A performance analysis system for outdoor high voltage vacuum circuit breakers

Abstract

The present invention relates to the technical field of circuit breaker performance testing, and specifically to a performance analysis system for an outdoor high-voltage vacuum circuit breaker, the system comprising: a performance calibration module, a fault prediction module, a load distribution identification module, a flow path analysis module, an energy consumption calculation module, and an optimization strategy formulation module. In the present invention, the performance calibration module utilizes comparative analysis of real-time monitoring data and historical performance data to achieve accurate correction of performance parameters, effectively reducing the risk of faults and enhancing the reliability of the system. In addition, the flow path analysis module reduces energy consumption and heat loss by optimizing the current flow path, which not only improves the operating efficiency of the power system, but also promotes the rational use of resources. The combined use of the performance calibration module and the flow path analysis module not only optimizes the performance management of the power system, but also improves the fault response and energy efficiency level, providing solid technical support for the efficient and sustainable operation of the power system.

Description

Technical Field

[0001] The invention relates to the technical field of circuit breaker performance testing, and in particular to an outdoor high-voltage vacuum circuit breaker performance analysis system. Background Art

[0002] Circuit breaker performance testing technology is a key technology field that focuses on the safe and reliable operation of power systems. In power systems, high-voltage circuit breakers are important equipment for protecting power grid safety and realizing circuit switching. This technical field includes testing and analyzing the electrical, mechanical and durability performance of high-voltage circuit breakers to ensure that they can reliably perform the tasks of opening and closing circuits in the power grid. In addition, circuit breaker performance testing also involves fault diagnosis, performance optimization and life prediction, aiming to improve the operating efficiency and safety of power equipment through technical means.

[0003] Among them, the outdoor high-voltage vacuum circuit breaker performance analysis system is a device or system specially designed to evaluate and analyze the performance of outdoor high-voltage vacuum circuit breakers. Its main purpose is to ensure the reliability and safety of the power system by accurately testing and analyzing the performance of the circuit breaker in actual operation. This includes the evaluation of key parameters such as the circuit breaker's breaking capacity, closing withstand current, mechanical operating performance, and insulation performance. Through these analyses, potential failures can be prevented, the service life of the equipment can be extended, and the stability and efficiency of the power system can be improved.

[0004] Traditional systems often rely on relatively crude performance monitoring and efficiency assessment methods, which make it difficult to accurately identify performance deviations and optimize energy efficiency in real time, resulting in untimely fault handling responses and serious energy waste. For example, inefficient current flow paths are not effectively identified and optimized, which may cause unnecessary energy consumption and increase system operating costs. In addition, failure to accurately predict the occurrence of faults may lead to increased equipment damage and even affect the stability of power supply to the grid. These problems not only affect the economy and safety of the power system, but also limit its ability to adapt to future challenges. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a performance analysis system for outdoor high-voltage vacuum circuit breaker.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an outdoor high-voltage vacuum circuit breaker performance analysis system comprises:

[0007] The performance calibration module collects key performance indicators based on real-time monitoring data, analyzes arc extinction time and contact wear data, compares the differences with historical performance data, adjusts performance prediction model parameters, and generates performance parameter corrections;

[0008] The fault prediction module analyzes the matching degree between historical fault data and current performance data through the performance parameter correction, identifies similar fault modes, predicts potential fault points, and obtains potential fault indicators;

[0009] The load distribution identification module compares the current and historical load data according to the potential fault indicators, identifies abnormal load patterns, attributes the environmental conditions that cause performance deviations, and obtains an abnormal load map;

[0010] The flow path analysis module evaluates the efficiency of the current flow path based on the abnormal load map, analyzes the multi-path current density and temperature rise data, and based on the energy efficiency screening area, sets the area below the preset value as a low energy efficiency area, and establishes a low efficiency area identification;

[0011] The energy consumption calculation module uses the inefficient area identifier to calculate the energy consumption and heat loss of multiple load points, identifies abnormal paths based on energy consumption, identifies optimization points, and generates an energy efficiency abnormal point set;

[0012] The optimization strategy formulation module analyzes the current distribution and flow path according to the energy efficiency abnormal point set, adjusts the load distribution, optimizes the current flow path, and obtains a path optimization solution.

[0013] As a further solution of the present invention, the performance parameter correction includes current regulation data, voltage correction data, and temperature deviation data; the potential fault indicators are specifically fault probability assessment, fault type identification, and fault time prediction; the abnormal load spectrum includes load peak characteristics, abnormal operation mode identification, and environmental influencing factor analysis; the inefficiency area identification is specifically a current intensive area identification, a heat loss high area identification, and an efficiency reduction path identification; the energy efficiency abnormal point set includes a high energy consumption point mark, a heat loss concentration point mark, and an inefficiency line mark; the path optimization plan is specifically a current path adjustment plan, loss reduction measures, and energy efficiency improvement measures.

