Busbar fault diagnosis method and system for photovoltaic energy storage system

Abstract

The present disclosure provides a bus duct fault diagnosis method and system for a photovoltaic energy storage system, which relates to the field of equipment fault diagnosis technology. The method includes: performing multiple warning tests on the bus duct under multiple current data according to multi-dimensional warning indicators to build a current-warning threshold database; inputting the predicted current into the current-warning threshold database to match and obtain a multi-dimensional fault warning threshold; judging the sensor monitoring data of the bus duct based on the multi-dimensional fault warning threshold; marking the abnormal duct number according to the judgment result; and performing bus duct fault inspection and repair based on the abnormal duct number. The present disclosure can solve the technical problem in the prior art that the accuracy of the bus duct warning judgment is low due to the inability to set an accurate fault warning threshold in combination with the actual situation of photovoltaic power generation, resulting in the inability to timely and effectively perform bus duct fault warning. It can improve the accuracy and timeliness of the fault warning, thereby timely and effectively performing fault abnormality warning.

Description

Technical Field

[0001] The present disclosure relates to the technical field of equipment fault diagnosis, and in particular to a bus duct fault diagnosis method and system for a photovoltaic energy storage system. Background Art

[0002] The bus duct of a photovoltaic energy storage system is a device used to connect and transmit electrical energy between photovoltaic panels and energy storage equipment. Its main function is to collect the DC power generated by photovoltaic panels and transmit it to the energy storage equipment for storage, or to invert it into AC power and supply it to the load.

[0003] Since photovoltaic power generation is greatly affected by environmental factors such as sunlight and weather, the generated current fluctuates greatly. When the current in the bus duct increases, the temperature rise of the bus duct will increase, and it will also cause the bus duct to vibrate and generate noise. These conditions are normal when the current flowing through is large; however, when the current flowing through is small, these conditions may be a precursor to bus duct failure. Therefore, the existing use of fixed warning thresholds in dimensions such as temperature, vibration, and noise for fault warning will make the warning threshold less compatible with the actual situation of photovoltaic power generation, resulting in inaccurate warning threshold setting and low accuracy of bus duct warning judgment.

[0004] In summary, the existing bus duct fault diagnosis method is unable to set an accurate fault warning threshold based on the actual situation of photovoltaic power generation, resulting in low accuracy of bus duct warning judgment, causing the technical problem of being unable to provide timely and effective bus duct fault warning. Summary of the Invention

[0005] The purpose of the present disclosure is to provide a busbar duct fault diagnosis method and system for a photovoltaic energy storage system, so as to solve the technical problem that the existing busbar duct fault diagnosis method cannot set an accurate fault warning threshold based on the actual situation of photovoltaic power generation, resulting in low accuracy of busbar duct warning judgment and the inability to provide timely and effective busbar duct fault warning.

[0006] In view of the above problems, the present disclosure provides a busbar fault diagnosis method and system for a photovoltaic energy storage system.

[0007] In the first aspect, the present disclosure provides a bus duct fault diagnosis method for a photovoltaic energy storage system, the method is implemented by a bus duct fault diagnosis system for a photovoltaic energy storage system, wherein the method includes: transmitting the light intensity and real-time temperature collected within a preset time window to a current prediction channel for current prediction, and outputting the predicted current, wherein the current prediction channel is constructed based on the target photovoltaic device; obtaining an expected current threshold, performing multiple warning tests on the bus duct under multiple current data within the expected current threshold according to multi-dimensional warning indicators, and constructing a current-warning threshold database according to the test results; inputting the predicted current into the current-warning threshold database, matching to obtain a fault warning threshold, wherein the fault warning threshold includes a temperature warning threshold, a vibration warning threshold and a noise warning threshold; receiving a sensor monitoring data set for the bus duct, wherein the sensor monitoring data includes a temperature monitoring data set. The sensor monitoring data is collected from a plurality of bus ducts and a plurality of bus ducts, and each bus duct corresponds to a sensor monitoring data, wherein the sensor monitoring data is marked with a bus duct number; the temperature warning threshold is corrected according to the real-time temperature to obtain an updated temperature warning threshold, and the sensor monitoring data in the sensor monitoring data set are judged based on the updated temperature warning threshold, the vibration warning threshold and the noise warning threshold; when the temperature monitoring data does not meet the updated temperature warning threshold and / or the vibration monitoring data meets the vibration warning threshold and / or the noise monitoring data meets the noise warning threshold, the corresponding bus duct number is extracted and marked as abnormal to obtain an abnormal bus duct number set; positioning and identification are performed based on the abnormal bus duct number set, and the position coordinates of the abnormal bus duct are sent to the nearest fault inspection and maintenance personnel to perform bus duct fault inspection and maintenance.

[0008] In a second aspect, the present disclosure further provides a busbar fault diagnosis system for a photovoltaic energy storage system, which is used to execute a busbar fault diagnosis method for a photovoltaic energy storage system as described in the first aspect, wherein the system includes: a current prediction module, the current prediction module is used to transmit the light intensity and real-time temperature collected within a preset time window to a current prediction channel for current prediction, and output the predicted current, the current prediction channel is constructed based on the target photovoltaic device; a current-warning threshold database construction module, the current-warning threshold database construction module is used to obtain an expected current threshold, perform multiple warning tests on the busbar under multiple current data within the expected current threshold according to multi-dimensional warning indicators, and construct a current-warning threshold database according to the test results; a fault warning threshold matching module, the fault warning threshold matching module is used to input the predicted current into the current-warning threshold database, match and obtain a fault warning threshold, the fault warning threshold includes a temperature warning threshold, a vibration warning threshold and a noise warning threshold; a sensor monitoring data set receiving module, the sensor monitoring data set receiving module is used to receive a sensor monitoring data set of the busbar, The sensor monitoring data includes temperature monitoring data, vibration monitoring data and noise monitoring data, and each slot of the bus duct corresponds to a sensor monitoring data, wherein the sensor monitoring data is marked with a slot number; a sensor monitoring data judgment module, the sensor monitoring data judgment module is used to correct the temperature warning threshold according to the real-time temperature to obtain an updated temperature warning threshold, and judge the sensor monitoring data in the sensor monitoring data set based on the updated temperature warning threshold, vibration warning threshold and noise warning threshold; an abnormal slot number set acquisition module, the abnormal slot number set acquisition module is used to extract and mark the corresponding slot number as abnormal when the temperature monitoring data does not meet the updated temperature warning threshold and / or the vibration monitoring data meets the vibration warning threshold and / or the noise monitoring data meets the noise warning threshold, so as to obtain an abnormal slot number set; a bus duct fault inspection and maintenance module, the bus duct fault inspection and maintenance module is used to perform positioning and identification based on the abnormal slot number set, send the abnormal slot position coordinates to the nearest fault inspection and maintenance personnel, and perform bus duct fault inspection and maintenance.

