Method and platform for analyzing abnormal operation of low-voltage cabinet circuit breaker

Abstract

The invention discloses a low-voltage cabinet circuit breaker operation abnormity analysis method and platform, and relates to the technical field of electrical equipment monitoring, and the method comprises the steps: collecting the operation data of a low-voltage cabinet circuit breaker from a plurality of monitoring terminals, and constructing a historical operation database; establishing a circuit breaker operation state evaluation model; predicting the operation state of the circuit breaker in a future time period to obtain predicted operation state data; comparing the actual operation state data with the predicted operation state data of the circuit breaker; performing anomaly analysis on the circuit breaker, and determining a plurality of abnormal data indexes; and formulating a troubleshooting scheme. The technical problems that in the prior art, the operation state of the low-voltage cabinet circuit breaker cannot be comprehensively monitored in real time, the abnormity is difficult to accurately analyze, and the operation reliability and stability of the low-voltage cabinet circuit breaker cannot be effectively guaranteed are solved, and the purposes of comprehensively monitoring the operation state of the low-voltage cabinet circuit breaker, accurately analyzing the abnormity and effectively guaranteeing the reliability and stability of the low-voltage cabinet circuit breaker are achieved. And the operation reliability and stability of the low-voltage cabinet circuit breaker are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment monitoring, and particularly to a method and platform for analyzing abnormal operation of low-voltage switchgear circuit breakers. Background Art

[0002] With the wide application and continuous development of the power system, as a key device for power distribution and control, the reliability and stability of the operation of low-voltage switchgear circuit breakers are of crucial importance. In actual operation, low-voltage switchgear circuit breakers face a complex working environment and diverse load changes, which makes them prone to various abnormal operation problems. However, existing monitoring and analysis means often have difficulty in comprehensively and real-time monitoring the operation status of low-voltage switchgear circuit breakers, and also cannot accurately analyze the root causes of abnormal conditions. Traditional methods mostly rely on manual regular inspections, which are not only inefficient but also difficult to detect potential fault hazards. At the same time, in terms of fault troubleshooting and maintenance, there is a lack of scientific and systematic methods, resulting in a long fault handling time and affecting the normal operation of the power system.

[0003] There are technical problems in the prior art that it is impossible to comprehensively and real-time monitor the operation status of low-voltage switchgear circuit breakers, difficult to accurately analyze abnormalities, and unable to effectively ensure their operation reliability and stability. Summary of the Invention

[0004] This application provides a method and platform for analyzing abnormal operation of low-voltage switchgear circuit breakers, which are used to solve the technical problems in the prior art that it is impossible to comprehensively and real-time monitor the operation status of low-voltage switchgear circuit breakers, difficult to accurately analyze abnormalities, and unable to effectively ensure their operation reliability and stability.

[0005] In view of the above problems, this application provides a method and platform for analyzing abnormal operation of low-voltage switchgear circuit breakers.

[0006] In the first aspect of this application, a method for analyzing abnormal operation of low-voltage switchgear circuit breakers is provided. The method includes:

[0007] Collecting operation data of low-voltage switchgear circuit breakers from multiple monitoring terminals to construct a historical operation database; using the data in the historical operation database and combining with the operation principle of the circuit breaker to establish an operation status evaluation model of the circuit breaker; using the operation status evaluation model to predict the operation status of the circuit breaker in the future time period to obtain predicted operation status data; comparing the actual operation status data of the circuit breaker with the predicted operation status data; if there is a difference between the actual operation status data and the predicted operation status, then performing abnormal analysis on the real-time data of the current operation of the circuit breaker to determine multiple abnormal data indicators; and formulating a fault troubleshooting plan according to the multiple abnormal data indicators in combination with the fault tree analysis method.

[0008] In the second aspect of the present application, a low-voltage switchgear circuit breaker operation anomaly analysis platform is provided. The platform includes:

[0009] A historical operation database construction module, which is used to collect the operation data of the low-voltage switchgear circuit breaker from multiple monitoring terminals and construct a historical operation database; an operation status evaluation model establishment module, which is used to utilize the data in the historical operation database and combine the circuit breaker operation principle to establish a circuit breaker operation status evaluation model; a predicted operation status data acquisition module, which is used to use the operation status evaluation model to predict the operation status of the circuit breaker in a future period to obtain predicted operation status data; an operation status data comparison module, which is used to compare the actual operation status data of the circuit breaker with the predicted operation status data; an abnormal data index determination module, which is used to perform abnormal analysis on the real-time data of the current operation of the circuit breaker if there is a difference between the actual operation status data and the predicted operation status, and determine multiple abnormal data indexes; a fault troubleshooting plan formulation module, which is used to formulate a fault troubleshooting plan according to the multiple abnormal data indexes and in combination with the fault tree analysis method.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] Collect the operation data of the low-voltage switchgear circuit breaker from multiple monitoring terminals and construct a historical operation database; utilize the data in the historical operation database and combine the circuit breaker operation principle to establish a circuit breaker operation status evaluation model; use the operation status evaluation model to predict the operation status of the circuit breaker in a future period to obtain predicted operation status data; compare the actual operation status data of the circuit breaker with the predicted operation status data; if there is a difference between the actual operation status data and the predicted operation status, perform abnormal analysis on the real-time data of the current operation of the circuit breaker and determine multiple abnormal data indexes; formulate a fault troubleshooting plan according to the multiple abnormal data indexes and in combination with the fault tree analysis method. It achieves the technical effect of realizing comprehensive monitoring of the operation status of the low-voltage switchgear circuit breaker, accurate abnormal analysis, and improving the reliability and stability of the operation of the low-voltage switchgear circuit breaker. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0013] Figure 1 It is a flowchart of the low-voltage switchgear circuit breaker operation anomaly analysis method provided by the embodiment of the present application;