[0014] As a further solution of the present invention, the performance calibration module includes:

[0015] The performance difference identification submodule uses the support vector machine algorithm based on real-time monitoring data to analyze arc extinction performance and contact resistance data, compare historical performance records, identify performance fluctuations, distinguish performance degradation trends, and generate difference identification results;

[0016] The performance trend analysis submodule quantifies the change of arc extinction performance, evaluates the impact of contact resistance fluctuation, predicts future performance trends, and obtains a performance change map based on the difference identification results;

[0017] The model parameter optimization submodule adjusts the circuit breaker simulation test parameters based on the performance change map, performs closing and opening cycle tests, collects adjusted performance data, analyzes data changes, and generates performance parameter corrections.

[0018] As a further solution of the present invention, the support vector machine algorithm adopts the formula:

[0019]

[0020] in, To monitor the arc extinction performance and contact resistance data in real time, is the support vector, is the class label, is the weight of the support vector, is the bias term, To adjust the parameters, To add feature weights, represents the inner product of the newly added feature vector and the input data, For an additional adjustment bias term, represents the newly introduced feature vector.

[0021] As a further solution of the present invention, the load distribution identification module includes:

[0022] The load data comparison submodule, based on the potential fault indicators, identifies abnormal current fluctuations by comparing and analyzing the current waveform in real time with historical data, and uses a statistical model to compare waveform feature differences to generate abnormal current feature indicators;

[0023] The abnormal pattern recognition submodule analyzes the correlation between the abnormal pattern and the operation frequency based on the flow abnormality characteristic index and the operation number and the current waveform relationship diagram, locates the performance deviation area caused by frequent operation, and obtains the frequent operation deviation index;

[0024] The environmental condition attribution module tracks and analyzes the ambient temperature data based on the frequent operation deviation index, compares the temperature changes during the abnormal mode, evaluates the impact of temperature fluctuations on circuit breaker performance, and generates an abnormal load map.

[0025] As a further solution of the present invention, the flow path analysis module includes:

[0026] The current path efficiency analysis submodule determines the efficiency of multiple current paths based on the abnormal load map through arc consumption analysis, calculates the energy loss of each path, identifies the paths with low energy efficiency, and generates a current path efficiency evaluation;

[0027] The multi-path current density analysis submodule uses mechanical durability test data to compare the current density and mechanical properties of multiple current paths based on the current path performance evaluation, evaluates the load bearing capacity and durability of the paths, and obtains a load bearing rating;

[0028] The energy efficiency screening area setting submodule is based on the load bearing capacity rating and adopts a decision tree algorithm to analyze the thermal stability data of the insulation material, inspect the insulation material, analyze the thermal stability characteristics, record the temperature changes under differentiated load conditions, identify the area with abnormally high temperature, and establish the low energy efficiency area identification.

[0029] As a further solution of the present invention, the decision tree algorithm adopts the formula

[0030]

[0031] in, is the temperature data set of the insulation material, For the selected attribute, Is an attribute The overall estimated value of is in the attributes The value of hour A subset of and They are and The number of samples, and They are and The entropy of is the adjustment factor, For attribute value The weight of .

[0032] As a further solution of the present invention, the energy consumption calculation module includes:

[0033] The energy consumption and heat loss calculation submodule measures the energy consumption and heat loss of multiple load points under differentiated operating conditions based on the identification of the inefficient areas through breaking times analysis, identifies the areas with large energy losses in combination with the contact resistance measurement results, and generates a comprehensive energy consumption and heat loss index set;

[0034] The energy consumption abnormal path identification submodule captures the changes in insulation performance evaluation and contact resistance in the current flow path based on the comprehensive index set of energy consumption and heat loss, marks the path with abnormal energy consumption due to reduced insulation performance or increased contact resistance, and obtains the abnormal energy consumption channel;

[0035] The optimization point identification submodule is based on the abnormal energy consumption channel, monitors the current and voltage parameters in the channel, records the energy consumption data of differentiated nodes, compares the energy consumption differences of multiple nodes, identifies nodes with abnormally increased resistance, analyzes the contact resistance or insulation performance problems of the nodes, sets points for reducing energy consumption by optimizing contact resistance or improving insulation performance, and establishes an energy efficiency abnormal point set.

[0036] Compared with the prior art, the advantages and positive effects of the present invention are:

[0037] In the present invention, the performance calibration module uses the comparative analysis of real-time monitoring data and historical performance data to achieve accurate correction of performance parameters, effectively reduce the risk of failure, and enhance the reliability of the system. In addition, the flow path analysis module not only improves the operating efficiency of the power system, but also promotes the rational use of resources by optimizing the current flow path and reducing energy consumption and heat loss. The combined use of the performance calibration module and the flow path analysis module not only optimizes the performance management of the power system, but also improves the fault response and energy efficiency level, providing solid technical support for the efficient and sustainable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a system flow chart of the present invention;

[0039] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0040] Figure 3 is a flow chart of the performance calibration module of the present invention;

[0041] Figure 4 This is a flow chart of the fault prediction module of the present invention;

[0042] Figure 5 It is a flow chart of the load distribution identification module of the present invention;

[0043] Figure 6 is a flow chart of the flow path analysis module of the present invention;

[0044] Figure 7 It is a flow chart of the energy consumption calculation module of the present invention;

[0045] Figure 8 A module flow chart is developed for the optimization strategy of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. Embodiment 1