[0009] One or more technical solutions provided in this disclosure have at least the following technical effects or advantages:

[0010] 1. Current prediction is performed by transmitting the light intensity and real-time temperature collected within a preset time window to a current prediction channel, and the predicted current is output. The current prediction channel is constructed based on the target photovoltaic device; an expected current threshold is obtained, and multiple warning tests are performed on the bus duct under multiple current data within the expected current threshold according to multi-dimensional warning indicators, and a current-warning threshold database is constructed according to the test results; the predicted current is input into the current-warning threshold database, and a fault warning threshold is obtained by matching. The fault warning threshold includes a temperature warning threshold, a vibration warning threshold, and a noise warning threshold; a sensor monitoring data set of the bus duct is received, wherein the sensor monitoring data includes temperature monitoring data, vibration monitoring data, and noise monitoring data, and each slot of the bus duct corresponds to a sensor Monitoring data, wherein the sensor monitoring data is marked with a slot number; the temperature warning threshold is corrected according to the real-time temperature to obtain an updated temperature warning threshold, and the sensor monitoring data in the sensor monitoring data set are judged based on the updated temperature warning threshold, vibration warning threshold and noise warning threshold; when the temperature monitoring data does not meet the updated temperature warning threshold and / or the vibration monitoring data meets the vibration warning threshold and / or the noise monitoring data meets the noise warning threshold, the corresponding slot number is extracted and marked as abnormal to obtain an abnormal slot number set; positioning and identification are performed based on the abnormal slot number set, and the abnormal slot position coordinates are sent to the nearest fault repair personnel to perform bus duct fault repair and maintenance. In other words, the above method can solve the technical problem that the existing bus duct fault diagnosis method cannot set an accurate fault warning threshold in combination with the actual situation of photovoltaic power generation, resulting in low accuracy of bus duct warning judgment and the inability to perform bus duct fault warning in a timely and effective manner.

[0011] 2. By dynamically adjusting the fault warning threshold based on photovoltaic power generation prediction, the matching degree between the fault warning threshold and the actual power generation of photovoltaic equipment can be improved, thereby improving the accuracy of the fault warning threshold setting.

[0012] 3. By combining fault warning thresholds in multiple dimensions to make early warning judgments on the real-time operating status of the bus duct, the accuracy and timeliness of bus duct fault warnings can be improved, so that fault abnormalities can be warned in a timely and effective manner to avoid major safety losses.

[0013] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented in accordance with the contents of the description. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the specific implementation methods of the present disclosure are listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without any creative work.

[0015] Figure 1 This is a flow chart of a busbar fault diagnosis method for a photovoltaic energy storage system disclosed herein;

[0016] Figure 2 A schematic diagram of a process for constructing a current prediction channel in a busbar fault diagnosis method for a photovoltaic energy storage system disclosed herein;

[0017] Figure 3 The present invention discloses a schematic structural diagram of a busbar fault diagnosis system for a photovoltaic energy storage system.

[0018] Description of reference numerals:

[0019] Current prediction module 11, current-warning threshold database construction module 12, fault warning threshold matching module 13, sensor monitoring data set receiving module 14, sensor monitoring data judgment module 15, abnormal slot number set acquisition module 16, busbar fault inspection and repair module 17. DETAILED DESCRIPTION

[0020] The present disclosure provides a busbar duct fault diagnosis method and system for a photovoltaic energy storage system, thereby solving the technical problem that the existing busbar duct fault diagnosis method cannot set an accurate fault warning threshold value in combination with the actual situation of photovoltaic power generation, resulting in low accuracy of busbar duct warning judgment and the inability to provide busbar duct fault warning in a timely and effective manner. The present disclosure achieves the technical effect of improving the accuracy and timeliness of busbar duct fault warning, thereby enabling timely and effective busbar duct fault abnormality warning.

[0021] Below, the technical solutions in the present disclosure will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments of the present disclosure. It should be understood that the present disclosure is not limited to the example embodiments described herein. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure. It should also be noted that, for the convenience of description, only the parts related to the present disclosure, rather than all of them, are shown in the accompanying drawings.

[0022] Example 1

[0023] Please see the attached Figure 1 The present disclosure provides a busbar fault diagnosis method for a photovoltaic energy storage system, wherein the method is applied to a busbar fault diagnosis system for a photovoltaic energy storage system, and the method specifically comprises the following steps:

[0024] Step 1: Transmitting the light intensity and real-time temperature collected within a preset time window to a current prediction channel for current prediction, and outputting the predicted current, wherein the current prediction channel is constructed based on the target photovoltaic device;

[0025] Specifically, a preset time window is first acquired. The preset time window is a relatively short period of time that can be set by those skilled in the art based on actual conditions, for example, 30 minutes, 60 minutes, etc. Then, within the preset time window, multiple sensors are used to collect the current light intensity and real-time ambient temperature of the photovoltaic device to obtain light intensity data and real-time temperature data.

[0026] The light intensity data and real-time temperature data are then fed into a pre-built current prediction channel to generate a current prediction result, the predicted current. This current prediction channel is built based on the target photovoltaic device and is a feedforward neural network model that can be iteratively optimized in machine learning. This model is trained using supervised historical datasets. This predicted current provides support for matching fault warning data in the next step.