[0014] Figure 2Schematic diagram of the structure of the abnormal operation analysis platform for low-voltage switchgear circuit breakers provided by the embodiments of the present application.

[0015] Explanation of reference numerals in the drawings: Historical operation database construction module 10, operation status evaluation model establishment module 20, predicted operation status data acquisition module 30, operation status data comparison module 40, abnormal data index determination module 50, fault troubleshooting plan formulation module 60. Detailed implementation manners

[0016] The present application provides a method and platform for analyzing abnormal operation of low-voltage switchgear circuit breakers, which are used to solve the technical problems existing in the prior art, such as the inability to comprehensively and real-time monitor the operation status of low-voltage switchgear circuit breakers, the difficulty in accurately analyzing abnormalities, and the inability to effectively ensure their operation reliability and stability.

[0017] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0018] Embodiment 1, as Figure 1 shown, the present application provides a method for analyzing abnormal operation of low-voltage switchgear circuit breakers, and the method includes:

[0019] Step S100: Collect operation data of the low-voltage switchgear circuit breaker from multiple monitoring terminals and construct a historical operation database.

[0020] Specifically, to achieve comprehensive and accurate analysis of the operation status of the low-voltage switchgear circuit breaker, it is necessary to collect its operation data from multiple monitoring terminals, and then construct a historical operation database. According to the structural characteristics and operation principles of the low-voltage switchgear circuit breaker, multiple monitoring points closely related to the operation status are determined. These monitoring points are evenly distributed at different key parts of the circuit breaker, such as contacts, coils, bus connections, etc., so as to capture operation data in all directions. Then, suitable sensors are deployed at each monitoring point, and these sensors can collect various operation data such as current, voltage, temperature, and switch status in real time. As time goes by, a large amount of collected data will be integrated at a preset time interval, such as every minute or every hour. The integration process includes operations such as unifying the data format and eliminating duplicate data, and then the processed data is stored in a dedicated database, thus successfully constructing a historical operation database. This database aggregates the long-term operation data of the circuit breaker, providing a solid data foundation for subsequent in-depth analysis of its operation status, establishing an evaluation model, and predicting future operation trends.

[0021] Step S200: Establish an evaluation model for the operating state of the circuit breaker by using the data in the historical operation database and combining with the operating principle of the circuit breaker.

[0022] Specifically, first, carefully screen and preprocess the data in the historical operation database to remove noise, outliers, and duplicate data, ensuring the accuracy and consistency of the data. Subsequently, deeply study the operating principle of the circuit breaker to clarify the internal relationship between key operating parameters such as current, voltage, temperature, and contact wear and the operating state of the circuit breaker. Based on these key parameters, determine the input features of the model, which will be used as the basis for the model to judge the operating state of the circuit breaker. Then, combine the actual requirements and data characteristics to select a suitable modeling method, such as support vector machine, etc., to construct an initial operating state evaluation model. Use part of the data in the historical operation database as the training set to repeatedly train and optimize the model, and continuously adjust the parameters of the model to improve the accuracy and reliability of the model. Then use another part of the data as the validation set to verify and evaluate the trained model to ensure that the model can accurately evaluate the operating state of the circuit breaker. After continuous optimization and verification, finally establish an evaluation model that can accurately reflect the operating state of the circuit breaker, providing strong support for subsequent operating state prediction and fault diagnosis.

[0023] Step S300: Use the operating state evaluation model to predict the operating state of the circuit breaker in the future time period to obtain predicted operating state data.