[0048] See also Figure 1 to Figure 2 , an outdoor high-voltage vacuum circuit breaker performance analysis system comprises:

[0049] The performance calibration module collects key performance indicators based on real-time monitoring data, analyzes arc extinction time and contact wear data, compares the differences with historical performance data, adjusts performance prediction model parameters, and generates performance parameter corrections;

[0050] The fault prediction module analyzes the matching degree between historical fault data and current performance data through performance parameter correction, identifies similar fault modes, predicts potential fault points, and obtains potential fault indicators;

[0051] The load distribution identification module compares the current and historical load data based on potential fault indicators, identifies abnormal load patterns, attributes the environmental conditions that cause performance deviations, and obtains abnormal load maps;

[0052] The flow path analysis module evaluates the efficiency of the current flow path based on the abnormal load map, analyzes the multi-path current density and temperature rise data, and based on the energy efficiency screening area, sets the area below the preset value as a low energy efficiency area, and establishes the low efficiency area identification;

[0053] The energy consumption calculation module uses the inefficient area identification to calculate the energy consumption and heat loss of multiple load points, identifies abnormal paths based on energy consumption, identifies optimization points, and generates energy efficiency abnormal point sets;

[0054] The optimization strategy formulation module analyzes the current distribution and flow path according to the energy efficiency abnormal point set, adjusts the load distribution, optimizes the current flow path, and obtains the path optimization plan.

[0055] Performance parameter correction includes current regulation data, voltage correction data, and temperature deviation data. Potential fault indicators include fault probability assessment, fault type identification, and fault time prediction. Abnormal load maps include load peak characteristics, abnormal operation mode identification, and environmental influencing factor analysis. Inefficient area identification includes current intensive area identification, high heat loss area identification, and efficiency reduced path identification. The energy efficiency abnormal point set includes high energy consumption point marking, heat loss concentration point marking, and inefficient line marking. The path optimization plan includes current path adjustment plan, loss reduction measures, and energy efficiency improvement measures.

[0056] See also Figure 2 and Figure 3 , the performance calibration module includes:

[0057] The performance difference identification submodule uses the support vector machine algorithm based on real-time monitoring data to analyze arc extinction performance and contact resistance data, compare historical performance records, identify performance fluctuations, distinguish performance degradation trends, and generate difference identification results;

[0058] The performance difference identification submodule is based on real-time monitoring data and adopts the support vector machine algorithm. First, the arc extinction performance data Dxe and contact resistance data Dr are collected, which are obtained from the hourly sensor readings of the equipment. These real-time data are compared with the historical records Hxe and Hr, where Hxe and Hr are the average values ​​of arc extinction performance and contact resistance in the same period of the past year, respectively. The deviation rate between real-time data and historical average is calculated by δi= (Di-Hi) / Hi, where Di is the current monitoring value, Hi is the historical average value, and i represents Dxe or Dr. If δi exceeds the preset threshold ψ (such as 10%), it is determined to be a performance fluctuation. By comparing Dxe and Dr with their historical data item by item and analyzing the trend, if the measurement results δi increase for three consecutive times, it is considered that the performance has a degradation trend, and the difference identification result Rd is generated, indicating the specific fluctuation and degradation indicators.

[0059] The support vector machine algorithm uses the formula:

[0060]

[0061] in, To monitor the arc extinction performance and contact resistance data in real time, is the support vector, is the class label, is the weight of the support vector, is the bias term, To adjust the parameters, To add feature weights, represents the inner product of the newly added feature vector and the input data, For an additional adjustment bias term, represents the newly introduced feature vector.

[0062] First, real-time monitoring data of arc extinction performance and contact resistance is collected as input , then, use the support vectors in the trained SVM model and the corresponding class labels And weight Calculate inner product , then, add the new feature vector , which are derived from environmental monitoring such as changes in temperature and humidity, and the weights of these new features are calculated and with The inner product of , through the parameters and Make adjustments, and finally, add all these values ​​together, plus the bias term and additional adjustment bias , get the final classification result, weight and The specific value of is obtained by optimizing the loss function. This process involves cross-validation and grid search to ensure the generalization ability and prediction accuracy of the model and complete the task of identifying performance differences.

[0063] The performance trend analysis submodule quantifies the change in arc extinction performance based on the difference recognition results, evaluates the impact of contact resistance fluctuations, predicts future performance trends, and obtains a performance change map;

[0064] The performance trend analysis submodule quantitatively analyzes the change in arc extinction performance based on the difference identification result Rd. Calculate the arc extinction time change rate ΔTxe=(Txe current-Txe past) / Txe past, where Txe current is the currently measured arc extinction time and Txe past is the historical average arc extinction time. At the same time, evaluate the impact of the fluctuation of the contact resistance Rr and calculate the fluctuation rate ΔRr=(Rr current-Rr past) / Rr past. Combine these change rates with historical performance data, use linear regression analysis to predict future performance trends, and draw a performance change map Sp based on the analysis results, which includes the prediction curves of arc extinction time and contact resistance.

[0065] The model parameter optimization submodule adjusts the circuit breaker simulation test parameters based on the performance change map, performs closing and opening cycle tests, collects adjusted performance data, analyzes data changes, and generates performance parameter corrections.

