Distribution automation circuit breaker interrupting capability evaluation method

Abstract

The invention discloses a method for evaluating the breaking capacity of a distribution automation circuit breaker, and relates to the technical field of circuit breakers. In order to solve the problems that analysis of the carrying capacity of a power distribution network cannot directly meet the requirement for accurate evaluation of the breaking capacity of a circuit breaker, short-circuit current at the moment of a fault cannot be accurately obtained in actual operation, and the breaking capacity of the circuit breaker in a complex fault scene is difficult to evaluate. According to the method, the circuit breaker information is comprehensively collected, the knowledge graph is utilized to optimize the evaluation standard, the power distribution network simulation model is accurately constructed, accurate calculation of the short-circuit current and the interruption capacity is achieved, multiple influence factors are comprehensively considered, the evaluation accuracy is remarkably improved, scientific maintenance suggestions are generated in combination with performance characteristic analysis, blind maintenance is avoided, and the maintenance efficiency is improved. Meanwhile, based on a simulation model of a power distribution network, automation of an evaluation process is achieved, equipment state information is synchronized in real time, the operation risk of a power system is effectively reduced, and the power distribution automation management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit breakers, and in particular to a method for evaluating the interrupting capacity of a distribution automation circuit breaker. Background Art

[0002] Existing circuit breaker interrupting capacity assessment methods often rely on a single indicator or simple parameter comparison, failing to fully consider the complexity of the distribution network, the actual operating conditions of the circuit breaker, and the influence of multiple factors. This results in deviations between the assessment results and the actual operating status. Patent application publication number CN112215508A discloses a method and apparatus for analyzing the carrying capacity of a distribution network. The method comprises statistically obtaining multiple evaluation indicators for the distribution network based on historical operating data; normalizing each of the multiple evaluation indicators to obtain a normalized value corresponding to each evaluation indicator; and performing a weighted calculation on the normalized values ​​corresponding to all evaluation indicators based on a predetermined weight corresponding to each evaluation indicator to obtain a comprehensive evaluation value of the distribution network's carrying capacity. By normalizing each evaluation indicator, indicators of different dimensions and orders of magnitude are converted into comparable data of the same dimensions and orders of magnitude. The entire analysis process is computationally simple. By quantitatively analyzing the distribution network's carrying capacity, the impact of integrated energy sources and multiple access entities on the distribution network is intuitively and clearly reflected.

[0003] Although the above patents have improved the ability to assess the operating status of distribution networks, the following problems still exist:

[0004] In the existing technology, the analysis of the distribution network's carrying capacity cannot directly meet the needs of accurately evaluating the circuit breaker's interrupting capacity. In actual operation, the location and type of short-circuit faults are random, and factors such as transition resistance will cause the short-circuit current to show dynamic changes. It is impossible to accurately obtain the short-circuit current at the moment of the fault, and it is difficult to evaluate the circuit breaker's interrupting capacity in complex fault scenarios. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for evaluating the interrupting capacity of distribution automation circuit breakers. By constructing a multi-dimensional information system and evaluation model, the complexity of the distribution network, the actual operating conditions of the circuit breaker and the influence of multiple factors are comprehensively considered, and combined with simulation and dynamic parameter correction, the short-circuit current and interrupting capacity are accurately calculated to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for evaluating the interrupting capacity of a distribution automation circuit breaker, comprising:

[0008] Collect circuit breaker information, including rated parameters, historical operating data, and manufacturer information, to obtain evaluation standards and indicators for circuit breaker interrupting capacity;

[0009] By building a simulation model of the power distribution network, short-circuit faults at different locations of the circuit breaker are simulated, and the short-circuit current is calculated based on the fault type and location of the short-circuit fault. At the same time, the interrupting capacity is calculated based on the rated voltage and rated breaking current of the circuit breaker.

[0010] The calculated short-circuit current is compared with the interrupting capacity. Combined with the evaluation standards and indicators of the circuit breaker's interrupting capacity, the circuit breaker's interrupting capacity under different fault conditions is evaluated, and maintenance recommendations are generated based on the evaluation results.

[0011] Furthermore, collecting circuit breaker information also includes:

[0012] Preprocess the acquired circuit breaker information, extract features from the preprocessed circuit breaker information, and identify key feature vectors;

[0013] Construct an information correlation calculation model to quantify the correlation between various pieces of circuit breaker information based on the similarity of key feature vectors;

[0014] Determine the potential relationship between circuit breaker information based on the degree of association, classify and mark the circuit breaker information related to the evaluation criteria and indicators, and determine the association relationship between circuit breaker information;

[0015] The circuit breaker information, evaluation criteria and indicators are used as nodes, and the association between circuit breaker information is used as edges to construct a knowledge graph. Based on the constructed knowledge graph, the evaluation criteria and indicators initially obtained are optimized and adjusted.

[0016] Furthermore, the evaluation criteria and indicators of the circuit breaker interrupting capacity are obtained, including:

[0017] Determine the rated interrupting capacity of the rated circuit breaker based on the collected rated parameters of the circuit breaker, and at the same time, obtain the power parameters of the distribution network and determine the maximum short-circuit capacity of the distribution network based on the power parameters;

[0018] Determine the correction factor for interrupting capacity based on the location attributes of the circuit breaker in the distribution network, and set the interrupting capacity evaluation standard based on the rated interrupting capacity;

[0019] Determine the type and location of short-circuit faults based on the topology and operating characteristics of the distribution network, and calculate the short-circuit current distribution characteristics;

[0020] Compare the calculated short-circuit current with the rated parameters such as the circuit breaker's rated breaking current to set the actual short-circuit current withstand capacity index;

[0021] Combined with the collected data on the distribution network's demand for rapid disconnection of short-circuit faults, the upper limit of the disconnection time is determined, and based on the collected rated parameters, the disconnection time evaluation standard is comprehensively set.