[0024] Specifically, to obtain the predicted operating state data of the circuit breaker in the future time period, first accurately obtain the real-time operating data of the circuit breaker at the current moment from various current monitoring devices, and these data cover key operating parameters such as current, voltage, and temperature. Then, input these real-time and accurate operating data into the established operating state evaluation model without error. Since the operating state evaluation model is trained and optimized based on historical operating data and the operating principle of the circuit breaker and has the ability to analyze and predict the operating state, it can predict the future operating state of the circuit breaker based on the input real-time data. To ensure the accuracy and reliability of the prediction, multiple simulation predictions will be carried out, and each simulation prediction will produce slightly different results based on different random factors. After multiple simulation predictions are completed, perform statistical analysis on these results and calculate the confidence interval of the prediction results. The confidence interval can reflect the credibility range of the prediction results. By comprehensively considering the results of multiple simulation predictions and the confidence interval, finally determine the relatively reliable predicted operating state data of the circuit breaker in the future time period, which will provide an important reference basis for subsequent judgment of whether the circuit breaker is operating abnormally.

[0025] Step S400: Compare the actual operating state data of the circuit breaker with the predicted operating state data.

[0026] Specifically, to accurately evaluate the operating status of the low-voltage switchboard circuit breaker, it is necessary to carefully compare its actual operating status data and predicted operating status data. The actual operating status data of the circuit breaker at present is obtained in real time from the real-time monitoring system. These data include various key operating parameters such as current, voltage, temperature, and the number of switch operations, accurately reflecting the real operating situation of the circuit breaker at this moment. At the same time, the predicted operating status data previously obtained through the operating status evaluation model is called. These data are the estimations of the operating status of the circuit breaker within the same or similar time periods. Each parameter in the two sets of data is compared one by one, not only comparing the numerical magnitudes, but also analyzing whether the data change trends are consistent. For example, compare the actual current value with the predicted current value to observe whether the deviation between the two is within a reasonable range; analyze the actual voltage fluctuation trend and the predicted voltage fluctuation trend to determine whether there are significant differences. Through this comprehensive and detailed comparison, the difference between the actual operating status of the circuit breaker and the expectation can be clearly judged, providing a key basis for subsequent judgment of whether there are abnormalities in the circuit breaker and determination of the degree of abnormality.

[0027] Step S500: If there are differences between the actual operating status data and the predicted operating status, perform abnormal analysis on the real-time data of the current operation of the circuit breaker to determine multiple abnormal data indicators.

[0028] Specifically, when it is found that there are differences between the actual operating status data and the predicted operating status of the circuit breaker, the abnormal analysis process of the real-time data of the current operation of the circuit breaker is immediately started. First, based on a large amount of accumulated normal operating data and professional experience in the past, a reasonable threshold range of normal operating data is set, which covers the normal fluctuation ranges of various key operating parameters such as current, voltage, and temperature. Then, the real-time data of the current operation of the circuit breaker is compared with the set threshold range one by one, and the data exceeding the threshold is screened out. These data exceeding the threshold may be single-parameter abnormalities or multiple-parameter abnormalities at the same time. Next, perform correlation analysis on these data exceeding the threshold, considering the mutual influence and potential connection between different parameters. For example, an abnormal increase in current may be related to an abnormal increase in temperature. Through this correlation analysis, comprehensive judgment is made from multiple perspectives to determine multiple abnormal data indicators that can accurately reflect the abnormal situation of the circuit breaker. These abnormal data indicators provide key clues and important bases for subsequent in-depth analysis of the fault causes and formulation of fault troubleshooting plans.

[0029] Step S600: According to the multiple abnormal data indicators, combined with the fault tree analysis method, formulate a fault troubleshooting plan.

[0030] Specifically, after obtaining multiple abnormal data indicators, the fault tree analysis method is used to formulate a fault troubleshooting plan. Taking these abnormal data indicators as the top events of the fault tree, and according to the logical relationship of fault occurrence, the direct and indirect causes leading to these abnormalities are analyzed layer by layer downward to construct a complete fault tree structure. In the fault tree, each node represents a fault event, and the branches represent the causal relationships between faults. Subsequently, based on historical data, experience, and relevant technical materials, the occurrence probabilities of each fault event in the fault tree are evaluated. According to these occurrence probabilities, all fault events are sorted by importance to determine which fault events are more likely to cause abnormalities and which have a greater impact on the normal operation of the circuit breaker. According to the importance sorting results, the fault events with high occurrence probabilities and great impacts are preferentially investigated to determine the sequence of fault troubleshooting and formulate a detailed fault troubleshooting plan. The tools, methods, and operation procedures required for each troubleshooting step are clearly defined in the plan to ensure that the root cause of the circuit breaker fault can be found efficiently and accurately.

[0031] In a possible implementation manner, step S100 further includes:

[0032] Step S110: Determine multiple monitoring points related to the operation of the low-voltage cabinet circuit breaker, and the multiple monitoring points are distributed at different key parts of the circuit breaker.

[0033] Step S120: Deploy sensors at each monitoring point to collect operation data in real time.

[0034] Step S130: Integrate and store the collected data at set time intervals to establish the historical operation database.