[0066] The model parameter optimization submodule adjusts the circuit breaker simulation test parameters to match the actual operating status based on the performance change map Sp. The simulation test parameters are set to include current I test, voltage U test, and operation cycle Op cycle, where I test and U test are adjusted according to the trend of arc extinction time and contact resistance in the performance change map, and Op cycle is determined according to the performance degradation rate. Perform closing and breaking cycle test Tc to simulate actual operating conditions and collect performance data Dnew after operation, including new arc extinction time Txe new and contact resistance Rr new. Compare the data before and after adjustment, analyze the performance changes, identify the adjustment effect, generate the performance parameter correction result Rp, and indicate the performance improvement after the test parameters are adjusted.

[0067] See also Figure 2 and Figure 4 , the fault prediction module includes:

[0068] The performance data matching submodule is based on performance parameter correction. By comparing the insulation performance test results with the current contact resistance measurement data, the historical fault cases are screened based on the degree of fit, the fault frequency and conditions of the historical fault cases are analyzed, and the performance matching index is generated.

[0069] The performance data matching submodule is based on the performance parameter correction result Rp. By comparing the insulation performance test result with the current contact resistance measurement data, the deviation value Dp between the insulation performance test result and the contact resistance measurement data is first calculated. Dp=|Rt-Rc|, where Rt represents the insulation performance test result and Rc represents the contact resistance measurement data. Then, the performance data matching submodule uses the deviation value Dp to compare with the corresponding deviation value in each case recorded in the historical fault database, and calculates the fit Pf of each historical fault case with the current situation. Pf=1-(Dp-Dh) / Dh, where Dh represents the deviation value in the historical case. The fit Pf obtained by calculation is used to screen out fault cases with high fit, that is, to screen out all historical fault cases with Pf values ​​greater than the set threshold Tf. These screened cases will be further used to analyze the frequency Fh of fault occurrence and the specific conditions Ch of its occurrence. The frequency and conditions of each fault case are recorded in the database of the performance data matching submodule. Finally, according to the above analysis results, the performance data matching submodule generates a performance matching index PMI, PMI = Σ(Pf *Fh), where Σ represents the summation of all screened fault cases.

[0070] The fault mode identification submodule analyzes the common features in the fault cases based on the performance matching index, identifies the fault occurrence patterns and trends by comparing the breaking capacity detection and closing capacity evaluation results, and obtains the fault mode map;

[0071] The fault mode recognition submodule is based on the performance matching index PMI. First, the common feature Fct is extracted from the high-fitting fault cases provided by the performance matching submodule. Fct consists of the fault conditions and symptoms that frequently appear in each fault case. Through statistical analysis, the frequency Fr of each feature in the fault case is calculated to determine the main fault feature set, which includes the most common fault types and their environmental conditions. Then, the fault mode recognition submodule uses the breaking capacity detection result Ka and the closing capacity evaluation result Kc of the current device to compare these results with the corresponding parameters in the main fault feature set. For example, by calculating the correlation Ra between the breaking capacity Ka and the breaking failure feature in the fault feature, and the correlation Rc between the closing capacity Kc and the closing failure feature, the calculation formulas of Ra and Rc are Ra = 1-|Ka- Ftka| / Ftka, Rc = 1 -|Kc- Ftkc| / Ftkc, where Ftka and Ftkc represent the standard values ​​of the breaking and closing capacities in the fault feature set, respectively. By comparing these correlations, the fault mode identification submodule can identify the fault mode that is closest to the current performance and classify it to form a fault mode map Mg.

[0072] The potential fault point prediction submodule sets the potential fault point location based on the fault mode map and compares the fault mode with the current equipment status. It predicts the future fault development path and generates a fault trend path map by analyzing the actual operation data and simulation test results.

[0073] The potential fault point prediction submodule is based on the fault mode spectrum Mg. First, it compares and analyzes the current state data Cs of the equipment and the parameters of each fault mode in the spectrum. The equipment state data includes but is not limited to key performance indicators such as temperature, pressure, and current during equipment operation. By calculating the matching degree Mp of each fault mode, the formula Mp=Σ(1-|Ftm-Csm| / Csm) is used, where Ftm represents the parameters of a certain fault mode in the spectrum, and Csm represents the parameter value of the corresponding current equipment state. Fault modes with high matching degrees indicate that the current state of the equipment is similar to the fault mode, and the possibility of failure is high. Then, the module locates these fault modes with high matching degrees and defines the potential fault point locations Lp. These locations may be the contact surface of mechanical parts, the connection points of circuits, or any component areas where failures occur frequently. After determining these locations, the submodule uses the actual operation data Od and the simulation test results Mr for analysis. The simulation test predicts the development path of potential faults by simulating the equipment response when the fault occurs. The core of the analysis is to calculate the difference Df between the device's response under simulated fault conditions and normal operating data, using the formula Df = Σ|Od- Mr| / Mr, where Od represents the data of the device in actual operation and Mr represents the predicted data under simulated fault conditions. Through this analysis, the potential fault point prediction submodule can depict the development trend of the fault and generate a fault trend path diagram Tp.

