions can be partitioned with the same grid to determine the maximum, average and minimum values ​​of the pixel values ​​of each partition; that is, the second characteristic parameter includes: parameters representing the maximum, minimum and average values ​​of the pixel values ​​of each partition; the feature extraction of monitoring data mainly extracts the maximum, minimum and average values ​​of the monitored current and voltage; that is, the third characteristic parameter includes: parameters representing the maximum, minimum and average values ​​of the voltage and current; the secondary detection analysis library is a pre-analysis configuration, and the fault analysis results in the analysis library are associated with the fault analysis parameter set in a one-to-one correspondence;

[0061] The photovoltaic component fault detection method of the present invention locates the photovoltaic component suspected of fault by monitoring the component, collects data for fault confirmation by a patrol device, and analyzes the data in combination with the monitoring data of the monitoring component to ensure the accuracy and effectiveness of the analysis and avoid the occurrence of misjudgment.

[0062] In order to accurately determine the suspected fault, in one embodiment, a monitoring analysis is performed based on the monitoring data and the environmental data to determine the suspected faulty photovoltaic module as a target to be detected, including:

[0063] According to the environmental data, the corresponding monitoring range is determined from the pre-configured monitoring range determination library; the environmental characteristic parameters corresponding to the environmental data in the monitoring range determination library are correspondingly associated with each monitoring range; that is, the monitoring range determination library can be used to determine the approximate range of power generation effects according to the current environment of the photovoltaic power generation component;

[0064] Based on the monitoring range, monitoring data and pre-configured judgment rules, the target to be detected is determined. The monitoring data can be understood as the actual power generation effect. The difference between the actual power generation effect and the estimated power generation effect can be used to determine whether the photovoltaic component may have a fault;

[0065] Among them, based on the monitoring scope, monitoring data and pre-configured judgment rules, the target to be detected is determined, including:

[0066] Determine whether the monitoring data is within the monitoring range; if not, take the photovoltaic module corresponding to the monitoring data as the target to be detected.

[0067] When the target to be detected is not determined for a long time, the autonomous detection mode can be entered. At this time, the target to be detected is determined based on the monitoring range, monitoring data and pre-configured judgment rules, which also includes:

[0068] Configure a trigger parameter; when the target to be detected is not determined according to the judgment rule of whether the monitoring data is within the monitoring range, the trigger parameter is increased by one (the trigger parameter is updated every half an hour); when the target to be detected is determined according to the judgment rule of whether the monitoring data is within the monitoring range, the trigger parameter is reset; use the trigger parameter to control whether to enter the autonomous detection;

[0069] When the trigger parameter is greater than or equal to the preset trigger threshold (any value between 2 and 100), the risk value of each photovoltaic module is determined based on the monitoring range and monitoring data;

[0070] Arrange the risk values ​​in descending order and use the photovoltaic modules with the first preset number as targets to be detected.

[0071] Among them, the risk value of each photovoltaic module is determined based on the monitoring scope and monitoring data, including:

[0072] Divide the monitoring scope into risk segments, determine multiple regional scopes and the corresponding risk assessment values ​​for each regional scope;

[0073] Extract the data of the most recent preset number of times (3 to 10 times) from the monitoring data; the monitoring data is essentially the output voltage and current of the photovoltaic module in any configured time period from 2 minutes to 30 minutes;

[0074] Determine the area range corresponding to each data, and then determine the risk assessment value corresponding to each data;

[0075] The risk assessment values ​​of each data are weighted averaged to obtain a first assessment value; the shorter the data is from the current time, the greater the weighting coefficient;

[0076] Determine the conversion efficiency of each photovoltaic module based on monitoring data and pre-configured conversion efficiency analysis; conversion efficiency is the corresponding relationship between light energy and electrical energy; determine the standard power generation through the current environment; conversion efficiency is the ratio between actual power generation and standard power generation;

[0077] According to the conversion efficiency, a pre-configured conversion efficiency and second evaluation value correspondence table is queried to determine the second evaluation value; the conversion efficiency and second evaluation value correspondence table is pre-configured, and the second evaluation value and the conversion efficiency are associated one-to-one in the table;

