A method and equipment for reliability assessment of intelligent miniature switchgear

Abstract

This invention discloses an intelligent method and device for reliability assessment of small switchgear, relating to the field of intelligent switchgear technology. The method includes the following steps: collecting switchgear operating status data; calculating a first reliability assessment parameter based on the operating status data; acquiring switchgear lifecycle data and a second reliability assessment parameter; calculating a dynamic threshold range corresponding to the second reliability assessment parameter through collaborative learning of shared trees and specialized trees, and constraint consistency correction; and performing correlation analysis on the first reliability assessment parameter and the dynamic threshold range to obtain the switchgear reliability assessment result. This invention addresses the problem of one-sided dimensions in traditional reliability assessments of small switchgear.

Description

Technical Field

[0001] This invention relates to the field of intelligent switchgear technology, and more specifically, to an intelligent small switchgear reliability assessment method and device. Background Technology

[0002] In modern power systems, intelligent miniature switchgear, as a core device for power distribution and control, is widely used in various substations, distribution rooms, and industrial sites. Its operational reliability directly affects the stability and security of power supply. As power systems develop towards intelligence and distributed systems, higher requirements are placed on the reliability assessment of switchgear, and the shortcomings of traditional assessment methods are becoming increasingly apparent.

[0003] For example, the invention patent announcement CN120233176A discloses a method for evaluating the operational reliability of switchgear. This method, belonging to the field of switchgear, includes: collecting the rated voltage and rated current values ​​of multiple sets of drive modules and power modules in the target switchgear; performing electrical variable protection tests on each set of drive modules in the target switchgear to obtain the corresponding electrical variable protection test results; performing over-temperature protection tests on each set of power modules in the target switchgear to obtain the corresponding over-temperature test results; performing phase loss protection tests on the target switchgear to obtain the corresponding phase loss test results; and comprehensively evaluating the reliability of the target switchgear based on all the test results.

[0004] The above-disclosed technical solutions have at least the following technical problems:

[0005] Current reliability assessment methods for small substation switchgear focus on a relatively singular parameter dimension, neglecting the impact of environmental factors and mechanical performance on equipment operational reliability. This results in a limited range of assessment parameters, failing to fully reflect the actual operating status of the switchgear and thus affecting the accuracy and reliability of the assessment results. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent small switchgear reliability assessment method and device. By collaborative learning and constraint consistency correction of shared trees and specialized trees, combined with a coupled rule base, multi-dimensional dynamic reliability assessment results are generated to solve the problem of one-sided dimensions in traditional switchgear reliability assessment.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for reliability assessment of an intelligent small switchgear includes the following steps: collecting switchgear operating status data, calculating a first reliability assessment parameter of the switchgear based on the operating status data; acquiring switchgear lifecycle data and a second reliability assessment parameter, calculating the dynamic threshold range corresponding to the second reliability assessment parameter through collaborative learning of shared tree and specialized tree and constraint consistency correction; performing correlation analysis on the first reliability assessment parameter and the dynamic threshold range to obtain the switchgear reliability assessment result.

[0009] In a preferred embodiment, the first reliability assessment parameters of the switchgear include: partial discharge phase distribution entropy, opening and closing coil current waveform distortion rate, dew point-surface temperature difference, and ultrasonic attenuation coefficient of the insulating tie rod.

[0010] In a preferred embodiment, the step of calculating the dynamic threshold range corresponding to the second reliability assessment parameter through collaborative learning of shared trees and specialized trees and constraint consistency correction specifically involves: performing feature structuring processing on the switchgear lifecycle data and the second reliability assessment parameter to generate a structured feature vector; the second reliability assessment parameter is obtained through historical fault data; modeling the structured feature vector using a collaborative learning algorithm of shared trees and specialized trees; generating multiple decision trees through multiple rounds of iterative training; fusing the prediction results of shared trees and specialized trees; and outputting the initial threshold range of the second assessment parameter; and performing constraint consistency correction on the output initial threshold range to obtain the dynamic threshold range.