[0074] The fault indicator generation submodule analyzes the fault mode, current status of the equipment and predicted fault development path based on the fault trend path diagram, extracts key fault indicators, determines the fault warning level and corresponding response measures, and obtains potential fault indicators.

[0075] Based on the fault trend path map Tp, the fault indicator generation submodule first performs an in-depth analysis to extract the key fault indicators Kf that affect equipment performance and safety. This step involves comprehensive consideration of the fault mode, the current state of the equipment Cs, and the data in the predicted fault development path Tp. For each potential fault mode, the submodule calculates its impact weight Wi, using the formula Wi= Pi*Si, where Pi represents the probability of the fault mode occurring and Si represents the severity of the impact of the fault mode on equipment performance. Subsequently, the fault indicator generation submodule determines the fault warning level Lw based on the severity of the key fault indicator Kf and its possible consequences. The warning level is divided according to the range of Kf values. For example, a Kf value below a certain threshold Tl may indicate a low risk, while a value above another threshold Th indicates a high risk. The specific threshold setting is based on historical data analysis and risk assessment criteria. For different warning levels, the fault indicator generation submodule formulates corresponding response measures Sr. These measures include but are not limited to immediate shutdown inspection, increased monitoring frequency, and adjustment of operating parameters. Each response measure is designed for a specific fault mode and fault warning level, aiming to minimize or avoid the impact of the fault. Based on the above analysis and decision-making process, the fault indicator generation submodule outputs the potential fault indicator Rf.

[0076] See also Figure 2 and Figure 5 , the load distribution identification module includes:

[0077] The load data comparison submodule is based on potential fault indicators. It identifies abnormal current fluctuations by comparing and analyzing the real-time current waveform with historical data. It uses statistical models to compare the waveform feature differences and generates current abnormal feature indicators.

[0078] Based on the potential fault indicator Rf, the load data comparison submodule first performs a comparative analysis of the real-time monitoring current waveform Iw real-time and the historical current waveform Iw history. The real-time current waveform Iw real-time comes from the real-time sensor data of the circuit breaker, and Iw history is the average current waveform in the same time period in the past. By calculating the difference coefficient ΔIw=(Iw real-time-Iw history) / Iw history between the two, abnormal current fluctuations are identified. Statistical models, such as standard deviation and variance analysis, are further used to compare the waveform feature differences between real-time and historical data, thereby generating the current abnormal feature indicator If and clarifying the amplitude and frequency of current fluctuations.

[0079] The abnormal pattern recognition submodule is based on the flow abnormality characteristic index and uses the relationship diagram between the number of operations and the current waveform to analyze the correlation between the abnormal pattern and the operation frequency, locate the performance deviation area caused by frequent operations, and obtain the frequent operation deviation index;

[0080] The abnormal pattern recognition submodule constructs a relationship diagram between the number of operations N operations and the current waveform Iw based on the current abnormal characteristic index If, and identifies the correlation between the abnormal pattern and the operation frequency through statistical analysis. In particular, the change trend of Iw when N operations increase is analyzed to identify the performance deviation caused by frequent operations. The frequent operation deviation index Id=Σ(ΔIw / N operations) is calculated, and the current waveform changes caused by each operation are summarized to obtain the key operation area that affects the performance of the circuit breaker.

[0081] The environmental condition attribution module tracks and analyzes the ambient temperature data based on the frequent operation deviation index, compares the temperature changes during the abnormal mode, evaluates the impact of temperature fluctuations on circuit breaker performance, and generates abnormal load maps.

[0082] The environmental condition attribution module tracks and analyzes the environmental temperature data Tenvironment based on the frequent operation deviation index Id. The temperature change during the abnormal mode is compared by ΔT = Tenvironment Current - Tenvironment History, where Tenvironment Current is the current temperature data and Tenvironment History is the average temperature data for the same period in history. By analyzing the relationship between ΔT and Id, the impact of temperature fluctuations on circuit breaker performance is evaluated, and an abnormal load map St is generated. The map depicts the correlation between temperature fluctuations and current abnormal characteristics, helping to locate performance degradation caused by environmental conditions.

[0083] See also Figure 2 and Figure 6 , the flow path analysis module includes:

[0084] The current path efficiency analysis submodule measures the efficiency of multiple current paths based on the abnormal load spectrum through arc consumption analysis, calculates the energy loss of each path, identifies the paths with low energy efficiency, and generates the current path efficiency evaluation;

[0085] When the current path efficiency analysis submodule is started, the abnormal load spectrum St is first defined, which is based on the record of abnormal current load events in the past. Through a detailed review of historical data, the spectrum St maps the specific circumstances of abnormal current load, including key parameters such as time, duration, and scope of impact. Next, the submodule collects real-time current data and compares it with St to find potential anomalies. Once the abnormal pattern in the spectrum is matched, the arc consumption analysis program is started. The program calculates the energy loss caused by the arc by simulating the impact of the arc on the current path. This process involves a comprehensive evaluation of the probability of arc occurrence, duration, and impact on the circuit. After summarizing these data, the energy loss value of each current path is calculated. The paths are sorted by loss value, the path with the lowest efficiency is identified, and a current path efficiency evaluation report is formed.