[0022] Furthermore, a simulation model of the power distribution network is constructed, specifically including:

[0023] Build a distribution network simulation model framework based on the collected distribution network topology information, circuit breaker rated parameters, and historical operation data, and perform consistency verification between the collected circuit breaker information and the distribution network simulation model;

[0024] Establish a dynamic parameter update mechanism to obtain the changing patterns of the circuit breaker's operating characteristics based on the time series of the circuit breaker information in the distribution network. Utilize the circuit breaker's historical operating data and combine it with the power parameters of the distribution network to adjust the electrical parameters in the distribution network simulation model in real time.

[0025] Combined with the determined short-circuit fault type and location, a multi-dimensional fault scenario library of different fault types is constructed, and the simulation model of the distribution network is simulated based on each fault scenario in the multi-dimensional fault scenario library.

[0026] Furthermore, the construction of the simulation model of the power distribution network also includes:

[0027] Generate a fault probability distribution based on the operating status and historical fault data of the distribution network. During the simulation, randomly trigger short-circuit faults of different types and locations based on the fault probability distribution. Simultaneously, simulate the nonlinear variation characteristics of the fault transition resistance.

[0028] Based on the simulation results, the short-circuit current size, current waveform and voltage change data at both ends of the circuit breaker at the fault point are recorded in real time. The real-time recorded data are sorted and analyzed, and the analysis results are compared and verified with the actual distribution network operation data.

[0029] Furthermore, the calculation of short-circuit current also includes:

[0030] Obtain historical fault data, determine the current calculation method based on the fault characteristics of each fault type, and establish a method mapping rule library based on the correspondence between fault types and current calculation methods;

[0031] Real-time monitoring of the power distribution network, capturing short-circuit fault signals. When a short-circuit fault is triggered, the fault location information is extracted, the current network topology is obtained, and the optimal calculation method is matched based on the method mapping rule library.

[0032] At the same time, for asymmetric faults, positive-sequence, negative-sequence and zero-sequence networks are constructed, and the sequence currents of the short-circuit points of each sequence network are calculated respectively. According to the boundary conditions of the fault type, the sequence currents of the short-circuit points of each sequence network are superimposed to obtain the three-phase short-circuit current, and the calculation results are output.

[0033] Furthermore, the calculation of interruption capacity also includes:

[0034] Extract key parameters such as rated voltage, rated current, rated breaking current, and rated making current from circuit breaker information, and calculate the interrupting capacity of the circuit breaker based on the extracted rated voltage and rated breaking current;

[0035] Based on the probability distribution of different fault types, the calculation results are assigned fault type weights, and the calculated value of the interruption capacity is corrected based on the fault type weights;

[0036] The calculated and corrected interruption capacity is compared with the preset evaluation criteria, and the rationality of the calculation results is evaluated based on the comparison results.

[0037] Furthermore, based on the probability distribution of different fault types, the calculation results are assigned fault type weights, and the calculated value of the interruption capacity is corrected based on the fault type weights, including:

[0038] Extract historical fault datasets;

[0039] Performing fault type identification on the historical fault data set to obtain the fault type appearing in the historical fault data set;

[0040] Extracting the percentage of occurrences of each fault type, and using the percentage of occurrences of each fault type to generate a fault probability corresponding to the fault type;

[0041] Extracting the preset initial fault weight value corresponding to each fault type;

[0042] Obtaining an adjusted weight corresponding to the fault type using the fault occurrence probability corresponding to the fault type and a preset initial fault weight value as the fault type weight;

[0043] The calculated value of the interruption capacity is corrected using the fault type weight corresponding to the fault type.

[0044] Furthermore, the calculated value of the interruption capacity is corrected using the fault type weight corresponding to the fault type, including:

[0045] Retrieving the fault type weight;

[0046] Extract the weight value change rate of the fault type weight corresponding to each fault type compared to its corresponding preset initial fault weight value;

[0047] Comparing the weight value change rate with a preset weight value change rate threshold;

[0048] Extracting the fault type corresponding to the weight value change rate exceeding the preset weight value change rate threshold as the target fault type;

[0049] The calculated value of the interruption capacity is corrected using the fault type weight of the target fault type to obtain the corrected interruption capacity.

[0050] Furthermore, the evaluation of the circuit breaker's breaking capacity under different fault conditions also includes: performing at least one simulated short-circuit breaking test on the circuit breaker based on the detection circuit, simulating conditions of different short-circuit current sizes and durations, and obtaining monitoring data during the circuit breaker breaking process in real time, determining the correlation between the monitoring data, analyzing the circuit breaker's breaking performance characteristics based on the correlation between the monitoring data, and determining the circuit breaker's breaking performance indicators based on the breaking performance characteristics, and evaluating the stability of the circuit breaker's breaking capacity under different working conditions.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] By comprehensively collecting circuit breaker information, optimizing evaluation criteria using knowledge graphs, and accurately constructing distribution network simulation models, accurate calculations of short-circuit current and interrupting capacity are achieved. By comprehensively considering multiple influencing factors, the accuracy of evaluation is significantly improved. Scientific maintenance recommendations are generated in combination with performance characteristic analysis to avoid blind maintenance and ensure stable operation of circuit breakers. At the same time, based on the simulation model of the distribution network, the evaluation process is automated, and equipment status information is synchronized in real time to facilitate operation and maintenance management, effectively reducing the operation risks of the power system and improving the efficiency of distribution automation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of the method for evaluating the interrupting capacity of a distribution automation circuit breaker according to the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] To address the technical issues that the existing technology for analyzing the carrying capacity of distribution networks cannot directly meet the demand for accurate evaluation of the interrupting capacity of circuit breakers, in actual operation, the location and type of short-circuit faults are random, and factors such as transition resistance will cause the short-circuit current to show dynamic changes. It is impossible to accurately obtain the short-circuit current at the moment of the fault, and it is difficult to evaluate the interrupting capacity of circuit breakers in complex fault scenarios. Figure 1 , this embodiment provides the following technical solutions:

[0056] A method for evaluating the interrupting capacity of a distribution automation circuit breaker, comprising:

[0057] Collect circuit breaker information, including circuit breaker rated parameters, historical operating data, and manufacturer information; circuit breaker rated parameters, such as rated voltage, rated current, rated breaking current, and rated closing current; historical operating data, including operation records, fault records, and operating temperature monitoring data; manufacturer information, such as manufacturer name, product model, and manufacturing date, and also includes:

[0058] Preprocess the acquired circuit breaker information, extract features from the preprocessed circuit breaker information, and identify key feature vectors;

[0059] Construct an information correlation calculation model to quantify the correlation between various pieces of circuit breaker information based on the similarity of key feature vectors;

[0060] Determine the potential relationship between circuit breaker information based on the degree of association, classify and mark the circuit breaker information related to the evaluation criteria and indicators, and determine the association relationship between circuit breaker information;

[0061] Build a knowledge graph using circuit breaker information, evaluation criteria, and indicators as nodes and relationships between circuit breaker information as edges. Optimize and adjust the initially acquired evaluation criteria and indicators based on the constructed knowledge graph.

[0062] Obtain evaluation standards and indicators for circuit breaker interrupting capacity, using interrupting capacity, actual short-circuit current carrying capacity, and breaking time as primary evaluation indicators, while also considering indicators such as arc energy and equipment aging.

[0063] By building a simulation model of the power distribution network, short-circuit faults at different locations of the circuit breaker are simulated, and the short-circuit current is calculated based on the fault type and location of the short-circuit fault. At the same time, the interrupting capacity is calculated based on the rated voltage and rated breaking current of the circuit breaker.

[0064] Compare the calculated short-circuit current with the interrupting capacity. Combined with the evaluation standards and indicators of the circuit breaker's interrupting capacity, evaluate the circuit breaker's interrupting capacity under different fault conditions and generate maintenance recommendations based on the evaluation results.

[0065] In this embodiment, the evaluation of the circuit breaker's breaking capacity under different fault conditions also includes: performing at least one simulated short-circuit breaking test on the circuit breaker based on the detection circuit, simulating conditions of different short-circuit current sizes and durations, and obtaining monitoring data such as voltage changes and breaking time during the circuit breaker breaking process in real time, determining the correlation between the monitoring data, analyzing the circuit breaker's breaking performance characteristics such as arc extinguishing characteristics and contact wear characteristics based on the correlation between the monitoring data, determining the circuit breaker's breaking performance indicators based on the breaking performance characteristics, and evaluating the stability of the circuit breaker's breaking capacity under different working conditions.

[0066] In this embodiment, the interrupting capacity is calculated as the product of the rated voltage and the rated breaking current. The actual short-circuit current carrying capacity is obtained by comparing the short-circuit current value measured by the simulated fault with the rated parameter. The breaking time is defined as the time from the occurrence of the short-circuit fault to the separation of the circuit breaker contacts and the successful interruption of the current.

[0067] In this embodiment, when collecting circuit breaker rated parameters, the rated voltage, rated current, rated breaking current, and rated making current are obtained from the circuit breaker equipment nameplate or product manual; the operation records in the historical operation data are obtained through the power automation system or the equipment's own operation recording device; the fault records are queried from the power system fault management database; and the operating temperature monitoring data is uploaded and stored in real time through the data acquisition system using temperature sensors installed on the circuit breaker body or key parts.

[0068] In this embodiment, by collecting multi-dimensional circuit breaker information, constructing a knowledge graph to optimize the evaluation standard, combining distribution network simulation to simulate various fault scenarios, accurately calculating short-circuit current and interrupting capacity, and comprehensively evaluating the interrupting capacity with multiple indicators, the influence of factors such as environment and equipment aging is fully considered, thereby greatly improving the evaluation accuracy and providing a reliable basis for judging the operating status of the circuit breaker. Based on the accurate interrupting capacity evaluation results and combined with the analysis of circuit breaker performance characteristics, targeted maintenance suggestions are generated to achieve reasonable resource allocation, ensure safe and reliable operation of the circuit breaker, and reduce the probability of power accidents. The evaluation results and maintenance information are synchronized in real time, which makes it convenient for operators to grasp the equipment status and improve the level of distribution automation management.

[0069] In this embodiment, the evaluation criteria and indicators for the circuit breaker interrupting capability are obtained, specifically including:

[0070] Determine the rated interrupting capacity of the circuit breaker based on the collected rated parameters of the circuit breaker. At the same time, obtain the power parameters of the distribution network, including power supply capacity, system impedance, etc., and determine the maximum short-circuit capacity of the distribution network based on the power parameters.

[0071] The correction factor for the interrupting capacity is determined based on the location of the circuit breaker in the distribution network (e.g., main line circuit breaker, branch line circuit breaker). Based on the rated interrupting capacity, the interrupting capacity evaluation standard is set, requiring the circuit breaker interrupting capacity to be 1.2-1.5 times greater than the system's maximum short-circuit capacity, i.e., the corresponding correction factor.