[0027] Step 2: Obtain the expected current threshold, perform multiple early warning tests on the bus duct under multiple current data within the expected current threshold based on multi-dimensional early warning indicators, and build a current-early warning threshold database based on the test results;

[0028] Specifically, first, the expected current threshold is obtained. The expected current threshold refers to the power generation current range of the photovoltaic device during power transmission, which can be constructed by extracting the minimum current value and the maximum current value in the historical power generation data of the photovoltaic device. Then, according to the multi-dimensional warning index, multiple warning tests are performed on the bus duct under multiple current data within the expected current threshold. Since the temperature rise of the bus duct increases when the current in the bus duct increases, it also causes the bus duct to vibrate and generate noise. Therefore, the multi-dimensional warning index is set as a temperature index, a vibration index and a noise index, and multiple warning test results are obtained. A current-warning threshold database is constructed based on the multiple warning test results, wherein the current-warning threshold database stores multiple current values ​​and multiple warning threshold sets, and each current value corresponds to a warning threshold set.

[0029] By conducting multiple warning tests to build a current-warning threshold database, the accuracy of the current-warning threshold database setting can be improved. At the same time, it provides support for the next step of warning threshold matching under actual current conditions, which can improve the efficiency and accuracy of warning threshold matching.

[0030] Step 3: inputting the predicted current into the current-warning threshold database, matching and obtaining a fault warning threshold, wherein the fault warning threshold includes a temperature warning threshold, a vibration warning threshold, and a noise warning threshold;

[0031] Specifically, the predicted current is input into the current-warning threshold database for matching, and the corresponding fault warning threshold is obtained. The fault warning threshold includes a temperature warning threshold, a vibration warning threshold, and a noise warning threshold. The temperature warning threshold is the warning temperature; the vibration warning threshold is one or more abnormal vibration signals, including abnormal vibration frequency and abnormal amplitude; and the noise warning threshold is one or more abnormal noise signals, including abnormal noise frequency, abnormal noise pitch, and abnormal noise intensity. Obtaining the fault warning threshold provides a basis for the next step of conducting a real-time early warning judgment of the bus duct status.

[0032] Step 4: Receive a sensor monitoring data set of the bus duct, wherein the sensor monitoring data includes temperature monitoring data, vibration monitoring data, and noise monitoring data, and each slot of the bus duct corresponds to a sensor monitoring data, wherein the sensor monitoring data is marked with a slot number;

[0033] Specifically, the bus duct is composed of multiple trough units, each trough unit has a corresponding unique number, and each trough unit is configured with a temperature sensor, a vibration sensor and a noise sensor, where the number of sensors is one or more and can be set based on the actual length and area of ​​the trough unit. If there are multiple sensors, the sensor monitoring data is the average of the monitoring data of multiple sensors.

[0034] Multiple monitoring data collection nodes are set within the preset time window, where the monitoring data collection nodes can be set according to actual needs. The higher the quality of the demand warning, the shorter the time interval of the monitoring data collection nodes. For example, if the preset time window is 30 minutes, the time interval of the monitoring data collection nodes can be set to 1 minute. Sensor monitoring data of the bus duct is received at the multiple monitoring data collection nodes to obtain a sensor monitoring data set, where the sensor monitoring data includes temperature monitoring data, vibration monitoring data, and noise monitoring data. Each slot of the bus duct corresponds to a sensor monitoring data, and the sensor monitoring data is marked with the slot number.

[0035] By acquiring the real-time sensor monitoring data of the bus duct, the real-time operating status information of the bus duct can be intuitively obtained, and at the same time, data support is provided for the fault warning judgment of the bus duct.

[0036] Step 5: Correcting the temperature warning threshold according to the real-time temperature to obtain an updated temperature warning threshold, and judging the sensor monitoring data in the sensor monitoring data set based on the updated temperature warning threshold, vibration warning threshold, and noise warning threshold;

[0037] Specifically, since the matched temperature warning threshold is analyzed and obtained at a fixed ambient temperature, it is necessary to calibrate the temperature warning threshold based on the real-time temperature to further improve the accuracy of the temperature warning threshold setting and obtain an updated temperature warning threshold. The temperature monitoring data in the sensor monitoring data is then judged based on the updated temperature warning threshold, the vibration monitoring data in the sensor monitoring data is judged based on the vibration warning threshold, and the noise monitoring data in the sensor monitoring data is judged based on the noise warning threshold.

[0038] Step 6: When the temperature monitoring data does not meet the updated temperature warning threshold and / or the vibration monitoring data meets the vibration warning threshold and / or the noise monitoring data meets the noise warning threshold, the corresponding slot numbers are extracted and marked as abnormal to obtain an abnormal slot number set;

[0039] Specifically, when the temperature monitoring data is greater than the updated temperature warning threshold and / or the vibration monitoring data meets the vibration warning threshold and / or the noise monitoring data meets the noise warning threshold, wherein the vibration monitoring data meets the vibration warning threshold means that the vibration monitoring data meets any one of the abnormal vibration signals in the vibration warning threshold; the noise monitoring data meets the noise warning threshold means that the noise monitoring data meets any one of the abnormal noise signals in the noise warning threshold, then the slot number corresponding to the sensor monitoring data is extracted, and the slot number is marked as abnormal to obtain multiple abnormal slot numbers, and an abnormal slot number set is formed based on the multiple abnormal slot numbers.

[0040] By obtaining the abnormal slot number set, it provides support for the accurate positioning of the abnormal slot, and at the same time can improve the efficiency and accuracy of abnormal slot fault repair.

[0041] Step 7: Perform positioning and identification based on the abnormal slot number set, and send the abnormal slot position coordinates to the nearest fault repair personnel to perform bus duct fault repair and maintenance.

[0042] Specifically, the abnormal slot is accurately located based on the abnormal slot number set, and the abnormal slot coordinates are determined. The position coordinates of multiple fault maintenance personnel currently on duty are obtained, and the abnormal slot maintenance task is assigned based on the multiple fault maintenance personnel's position coordinates. The abnormal slot position coordinates are sent to the nearest fault maintenance personnel, and the fault maintenance personnel finally perform bus duct fault maintenance and repair based on the received abnormal slot position coordinates.

[0043] Specifically, the busbar duct fault diagnosis method for a photovoltaic energy storage system is applied to a busbar duct fault diagnosis system for a photovoltaic energy storage system.