[0086] The multi-path current density analysis submodule is based on the current path performance evaluation. It uses mechanical durability test data to compare the current density and mechanical properties of multiple current paths, evaluate the load bearing capacity and durability of the paths, and obtain the load bearing rating;

[0087] The multi-path current density analysis submodule receives the current path performance evaluation report, and combines the energy efficiency performance of each path identified in the report with the mechanical durability test data, which includes the compressive strength, tensile strength and other mechanical performance indicators of the path material. By comparing the current density of each path with these mechanical properties, the load bearing capacity of each path at different current densities is analyzed. Using a specific formula such as Ei=Fj / Lk (Ei represents the current density of path i, Fj represents the mechanical strength of path j, and Lk is the length of path k), each path is evaluated for durability and a load bearing rating is obtained.

[0088] The energy efficiency screening area setting submodule is based on the load bearing capacity rating and adopts a decision tree algorithm to analyze the thermal stability data of the insulation material, inspect the insulation material, analyze the thermal stability characteristics, record the temperature changes under differentiated load conditions, identify areas with abnormally high temperatures, and establish low energy efficiency area identification.

[0089] The energy efficiency screening area setting submodule uses a decision tree algorithm to further analyze the thermal stability data of the insulation material based on the load bearing rating. This analysis includes collecting thermal response data of the insulation material at different current densities, such as thermal expansion coefficient, thermal conductivity, etc. Based on these data, the submodule calculates the maximum temperature that the material may reach under actual operating conditions and compares it with the thermal stability threshold of the material. If the calculated temperature exceeds the threshold, the path is marked as an area of ​​abnormally high temperature. Through these analysis steps, the submodule identifies areas where energy efficiency may decrease under differentiated load conditions, and sets low-efficiency area identification for these areas to facilitate subsequent monitoring and maintenance. This process involves not only data collection and analysis, but also a comprehensive evaluation of different current paths and insulation material characteristics, aiming to improve the overall energy efficiency and safety of the system.

[0090] The decision tree algorithm uses the formula

[0091]

[0092] in, is the temperature data set of the insulation material, For the selected attribute, Is an attribute The overall estimated value of is in the attributes The value of hour A subset of and They are and The number of samples, and They are and The entropy of is the adjustment factor, For attribute value The weight of .

[0093] First, analyze the temperature data set of the insulation material and the selected attributes , then calculate The total entropy and each subset Entropy , and calculate the relative size of each subset , based on historical data and attributes The actual influence of each Weight ,These weights are determined by analyzing the influence and frequency of attribute values ​​in historical data, and then all these values ​​are substituted into the improved information gain formula, and finally the coefficients are adjusted , determined based on cross-validation or other statistical methods to optimize the overall performance of the model, and ultimately identify and mark low-efficiency areas, thereby improving the accuracy of identifying temperature anomaly areas.

[0094] See also Figure 2 and Figure 7 , the energy consumption calculation module includes:

[0095] The energy consumption and heat loss calculation submodule is based on the identification of inefficient areas. By analyzing the number of disconnections, it measures the energy consumption and heat loss of multiple load points under differentiated operating conditions. Combined with the contact resistance measurement results, it identifies areas with large energy losses and generates a comprehensive energy consumption and heat loss indicator set.

[0096] The energy consumption and heat loss calculation submodule first receives the identification information of the inefficient areas, which is obtained by the previous module based on the current path efficiency analysis. For these identified areas, the submodule starts the breaking frequency analysis program, which identifies which load points have abnormally increased breaking times under differentiated operating conditions by monitoring the breaking operation frequency during system operation. Breaking operations can cause temporary energy loss in the electrical system, especially under high-load operating conditions. Through this analysis, the submodule measures and records the energy consumption and heat loss data of each load point, and the data collection covers energy consumption under different operating conditions. It is further combined with the contact resistance measurement results, which are obtained by direct resistance measurement of the contact points of the load points to evaluate the health of the contact points. The energy consumption data is combined with the contact resistance data to identify areas where energy losses increase significantly under specific operating conditions, forming a comprehensive set of energy consumption and heat loss indicators.

[0097] The energy consumption abnormal path identification submodule captures the changes in insulation performance evaluation and contact resistance in the current flow path based on the comprehensive index set of energy consumption and heat loss, marks the path with abnormal energy consumption due to reduced insulation performance or increased contact resistance, and obtains the abnormal energy consumption channel;

[0098] The energy consumption abnormal path identification submodule uses the comprehensive energy consumption and heat loss indicator set as input, focusing on analyzing the insulation performance evaluation and dynamic changes of contact resistance in the current flow path. By comparing data at different time points, those paths that show abnormal energy consumption due to reduced insulation performance or increased contact resistance are identified. This analysis relies on high-precision data collection and in-depth data comparison to ensure accurate identification of problem paths. After marking, the summarized information is used to form a list of abnormal energy consumption channels, which details the location, properties and impact of each abnormal path on the system energy efficiency.