[0072] Determine the type and location of short-circuit faults based on the topology and operating characteristics of the distribution network, and calculate the short-circuit current distribution characteristics;

[0073] Compare the calculated short-circuit current with the rated parameters such as the circuit breaker's rated breaking current, and set the actual short-circuit current withstand capacity index, for example, stipulating that the actual short-circuit current peak value does not exceed 90% of the rated breaking current, and the effective value does not exceed 80%;

[0074] Combined with the collected data on the distribution network's demand for rapid disconnection of short-circuit faults, the upper limit of the disconnection time is determined. Based on the collected rated parameters, the disconnection time evaluation standard is comprehensively set. For example, in urban distribution networks, the disconnection time is required to not exceed 50 milliseconds.

[0075] In this embodiment, by determining the short-circuit fault type and location to calculate the short-circuit current distribution characteristics, and determining the breaking time standard in combination with network requirements, an evaluation index system is constructed from multiple aspects to comprehensively cover the key factors affecting the evaluation of the circuit breaker's interrupting capacity, avoid the one-sidedness of the evaluation caused by a single factor, and set the interrupting capacity correction coefficient according to the position attribute of the circuit breaker in the distribution network, so that the evaluation standard is more in line with the actual operating conditions of circuit breakers in different positions. At the same time, the upper limit of the breaking time is clarified and the evaluation standard is set in combination with the rated parameters, so that the circuit breaker's ability to evaluate the breaking speed is more accurate, effectively avoiding the evaluation error caused by the ambiguity of the indicators, and thus more accurately evaluating the circuit breaker's interrupting capacity under different fault conditions.

[0076] In this embodiment, constructing a simulation model of a power distribution network specifically includes:

[0077] Based on the collected distribution network topology information, circuit breaker rated parameters, and historical operating data, a distribution network simulation model framework is built, including a modular structure such as the power supply layer, transmission line layer, transformer layer, load layer, and circuit breaker layer. The collected circuit breaker information is then checked for consistency with the distribution network simulation model to ensure that the simulation model parameters of the distribution network are completely consistent with the actual equipment parameters.

[0078] Establish a dynamic parameter update mechanism to obtain the changing patterns of the circuit breaker's operating characteristics based on the time series of circuit breaker information in the distribution network. Utilize the circuit breaker's historical operating data, combined with the power parameters of the distribution network, to adjust the electrical parameters in the distribution network simulation model in real time, forming a dynamic topology model that reflects the actual operating status.

[0079] Based on the determined short-circuit fault type and location, a multi-dimensional fault scenario library of different fault types is constructed. The fault scenario library includes different locations such as the line headend, middle end, and end end, and also contains multiple fault types such as three-phase short circuit, two-phase short circuit, and single-phase ground short circuit. The simulation model of the distribution network simulates each fault scenario based on the multi-dimensional fault scenario library.

[0080] Generate a fault probability distribution based on the operating status and historical fault data of the distribution network. During the simulation process, short-circuit faults of different types and locations are randomly triggered based on the fault probability distribution. Simultaneously, the nonlinear variation characteristics of the fault transition resistance are simulated to achieve intelligent simulation of complex fault scenarios such as transient faults and permanent faults, which is closer to the fault conditions of the actual distribution network.

[0081] Based on the simulation results, the short-circuit current size, current waveform and voltage change data at both ends of the circuit breaker at the fault point are recorded in real time. The real-time recorded data are sorted and analyzed, and the analysis results are compared and verified with the actual distribution network operation data.

[0082] In this embodiment, the combination of a multi-dimensional fault scenario library and an intelligent fault simulation method can comprehensively cover various fault types and locations, simulate complex fault scenarios, and highly restore the actual operating conditions of the distribution network, providing an accurate simulation environment for the evaluation of the circuit breaker's interrupting capacity, making the evaluation results closer to reality, and recording the short-circuit current, current waveform and voltage change data at both ends of the circuit breaker at the fault point in real time during the simulation process, effectively improving the credibility and effectiveness of the evaluation results, and being able to evaluate the circuit breaker's interrupting capacity under different probability fault scenarios.

[0083] In this embodiment, calculating the short-circuit current further includes:

[0084] Obtain historical fault data, determine the current calculation method based on the fault characteristics of each fault type, and establish a method mapping rule library based on the correspondence between fault types and current calculation methods;

[0085] Real-time monitoring of the power distribution network, capturing short-circuit fault signals. When a short-circuit fault is triggered, the fault location information is extracted, the current network topology is obtained, and the optimal calculation method is matched based on the method mapping rule library.

[0086] For three-phase short-circuit faults, the phase component method is preferred for rapid calculation. For asymmetric faults, such as two-phase short circuits and single-phase ground faults, positive-sequence, negative-sequence, and zero-sequence networks are established based on power system parameters. The positive-sequence network is drawn according to the normal operating wiring diagram, the negative-sequence network assumes the power source electromotive force is zero, and the zero-sequence network is constructed based on the neutral point grounding method and the zero-sequence impedance characteristics of the components.

[0087] At the same time, for asymmetric faults, positive-sequence, negative-sequence and zero-sequence networks are constructed, and the sequence currents of the short-circuit points of each sequence network are calculated respectively. According to the boundary conditions of the fault type, the sequence currents of the short-circuit points of each sequence network are superimposed to obtain the three-phase short-circuit current, and the calculation results are output to realize intelligent adaptation of the calculation method and improve calculation efficiency and accuracy.