[0035] Specifically, in the initial stage of constructing the historical operation database of the low-voltage cabinet circuit breaker, the structural composition, working principle, and past fault cases of the low-voltage cabinet circuit breaker need to be comprehensively considered. Structurally, the circuit breaker includes multiple key parts such as moving and static contacts, arc extinguishing chambers, trip devices, and operating mechanisms. Among them, the moving and static contacts are the direct components for current conduction and interruption, and their states directly affect the on-off of the circuit. Monitoring here can obtain key information such as contact resistance and switching times; the arc extinguishing chamber is responsible for extinguishing the arcs generated during opening and closing. Monitoring parameters such as internal pressure and temperature can judge whether the arc extinguishing effect is good; the trip device is an important device for protecting the circuit. Monitoring data such as its operating current and operating time helps to evaluate whether the protection function is normal; the operating mechanism controls the opening and closing operations of the circuit breaker. Monitoring indicators such as its mechanical movement stroke and operating force can judge whether the mechanism operates smoothly. Based on the importance of these key parts to the operation of the circuit breaker, multiple monitoring points are determined and reasonably distributed at the above key parts, laying a foundation for accurately collecting operation data subsequently.

[0036] Appropriate sensors are selected based on the environmental conditions at each monitoring point and the type of data required to be collected. High-precision current and temperature sensors are installed at the circuit breaker contacts, where precise monitoring of current, temperature, and other data is required. The current sensors capture the magnitude and changes of the current in real time, while the temperature sensors quickly sense temperature changes in the contacts caused by the passage of current. Pressure and gas composition sensors are deployed near the arc extinguishing chamber to monitor parameters such as internal pressure and gas composition. Sensors that monitor operating current and time are installed at the trip unit. Displacement and force sensors are installed on key moving parts of the operating mechanism to monitor mechanical travel and operating force. These sensors are precisely installed at each monitoring point and connected to the data acquisition system for real-time data collection. The sensors convert the detected physical quantities into electrical or digital signals, which are continuously transmitted to the data acquisition system to ensure timely and continuous operational data.

[0037] Data is consolidated and stored at pre-set intervals, which can be set to every minute, every five minutes, or other appropriate durations based on actual needs. Within each interval, the sensor data is collated, removing any errors or abnormal fluctuations. The processed data is then stored in a database according to specific formats and rules, successfully establishing a historical operation database. This database integrates various data from the circuit breaker's long-term operation, providing solid data support for subsequent in-depth analysis of the circuit breaker's operating status, the development of evaluation models, and the prediction of future trends.

[0038] In one possible implementation, step S200 further includes:

[0039] Step S210: setting the operating status assessment model to have a structure of a data preprocessing layer, a feature extraction layer, and an assessment decision layer.

[0040] Step S220: The data preprocessing layer is used to clean and normalize the historical operation data.

[0041] Step S230: The feature extraction layer extracts feature parameters reflecting the operating status of the circuit breaker from the preprocessed data according to the operating principle of the circuit breaker.

[0042] Step S240: The evaluation decision layer uses a preset evaluation algorithm to evaluate the operating status based on the extracted characteristic parameters.

[0043] Step S250: using part of the data in the historical operation database as a training set, training and optimizing the operation status assessment model, and establishing the circuit breaker operation status assessment model.

[0044] Specifically, when establishing the circuit breaker operation status evaluation model, the multi-layer perceptron (MLP) algorithm in deep learning is used to implement the entire process from data processing to model training. The operation status evaluation model is designed based on the multi-layer perceptron structure, which is divided into a data preprocessing layer, a feature extraction layer, and an evaluation decision layer. The data preprocessing layer is responsible for preliminary data regularization. The feature extraction layer mines data features through the neuron connections in the MLP hidden layer. The evaluation decision layer gives the evaluation result using the output layer of the MLP.

[0045] The data preprocessing layer uses data cleaning algorithms to identify and remove error values, duplicate values, and outliers in the historical operation data. For example, by setting reasonable ranges for parameters such as current and voltage, obvious error data can be screened out. Then, normalization processing is performed. The Min-Max normalization method is used to map the parameter data to the interval [0, 1], so that data with different dimensions are on the same scale, facilitating subsequent processing.

[0046] The feature extraction layer relies on the powerful feature learning ability of the multi-layer perceptron to mine features from the preprocessed data according to the operation principle of the circuit breaker. For example, the current change is related to the contact state of the circuit breaker, and the voltage fluctuation reflects the circuit load condition, etc. The MLP automatically learns these complex non-linear relationships through the weight connections between different neurons, and extracts key feature parameters such as overload duration and voltage fluctuation frequency that reflect the operation state of the circuit breaker from a large amount of data.

[0047] After receiving the feature parameters output by the feature extraction layer, the evaluation decision layer uses a preset evaluation algorithm based on the multi-layer perceptron to perform operation status evaluation. This layer converts the feature parameters into specific operation status categories, such as normal, minor fault, severe fault, etc., through the output layer of the MLP. During the training process, by adjusting the weights of the MLP, the model output result continuously approaches the true operation status label to achieve accurate evaluation.