[0011] In a preferred embodiment, the method of modeling structured feature vectors using a shared tree and specialized tree collaborative learning algorithm specifically involves: processing the structured feature vectors using a shared tree model to extract the probability distribution and importance of the features; generating an initial range of evaluation parameters through multiple iterations; and outputting the initial range to the specialized tree through a bidirectional feedback mechanism. Then, using the specialized tree model, based on the initial range and the bidirectional feedback mechanism, the method performs refined learning on specific anomalies to generate discrimination rules and provides feedback to correct any uncovered feature regions in the shared tree model.

[0012] In a preferred embodiment, the bidirectional feedback mechanism specifically comprises: using a shared tree model to feed back the output probability distribution and feature importance information to a specialized tree; using the specialized tree model to determine key features and optimize the splitting strategy based on the feedback from the shared tree, refining the modeling of specific anomalies, and generating discrimination rules; feeding back the local high-risk parameters and their discrimination rules identified by the specialized tree to the shared tree, and correcting the feature regions not covered in the global model and adjusting the weights of relevant features through the shared tree.

[0013] In a preferred embodiment, the constraint consistency correction includes basic constraint correction and associated constraint correction.

[0014] In a preferred embodiment, the correlation analysis is performed using a coupled rule base. The method for constructing the coupled rule base is as follows: performing correlation analysis on the first reliability assessment parameters to identify the interaction relationships between the parameters; using a constrained Apriori algorithm to mine association rules for the first reliability assessment parameters to generate an initial association rule set; calculating the confidence of each rule in the association rule set and performing filtering and optimization based on a preset confidence threshold; and summarizing the optimized association rules to construct the coupled rule base.

[0015] In a preferred embodiment, the step of using the constrained Apriori algorithm to mine association rules for the first reliability assessment parameter specifically involves: introducing prior constraints based on a dynamic threshold backpropagation model and removing combinations that do not meet the constraints; and dynamically adjusting the threshold range of the first reliability assessment parameter according to the risk level and importance based on the dynamic threshold backpropagation model.

[0016] In a preferred embodiment, the switchgear reliability assessment result includes a first assessment result based on a first reliability assessment parameter and a dynamic threshold range, and a second assessment result based on the first reliability assessment parameter and a coupled rule base.

[0017] An electronic device, characterized in that the electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the intelligent small switch cabinet reliability assessment method as described in any one of claims 1 to 9.

[0018] The present invention relates to the technical effects and advantages of an intelligent small switchgear reliability assessment method and equipment:

[0019] This invention is based on collaborative learning of shared trees and specialized trees, constraint consistency correction, and combines switchgear lifecycle data and second reliability assessment parameters to output the dynamic threshold range of switchgear assessment parameters. Then, based on the first reliability assessment parameters of the switchgear, the dynamic threshold range, and the coupling rule base, the reliability assessment result of the small switchgear is obtained. This realizes the dynamic adjustment of the threshold range and the supplementation of the correlation between different parameters, enhances the accuracy of the assessment results, and effectively solves the problem of one-sided dimensions in the traditional reliability assessment of small switchgear. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a reliability assessment method for an intelligent small switchgear provided in an embodiment of the present invention.

[0021] Figure 2This is a schematic diagram of the electronic device structure of an intelligent small switchgear reliability assessment device provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1, Figure 1 This invention provides a method for reliability assessment of an intelligent small switchgear, comprising the following steps:

[0024] S1, collect switchgear operating status data, and calculate the first reliability assessment parameters of the switchgear based on the operating status data;

[0025] S2, acquire switch cabinet lifecycle data and second reliability assessment parameters, and calculate the dynamic threshold range corresponding to the second reliability assessment parameters through collaborative learning of shared tree and special tree and constraint consistency correction;

[0026] S3, perform a correlation analysis on the first reliability assessment parameter and the dynamic threshold range to obtain the switch cabinet reliability assessment result.