[0088] In this embodiment, calculating the interruption capacity also includes:

[0089] Extract key parameters such as rated voltage, rated current, rated breaking current, and rated making current from circuit breaker information, and calculate the interrupting capacity of the circuit breaker based on the extracted rated voltage and rated breaking current;

[0090] Based on the probability distribution of different fault types, the calculation results are assigned fault type weights, and the calculated interrupting capacity value is corrected based on the fault type weights. For example, for fault types that may cause a surge in short-circuit current, the calculated interrupting capacity value is increased to make it more in line with actual operation needs;

[0091] The calculated and corrected interruption capacity is compared with the preset evaluation criteria, and the rationality of the calculation results is evaluated based on the comparison results.

[0092] Specifically, based on the probability distribution of different fault types, the calculation results are assigned fault type weights, and the calculated interruption capacity value is corrected based on the fault type weights, including:

[0093] Extract historical fault datasets;

[0094] Performing fault type identification on the historical fault data set to obtain the fault type appearing in the historical fault data set;

[0095] Extracting the percentage of occurrences of each fault type, and using the percentage of occurrences of each fault type to generate a fault probability corresponding to the fault type;

[0096] The probability of the fault occurring corresponding to the fault type is obtained by the following formula:

[0097]

[0098] Where P represents the probability of occurrence of the fault type; x represents the preset time attenuation coefficient, which ranges from 0.7 to 1.3; ΔT represents the time interval between the current moment and the last time the fault type occurred; A represents the number of locations where the fault type occurs; A t represents the total number of monitoring locations for fault monitoring of the distribution network; C represents the spatial aggregation degree corresponding to the fault type; C xrepresents the preset spatial clustering reference value. Specifically, exp(-x*ΔT) reflects the decay of the fault probability over time. That is, the longer the time since the last occurrence of a fault type, the lower the probability of that fault type recurring; the shorter the time interval, the higher the probability of recurrence. This reflects the timeliness of fault occurrence: a new fault has a greater impact on the probability of its recurrence in the near future, while this impact gradually decreases over time. The spatial aggregation degree C corresponding to the fault type and the preset spatial aggregation degree reference value C x The ratio of y is multiplied by the proportional coefficient y (the specific value range is not mentioned in the formula, but it is speculated that it is a parameter used to adjust the degree of spatial clustering). The spatial clustering C measures the degree to which faults occur in a certain area. This reflects the impact of the spatial clustering of faults on the probability of fault occurrence. When the spatial clustering degree C is large and the y value is appropriate, this component has a greater impact on the fault probability; otherwise, it has a smaller impact. This comprehensively reflects the spatial distribution breadth and clustering characteristics of the fault. The sum of the two components, taken as a whole, reflects the combined impact of the fault on the probability of occurrence in the spatial dimension. The above two calculation components are multiplied to comprehensively consider the impact of both time and space on the probability of fault occurrence. This is not a simple linear superposition, but rather reflects the interaction between the various factors through a specific functional relationship. The resulting fault probability corresponding to the fault type is derived by comprehensively considering both time factors (the time interval since the last fault) and spatial factors (the number of fault locations and their spatial clustering degree). This is used in subsequent operations such as fault type weighting based on the fault probability distribution of different fault types, providing a basis for power system fault analysis and treatment.

[0099] Extracting the preset initial fault weight value corresponding to each fault type;

[0100] Obtaining an adjusted weight corresponding to the fault type using the fault occurrence probability corresponding to the fault type and a preset initial fault weight value as the fault type weight;

[0101] The fault type weight corresponding to each fault type is obtained by the following formula:

[0102]

[0103] Where w represents the fault type weight corresponding to each fault type; w0 represents the preset initial fault weight value corresponding to each fault type; P represents the fault occurrence probability corresponding to the fault type; P x Indicates the average probability of failure corresponding to all fault types; specifically, the value range of the sine function sin() is between [-1,1]. Multiply the result of (P-Px) by It is then used as the independent variable of the sine function, which uses the nonlinear characteristics of the sine function to map the degree of deviation of the probability of occurrence of the fault type from the average value to the interval [-1,1]. Based on the preset initial fault weight value w0 for each fault type, it is adjusted using the proportional coefficient obtained from the above calculation. Based on the deviation of the fault type's probability from the average probability, a nonlinear transformation of the sine function and subsequent addition operations are used to determine the adjustment ratio, resulting in the adjusted fault type weight w. w is the fault type weight obtained by adjusting the preset initial fault weight value, taking into account the difference between the probability of a specific fault type and the average probability of all fault types. It reflects the relative importance of the fault type within the overall fault type distribution, taking into account the fault probability distribution, and is used to subsequently correct the calculated interrupting capacity value. The formula calculates the difference between the probability of a specific fault type and the average probability and performs a nonlinear transformation using the sine function, accurately reflecting the impact of relative differences in fault type probability on the weight. Compared to simple linear adjustments or fixed weight settings that do not consider probability differences, this method more accurately determines fault type weights based on the fault probability distribution, ensuring that the weights are more closely aligned with actual fault conditions. Utilizing the nonlinear characteristics of the sine function for adjustment allows for more flexible handling of varying degrees of probability deviation. For fault types with large deviations, the adjustment amplitude can be appropriately increased based on the characteristics of the sine function. For fault types with small deviations, the adjustment amplitude is correspondingly smaller. This avoids the potential over-adjustment or under-adjustment issues that can arise from linear adjustment, further improving the accuracy of fault type weights. This formula is adaptable to a variety of fault probability distributions. Whether the fault type probability distribution is uniform or has significant high and low variance, the fault type weights can be appropriately adjusted by calculating the probability difference and utilizing the sine function transformation. For example, if the probability of some fault types is concentrated within a certain range, while the probability of individual fault types deviates significantly, the formula can accurately capture this difference and adjust the weights. As the power system operates, the fault probability distribution may change. This formula can recalculate the probability difference and adjust the weights based on real-time or updated fault probability, dynamically adapting to changes in fault probability. This ensures accurate fault type weights are obtained at different stages, making subsequent weight-based operations (such as correcting the calculated interrupting capacity) more efficient and effective. The calculated interrupting capacity value is corrected using the fault type weight corresponding to the fault type.