[0048] Select part of the data from the historical operation database as the training set, and use the stochastic gradient descent method to train and optimize the multi-layer perceptron model. During the training process, the model continuously adjusts the weights and biases between the neurons of each layer, so that the loss function value between the prediction result of the model on the training set and the true label continuously decreases. After multiple rounds of training, when the performance index of the model on the validation set reaches the preset standard, the training is stopped, and a circuit breaker operation status evaluation model based on the multi-layer perceptron is successfully established, enabling it to accurately evaluate the operation status of the circuit breaker.

[0049] In a possible implementation manner, step S300 further includes:

[0050] Step S310: Obtain the real-time operation data of the circuit breaker at the current moment.

[0051] Step S320: Input the real-time operation data into the operation status evaluation model to predict the future operation status.

[0052] Step S330: Calculate the confidence interval of the prediction result through multiple simulation predictions to determine the predicted operation status data.

[0053] Specifically, the real-time operation data of the circuit breaker at the current moment is obtained through sensors deployed at each key part of the circuit breaker and the connected data acquisition system. These sensors continuously monitor various operation parameters of the circuit breaker, such as the magnitude of the current, the value of the voltage, the temperature change, the opening and closing state of the contacts, etc. The data acquisition system collects this information at an extremely high frequency and conducts preliminary processing and integration to ensure that the obtained real-time operation data is accurate, complete, and can truly reflect the current operation status of the circuit breaker.

[0054] Input the obtained real-time operation data into the previously established operation status evaluation model. This model has been trained and optimized with a large amount of historical operation data and has learned the complex relationship between the operation data and the operation status of the circuit breaker. The real-time operation data serves as the input of the model, and the model predicts the future operation status of the circuit breaker based on its internal algorithms and parameter settings. For example, the model will predict whether the circuit breaker will experience abnormal conditions such as overload and short circuit in the future, as well as the probability of normal operation, based on the current trends of the current and voltage and in combination with the operation performance of the circuit breaker in similar situations in historical data.

[0055] To improve the reliability and accuracy of the prediction, the confidence interval of the prediction result is calculated through multiple simulation predictions. Each time a simulation prediction is performed, the model operates based on the real-time operation data and its own algorithms. However, due to the inherent uncertainty of the model itself, the results of multiple simulation predictions will vary. By statistically analyzing these different prediction results, the confidence interval of the prediction result is calculated. The confidence interval represents the range within which the true future operation status may occur at a certain probability level. For example, at a 95% confidence interval, it is predicted that the future current value of the circuit breaker will fluctuate within a certain range. By comprehensively considering the results of multiple simulation predictions and the calculated confidence interval, more reliable predicted operation status data is finally determined, providing a favorable basis for subsequent judgment of whether the circuit breaker is operating abnormally and for taking corresponding measures in advance.

[0056] In a possible implementation manner, step S310 further includes:

[0057] Step S311: Set the threshold range of the normal operation data, compare the real-time operation data with the threshold range, and obtain the data that exceeds the threshold.

[0058] Step S312: Perform correlation analysis on the data exceeding the threshold to determine multiple abnormal data indicators.

[0059] Specifically, first, set the threshold range of normal operating data based on the design standards of the circuit breaker, historical operating data, and industry experience. This threshold range covers various parameters closely related to the operation of the circuit breaker, such as current, voltage, temperature, etc. For example, according to the rated current of the circuit breaker and its heat generation characteristics during normal operation, determine the normal upper limit value of the current and the reasonable fluctuation range of the temperature; based on the voltage requirements of the circuit design and the voltage withstand performance of the circuit breaker, set the upper and lower threshold values of the voltage. Then, compare the real-time collected operating data of the circuit breaker with these set threshold ranges one by one. Through this comparison method, the data exceeding the threshold can be quickly screened out, and these data become important clues for judging whether the circuit breaker is operating abnormally. For example, when the real-time monitored current value is higher than the set current upper limit threshold, or the temperature exceeds the normal temperature range, these data exceeding the threshold will be marked and extracted.

[0060] Because there are often certain internal relationships among the operating parameters of the circuit breaker, the abnormality of a single parameter may be correlated with the changes of other parameters. For example, an abnormal increase in current may cause the temperature to rise, and abnormal voltage fluctuations may also affect the stability of the current. Therefore, use data analysis methods and in-depth understanding of the operating principle of the circuit breaker to study the correlation between these data exceeding the threshold. Through this correlation analysis, the abnormal information hidden behind the data can be excavated, and multiple abnormal data indicators can be determined. These abnormal data indicators are no longer isolated single abnormal data, but key indicators obtained through comprehensive analysis that can more accurately reflect the abnormal operation of the circuit breaker. For example, through correlation analysis, it is found that when the current exceeds the threshold and the temperature also rises abnormally synchronously, the combination of these two parameters is used as an abnormal data indicator to more accurately judge the possible fault types of the circuit breaker, providing a strong basis for formulating a subsequent fault troubleshooting plan.