[0027] This embodiment acquires switchgear lifecycle data and second reliability assessment parameters. Through collaborative learning of shared trees and specialized trees, and constraint consistency correction, it calculates the dynamic threshold range corresponding to the second reliability assessment parameters. Then, combined with the coupled rule base, it evaluates the correlation between the first reliability assessment parameters and generates the final result. The core innovation lies in modeling through collaborative learning of shared trees and specialized trees, and obtaining the dynamic threshold range through constraint consistency correction. It infers the actual failure boundary from the second assessment parameters of switchgear lifecycle data and historical failure cases, and outputs the dynamic threshold range, generating a personalized dynamic threshold range for each switchgear that changes with its state and environment. The coupled rule base, through multi-parameter correlation analysis, adds depth to the structural characteristics of small switchgear, improving the assessment results. This solves the problem of one-sided dimensions in traditional reliability assessment.

[0028] S1, collect switchgear operating status data, and calculate the first reliability assessment parameters of the switchgear based on the operating status data;

[0029] In this embodiment, the first reliability assessment parameters of the switchgear include: partial discharge phase distribution entropy, opening and closing coil current waveform distortion rate, dew point-surface temperature difference, and ultrasonic attenuation coefficient of the insulating tie rod.

[0030] It should be noted that the partial discharge phase distribution entropy is calculated by using a highly sensitive sensor installed near the busbar in a small switchgear to collect partial discharge pulses during the voltage cycle, counting the number of pulses in each phase, and the calculation formula is as follows:

[0031]

[0032] In the formula, pi is the pulse proportion of the i-th phase interval, and H is the distribution entropy (normal range <1.2, aging >1.8).

[0033] It should be noted that the distortion rate of the opening and closing coil current waveform is obtained by using a Hall current sensor connected in series in the opening and closing coil circuit of the small switchgear. The current waveform is collected when the opening and closing action occurs. Fourier transforms are performed on the collected current waveform and the standard current waveform to obtain their respective spectral representations. The distortion rate is then calculated by measuring the difference between the two spectra. The specific formula is as follows:

[0034]

[0035] In the formula, For the nth harmonic current, TND is the fundamental frequency, and TND is the total harmonic distortion of the output (normal <3%, mechanical abnormality >5%).

[0036] It should be noted that the dew point-surface temperature difference is calculated in real time by integrating a dew point sensor and an infrared temperature sensor in a small switch cabinet (normal > 2℃, condensation risk ≤ 0℃).

[0037] It should be noted that the ultrasonic attenuation coefficient of the insulating tie rod is determined by ultrasonic probes attached to both ends of the tie rod of the small switchgear. Ultrasonic waves are injected at the transmitting end, and the amplitude attenuation is detected at the receiving end. The specific formula is as follows:

[0038]

[0039] In the formula, For the transmission amplitude, To receive amplitude, The attenuation coefficient is (normal <5%, crack >20%).

[0040] S2, acquire switch cabinet lifecycle data and second reliability assessment parameters, and calculate the dynamic threshold range corresponding to the second reliability assessment parameters through collaborative learning of shared tree and special tree and constraint consistency correction;

[0041] In this embodiment, the dynamic threshold range corresponding to the second reliability assessment parameter is calculated through collaborative learning of the shared tree and the specialized tree, as well as constraint consistency correction, specifically as follows:

[0042] The switchgear lifecycle data and the second reliability assessment parameters are processed by feature structuring to generate a structured feature vector. The second reliability assessment parameters are obtained through historical fault data.

[0043] The structured feature vector is modeled using a shared tree and specialized tree collaborative learning algorithm. Multiple decision trees are generated through multiple rounds of iterative training. The prediction results of the shared tree and specialized tree are then fused to output the initial threshold range of the second evaluation parameter.

[0044] Constraint consistency correction is applied to the initial threshold range of the output to obtain the dynamic threshold range.

[0045] It should be noted that the switchgear lifecycle data includes: initial value of insulation resistance at the factory, reference value of partial discharge, years of operation, cumulative number of opening and closing cycles, and annual load rate curve;

[0046] It should be noted that the second reliability assessment parameter refers to the outlier values ​​of the following four parameters when failures occurred in historical cases: partial discharge phase distribution entropy, opening and closing coil current waveform distortion rate, dew point-surface temperature difference, and ultrasonic attenuation coefficient of the insulating tie rod.