[0099] The optimization point identification submodule is based on the abnormal energy consumption channel, monitors the current and voltage parameters in the channel, records the energy consumption data of differentiated nodes, compares the energy consumption differences of multiple nodes, identifies nodes with abnormally increased resistance, analyzes the contact resistance or insulation performance problems of the nodes, sets points for reducing energy consumption by optimizing contact resistance or improving insulation performance, and establishes an energy efficiency abnormal point set.

[0100] Based on the detailed information of the abnormal energy consumption channel, the optimization point identification submodule further monitors the current and voltage parameters of each node in the channel. The energy consumption data of different nodes under different operating conditions are recorded by high-frequency sampling, and the data are compared using difference analysis technology to identify those nodes with abnormally high resistance. These nodes are often the main cause of low energy efficiency of the system. For these nodes, the submodule deeply analyzes their contact resistance or insulation performance problems, and determines the best strategy to significantly reduce energy consumption by optimizing contact resistance or improving insulation performance through technical evaluation. The analysis results of this stage will be used to formulate specific optimization measures, and finally form a set of energy efficiency abnormal points.

[0101] See also Figure 2 and Figure 8 , the optimization strategy formulation module includes:

[0102] The current distribution analysis submodule is based on the energy efficiency abnormal point set. Through the analysis of current distribution and flow path, it draws the current flow path diagram, marks the current intensity of multiple nodes, calculates and compares the current intensity and resistance value of multiple nodes, identifies the nodes with abnormally increased or decreased current intensity, and compares the current distribution diagram under normal conditions to find out the nodes and paths with uneven current distribution. According to the abnormal current intensity and resistance value, it identifies the hot spot area and generates the current abnormal hot spot identification;

[0103] The work of the current distribution analysis submodule begins with an in-depth analysis of the energy efficiency anomaly point set, which provides detailed information about the specific locations in the system where energy efficiency is poor. First, the submodule uses current distribution theory to analyze the current flow path between each node. By monitoring the direction and intensity of the current in the system in real time, the module draws a detailed current flow path diagram, which includes the current intensity annotation of each node. Next, the submodule calculates and compares the current intensity and resistance value of each node, and identifies those nodes with abnormally increased or decreased current intensity based on the current and resistance measurement data. By comparing with the current distribution diagram under normal operating conditions, nodes and paths with uneven current distribution can be found. Based on the abnormal data of current intensity and resistance value, the submodule further identifies the hot spots in the system and finally generates a current abnormal hot spot identification report.

[0104] The flow path optimization submodule evaluates the efficiency of the existing current path based on the identification of abnormal current hot spots, and replans the current flow path in combination with arc disturbance analysis and contactor wear assessment to obtain an optimized current path design;

[0105] The flow path optimization submodule receives the results of the current abnormal hotspot identification and uses this as a basis to evaluate the efficiency of the existing current path. Combined with arc disturbance analysis and contactor wear assessment, the submodule identifies key factors that affect the efficiency of current transmission, such as the damage that the instantaneous high temperature generated by the arc may cause to the circuit, and the increase in contact resistance caused by contactor wear. Based on these analysis results, the submodule proposes suggestions for replanning the current flow path to reduce energy loss and improve system efficiency. The optimized current path design not only takes into account the optimization of electrical parameters, but also includes improvements to the physical layout to achieve higher operating efficiency and reliability.

[0106] The load adjustment strategy submodule is based on optimizing the current path design, analyzing the impact of insulation material aging and contact resistance changes on current transmission efficiency, collecting current intensity and resistivity data, comparing initial performance parameters, determining the location and extent of performance degradation, performing real-time status assessment of insulation materials, updating the load distribution plan in real time, and establishing a load adjustment strategy.

[0107] Based on the optimized current path design, the load adjustment strategy submodule further analyzes the impact of insulation material aging and contact resistance changes on current transmission efficiency. By collecting real-time data on current intensity and resistivity and comparing it with the system's initial performance parameters, the submodule determines the specific location and extent of performance degradation. This analysis helps the submodule to conduct real-time status assessment of insulation materials and promptly detect trends in insulation performance degradation, so that intervention can be made before the problem becomes a serious failure. Based on these analysis and evaluation results, the submodule updates the load distribution plan in real time to ensure that the system maintains optimal performance under various operating conditions. By establishing a load adjustment strategy, the module achieves dynamic management of the system's operating status, optimizes energy consumption, and improves system reliability and safety.