[0104] The above technical solution achieves the following: by extracting historical fault data sets, identifying fault types, calculating the occurrence percentage of each fault type, and generating a fault probability. This accurately captures the historical occurrence patterns of different fault types, enabling subsequent weight assignment based on accurate fault probability information. This allows the calculated interrupting capacity to be more accurately aligned with actual fault conditions, improving calculation accuracy. Adjusted weights (i.e., fault type weights) are derived by combining the fault probability with preset initial fault weights. This comprehensive consideration leverages both the initial weights set based on historical experience and the actual fault probability, resulting in a more rational weight assignment, more accurate corrections to the calculated interrupting capacity, and improved overall accuracy. Analysis based on historical fault data sets is reliable and reflects the fault conditions experienced during actual equipment operation. This foundation for fault type identification, probability calculation, and weight assignment provides solid data support for correcting the calculated interrupting capacity, enhancing the reliability of the results. As equipment operates, the distribution of fault types may change. This solution dynamically adapts to changes in fault types by continuously updating the historical fault data sets, recalculating fault probabilities, and adjusting fault type weights, ensuring the reliability of corrected interrupting capacity calculations. Accurately correcting calculated interrupting capacity values ​​helps rationally allocate power system resources, such as selecting equipment with appropriate interrupting capacity. This prevents inaccurate calculations that result in equipment interrupting capacity being too low to handle faults, or too high, which wastes resources, thereby ensuring safe and stable power system operation. Correcting calculated interrupting capacity values ​​based on accurate fault type weights allows for more precise assessment of equipment's ability to respond to different fault types, enabling early identification of potential risks and implementation of targeted preventive measures to mitigate potential system damage and improve power system safety.

[0105] Specifically, the calculated value of the interruption capacity is corrected using the fault type weight corresponding to the fault type, including:

[0106] Retrieving the fault type weight;

[0107] Extract the weight value change rate of the fault type weight corresponding to each fault type compared to its corresponding preset initial fault weight value;

[0108] Comparing the weight value change rate with a preset weight value change rate threshold;

[0109] Extracting the fault type corresponding to the weight value change rate exceeding the preset weight value change rate threshold as the target fault type;

[0110] The calculated value of the interruption capacity is corrected using the fault type weight of the target fault type to obtain the corrected interruption capacity;

[0111] The corrected interruption capacity is obtained by the following formula:

[0112]

[0113] Where D represents the interruption capacity after correction; D0 represents the interruption capacity before correction; n represents the number of target fault types; P mi represents the fault type weight corresponding to the i-th target fault type; P yb represents the standard deviation of the fault type weight corresponding to the non-target fault type; P y represents the average weight of the fault type corresponding to the non-target fault type; ε represents the preset minimum parameter, which is used to prevent the numerator from being zero.

[0114] This section reflects the relative importance of the target fault type among all fault types. The larger its value, the greater the deviation of the target fault type weight from the non-target fault type weight. In other words, the target fault type has a more prominent impact on the system than the non-target fault type, and requires more attention when revising the interrupting capacity. D is the corrected interrupting capacity obtained by adjusting the pre-correction interrupting capacity after comprehensively considering the characteristics of the target fault type weight and the non-target fault type weight. It reflects the reasonable interrupting capacity value required to ensure the safe and stable operation of the system while considering the differences in the impact of different fault types on the system. It is used to guide the selection of relevant equipment and the setting of operating parameters in the power system.

[0115] The above technical solution achieves the following: By calculating the rate of change in weight values ​​and comparing them with a threshold, it can accurately screen target fault types with significant weight changes. These fault types may significantly alter their impact on the system. Identifying these fault types focuses on key factors, allowing the interruption capacity to be corrected based on the fault types with the greatest impact on the system. This improves the accuracy of the corrected interruption capacity calculation and better meets actual operational needs. By considering the rate of change in fault type weights relative to the preset initial weights, the solution can dynamically adapt to changes in the fault type's impact on the system. As the system operates, factors such as the probability of a fault type's occurrence change, which can cause weights to change. The solution promptly responds to these dynamic changes and corrects the calculated interruption capacity based on the latest weights, ensuring the accuracy of the calculated results. The interruption capacity is corrected for target fault types (with a rate of change in weight values ​​exceeding the threshold) to avoid interference from a large number of fault types with smaller weight changes. Targeted treatment of key fault types effectively reduces calculation errors caused by minor factors, making the corrected interruption capacity more reliable and providing a stronger guarantee for safe and stable system operation. As fault conditions evolve during system operation, the solution continuously monitors the rate of change in weights to adapt to changes in fault type characteristics over the long term. Continuously updating the target fault type and correcting the interruption capacity allows the system to operate based on reliable interruption capacity values ​​at different stages, thereby enhancing system reliability. Accurately correcting the interruption capacity allows for reasonable resource allocation based on the actual critical fault type. This avoids improper equipment selection due to inaccurate interruption capacity calculations, prevents equipment from failing to interrupt properly in the event of a fault, or prevents waste of resources due to over-configuration, ensures that the system can effectively isolate the fault point in the event of a fault, and improves system safety. Correcting the interruption capacity based on the target fault type weight allows for a more accurate assessment of the system's risk under critical fault types. Implementing preventive measures for these critical faults in advance, such as strengthening equipment maintenance and adjusting operating strategies, can reduce the degree of harm to the system when a fault occurs, further improving system safety.