[0047] In this embodiment, the feature structuring process specifically includes:

[0048] Lifecycle data and second reliability assessment parameters are divided into different dimensions based on their characteristics, including: basic dimension, time series dimension, and environmental dimension.

[0049] The features are processed using the Z-score normalization method to form a normalized feature row vector corresponding to the evaluation parameters.

[0050] It should be noted that the classification into different dimensions is based on the specific type of the parameter. The initial value of the factory insulation resistance and the benchmark value of the partial discharge are classified into the basic dimension; parameters that change cumulatively over time, such as the cumulative number of opening and closing cycles and the number of years of operation, are classified into the time-series dimension; and parameters that are related to the external environment are classified into the environmental dimension.

[0051] It should be noted that Z-score standardization of features can eliminate differences in units and orders of magnitude, allowing the different parameters to be learned collaboratively in the same model. The specific formula is as follows:

[0052]

[0053] In the formula These are the original eigenvalues. This is the mean of the feature in the sample. This represents the standard deviation of the feature.

[0054] It should be noted that the standard deviation mentioned The calculation formula is:

[0055]

[0056] In the formula The standard deviation of the feature The number of samples for this feature. For the first feature One original sample value.

[0057] In this embodiment, the method for collaborative learning and constraint consistency correction of the shared tree and the specialized tree includes a dynamic threshold back-calculation model.

[0058] In this embodiment, the method of modeling structured feature vectors using a shared tree and specialized tree collaborative learning algorithm specifically involves:

[0059] The shared tree model is used to process structured feature vectors, extract the probability distribution and importance of features, generate the initial range of evaluation parameters through multiple iterations, and output them to the specialization tree through a two-way feedback mechanism;

[0060] By utilizing a specialized tree model, based on an initial range and a two-way feedback mechanism, we can refine the learning for specific anomalies, generate discrimination rules, and provide feedback to correct the uncovered feature regions of the shared tree model.

[0061] In this embodiment, the process of shared tree learning modeling is specifically as follows:

[0062] In each round of splitting, the CART algorithm iterates through all features and their possible split points, calculates the Gini index of each feature at a given split point, and selects the feature with the smallest Gini index for splitting.

[0063] After selecting the optimal features and split points, the data is recursively split into two subsets. Each subset continues to split until a stopping condition is met (e.g., the tree depth reaches a preset maximum value, or the node purity reaches a threshold).

[0064] Then, at each leaf node, output the probability distribution of the corresponding category; this probability distribution represents the probability that the system will experience a certain risk under the feature conditions corresponding to that node.

[0065] It should be noted that the CART algorithm is specifically as follows:

[0066] The input parameters are divided into two parts based on the main features of the structured feature vector.

[0067] Mean squared error was used as a purity metric to calculate the purity of the bisectioned sample groups, and the purity calculation results were used to recursively split each group of samples.

[0068] The splitting process stops when the disorder of the split sample group is lower than the preset disorder threshold, or when the number of samples in the sample group is less than the preset threshold.

[0069] The average value of the parameters of each sub-sample group obtained by the final division is calculated, and the average value is used as the threshold output of the corresponding parameter.

[0070] It should be noted that the specific formula for selecting the optimal feature and split point is as follows:

[0071]

[0072] In the formula, It is the first The smaller the Gini index, the purer the nodes are.

[0073] In this embodiment, the process of learning and modeling the specialty tree is as follows:

[0074] The split is performed by selecting the features most relevant to the output of the shared tree. The specialized tree will pay more attention to the boundary values ​​of these features, especially in areas with higher risk.

[0075] Continue recursively splitting, selecting the best features and split points each time to maximize the purity after splitting;

[0076] At the leaf nodes of the specialization tree, more detailed risk assessment results are output; the final output is a refined risk assessment result for specific features.

[0077] It should be noted that the specific formula for the risk assessment results output by integrating the shared tree and the specialized tree is as follows:

[0078]

[0079] In the formula, This indicates the proportion of the shared tree in the final risk assessment. This represents the probability of risk assessment based on global features. This represents the probability of risk assessment based on local features.