[0044] First, the light intensity and real-time temperature collected within a preset time window are transmitted to the current prediction channel for current prediction, and the predicted current is output. The current prediction channel is constructed based on the target photovoltaic device; then the expected current threshold is obtained, and multiple warning tests are performed on the bus duct under multiple current data within the expected current threshold according to the multi-dimensional warning indicators, and a current-warning threshold database is constructed according to the test results; the predicted current is further input into the current-warning threshold database, and the fault warning threshold is matched to obtain the fault warning threshold, which includes the temperature warning threshold, the vibration warning threshold and the noise warning threshold; on the other hand, the sensor monitoring data set of the bus duct is received, wherein the sensor monitoring data includes the temperature monitoring data, the vibration monitoring data and the noise monitoring data, and each slot of the bus duct corresponds to a The sensor monitoring data is collected, wherein the sensor monitoring data is marked with a slot number; the temperature warning threshold is corrected according to the real-time temperature to obtain an updated temperature warning threshold, and the sensor monitoring data in the sensor monitoring data set are judged based on the updated temperature warning threshold, vibration warning threshold and noise warning threshold; when the temperature monitoring data does not meet the updated temperature warning threshold and / or the vibration monitoring data meets the vibration warning threshold and / or the noise monitoring data meets the noise warning threshold, the corresponding slot number is extracted and marked as abnormal to obtain an abnormal slot number set; finally, positioning and identification are performed based on the abnormal slot number set, and the abnormal slot position coordinates are sent to the nearest fault inspection and maintenance personnel to perform bus duct fault inspection and maintenance.

[0045] By dynamically adjusting the fault warning threshold based on photovoltaic power generation prediction, the accuracy of the fault warning threshold setting can be improved. At the same time, the real-time operation status of the bus duct can be warned based on the fault warning thresholds in multiple dimensions, which can improve the accuracy and timeliness of the bus duct fault warning. Therefore, the abnormal bus duct fault warning can be carried out in a timely and effective manner to avoid major safety losses.

[0046] Further, as attached Figure 2 As shown, step one of the present disclosure includes:

[0047] Obtaining basic indicator data of the target photovoltaic equipment, wherein the basic indicator data includes equipment type, installed capacity, and photoelectric conversion efficiency;

[0048] Based on the industrial big data, photovoltaic power generation related data is retrieved using the basic indicator data as a retrieval condition to obtain a plurality of sample power generation data, wherein the sample power generation data includes sample light intensity, sample temperature and sample power generation current;

[0049] performing data cleaning on the plurality of sample power generation data to obtain a plurality of standard sample power generation data;

[0050] The plurality of standard sample power generation data are used as training data, and supervised learning is performed on the current prediction channel constructed based on the BP neural network to obtain a current prediction channel that meets expected indicators.

[0051] Specifically, first, basic indicator data of the target photovoltaic equipment is obtained, wherein the basic indicator data includes equipment type, installed capacity and photoelectric conversion efficiency, and those skilled in the art can set it according to the actual situation of the target photovoltaic equipment.

[0052] Industrial big data refers to a series of technologies and methods that enable the mining and display of value within massive amounts of industrial data. These technologies include data planning, data collection, and analytical mining. Based on industrial big data technology, this method uses the basic indicator data of target photovoltaic equipment as search criteria to retrieve photovoltaic power generation-related data. This method obtains multiple sample power generation data, including sample light intensity, sample temperature, and sample power generation current.

[0053] The plurality of sample power generation data are cleaned according to a preset data cleaning scheme. The preset data cleaning scheme includes at least steps such as data deduplication, missing value supplementation, and outlier processing. These are commonly used data processing methods by those skilled in the art and are not described in detail here. This results in a plurality of cleaned standard sample power generation data. By performing data cleaning on the sample power generation data, the accuracy of the obtained standard sample power generation data can be improved, thereby indirectly improving the accuracy of the current prediction channel training.

[0054] A current prediction channel is constructed based on a BP neural network. This channel is a feedforward neural network model that can be iteratively optimized in machine learning. This channel is obtained through supervised training using historical datasets. The channel includes an input layer, a hidden layer, and an output layer. The input data of the input layer is light intensity and temperature, and the output data is the generated current. The current prediction channel is then supervised trained using the multiple standard sample power generation data as training data.

[0055] The method for supervised training of the current prediction channel is as follows: first, the training data is divided into a sample training set and a sample verification set according to a preset data division ratio. The preset data division ratio can be set according to the actual amount of sample data. Under normal circumstances, the sample training set accounts for 80% and the sample verification set accounts for 20%. Then, supervised training is performed on the current prediction channel using the sample training set. First, first sample training data is randomly selected from the sample training set, and supervised training is performed on the current prediction channel using the first sample training data to obtain a first generated current. The first generated current is compared with the first sample generated current in the first sample training data. When the two are consistent, second sample training data is randomly selected to perform supervised training on the current prediction channel. When the two are inconsistent, a deviation value between the first generated current and the first sample generated current in the first sample training data is calculated, and the weight parameters of the current prediction channel are optimized and adjusted according to the deviation value. Then, second sample training data is randomly selected to perform supervised training on the current prediction channel. Iterative training is continuously performed using the sample data set. When the output result of the current prediction channel tends to converge, the current prediction channel is verified and trained using the sample verification set until the accuracy of the output result of the current prediction channel meets the expected indicator. Then, a trained current prediction channel is obtained. The expected indicator is an output accuracy indicator, which can be set according to actual needs. The higher the required accuracy, the larger the output accuracy indicator. For example, the output accuracy indicator can be set to an accuracy of 95%.

[0056] By constructing a current prediction channel based on a BP neural network and obtaining corresponding sample power generation training data to perform supervised training on the current prediction channel, support is provided for power generation current prediction and the accuracy of power generation current prediction can be improved.

[0057] Furthermore, step 2 of the present disclosure includes:

[0058] The multi-dimensional early warning indicators include temperature indicators, vibration indicators and noise indicators;

[0059] Dividing the expected current threshold into equal values ​​based on a preset analysis step to obtain a plurality of current data;

[0060] Constructing a bus duct twin model, and sequentially performing multiple early warning tests under multiple current data using the bus duct twin model to obtain multiple early warning indicator sets;

[0061] Specifically, the method for constructing a current-warning threshold database is as follows: first, a multi-dimensional warning indicator is obtained, wherein the multi-dimensional warning indicator includes a temperature indicator, a vibration indicator, and a noise indicator. Then, the expected current threshold is divided into equal values ​​based on a preset analysis step to obtain multiple current data, wherein the preset analysis compensation can be set according to the range of the expected current threshold and the actual accuracy requirement. The smaller the preset analysis step, the higher the analysis accuracy. For example, assuming that the expected current threshold is 100 to 200 amps, the preset analysis step can be set to 0.5 amps, that is, a current data is set every 0.5 amps, such as 100.5 amps, 101 amps, etc.