[0078] Calculate the difference between each adjacent data, and construct a stable analysis vector based on the difference; mainly calculate the difference between the power generation corresponding to each data; arrange the difference in time sequence to form a stable analysis vector;

[0079] Using the stability analysis vector, querying a pre-configured stability analysis library to determine a third evaluation value; the third evaluation value in the stability analysis library is associated with the stability analysis vector in a one-to-one correspondence;

[0080] The weighted sum of the first evaluation value, the second evaluation value and the third evaluation value is calculated to obtain the risk value. The weighted coefficients corresponding to the first evaluation value, the second evaluation value and the third evaluation value are pre-configured.

[0081] Since it is an active detection, there is no high purpose orientation, that is, the targets to be detected may be good, so there is no situation where the suspected fault is in a tight time; however, if the inspection of the inspection components is only to detect the components with higher risk value assessment, it is a waste of the inspection opportunity; therefore, in one embodiment, the photovoltaic component fault detection method also includes:

[0082] After the autonomous detection mode is triggered, a distribution map of photovoltaic modules is obtained; the selected target to be detected is marked in the distribution map of photovoltaic modules;

[0083] Construct a patrol path; the patrol path starts from a preset starting position and returns to a preset ending position and passes through the target to be detected in sequence;

[0084] The patrol time of the patrol path is estimated; when the estimated patrol time is greater than or equal to the pre-configured patrol time (the maximum single flight time of the drone multiplied by the preset coefficient, which is any value between 0.6 and 0.9) and less than or equal to the alert time (the maximum single flight time of the drone), the patrol path will not be updated; the patrol time estimation mainly includes estimating the moving distance divided by the moving speed of the drone, the moving time and the shooting stop time of each photoelectric component;

[0085] When it is less than the inspection time, the inspection path is updated; when it is greater than the warning time, the targets to be detected are grouped, and the inspection path is constructed for each group, and then the inspection path is updated and judged;

[0086] Among them, the updates to the inspection routes include:

[0087] Extract the risk values ​​of the photoelectric components within a distance range from the inspection path; sort the risk values, set the photoelectric components with the largest risk values ​​as the targets to be detected in turn, and update the inspection path; until the inspection time of the updated inspection path is greater than or equal to the pre-configured inspection time and less than or equal to the warning time;

[0088] In order to improve the updating efficiency, the distance range can be determined first to reduce the optoelectronic components that need to be analyzed; the value of the distance range is the same as the value obtained by subtracting a dwell time from the difference between the pre-configured inspection time and the inspection time of the inspection path and dividing it by the moving speed of the drone.

[0089] In one embodiment, Figure 2 As shown, the photovoltaic component fault detection method also includes:

[0090] Step S11: obtaining usage data of the photovoltaic module that has failed, performance data of each component, and location data of the setting location as analysis data;

[0091] Step S12: Building a component evaluation database based on the analysis data;

[0092] Step S13: Processing the usage data of the photovoltaic module, the performance data of each component and the location data of the setting location to obtain an evaluation parameter set;

[0093] Step S14: determining the evaluation result of the photovoltaic module according to the evaluation parameter set and the module evaluation database;

[0094] Step S15: When the evaluation result meets the preset secondary detection and analysis conditions, a secondary detection and analysis is performed.

[0095] In one embodiment, Figure 3 As shown, the photovoltaic component fault detection method further includes:

[0096] Step S21: performing risk assessment on the operation data of the photovoltaic module according to the pre-configured operation assessment database;

[0097] Step S22: When the assessed operational risk meets the secondary detection and analysis conditions, a secondary detection and analysis is performed.

[0098] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A photovoltaic module fault detection method, characterized in that: include: Acquiring monitoring data of the photovoltaic components monitored by the monitoring components through the monitoring components corresponding to each photovoltaic component; Conduct a monitoring analysis based on the monitoring data and environmental data to identify the PV panels with suspected faults as the targets to be tested; Conduct inspection and detection of the target to be inspected through the inspection device to obtain inspection and detection data; Conduct secondary detection and analysis on the inspection data and monitoring data to obtain fault analysis results.