[0080] In this embodiment, the bidirectional feedback mechanism is specifically as follows:

[0081] The shared tree model is used to feed back the output probability distribution and feature importance information to the specialized tree;

[0082] By using a specialized tree model, key features are determined based on shared tree feedback and the splitting strategy is optimized. Detailed modeling is then performed on specific anomalies to generate discrimination rules.

[0083] The local high-risk parameters and their discrimination rules identified by the specialized tree are fed back to the shared tree. The shared tree corrects the feature regions not covered in the global model and adjusts the weights of the relevant features.

[0084] In this embodiment, before the shared tree and specialized tree learn together, Z-score preprocessing is required for all samples to calculate the initial sample weights. The calculation formula is:

[0085]

[0086] In the formula, The baseline weighting coefficient represents the basic weight without other adjustment factors. Let i be the measure of uncertainty for sample i. Regarding uncertainty The mapping function is used to transform uncertainty into weight correction amounts. This is an indicator of the rarity or representativeness of sample i, reflecting the scarcity of the sample within the overall data distribution. , These are the weighting coefficients for uncertainty correction and scarcity correction, respectively. Let i be the observation value of sample i in feature dimension k. The dynamic threshold center position of feature k. This is a function of how much a sample deviates from the threshold, used to reflect the contribution of a sample to the learning of the threshold boundary. The weighting coefficient is used to correct for threshold deviation.

[0087] In this embodiment, the process of feeding back the shared tree to the specialized tree and the specialized tree determining key features and optimizing the splitting strategy based on the feedback information from the shared tree is as follows:

[0088] The shared tree outputs the probability distribution of samples under each risk category and feeds this probability distribution back to the specialized tree as a soft label. When receiving sample input, the specialized tree uses this probability distribution along with the original feature vector of the sample as augmented input, thereby obtaining a smoother supervision effect during training and determining key features and optimizing the splitting strategy accordingly.

[0089] After receiving feedback suggestions from multiple special projects, the shared tree incorporates the rules provided by each project into the split search space as candidate split points using a candidate splitting method. For each candidate split *s*, a score is defined to characterize the split's discriminative power on the corrected samples. Splits with higher scores are selected first.

[0090] It should be noted that the formula for the candidate splitting alternative method is as follows:

[0091]

[0092] in: This is used to measure the contribution of partition S to the improvement of node purity; This represents a confidence measure considering parameter correlation.

[0093] It should be noted that the above The definition is as follows:

[0094]

[0095] In the formula, The sample set after correlation correction. The weights of sample i, To partition the set of samples that fall into the left child node L, Let i be an indicator function, if sample i belongs to If the value is 1, then the value is 1; otherwise, it is 0.

[0096] In this embodiment, the process of the shared tree correcting the uncovered feature regions in the global modeling and adjusting the weights of the partitioned features in the model based on feedback from the specialized tree is as follows:

[0097] For each specialized tree, high-risk nodes that meet certain conditions are identified in its leaf nodes. These conditions include: the number of samples in the leaf node is not less than a preset threshold, the high-risk probability exceeds a preset value, and the classification confidence is high. For leaf nodes that meet the conditions, the discrimination rules determined by the split path are extracted, and the node statistics are recorded to form a correction sample set. The correction samples are assigned higher weights and fed back to the shared tree to correct feature regions that are not covered or under-learned during the modeling process. This further optimizes the weight allocation of the shared tree in feature selection and boundary determination, thereby improving the overall accuracy and robustness of the model.

[0098] It should be noted that when the decision tree selects "waveform distortion rate of opening and closing coil > 12%" and "dew point temperature difference < 2℃" as splitting conditions in turn during the training process, all samples falling into the leaf node meet the above conditions. Therefore, the discrimination rule corresponding to the leaf node is "when the waveform distortion rate of opening and closing coil is greater than 12% and the dew point temperature difference is less than 2℃, it is judged as high risk".

[0099] In this embodiment, the constraint consistency correction includes basic constraint correction and associated constraint correction.

[0100] In this embodiment, the basic constraints are defined based on electrical industry standards and equipment operation specifications, setting initial upper and lower limits for the evaluation parameter thresholds.