[0062] A bus duct twin model is constructed based on digital twin technology, and then multiple early warning tests under multiple current data are performed in sequence through the bus duct twin model, and multiple early warning indicator sets are obtained according to the test results.

[0063] Furthermore, the present disclosure further includes the following steps:

[0064] Obtaining equipment specification data, operation control parameters, and working environment data of the bus duct;

[0065] In a visual simulation platform, simulation modeling of the bus duct equipment is performed based on the equipment specification data and the operation control parameters to obtain a twin model of the bus duct equipment;

[0066] The bus duct equipment twin model is environmentally configured based on the working environment data to obtain the bus duct twin model.

[0067] Specifically, the method for constructing a bus duct twin model is as follows: first, the equipment specification data, operation control parameters and working environment data of the bus duct are obtained. The equipment specification data includes information such as equipment type and metal busbar size. The operation control parameters include operating voltage, operating current and other data. The working environment data refers to the environmental parameters of the bus duct working area. The environmental parameter settings with the highest frequency of occurrence can be selected, including ambient temperature, ambient humidity and other data.

[0068] Digital twin technology is a method that creates highly simulated virtual models of real-world objects digitally, achieving a virtual representation of the state of a physical entity or system. It offers multiple advantages, including real-time performance, fidelity, interoperability, and closed-loop capabilities. Based on digital twin technology, bus duct equipment is simulated and modeled based on the equipment specifications and operational control parameters within a visual simulation platform. Commonly used visual simulation platforms include Blender and AutoCAD. Based on actual conditions, an appropriate visual simulation platform can be selected for simulation and modeling to produce a bus duct equipment twin model.

[0069] The bus duct equipment twin model is configured according to the working environment data to obtain a bus duct twin model. By building a bus duct twin model based on digital twin technology, the authenticity and accuracy of bus duct simulation tests can be improved, thereby improving the accuracy and rationality of early warning indicators.

[0070] Furthermore, the present disclosure further includes the following steps:

[0071] randomly selecting first current data from the plurality of current data;

[0072] Inputting the first current data into the bus duct twin model, performing multiple fault warning tests under a preset number threshold, and obtaining a plurality of first initial warning indicator sets, wherein the first initial warning indicator set includes a first temperature, first vibration characteristic data, and first noise characteristic data;

[0073] Performing data integration on the multiple first initial early warning indicator sets to obtain multiple first early warning indicator sets;

[0074] Extracting the first warning indicator with the highest occurrence frequency from the multiple first warning indicator sets, constructing a first warning indicator set, and adding the first warning indicator set to the multiple warning indicator sets.

[0075] Specifically, the method for obtaining multiple early warning indicator sets is as follows: first, randomly select the first current data from the multiple current data, and the first current data is any one of the multiple current data. Then, the first current data is input into the bus duct twin model, and multiple fault early warning tests are performed under a preset number threshold. The preset number threshold can be set according to actual conditions, wherein the larger the preset number threshold, the higher the accuracy of the early warning indicator, and multiple first initial early warning indicator sets are obtained, wherein the first initial early warning indicator set includes a first temperature, a first vibration characteristic data, and a first noise characteristic data, wherein the first vibration characteristic data refers to an abnormal vibration signal, such as: abnormal vibration frequency, abnormal amplitude, etc., and the first noise characteristic data refers to an abnormal noise signal, such as: abnormal noise frequency, abnormal noise tone, and abnormal noise intensity.

[0076] Data integration is performed on the multiple first initial warning indicator sets, where data integration refers to data classification and organization of the multiple first initial warning indicator sets to facilitate subsequent warning indicator analysis, thereby obtaining multiple first warning indicator sets. The first warning indicators with the highest frequency of occurrence from the multiple first warning indicator sets are then extracted, such as the temperature data, abnormal vibration signals, and abnormal noise signals with the highest frequency of occurrence, to obtain a first warning indicator set. The first warning indicator set is then added to the multiple warning indicator sets to obtain multiple warning indicator sets, wherein the current data corresponds to the warning indicator sets in a one-to-one manner. By generating multiple warning indicator sets, support is provided for the next step of constructing a current-warning threshold database.

[0077] A current-warning threshold database is constructed based on the mapping relationship between current data and warning indicator sets.

[0078] Specifically, multiple current intervals within the expected current threshold are set based on multiple current data, wherein the current interval is the current interval between two adjacent current data. Based on the decision tree principle, the current interval is used as the main node, and the warning indicator set corresponding to the current interval is used as the subsidiary node of the main node. Multiple current intervals and multiple warning indicator sets are used as filling data to form a current-warning threshold database.

[0079] By constructing a current-warning threshold database based on the principle of decision tree, it provides support for the next step of warning threshold matching, and at the same time can improve the accuracy and efficiency of warning threshold matching.

[0080] Furthermore, step five of the present disclosure includes:

[0081] Based on industrial big data, multiple historical monitoring logs of the bus duct are retrieved and multiple sample real-time temperatures and multiple sample temperature warning thresholds are extracted from the multiple historical monitoring logs, where the sample real-time temperatures and the sample temperature warning thresholds have a one-to-one correspondence;

[0082] Performing correlation analysis based on the real-time temperatures of the multiple samples and the temperature warning thresholds of the multiple samples to determine a temperature-threshold correlation coefficient;

[0083] Extracting the standard operating temperature from the working environment data, and calculating the temperature deviation between the real-time temperature and the standard operating temperature to obtain a temperature deviation value;

[0084] Performing a threshold deviation analysis on the temperature deviation value according to the temperature-threshold correlation coefficient to determine the threshold deviation value;

[0085] The temperature warning threshold is corrected by using the threshold deviation value to obtain the updated temperature warning threshold.