2. The photovoltaic module fault detection method according to claim 1, characterized in that: The monitoring component includes: a voltage monitoring unit and / or a current monitoring unit.

3. The photovoltaic module fault detection method according to claim 1, characterized in that: Conduct a monitoring analysis based on the monitoring data and environmental data to identify the suspected faulty PV panels as the targets to be tested, including: According to the environmental data, the corresponding monitoring range is determined from the pre-configured monitoring range determination library; Based on the monitoring scope, monitoring data and pre-configured judgment rules, the target to be detected is determined.

4. The photovoltaic module fault detection method according to claim 3, characterized in that: Based on the monitoring scope, monitoring data and pre-configured judgment rules, the target to be detected is determined, including: Determine whether the monitoring data is within the monitoring range; if not, take the photovoltaic module corresponding to the monitoring data as the target to be detected.

5. The photovoltaic module fault detection method according to claim 3, characterized in that: Based on the monitoring scope, monitoring data and pre-configured judgment rules, the target to be detected is determined, which also includes: Configure a trigger parameter; when the target to be detected is not determined according to the judgment rule of whether the monitoring data is within the monitoring range, the trigger parameter is increased by one; when the target to be detected is determined according to the judgment rule of whether the monitoring data is within the monitoring range, the trigger parameter is reset; When the trigger parameter is greater than or equal to the preset trigger threshold, the risk value of each photovoltaic module is determined based on the monitoring range and monitoring data; Arrange the risk values ​​in descending order and use the photovoltaic modules with the first preset number as targets to be detected.

6. The photovoltaic module fault detection method according to claim 5, characterized in that: Determine the risk value of each PV module based on the monitoring scope and monitoring data, including: Divide the monitoring scope into risk segments, determine multiple regional scopes and the corresponding risk assessment values ​​for each regional scope; Extract the data of the most recent preset number of times in the monitoring data; Determine the area range corresponding to each data, and then determine the risk assessment value corresponding to each data; Taking a weighted average of the risk assessment values ​​of each data to obtain a first assessment value; Determine the conversion efficiency of each PV module based on monitoring data and pre-configured conversion efficiency analysis; According to the conversion efficiency, a pre-configured conversion efficiency and a second evaluation value correspondence table is queried to determine the second evaluation value; Calculate the difference between each adjacent data and construct a stable analysis vector based on the difference; Using the stability analysis vector, querying a pre-configured stability analysis library to determine a third evaluation value; A weighted sum of the first evaluation value, the second evaluation value, and the third evaluation value is calculated to obtain a risk value.

7. The photovoltaic module fault detection method according to claim 1, characterized in that: The inspection device includes: a drone, a first image acquisition device and a second image acquisition device carried on the drone; The first image acquisition device is responsible for acquiring infrared images, and the second image acquisition device is responsible for acquiring visible light images.

8. The photovoltaic module fault detection method according to claim 7, characterized in that: Conduct secondary inspection and analysis on the inspection data and monitoring data to obtain fault analysis results, including: Extracting features of the infrared image, the visible light image and the monitoring data respectively to obtain a first feature parameter, a second feature parameter and a third feature parameter; Arranging the first characteristic parameter, the second characteristic parameter and the third characteristic parameter in order to form a fault analysis parameter set; The pre-configured secondary detection analysis library is indexed with the fault analysis parameter set to obtain the fault analysis result.

9. The photovoltaic module fault detection method according to claim 1, characterized in that: Also includes: Obtaining usage data of the failed photovoltaic module, performance data of each component, and location data of the installation location as analysis data; Build component evaluation database based on analysis data; Processing usage data of the photovoltaic module, performance data of each component, and location data of the installation location to obtain an evaluation parameter set; Determine the evaluation result of the photovoltaic module according to the evaluation parameter set and the module evaluation database; When the evaluation results meet the preset secondary detection and analysis conditions, secondary detection and analysis are carried out.

10. The photovoltaic module fault detection method according to claim 1, characterized in that: Also includes: Conduct risk assessment on the operation data of PV modules based on the pre-configured operation assessment database; When the assessed operational risk meets the conditions for secondary detection and analysis, secondary detection and analysis shall be carried out.

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