[0101] The correlation constraint correction is constructed based on the correlation between the second reliability assessment parameters and the physical characteristics of the switchgear.

[0102] It should be noted that for partial discharge phase distribution entropy, the limit for partial discharge in conventional switchgear is 10 pC, corresponding to an upper limit of 2.5 for phase distribution entropy; however, due to the lower insulation margin of small switchgear (insulation distance is 20% shorter than conventional switchgear), the threshold is tightened by 20%, hence the associated constraint is:

[0103] = 2.0, = 0.3 (lower limit to avoid misjudging normal micro-discharges);

[0104] It should be noted that the upper limit for the distortion rate of the opening and closing coils under normal mechanical operation is 10%; however, due to the compact structure of small switchgear and accelerated wear, the threshold is tightened to 10%, hence the associated constraint is:

[0105] = 9.0%, =0.5% (lower limit excludes sensor zero drift);

[0106] It should be noted that, considering the vibration amplification effect of the small cabinet (vibration value is 30% higher than that of the conventional cabinet), the risk of cracking is higher due to the attenuation coefficient of the insulating tie rod. Therefore, the associated constraint is:

[0107] = 18% (10% lower than the standard cabinet's 20%);

[0108] It should be noted that, regarding localized temperature differences within the cabinet, the risk of localized overheating is increased due to the limited heat dissipation of small cabinets (the heat dissipation area is only 60% of that of regular cabinets). Therefore, the associated constraints are as follows:

[0109] =7℃ (2℃ lower than the standard cabinet temperature of 9℃).

[0110] S3, perform a correlation analysis on the first reliability assessment parameter and the dynamic threshold range to obtain the switch cabinet reliability assessment result.

[0111] In this embodiment, the correlation analysis is performed using a coupling rule base, which is constructed as follows:

[0112] Correlation analysis was performed on the first reliability assessment parameters to identify the interaction relationships between the parameters;

[0113] A constrained Apriori algorithm is used to mine association rules for the first reliability assessment parameter to generate an initial set of association rules.

[0114] Calculate the confidence score of each rule in the association rule set, and perform filtering and optimization based on a preset confidence threshold;

[0115] Summarize and optimize the association rules to build a coupled rule library.

[0116] In this embodiment, the process of performing correlation analysis on the first reliability assessment parameter and identifying the interaction relationship between the parameters is as follows:

[0117] The parameter threshold range generated by the dynamic threshold backpropagation model is discretized and divided into insufficient, normal, and excessive levels according to the upper and lower limits of the threshold. The discretized levels correspond to different risk levels and are used as atomic terms of the Apriori algorithm.

[0118] Based on the correction of association constraints, the feasibility of atomic terms is determined. If an atomic term does not meet the physical boundary conditions, it is directly eliminated and does not enter the candidate set. During the candidate generation process, the association constraint predicate of the candidate combination is calculated. If the candidate does not meet the predicate, it is directly discarded.

[0119] It should be noted that the predicate is composed of causal relationships or empirical formulas between parameters, such as "the insulation resistance shall not be greater than 200MΩ when the temperature exceeds 80℃".

[0120] It should be noted that the generated parameter threshold range is discretized using the following formula:

[0121]

[0122] In the formula, and To evaluate the dynamic threshold range of the parameters .

[0123] It should be noted that during the rule filtering stage, a predetermined confidence threshold is set, and only rules with a confidence level greater than the threshold are retained, ensuring that the rules entering the rule base have high reliability and universality.

[0124] It should be noted that the confidence score calculation process for the combined rules is as follows:

[0125]

[0126] In the formula, Indicates the condition for combining parameters. This indicates the corresponding risk event.

[0127] In this embodiment, the step of using the constrained Apriori algorithm to mine association rules for the first reliability assessment parameter specifically involves:

[0128] Based on the dynamic threshold back-calculation model, prior constraints are introduced to remove combinations that do not meet the constraints.

[0129] By combining a dynamic threshold back-calculation model, the threshold range of the first reliability assessment parameter is dynamically adjusted according to the risk level and importance.