[0086] Specifically, the method for correcting the temperature warning threshold according to the real-time temperature to obtain an updated temperature warning threshold is as follows: first, based on industrial big data, information retrieval is performed using the bus duct as retrieval data to obtain multiple historical monitoring logs of the bus duct. Then, multiple sample real-time temperatures and multiple sample temperature warning thresholds are extracted from the multiple historical monitoring logs, where the sample real-time temperatures and the sample temperature warning thresholds have a one-to-one correspondence, and the sample real-time temperature refers to the ambient temperature of the bus duct during operation.

[0087] An association analysis algorithm is used to perform an association analysis between the temperature and the warning threshold based on the real-time temperatures of the multiple samples and the temperature warning thresholds of the multiple samples. Commonly used association analysis algorithms include the Apriori algorithm, the FP-Growth algorithm, the ECLAT algorithm, etc., which can be selected according to actual conditions to obtain a temperature-threshold correlation coefficient, where the temperature-threshold correlation coefficient refers to the impact of changes in ambient temperature on the temperature threshold. For example, when the ambient temperature drops by 5 degrees Celsius, the temperature threshold drops by 1 degree Celsius.

[0088] The standard operating temperature in the working environment data is extracted. The standard operating temperature is the temperature data manually set when constructing the busbar trunking twin model. The temperature deviation between the real-time temperature and the standard operating temperature is then calculated to obtain a temperature deviation value. A threshold deviation calculation is further performed on the temperature deviation value based on the temperature-threshold correlation coefficient to generate a threshold deviation value. Finally, the temperature warning threshold is corrected based on the threshold deviation value. That is, the threshold deviation value is subtracted from the temperature warning threshold to obtain an updated temperature warning threshold.

[0089] By correcting the temperature warning threshold according to the real-time temperature, the accuracy of setting the temperature warning threshold can be further improved, thereby improving the accuracy of temperature warning judgment.

[0090] Furthermore, step seven of the present disclosure includes:

[0091] Performing temperature deviation calculation on the updated temperature warning threshold and the temperature monitoring data to obtain a temperature deviation;

[0092] Performing a warning intensity analysis based on the temperature deviation to determine the warning intensity;

[0093] The warning intensity is input into a fault repair database for matching to obtain an optimized repair plan, which is then sent to corresponding fault repair personnel.

[0094] Specifically, before performing bus duct fault inspection and maintenance, a temperature deviation is first calculated between the updated temperature warning threshold and the temperature monitoring data to obtain a temperature deviation, where the temperature deviation is the difference between the temperature monitoring data and the updated temperature warning threshold. A warning intensity analysis is then performed based on the temperature deviation, where a greater temperature deviation indicates a greater warning intensity, thereby obtaining a warning intensity.

[0095] The warning intensity is input into the fault maintenance database for matching to obtain an optimized maintenance plan, and the optimized maintenance plan is sent to the corresponding fault maintenance personnel. The fault maintenance personnel performs bus duct fault maintenance and maintenance based on the optimized maintenance plan and the abnormal trough position coordinates.

[0096] The method for constructing the fault maintenance database is as follows: first, a plurality of bus duct maintenance logs are retrieved, and a plurality of historical warning intensities and corresponding maintenance plans are extracted based on the plurality of bus duct maintenance logs. Then, the plurality of historical warning intensities are clustered to obtain a plurality of warning intensity intervals and a corresponding plurality of maintenance plan sets; a plurality of maintenance plans are comprehensively evaluated in turn by an intelligent expert system, and the maintenance plan with the highest evaluation value is selected as the optimized maintenance plan corresponding to the warning intensity interval; a fault maintenance database is constructed based on the mapping relationship between the warning intensity interval and the optimized maintenance plan.

[0097] By building a fault maintenance database to optimize the maintenance plan matching, the efficiency of obtaining maintenance plans can be improved. At the same time, based on the optimized maintenance plan and the abnormal slot position coordinates, the corresponding slot is inspected and maintained, and the abnormal bus trough slot can be effectively handled in time to avoid major safety losses.

[0098] In summary, the busbar fault diagnosis method for a photovoltaic energy storage system provided by the present disclosure has the following technical effects:

[0099] 1. By dynamically adjusting the fault warning threshold based on photovoltaic power generation prediction, the accuracy of the fault warning threshold setting can be improved. At the same time, combining the fault warning thresholds of multiple dimensions to make early warning judgments on the real-time operating status of the bus duct can improve the accuracy and timeliness of bus duct fault warnings, thereby enabling timely and effective bus duct fault abnormality warnings to avoid major safety losses.

[0100] 2. By building a bus duct twin model based on digital twin technology, the authenticity and accuracy of the bus duct simulation test can be improved, thereby improving the accuracy and rationality of the early warning indicators.

[0101] 3. By correcting the temperature warning threshold according to the real-time temperature, the accuracy of the temperature warning threshold setting can be further improved, thereby improving the accuracy of the temperature warning judgment.

[0102] Example 2

[0103] Based on the same inventive concept as the busbar duct fault diagnosis method for a photovoltaic energy storage system in the aforementioned embodiment, the present disclosure also provides a busbar duct fault diagnosis system for a photovoltaic energy storage system, see the attached Figure 3 , the system comprising:

[0104] A current prediction module 11 is configured to transmit the light intensity and real-time temperature collected within a preset time window to a current prediction channel for current prediction and output a predicted current. The current prediction channel is constructed based on the target photovoltaic device.

[0105] A current-warning threshold database construction module 12 is used to obtain an expected current threshold, perform multiple warning tests on the bus duct under multiple current data within the expected current threshold based on multi-dimensional warning indicators, and construct a current-warning threshold database based on the test results;

[0106] A fault warning threshold matching module 13 is configured to input the predicted current into the current-warning threshold database and obtain a fault warning threshold by matching. The fault warning threshold includes a temperature warning threshold, a vibration warning threshold, and a noise warning threshold.