[0061] In a possible implementation manner, step S600 further includes:

[0062] Step S610: Construct a fault tree structure with the abnormal data indicators as fault events.

[0063] Step S620: Sort the fault events according to the occurrence probabilities of the fault events in the fault tree to generate an importance ranking result.

[0064] Step S630: Determine the order of fault troubleshooting according to the importance ranking result and formulate a fault troubleshooting plan.

[0065] Specifically, taking the previously determined abnormal data indicators as the starting fault events of the fault tree is the basis for constructing the fault tree structure. Based on an in-depth understanding of the working principle and fault mechanism of the circuit breaker, each abnormal data indicator is regarded as a top event, and then the direct and indirect causes leading to these abnormalities are analyzed layer by layer. For example, if the abnormal data indicator is "excessive current", the direct causes may be load short circuit, poor contact of the contacts, etc. These causes are used as the fault events in the next layer and connected with logical gates (such as "AND" gate, "OR" gate), and so on, gradually constructing a complete fault tree structure to clearly present the logical relationship between each fault event.

[0066] To achieve the importance ranking of each fault event in the fault tree according to its occurrence probability and generate the results, first, rely on the historical operation database to extract a large amount of historical data related to each fault event. For each fault event, count the frequency of its occurrence in the historical operation process, and then comprehensively consider factors such as the current operation duration of the circuit breaker, the use environment (such as temperature, humidity, load conditions, etc.), and the maintenance records. For example, if a certain fault event occurs frequently in a high-temperature and high-load environment, and the current circuit breaker is in a similar working condition, then the evaluation value of the occurrence probability of this fault event should be increased accordingly. Using probability analysis models such as Bayesian networks, taking the historical frequency data and the current working condition data as inputs, calculate the occurrence probability of each fault event under the current conditions. Sort the fault events from high to low according to the calculated occurrence probability. The higher the probability of a fault event, the higher its importance. After sorting, organize each fault event and its corresponding importance order to generate a detailed importance ranking result table, which will provide a key basis for determining the sequence of fault troubleshooting in the follow-up.

[0067] Determine the order of troubleshooting according to the importance ranking result and formulate a plan. First, extract the fault events ranked at the top from the importance ranking result. These high-importance fault events represent the most likely reasons for the abnormal circuit breaker. Taking the high-importance fault event of "load short circuit" as an example, determine the troubleshooting tools as a multimeter and an insulation resistance tester. Use the resistance range of the multimeter to measure the resistance of the load line. If the resistance value approaches zero, there may be a short circuit. Use the insulation resistance tester to detect the insulation resistance of the line to judge whether insulation damage causes the short circuit. Record these operation steps in detail in sequence to form a preliminary troubleshooting process. For subsequent fault events with slightly lower importance, such as "poor contact of the contact", determine to use an infrared thermometer and a contact resistance detector for troubleshooting. Use the infrared thermometer to detect the temperature of the contact. If the temperature rises abnormally, it may be caused by poor contact. Use the contact resistance detector to measure the contact resistance of the contact. If the resistance value exceeds the normal range, it can be determined that there is a contact problem. Similarly, arrange the operation steps in a reasonable order. Integrate the troubleshooting tools, methods and orders of all fault events to formulate a comprehensive troubleshooting plan. Clearly mark the corresponding fault events, operation methods, tools used and expected results for each troubleshooting step in the plan to ensure that the troubleshooting process is carried out in an orderly manner and the fault source can be located efficiently.

[0068] In a possible implementation manner, step S630 further includes:

[0069] Step S631: According to the troubleshooting plan, check each hardware device of the low-voltage cabinet circuit breaker one by one, mark the components that do not meet the preset inspection standards, determine the key maintenance components according to the marking results, and generate a key maintenance identifier.

[0070] Step S632: Add the key maintenance identifier to the maintenance strategy.

[0071] Specifically, according to the detailed fault troubleshooting plan, a comprehensive and meticulous one-by-one inspection is carried out on the hardware equipment of the low-voltage cabinet circuit breaker. During the inspection process, operations are strictly carried out in accordance with the preset inspection standards. For example, for the contact components of the circuit breaker, check whether there are oxidation and ablation marks on its surface, and measure whether the contact resistance of the contacts is within the specified range; for the release, detect whether its operating current and operating time are accurate; for the operating mechanism, check whether the connections of each mechanical component are firm and whether the movement is smooth, etc. Once a component does not meet the preset inspection standards, immediately mark it. The marking method can be to stick a prominent label on the component or to record the component name, number and specific abnormal conditions in detail in the equipment inspection record document. According to the marking results, comprehensively analyze the abnormal degree of each component and its impact on the overall performance of the circuit breaker to determine the key maintenance components. For example, if multiple marks are concentrated on the contact and release components and the abnormal conditions of these components are relatively serious, then the contacts and release are determined as the key maintenance components and a corresponding key maintenance identifier is generated. This identifier can be a specific code or symbol for quickly identifying the key maintenance components in the subsequent process.