[0130] In this embodiment, the process of dynamically adjusting the threshold range of the first reliability assessment parameter according to the risk level and importance is as follows:

[0131] By using a dynamic threshold back-calculation model to generate a threshold range, when the system risk level increases, the threshold range of the evaluation parameters is tightened, making the parameters more likely to trigger "anomaly" judgments, thereby enhancing the sensitivity to potential faults; when the risk level is low, the threshold range is widened to avoid generating too many false alarms.

[0132] It should be noted that the formula for this process is as follows:

[0133]

[0134] In the formula, and The corrected threshold. Risk level coefficient and This is an adjustment factor used to control the degree of threshold tightening.

[0135] In this embodiment, to verify the effectiveness of the method of the present invention, four small switchgear with different faults were selected as evaluation objects. The status and environmental data of the switchgear during operation were collected, the evaluation parameter values ​​were calculated, and an evaluation was performed in combination with the dynamic threshold to generate the evaluation result.

[0136] The evaluation results are shown in Table 1:

[0137] Table 1

[0138]

[0139] As can be seen from Table 1, the method of the present invention can achieve a one-time evaluation of evaluation parameters and dynamic thresholds for small switchgear under different operating conditions, laying the foundation for further evaluation.

[0140] For example, in Case 1, because the waveform distortion rate of the opening and closing coil current exceeded 5% and the dew point-surface temperature difference was below the threshold, the coil current abnormality risk was determined to be high and the condensation risk to be medium in a single assessment.

[0141] In Case 3, the waveform distortion rate of the opening and closing coil current exceeded 2%, and the entropy of the partial discharge phase distribution exceeded the standard. The time difference between the two exceeding the standard was less than 10 minutes. In one assessment, the coil current abnormality risk was determined to be medium and the partial discharge risk was also medium.

[0142] Case 4 was determined to have a medium risk of partial discharge and a medium risk of damage to the insulating tie rod due to excessive entropy of partial discharge phase distribution and ultrasonic attenuation coefficient of the insulating tie rod in a single assessment.

[0143] In this embodiment, the initial evaluation results are combined with the coupling rule base to perform correlation analysis on the parameters of the results and perform a second evaluation. The second evaluation results are shown in Table 2.

[0144] Table 2

[0145]

[0146] As can be seen from Table 2, the method of the present invention can accurately determine the risk level of different switch cabinets through the joint analysis of dynamic thresholds and rule bases, and give the cause of risk, providing a reliable basis for subsequent operation and maintenance.

[0147] For example, in Case 1, the first assessment determined that the risk of abnormal coil current was high and the risk of condensation was medium. By combining the coupling rule base, it was found that there was a rule that high current distortion rate + low dew point temperature difference -> high risk of coil contact oxidation. The generated second assessment result was high risk. The risk cause analysis was that poor contact of coil contacts and condensation formed a vicious cycle of "poor contact - condensation corrosion". In addition, the humidity accumulation in the compact space of the small switch cabinet further amplified the impact.

[0148] In Case 2, the initial assessment revealed three risk scenarios: moderate risk of partial discharge, moderate risk of condensation, and moderate risk of damage to the insulating tie rod. The rule base identified a rule: high phase distribution entropy + low dew point temperature difference + high ultrasonic attenuation coefficient -> extremely high risk of insulation breakdown. Given the severity of the problem, the second assessment would assess the risk level as extremely high. The risk cause analysis revealed a triple coupling of "discharge-condensation-structural damage," with concentrated electric fields in a compact space leading to short-term failure of the insulation system.

[0149] In Case 4, based on the results of a single assessment that the risk of partial discharge and the risk of damage to the insulating tie rod are both medium, and combined with the association of the coupling rule base, there is a rule that high phase distribution entropy + high ultrasonic attenuation coefficient -> significant risk of insulation tie rod breakage, with a risk level of high. The risk cause analysis is that partial discharge and physical damage to the insulating tie rod form a "discharge degradation - structural damage" coupling cycle, and the compact space of the small switch cabinet accelerates the insulation failure.