[0107] A sensor monitoring data set receiving module 14 is used to receive a sensor monitoring data set of the bus duct, wherein the sensor monitoring data includes temperature monitoring data, vibration monitoring data and noise monitoring data, and each slot of the bus duct corresponds to a sensor monitoring data, wherein the sensor monitoring data is marked with a slot number;

[0108] A sensor monitoring data judgment module 15 is configured to correct the temperature warning threshold according to the real-time temperature to obtain an updated temperature warning threshold, and to judge the sensor monitoring data in the sensor monitoring data set based on the updated temperature warning threshold, vibration warning threshold, and noise warning threshold;

[0109] The abnormal slot number set obtaining module 16 is used to extract and mark the corresponding slot number as abnormal when the temperature monitoring data does not meet the updated temperature warning threshold and / or the vibration monitoring data meets the vibration warning threshold and / or the noise monitoring data meets the noise warning threshold, so as to obtain an abnormal slot number set;

[0110] The bus duct fault repair module 17 is used to locate and identify the abnormal duct body based on the abnormal duct body number set, send the abnormal duct body position coordinates to the nearest fault repair personnel, and perform bus duct fault repair and maintenance.

[0111] Furthermore, the current prediction module 11 in the system is further configured to:

[0112] Obtaining basic indicator data of the target photovoltaic equipment, wherein the basic indicator data includes equipment type, installed capacity, and photoelectric conversion efficiency;

[0113] Based on the industrial big data, photovoltaic power generation related data is retrieved using the basic indicator data as a retrieval condition to obtain a plurality of sample power generation data, wherein the sample power generation data includes sample light intensity, sample temperature and sample power generation current;

[0114] performing data cleaning on the plurality of sample power generation data to obtain a plurality of standard sample power generation data;

[0115] The plurality of standard sample power generation data are used as training data, and supervised learning is performed on the current prediction channel constructed based on the BP neural network to obtain a current prediction channel that meets expected indicators.

[0116] Furthermore, the current prediction module 12 in the system is further configured to:

[0117] The multi-dimensional early warning indicators include temperature indicators, vibration indicators and noise indicators;

[0118] Dividing the expected current threshold into equal values ​​based on a preset analysis step to obtain a plurality of current data;

[0119] Constructing a bus duct twin model, and sequentially performing multiple early warning tests under multiple current data using the bus duct twin model to obtain multiple early warning indicator sets;

[0120] A current-warning threshold database is constructed based on the mapping relationship between current data and warning indicator sets.

[0121] Furthermore, the current prediction module 12 in the system is further configured to:

[0122] Obtaining equipment specification data, operation control parameters, and working environment data of the bus duct;

[0123] In a visual simulation platform, simulation modeling of the bus duct equipment is performed based on the equipment specification data and the operation control parameters to obtain a twin model of the bus duct equipment;

[0124] The bus duct equipment twin model is environmentally configured based on the working environment data to obtain the bus duct twin model.

[0125] Furthermore, the current prediction module 12 in the system is further configured to:

[0126] randomly selecting first current data from the plurality of current data;

[0127] Inputting the first current data into the bus duct twin model, performing multiple fault warning tests under a preset number threshold, and obtaining a plurality of first initial warning indicator sets, wherein the first initial warning indicator set includes a first temperature, first vibration characteristic data, and first noise characteristic data;

[0128] Performing data integration on the multiple first initial early warning indicator sets to obtain multiple first early warning indicator sets;

[0129] Extracting the first warning indicator with the highest occurrence frequency from the multiple first warning indicator sets, constructing a first warning indicator set, and adding the first warning indicator set to the multiple warning indicator sets.

[0130] Furthermore, the current prediction module 15 in the system is further configured to:

[0131] Based on industrial big data, multiple historical monitoring logs of the bus duct are retrieved and multiple sample real-time temperatures and multiple sample temperature warning thresholds are extracted from the multiple historical monitoring logs, where the sample real-time temperatures and the sample temperature warning thresholds have a one-to-one correspondence;

[0132] Performing correlation analysis based on the real-time temperatures of the multiple samples and the temperature warning thresholds of the multiple samples to determine a temperature-threshold correlation coefficient;

[0133] Extracting the standard operating temperature from the working environment data, and calculating the temperature deviation between the real-time temperature and the standard operating temperature to obtain a temperature deviation value;

[0134] Performing a threshold deviation analysis on the temperature deviation value according to the temperature-threshold correlation coefficient to determine the threshold deviation value;

[0135] The temperature warning threshold is corrected by using the threshold deviation value to obtain the updated temperature warning threshold.

[0136] Furthermore, the current prediction module 17 in the system is further configured to:

[0137] Performing temperature deviation calculation on the updated temperature warning threshold and the temperature monitoring data to obtain a temperature deviation;

[0138] Performing a warning intensity analysis based on the temperature deviation to determine the warning intensity;

[0139] The warning intensity is input into a fault repair database for matching to obtain an optimized repair plan, which is then sent to corresponding fault repair personnel.

[0140] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The bus duct fault diagnosis method and specific examples for a photovoltaic energy storage system in Example 1 are also applicable to the bus duct fault diagnosis system for a photovoltaic energy storage system in this embodiment. Through the detailed description of the bus duct fault diagnosis method for a photovoltaic energy storage system, those skilled in the art will clearly understand the bus duct fault diagnosis system for a photovoltaic energy storage system in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0141] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present disclosure. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments shown herein, but is intended to be construed in the widest manner consistent with the principles and novel features disclosed herein.

[0142] Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the present disclosure and its equivalents, the present disclosure is also intended to include these modifications and variations.