[0116] Furthermore, the formula proposed in the above technical solution comprehensively considers factors such as the standard deviation and mean of the target fault type weights and the non-target fault type weights, comprehensively reflecting the varying impacts of different fault types on the system. Compared to methods that only consider the weight of a single fault type or do not distinguish between target and non-target fault types, this method can more accurately correct the interruption capacity based on the actual distribution and importance of the fault types, making the corrected interruption capacity more aligned with the actual system operation requirements and improving the accuracy of the calculated results. By normalizing the difference between the target fault type weights and the non-target fault type weight characteristics and averaging multiple target fault types, the excessive influence of individual outliers on the results is avoided, resulting in more stable and accurate calculation results. This processing method effectively filters out interfering factors, highlights the overall relative deviation of the target fault type weights, and further improves the accuracy of the corrected interruption capacity. The interruption capacity correction based on the comparison of the target fault type weights and the non-target fault type weight characteristics closely reflects the distribution and importance of the actual fault types. When the probability or impact of a fault type in the system changes, the interruption capacity can be promptly and reliably adjusted by re-determining the target fault type and recalculating relevant parameters, ensuring safe and stable system operation. The preset minimum parameter ε prevents anomalies such as the numerator being zero during the calculation process, enhancing the robustness of the formula. Furthermore, by comprehensively considering multiple fault type weight characteristic parameters, the impact of fluctuations in a single parameter on the results is reduced, enabling the entire formula to reliably calculate reasonable interruption capacity corrections under varying fault type distributions. As the power system operates, the probability and importance of fault types may change, resulting in changes in the weight characteristics of target and non-target fault types. The formula recalculates relevant parameters based on these changes, dynamically adjusting the interruption capacity to adapt to the fault type distribution at different stages and maintaining effective system protection. Different power systems may have different fault type distributions and operating characteristics. By comprehensively considering multiple fault type weight characteristic parameters, the formula can adapt to the characteristics of different systems and reasonably adjust the interruption capacity based on actual fault conditions in different power systems, demonstrating strong adaptability.

[0117] In this embodiment, by constructing a method mapping rule library, combining the intelligent matching calculation method of real-time monitored fault information, and adopting differentiated calculation strategies for different fault types, the intelligent adaptation of the short-circuit current calculation method is realized, and the accuracy of the calculation results is greatly improved. It provides accurate current data support for the evaluation of the circuit breaker's interrupting capacity. When calculating the interrupting capacity, not only basic calculations are performed based on the rated parameters of the circuit breaker, but also a fault type weight distribution mechanism is introduced. Combined with the probability distribution of different fault types, the calculation results are corrected in a targeted manner to avoid evaluation deviations caused by conservative or insufficient calculations, enhance the fit between the interrupting capacity calculation results and the actual operating scenarios, and truly reflect the actual performance of the circuit breaker under different fault conditions, reduce manual intervention, and improve the automation level of the evaluation process.

[0118] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for evaluating the interrupting capacity of a distribution automation circuit breaker, characterized in that: include: Collect circuit breaker information, including rated parameters, historical operating data, and manufacturer information, to obtain evaluation standards and indicators for circuit breaker interrupting capacity; By building a simulation model of the power distribution network, short-circuit faults at different locations of the circuit breaker are simulated, and the short-circuit current is calculated based on the fault type and location of the short-circuit fault. At the same time, the interrupting capacity is calculated based on the rated voltage and rated breaking current of the circuit breaker. The calculated short-circuit current is compared with the interrupting capacity. Combined with the evaluation standards and indicators of the circuit breaker's interrupting capacity, the circuit breaker's interrupting capacity under different fault conditions is evaluated, and maintenance recommendations are generated based on the evaluation results.

2. A method for evaluating the interrupting capacity of a distribution automation circuit breaker according to claim 1, characterized in that: Collect circuit breaker information, including: Preprocess the acquired circuit breaker information, extract features from the preprocessed circuit breaker information, and identify key feature vectors; Construct an information correlation calculation model to quantify the correlation between various pieces of circuit breaker information based on the similarity of key feature vectors; Determine the potential relationship between circuit breaker information based on the degree of association, classify and mark the circuit breaker information related to the evaluation criteria and indicators, and determine the association relationship between circuit breaker information; The circuit breaker information, evaluation criteria and indicators are used as nodes, and the association between circuit breaker information is used as edges to construct a knowledge graph. Based on the constructed knowledge graph, the evaluation criteria and indicators initially obtained are optimized and adjusted.

3. A method for evaluating the interrupting capacity of a distribution automation circuit breaker according to claim 2, characterized in that: Obtain evaluation criteria and indicators for circuit breaker interrupting capacity, including: Determine the rated interrupting capacity of the rated circuit breaker based on the collected rated parameters of the circuit breaker, and at the same time, obtain the power parameters of the distribution network and determine the maximum short-circuit capacity of the distribution network based on the power parameters; Determine the correction factor for interrupting capacity based on the location attributes of the circuit breaker in the distribution network, and set the interrupting capacity evaluation standard based on the rated interrupting capacity; Determine the type and location of short-circuit faults based on the topology and operating characteristics of the distribution network, and calculate the short-circuit current distribution characteristics; Compare the calculated short-circuit current with the rated parameters such as the circuit breaker's rated breaking current to set the actual short-circuit current withstand capacity index; Combined with the collected data on the distribution network's demand for rapid disconnection of short-circuit faults, the upper limit of the disconnection time is determined, and based on the collected rated parameters, the disconnection time evaluation standard is comprehensively set.