[0108] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A performance analysis system for outdoor high-voltage vacuum circuit breaker, characterized in that: The system comprises: The performance calibration module collects key performance indicators based on real-time monitoring data, analyzes arc extinction time and contact wear data, compares the differences with historical performance data, adjusts performance prediction model parameters, and generates performance parameter corrections; The performance calibration module comprises: The performance difference identification submodule uses the support vector machine algorithm based on real-time monitoring data to analyze arc extinction performance and contact resistance data, compare historical performance records, identify performance fluctuations, distinguish performance degradation trends, and generate difference identification results; The performance trend analysis submodule quantifies the change of arc extinction performance, evaluates the impact of contact resistance fluctuation, predicts future performance trends, and obtains a performance change map based on the difference identification results; The model parameter optimization submodule adjusts the circuit breaker simulation test parameters based on the performance change map, performs closing and opening cycle tests, collects adjusted performance data, analyzes data changes, and generates performance parameter corrections; The support vector machine algorithm uses the formula: ； in, To monitor the arc extinction performance and contact resistance data in real time, is the support vector, is the class label, is the weight of the support vector, is the bias term, To adjust the parameters, To add feature weights, represents the inner product of the newly added feature vector and the input data, For an additional adjustment bias term, represents the newly introduced feature vector; The fault prediction module analyzes the matching degree between historical fault data and current performance data through the performance parameter correction, identifies similar fault modes, predicts potential fault points, and obtains potential fault indicators; The load distribution identification module compares the current and historical load data according to the potential fault indicators, identifies abnormal load patterns, attributes the environmental conditions that cause performance deviations, and obtains an abnormal load map; The flow path analysis module evaluates the efficiency of the current flow path based on the abnormal load map, analyzes the multi-path current density and temperature rise data, and based on the energy efficiency screening area, sets the area below the preset value as a low energy efficiency area, and establishes a low efficiency area identification; The flow path analysis module comprises: The current path efficiency analysis submodule determines the efficiency of multiple current paths based on the abnormal load map through arc consumption analysis, calculates the energy loss of each path, identifies the paths with low energy efficiency, and generates a current path efficiency evaluation; The multi-path current density analysis submodule uses mechanical durability test data to compare the current density and mechanical properties of multiple current paths based on the current path performance evaluation, evaluates the load bearing capacity and durability of the paths, and obtains a load bearing rating; The energy efficiency screening area setting submodule uses a decision tree algorithm based on the load bearing rating to analyze the thermal stability data of the insulation material, inspect the insulation material, analyze the thermal stability characteristics, record the temperature changes under differentiated load conditions, identify the area with abnormally high temperature, and establish a low energy efficiency area identification; The decision tree algorithm uses the formula ； in, is the temperature data set of the insulation material, For the selected attribute, Is an attribute The overall estimated value of is in the attributes The value of hour A subset of and They are and The number of samples, and They are and The entropy of is the adjustment factor, For attribute value The weight of The energy consumption calculation module uses the inefficient area identifier to calculate the energy consumption and heat loss of multiple load points, identifies abnormal paths based on energy consumption, identifies optimization points, and generates an energy efficiency abnormal point set; The optimization strategy formulation module analyzes the current distribution and flow path according to the energy efficiency abnormal point set, adjusts the load distribution, optimizes the current flow path, and obtains a path optimization solution.

2. The outdoor high-voltage vacuum circuit breaker performance analysis system according to claim 1, characterized in that: The performance parameter correction includes current regulation data, voltage correction data, and temperature deviation data; the potential fault indicators specifically include fault probability assessment, fault type identification, and fault time prediction; the abnormal load spectrum includes load peak characteristics, abnormal operation mode identification, and environmental influencing factor analysis; the inefficiency area identification specifically includes current intensive area identification, heat loss high area identification, and efficiency reduction path identification; the energy efficiency abnormal point set includes high energy consumption point mark, heat loss concentrated point mark, and inefficiency line mark; the path optimization plan specifically includes current path adjustment plan, loss reduction measures, and energy efficiency improvement measures.

3. The outdoor high-voltage vacuum circuit breaker performance analysis system according to claim 1, characterized in that: The load distribution identification module comprises: The load data comparison submodule, based on the potential fault indicators, identifies abnormal current fluctuations by comparing and analyzing the current waveform in real time with historical data, and uses a statistical model to compare waveform feature differences to generate abnormal current feature indicators; The abnormal pattern recognition submodule analyzes the correlation between the abnormal pattern and the operation frequency based on the flow abnormality characteristic index and the operation number and the current waveform relationship diagram, locates the performance deviation area caused by frequent operation, and obtains the frequent operation deviation index; The environmental condition attribution module tracks and analyzes the ambient temperature data based on the frequent operation deviation index, compares the temperature changes during the abnormal mode, evaluates the impact of temperature fluctuations on circuit breaker performance, and generates an abnormal load map.

4. The outdoor high-voltage vacuum circuit breaker performance analysis system according to claim 1, characterized in that: The energy consumption calculation module comprises: The energy consumption and heat loss calculation submodule measures the energy consumption and heat loss of multiple load points under differentiated operating conditions based on the identification of the inefficient areas through breaking times analysis, identifies the areas with large energy losses in combination with the contact resistance measurement results, and generates a comprehensive energy consumption and heat loss index set; The energy consumption abnormal path identification submodule captures the changes in insulation performance evaluation and contact resistance in the current flow path based on the comprehensive index set of energy consumption and heat loss, marks the path with abnormal energy consumption due to reduced insulation performance or increased contact resistance, and obtains the abnormal energy consumption channel; The optimization point identification submodule is based on the abnormal energy consumption channel, monitors the current and voltage parameters in the channel, records the energy consumption data of differentiated nodes, compares the energy consumption differences of multiple nodes, identifies nodes with abnormally increased resistance, analyzes the contact resistance or insulation performance problems of the nodes, sets points for reducing energy consumption by optimizing contact resistance or improving insulation performance, and establishes an energy efficiency abnormal point set.

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