Claims

1. A busbar fault diagnosis method for a photovoltaic energy storage system, characterized in that: The method comprises: Transmitting the light intensity and real-time temperature collected within a preset time window to a current prediction channel for current prediction, and outputting the predicted current, wherein the current prediction channel is constructed based on the target photovoltaic device; Obtain the expected current threshold, perform multiple early warning tests on the bus duct under multiple current data within the expected current threshold based on multi-dimensional early warning indicators, and build a current-early warning threshold database based on the test results; Inputting the predicted current into the current-warning threshold database, matching and obtaining a fault warning threshold, wherein the fault warning threshold includes a temperature warning threshold, a vibration warning threshold, and a noise warning threshold; Receive a sensor monitoring data set of the bus duct, wherein the sensor monitoring data includes temperature monitoring data, vibration monitoring data, and noise monitoring data, and each slot of the bus duct corresponds to a sensor monitoring data, wherein the sensor monitoring data is marked with a slot number; Correcting the temperature warning threshold according to the real-time temperature to obtain an updated temperature warning threshold, and judging the sensor monitoring data in the sensor monitoring data set based on the updated temperature warning threshold, the vibration warning threshold, and the noise warning threshold; When the temperature monitoring data does not meet the updated temperature warning threshold and / or the vibration monitoring data meets the vibration warning threshold and / or the noise monitoring data meets the noise warning threshold, the corresponding slot numbers are extracted and marked as abnormal to obtain an abnormal slot number set; Based on the abnormal busbar number set, the abnormal busbar position coordinates are sent to the nearest fault repair personnel to perform busbar fault repair and maintenance; Correcting the temperature warning threshold according to the real-time temperature to obtain an updated temperature warning threshold includes: Based on industrial big data, multiple historical monitoring logs of the bus duct are retrieved and multiple sample real-time temperatures and multiple sample temperature warning thresholds are extracted from the multiple historical monitoring logs, where the sample real-time temperatures and the sample temperature warning thresholds have a one-to-one correspondence; Performing correlation analysis based on the real-time temperatures of the multiple samples and the temperature warning thresholds of the multiple samples to determine a temperature-threshold correlation coefficient; Extracting the standard operating temperature from the working environment data, and calculating the temperature deviation between the real-time temperature and the standard operating temperature to obtain a temperature deviation value; Performing a threshold deviation analysis on the temperature deviation value according to the temperature-threshold correlation coefficient to determine the threshold deviation value; The temperature warning threshold is corrected by using the threshold deviation value to obtain the updated temperature warning threshold.

2. The method according to claim 1, wherein The current prediction channel is constructed based on the target photovoltaic device and includes: Obtaining basic indicator data of the target photovoltaic equipment, wherein the basic indicator data includes equipment type, installed capacity, and photoelectric conversion efficiency; Based on the industrial big data, photovoltaic power generation related data is retrieved using the basic indicator data as a retrieval condition to obtain a plurality of sample power generation data, wherein the sample power generation data includes sample light intensity, sample temperature and sample power generation current; performing data cleaning on the plurality of sample power generation data to obtain a plurality of standard sample power generation data; The plurality of standard sample power generation data are used as training data, and supervised learning is performed on the current prediction channel constructed based on the BP neural network to obtain a current prediction channel that meets expected indicators.

3. The method according to claim 1, wherein Based on multi-dimensional early warning indicators, multiple early warning tests are performed on the bus duct under multiple current data within the expected current threshold. Based on the test results, a current-early warning threshold database is constructed, including: The multi-dimensional early warning indicators include temperature indicators, vibration indicators and noise indicators; Dividing the expected current threshold into equal values ​​based on a preset analysis step to obtain a plurality of current data; Constructing a bus duct twin model, and sequentially performing multiple early warning tests under multiple current data using the bus duct twin model to obtain multiple early warning indicator sets; A current-warning threshold database is constructed based on the mapping relationship between current data and warning indicator sets.

4. The method according to claim 3, wherein Construct a bus duct twin model, including: Obtaining equipment specification data, operation control parameters, and working environment data of the bus duct; In a visual simulation platform, simulation modeling of the bus duct equipment is performed based on the equipment specification data and the operation control parameters to obtain a twin model of the bus duct equipment; The bus duct equipment twin model is environmentally configured based on the working environment data to obtain the bus duct twin model.

5. The method according to claim 3, wherein The bus duct twin model is used to sequentially perform multiple early warning tests under multiple current data to obtain multiple early warning indicator sets, including: randomly selecting first current data from the plurality of current data; Inputting the first current data into the bus duct twin model, performing multiple fault warning tests under a preset number threshold, and obtaining a plurality of first initial warning indicator sets, wherein the first initial warning indicator set includes a first temperature, first vibration characteristic data, and first noise characteristic data; Performing data integration on the multiple first initial early warning indicator sets to obtain multiple first early warning indicator sets; Extracting the first warning indicator with the highest occurrence frequency from the multiple first warning indicator sets, constructing a first warning indicator set, and adding the first warning indicator set to the multiple warning indicator sets.

6. The method according to claim 1, wherein Before performing bus duct troubleshooting and maintenance, also include: Performing temperature deviation calculation on the updated temperature warning threshold and the temperature monitoring data to obtain a temperature deviation; Performing a warning intensity analysis based on the temperature deviation to determine the warning intensity; The warning intensity is input into a fault repair database for matching to obtain an optimized repair plan, which is then sent to corresponding fault repair personnel.

7. A busbar fault diagnosis system for a photovoltaic energy storage system, characterized in that: The system is used to implement the busbar fault diagnosis method for a photovoltaic energy storage system according to any one of claims 1 to 6, comprising: A current prediction module, which is used to transmit the light intensity and real-time temperature collected within a preset time window to a current prediction channel for current prediction and output the predicted current. The current prediction channel is constructed based on the target photovoltaic device; A current-warning threshold database construction module is used to obtain an expected current threshold, perform multiple warning tests on the bus duct under multiple current data within the expected current threshold based on multi-dimensional warning indicators, and construct a current-warning threshold database based on the test results; a fault warning threshold matching module, the fault warning threshold matching module being used to input the predicted current into the current-warning threshold database and match to obtain a fault warning threshold, the fault warning threshold including a temperature warning threshold, a vibration warning threshold, and a noise warning threshold; A sensor monitoring data set receiving module, the sensor monitoring data set receiving module is used to receive a sensor monitoring data set of the bus duct, wherein the sensor monitoring data includes temperature monitoring data, vibration monitoring data and noise monitoring data, and each slot of the bus duct corresponds to a sensor monitoring data, wherein the sensor monitoring data is marked with a slot number; a sensor monitoring data judgment module, the sensor monitoring data judgment module being used to correct the temperature warning threshold according to the real-time temperature to obtain an updated temperature warning threshold, and to judge the sensor monitoring data in the sensor monitoring data set based on the updated temperature warning threshold, vibration warning threshold, and noise warning threshold; An abnormal slot number set obtaining module is used to extract and mark the corresponding slot number as abnormal when the temperature monitoring data does not meet the updated temperature warning threshold and / or the vibration monitoring data meets the vibration warning threshold and / or the noise monitoring data meets the noise warning threshold, so as to obtain an abnormal slot number set; A bus duct fault inspection and repair module is used to locate and identify the abnormal duct based on the abnormal duct number set, send the abnormal duct position coordinates to the nearest fault inspection and repair personnel, and perform bus duct fault inspection and maintenance.

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