4. A method for evaluating the interrupting capacity of a distribution automation circuit breaker according to claim 1, characterized in that: Build a simulation model of the power distribution network, including: Build a distribution network simulation model framework based on the collected distribution network topology information, circuit breaker rated parameters, and historical operation data, and perform consistency verification between the collected circuit breaker information and the distribution network simulation model; Establish a dynamic parameter update mechanism to obtain the changing patterns of the circuit breaker's operating characteristics based on the time series of the circuit breaker information in the distribution network. Utilize the circuit breaker's historical operating data and combine it with the power parameters of the distribution network to adjust the electrical parameters in the distribution network simulation model in real time. Combined with the determined short-circuit fault type and location, a multi-dimensional fault scenario library of different fault types is constructed, and the simulation model of the distribution network is simulated based on each fault scenario in the multi-dimensional fault scenario library.

5. A method for evaluating the interrupting capacity of a distribution automation circuit breaker according to claim 4, characterized in that: Build a simulation model of the power distribution network, including: Generate a fault probability distribution based on the operating status and historical fault data of the distribution network. During the simulation, randomly trigger short-circuit faults of different types and locations based on the fault probability distribution. Simultaneously, simulate the nonlinear variation characteristics of the fault transition resistance. Based on the simulation results, the short-circuit current size, current waveform and voltage change data at both ends of the circuit breaker at the fault point are recorded in real time. The real-time recorded data are sorted and analyzed, and the analysis results are compared and verified with the actual distribution network operation data.

6. A method for evaluating the interrupting capacity of a distribution automation circuit breaker according to claim 1, characterized in that: Calculates short-circuit current, also includes: Obtain historical fault data, determine the current calculation method based on the fault characteristics of each fault type, and establish a method mapping rule library based on the correspondence between fault types and current calculation methods; Real-time monitoring of the power distribution network, capturing short-circuit fault signals. When a short-circuit fault is triggered, the fault location information is extracted, the current network topology is obtained, and the optimal calculation method is matched based on the method mapping rule library. At the same time, for asymmetric faults, positive-sequence, negative-sequence and zero-sequence networks are constructed, and the sequence currents of the short-circuit points of each sequence network are calculated respectively. According to the boundary conditions of the fault type, the sequence currents of the short-circuit points of each sequence network are superimposed to obtain the three-phase short-circuit current, and the calculation results are output.

7. A method for evaluating the interrupting capacity of a distribution automation circuit breaker according to claim 6, characterized in that: Calculation of interruption capacity also includes: Extract key parameters such as rated voltage, rated current, rated breaking current, and rated making current from circuit breaker information, and calculate the interrupting capacity of the circuit breaker based on the extracted rated voltage and rated breaking current; Based on the probability distribution of different fault types, the calculation results are assigned fault type weights, and the calculated value of the interruption capacity is corrected based on the fault type weights; The calculated and corrected interruption capacity is compared with the preset evaluation criteria, and the rationality of the calculation results is evaluated based on the comparison results.

8. A method for evaluating the interrupting capacity of a distribution automation circuit breaker according to claim 7, characterized in that: Based on the probability distribution of different fault types, the calculation results are assigned fault type weights, and the calculated interruption capacity value is corrected based on the fault type weights, including: Extract historical fault datasets; Performing fault type identification on the historical fault data set to obtain the fault type appearing in the historical fault data set; Extracting the percentage of occurrences of each fault type, and using the percentage of occurrences of each fault type to generate a fault probability corresponding to the fault type; Extracting the preset initial fault weight value corresponding to each fault type; Obtaining an adjusted weight corresponding to the fault type using the fault occurrence probability corresponding to the fault type and a preset initial fault weight value as the fault type weight; The calculated value of the interruption capacity is corrected using the fault type weight corresponding to the fault type.

9. A method for evaluating the interrupting capacity of a distribution automation circuit breaker according to claim 8, characterized in that: The calculated value of the interruption capacity is corrected using the fault type weight corresponding to the fault type, including: Retrieving the fault type weight; Extract the weight value change rate of the fault type weight corresponding to each fault type compared to its corresponding preset initial fault weight value; Comparing the weight value change rate with a preset weight value change rate threshold; Extracting the fault type corresponding to the weight value change rate exceeding the preset weight value change rate threshold as the target fault type; The calculated value of the interruption capacity is corrected using the fault type weight of the target fault type to obtain the corrected interruption capacity.

10. A method for evaluating the interrupting capacity of a distribution automation circuit breaker according to claim 7, characterized in that: The evaluation of the circuit breaker's breaking capacity under different fault conditions also includes: conducting at least one simulated short-circuit breaking test on the circuit breaker based on the detection circuit, simulating conditions of different short-circuit current sizes and durations, and obtaining monitoring data during the circuit breaker breaking process in real time, determining the correlation between the monitoring data, analyzing the circuit breaker's breaking performance characteristics based on the correlation between the monitoring data, and determining the circuit breaker's breaking performance indicators based on the breaking performance characteristics, and evaluating the circuit breaker's breaking capacity stability under different working conditions.

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