[0072] Add the generated key maintenance identifier to the maintenance strategy. The maintenance strategy is a comprehensive plan covering all aspects of the maintenance of the low-voltage cabinet circuit breaker, including maintenance time, maintenance method, maintenance personnel arrangement, etc. The addition of the key maintenance identifier makes the maintenance strategy more targeted. When arranging maintenance work subsequently, the maintenance personnel can, according to the key maintenance identifier in the maintenance strategy, give priority to maintaining the key maintenance components. For example, arrange experienced technical personnel to repair or replace the key maintenance components; in terms of maintenance time, give more maintenance duration to the key maintenance components to ensure that the problems of these key components are completely solved, thereby ensuring the stable operation of the low-voltage cabinet circuit breaker.

[0073] Embodiment 2, based on the same inventive concept as the method for analyzing the abnormal operation of the low-voltage cabinet circuit breaker in the foregoing embodiment, as Figure 2 shown, the present application provides a platform for analyzing the abnormal operation of the low-voltage cabinet circuit breaker. The platform in the embodiments of the present application and the method embodiments are based on the same inventive concept. Among them, the platform includes:

[0074] A historical operation database construction module 10, which is used to collect the operation data of the low-voltage cabinet circuit breaker from multiple monitoring terminals and construct a historical operation database.

[0075] An operating state evaluation model establishment module 20, which is used to utilize the data in the historical operation database and combine the operating principle of the circuit breaker to establish an operating state evaluation model of the circuit breaker.

[0076] The predicted operating status data acquisition module 30 is used to predict the operating status of the circuit breaker in the future period by using the operating status evaluation model, and obtain the predicted operating status data.

[0077] The operating status data comparison module 40 is used to compare the actual operating status data of the circuit breaker with the predicted operating status data.

[0078] The abnormal data index determination module 50 is used to perform abnormal analysis on the real-time data of the current operation of the circuit breaker and determine multiple abnormal data indexes if there are differences between the actual operating status data and the predicted operating status.

[0079] The fault troubleshooting plan formulation module 60 is used to formulate a fault troubleshooting plan according to the multiple abnormal data indexes and in combination with the fault tree analysis method.

[0080] Furthermore, the platform is also used to implement the following functions:

[0081] Determine multiple monitoring points related to the operation of the low-voltage cabinet circuit breaker, and the multiple monitoring points are distributed at different key parts of the circuit breaker; deploy sensors at each monitoring point to collect operation data in real time; integrate and store the collected data at set time intervals to establish the historical operation database.

[0082] Furthermore, the platform is also used to implement the following functions:

[0083] Set the operating status evaluation model to have a structure of a data preprocessing layer, a feature extraction layer, and an evaluation decision layer; the data preprocessing layer is used to clean and normalize the historical operation data; the feature extraction layer extracts feature parameters reflecting the operating status of the circuit breaker from the preprocessed data according to the operating principle of the circuit breaker; the evaluation decision layer performs operating status evaluation based on the extracted feature parameters by using a preset evaluation algorithm; use part of the data in the historical operation database as a training set to train and optimize the operating status evaluation model to establish the circuit breaker operating status evaluation model.

[0084] Furthermore, the platform is also used to implement the following functions:

[0085] Obtain the real-time operation data of the circuit breaker at the current moment; input the real-time operation data into the operating status evaluation model to predict the future operating status; through multiple simulation predictions, calculate the confidence interval of the prediction result to determine the predicted operating status data.

[0086] Furthermore, the platform is also used to implement the following functions:

[0087] Set the threshold range of normal operating data, compare the real-time operating data with the threshold range, and obtain the data that exceeds the threshold; perform correlation analysis on the data that exceeds the threshold to determine multiple abnormal data indicators.

[0088] Further, the platform is also used to implement the following functions:

[0089] Construct a fault tree structure with the abnormal data indicators as fault events; sort the fault events according to the occurrence probabilities of the fault events in the fault tree to generate a sorting result of importance; determine the order of fault troubleshooting according to the sorting result of importance and formulate a fault troubleshooting plan.

[0090] Further, the platform is also used to implement the following functions:

[0091] According to the fault troubleshooting plan, check the hardware devices of the low-voltage cabinet circuit breakers one by one, mark the components that do not meet the preset inspection standards, determine the key maintenance components according to the marking results, and generate a key maintenance identifier; add the key maintenance identifier to the maintenance strategy.