[0150] Example 2, Figure 2 A schematic diagram of the electronic device structure of the intelligent small switchgear reliability assessment method of the present invention is provided. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the intelligent small switchgear reliability assessment method.

[0151] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0152] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0153] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0154] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0155] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0156] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A reliability assessment method for intelligent small switchgear, characterized in that, Includes the following steps: Collect switchgear operating status data and calculate the first reliability assessment parameters of the switchgear based on the operating status data; The process involves acquiring switchgear lifecycle data and second reliability assessment parameters. Through collaborative learning of shared trees and specialized trees, along with constraint consistency correction, the dynamic threshold range corresponding to the second reliability assessment parameters is calculated. Specifically, the switchgear lifecycle data and second reliability assessment parameters undergo feature structuring processing to generate structured feature vectors. The second reliability assessment parameters are obtained from historical fault data. A collaborative learning algorithm of shared trees and specialized trees is used to model the structured feature vectors. Multiple decision trees are generated through iterative training, and the prediction results of the shared trees and specialized trees are fused to output the initial threshold range of the second assessment parameters. Constraint consistency correction is applied to the output initial threshold range to obtain the dynamic threshold range. The modeling of the structured feature vectors using the collaborative learning algorithm of shared trees and specialized trees specifically involves processing the structured feature vectors using a shared tree model, extracting the probability distribution and importance of the features, generating the initial range of the assessment parameters through multiple iterations, and outputting it to the specialized tree through a bidirectional feedback mechanism. Using a specialized tree model, based on an initial range and a two-way feedback mechanism, we refine the learning for specific anomalies, generate discrimination rules, and provide feedback to correct the uncovered feature regions of the shared tree model. A correlation analysis was performed on the first reliability assessment parameter and the dynamic threshold range to obtain the switchgear reliability assessment result.

2. The intelligent miniature switchgear reliability assessment method according to claim 1, characterized in that, The first reliability assessment parameters of the switchgear include: partial discharge phase distribution entropy, current waveform distortion rate of opening and closing coils, dew point-surface temperature difference, and ultrasonic attenuation coefficient of insulating tie rod.

3. The intelligent miniature switchgear reliability assessment method according to claim 2, characterized in that, The bidirectional feedback mechanism is specifically as follows: The shared tree model is used to feed back the output probability distribution and feature importance information to the specialized tree; By using a specialized tree model, key features are determined based on shared tree feedback and the splitting strategy is optimized. Detailed modeling is then performed on specific anomalies to generate discrimination rules. The local high-risk parameters and their discrimination rules identified by the specialized tree are fed back to the shared tree. The shared tree corrects the feature regions not covered in the global model and adjusts the weights of the relevant features.

4. The intelligent miniature switchgear reliability assessment method according to claim 3, characterized in that, The constraint consistency correction includes basic constraint correction and associated constraint correction.

5. The intelligent miniature switchgear reliability assessment method according to claim 4, characterized in that, The correlation analysis is performed using a coupling rule base, which is constructed as follows: Correlation analysis was performed on the first reliability assessment parameters to identify the interaction relationships between the parameters; A constrained Apriori algorithm is used to mine association rules for the first reliability assessment parameter to generate an initial set of association rules. Calculate the confidence score of each rule in the association rule set, and perform filtering and optimization based on a preset confidence threshold; Summarize and optimize the association rules to build a coupled rule library.

6. The intelligent miniature switchgear reliability assessment method according to claim 5, characterized in that, The process of using a constrained Apriori algorithm to mine association rules for the first reliability assessment parameter is as follows: Based on the dynamic threshold back-calculation model, prior constraints are introduced to remove combinations that do not meet the constraints. By combining a dynamic threshold back-calculation model, the threshold range of the first reliability assessment parameter is dynamically adjusted according to the risk level and importance.

7. The intelligent miniature switchgear reliability assessment method according to claim 6, characterized in that, The switchgear reliability assessment results include a first assessment result based on a first reliability assessment parameter and a dynamic threshold range, and a second assessment result based on the first reliability assessment parameter and a coupled rule base.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the intelligent small switchgear reliability assessment method array as described in any one of claims 1 to 7.

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