[0092] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0094] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. Analysis method for abnormal operation of low-voltage switchgear circuit breakers, characterized in that The method includes: Collect the operation data of the low-voltage switchgear circuit breaker from multiple monitoring terminals and construct a historical operation database; Utilize the data in the historical operation database and combine with the operation principle of the circuit breaker to establish an operation state evaluation model for the circuit breaker; Apply the operation state evaluation model to predict the operation state of the circuit breaker in the future time period to obtain predicted operation state data; Compare the actual operation state data of the circuit breaker with the predicted operation state data; If there is a difference between the actual operation state data and the predicted operation state, perform anomaly analysis on the real-time data of the current operation of the circuit breaker to determine multiple anomaly data indicators; According to the multiple anomaly data indicators and in combination with the fault tree analysis method, formulate a fault troubleshooting plan.

2. The abnormal operation analysis method of the low-voltage cabinet circuit breaker according to claim 1, characterized in that Collect the operation data of the low-voltage switchgear circuit breaker from multiple monitoring terminals and construct a historical operation database, the method includes: Determine multiple monitoring points related to the operation of the low-voltage switchgear circuit breaker, and the multiple monitoring points are distributed at different key parts of the circuit breaker; Deploy sensors at each monitoring point to collect operation data in real time; Integrate and store the collected data at a set time interval to establish the historical operation database.

3. The abnormal operation analysis method of the low-voltage switchboard circuit breaker according to claim 1, characterized in that Utilize the data in the historical operation database and combine with the operation principle of the circuit breaker to establish an operation state evaluation model for the circuit breaker, the method includes: Set the operation state evaluation model to have a structure with a data preprocessing layer, a feature extraction layer, and an evaluation decision layer; The data preprocessing layer is used to clean and normalize the historical operation data; The feature extraction layer extracts feature parameters reflecting the operation state of the circuit breaker from the preprocessed data according to the operation principle of the circuit breaker; The evaluation decision layer performs operation state evaluation based on the extracted feature parameters by using a preset evaluation algorithm; Use part of the data in the historical operation database as a training set to train and optimize the operation state evaluation model to establish the operation state evaluation model for the circuit breaker.

4. The abnormal operation analysis method of the low-voltage cabinet circuit breaker according to claim 1, characterized in that, Apply the operation state evaluation model to predict the operation state of the circuit breaker in the future time period to obtain predicted operation state data, the method includes: Obtain the real-time operation data of the circuit breaker at the current moment; Input the real-time operation data into the operation state evaluation model to predict the future operation state; Through multiple simulation predictions, calculate the confidence interval of the prediction result to determine the predicted operation state data.

5. The abnormal operation analysis method of the low-voltage cabinet circuit breaker according to claim 4, characterized in that Perform anomaly analysis on the real-time data of the current operation of the circuit breaker to determine multiple anomaly data indicators, the method includes: Set the threshold range of normal operation data, compare the real-time operation data with the threshold range, and obtain the data exceeding the threshold; Perform correlation analysis on the data exceeding the threshold to determine multiple anomaly data indicators.

6. The abnormal operation analysis method of the low-voltage cabinet circuit breaker according to claim 1, characterized in that According to multiple anomaly data indicators and in combination with the fault tree analysis method, formulate a fault troubleshooting plan, the method includes: Construct a fault tree structure with the anomaly data indicators as fault events; Sort the fault events according to the occurrence probability of each fault event in the fault tree to generate an importance ranking result; According to the importance ranking result, determine the sequence of fault troubleshooting and formulate a fault troubleshooting plan.

7. The abnormal operation analysis method of the low-voltage switchgear circuit breaker according to claim 6, characterized in that, The method includes: According to the fault troubleshooting plan, check the hardware devices of the low-voltage switchgear circuit breaker one by one, mark the components that do not meet the preset inspection standards, determine the key maintenance components according to the marking results, and generate key maintenance identifiers; Add the key maintenance identifier to the maintenance strategy.

8. Low-voltage switchgear circuit breaker operation anomaly analysis platform, characterized in that, The platform is used to implement a method for analyzing abnormal operation of a low-voltage switchgear circuit breaker according to any one of claims 1-7. The platform includes: A historical operation database construction module, which is used to collect the operation data of the low-voltage switchgear circuit breaker from multiple monitoring terminals and construct a historical operation database; An operation status evaluation model establishment module, which is used to utilize the data in the historical operation database and combine the operation principle of the circuit breaker to establish an operation status evaluation model of the circuit breaker; A predicted operation status data acquisition module, which is used to use the operation status evaluation model to predict the operation status of the circuit breaker in the future time period and obtain predicted operation status data; An operation status data comparison module, which is used to compare the actual operation status data of the circuit breaker with the predicted operation status data; An abnormal data index determination module, which is used to perform abnormal analysis on the real-time data of the current operation of the circuit breaker and determine multiple abnormal data indexes if there is a difference between the actual operation status data and the predicted operation status; A fault troubleshooting plan formulation module, which is used to formulate a fault troubleshooting plan according to the multiple abnormal data indexes and in combination with the fault tree analysis method.

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