Insulation performance optimization method of high-voltage electrical equipment, equipment and medium

Abstract

The invention relates to the technical field of insulation design of high-voltage electrical equipment, and discloses an insulation performance optimization method and equipment for high-voltage electrical equipment and a medium. The method comprises the following steps: determining an electric field abnormal area according to geometric parameters and electric field distribution of the high-voltage electrical equipment; determining a voltage regulation strategy according to the electric field abnormal region; determining design parameters of a voltage-sharing element according to the voltage regulation strategy so as to obtain homogenized electric field distribution; determining an insulation performance evaluation result of the high-voltage electrical equipment in a system integration state according to the homogenized electric field distribution; and determining a collaborative optimization scheme for the insulation structure of the high-voltage electrical equipment according to the insulation performance evaluation result. The technical problems of low design efficiency, limited optimization ability and difficulty in guaranteeing result optimality caused by the fact that the whole process highly depends on artificial experience and lacks intelligent and data-driven decision support in the prior art can be at least solved.

Description

Technical Field

[0001] This application relates to the field of insulation design technology for high-voltage electrical equipment, and in particular to a method, equipment and medium for optimizing the insulation performance of high-voltage electrical equipment. Background Technology

[0002] In high-voltage power systems, the insulation reliability of various high-voltage electrical equipment (such as high-voltage circuit breakers) is the core of ensuring the safe and stable operation of the power grid. As equipment becomes more compact and operates at higher voltage levels, its internal electric field distribution becomes increasingly complex, and partial discharge has become a major cause of insulation degradation and even equipment failure.

[0003] Currently, the industry generally adopts an experience-based and iterative verification-based approach for the insulation design of high-voltage electrical equipment. This approach typically determines initial geometric parameters based on empirical formulas and standard parts libraries. Subsequently, a 3D model of the equipment is built using these parameters, and its internal electric field distribution is calculated through numerical simulation, generating a visualized electric field cloud map. Design engineers manually observe and analyze these cloud maps, using experience to identify areas with excessively high or unevenly distributed electric field strength. For any problems identified, engineers propose modifications based on personal experience or similar cases, such as adjusting the angle of a shield or changing the diameter of the equalizing ring. The modified design requires remodeling and simulation verification; the entire process often involves multiple manual adjustments and simulation cycles until the electric field distribution reaches an acceptable state, thereby determining the design parameters for each component.

[0004] However, the inventors have found at least the following technical problems in the related technologies: the entire process is highly dependent on human experience, lacks intelligent and data-driven decision support, resulting in low design efficiency, limited optimization capabilities, and difficulty in guaranteeing the optimality of the results. Summary of the Invention

[0005] One objective of this application is to provide a method, device, and medium for optimizing the insulation performance of high-voltage electrical equipment, at least to address the technical problem in related technologies where the entire process relies heavily on human experience, lacks intelligent and data-driven decision support, resulting in low design efficiency, limited optimization capabilities, and difficulty in guaranteeing optimal results.

[0006] To achieve the above objectives, some embodiments of this application provide the following aspects:

[0007] In a first aspect, some embodiments of this application provide a method for optimizing the insulation performance of high-voltage electrical equipment. The method includes: determining an abnormal electric field region based on the geometric parameters and electric field distribution of the high-voltage electrical equipment; determining a voltage regulation strategy based on the abnormal electric field region; determining the design parameters of a voltage equalization element based on the voltage regulation strategy to obtain a uniform electric field distribution; determining the insulation performance evaluation result of the high-voltage electrical equipment in a system integration state based on the uniform electric field distribution; and determining a collaborative optimization scheme for the insulation structure of the high-voltage electrical equipment based on the insulation performance evaluation result.

[0008] Secondly, some embodiments of this application also provide an electronic device, the electronic device comprising: one or more processors; and a memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method described above.

[0009] Thirdly, some embodiments of this application also provide a computer-readable medium having computer program instructions stored thereon, which can be executed by a processor to implement the method described above.

[0010] Compared with related technologies, the solution provided in this application achieves an organic unity from local problem localization to system performance improvement by constructing a technical closed loop of electric field anomaly region identification → voltage regulation strategy formulation → voltage equalization element parameter optimization → system insulation performance evaluation → collaborative optimization decision-making. Specifically, by identifying abnormal electric field regions based on the geometric parameters and electric field distribution of high-voltage electrical equipment, this method replaces simple judgments based on manual interpretation or fixed thresholds. It achieves quantitative analysis of the correlation between equipment geometric parameters and complex electric field distributions, solving the problem of inaccurate or missed identification of potential insulation weaknesses. By determining voltage regulation strategies based on abnormal electric field regions, it eliminates reliance on designers' personal experience and clarifies optimization directions through systematic correlation analysis, overcoming the limitation of voltage regulation strategies lacking sufficient scientific basis. The method determines the design parameters of voltage equalization components based on voltage regulation strategies, replacing iterative manual adjustments and simulation verifications. This achieves efficient optimization across a vast parameter space, resulting in refined and automated optimization of the electric field. Through insulation performance evaluation and collaborative optimization decisions in a system-integrated state, a closed-loop process of "local optimization - system verification - collaborative adjustment" is constructed, avoiding the adverse effects of independent improvements to single components on other parts and ensuring optimal balance and maximum improvement of insulation performance at the system-wide level. This data-driven, physical mechanism-integrated optimization process can effectively improve the insulation reliability of high-voltage electrical equipment, suppress partial discharge, extend equipment lifespan, and provide a systematic solution for the design and development of high-voltage equipment. Attached Figure Description

[0011] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0012] Figure 1 An exemplary flowchart of a method for optimizing the insulation performance of high-voltage electrical equipment, provided for some embodiments;

[0013] Figure 2 An exemplary structural diagram of an electronic device is provided for some embodiments. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] First Embodiment

[0016] The first embodiment relates to a method for optimizing the insulation performance of high-voltage electrical equipment. For example... Figure 1 As shown, the method may include the following steps:

[0017] Step S101: Determine the abnormal electric field region based on the geometric parameters and electric field distribution of the high-voltage electrical equipment;

[0018] Step S102: Determine a voltage regulation strategy based on the abnormal electric field region;

[0019] Step S103: Determine the design parameters of the voltage equalization element according to the voltage regulation strategy to obtain a uniform electric field distribution;

[0020] Step S104: Based on the homogenized electric field distribution, determine the insulation performance evaluation result of the high-voltage electrical equipment in the system integration state;

[0021] Step S105: Based on the insulation performance evaluation results, determine a collaborative optimization scheme for the insulation structure of the high-voltage electrical equipment.

[0022] The following sections will provide a detailed explanation of each of the above steps.

[0023] Specifically, regarding step S101, the high-voltage electrical equipment may include, but is not limited to, a high-voltage circuit breaker. The geometric parameters describe the specific shape, size, and layout of the high-voltage electrical equipment; the electric field distribution reflects the magnitude and direction of the internal electric field strength of the high-voltage electrical equipment under rated or overvoltage conditions. The abnormal electric field regions reflect potential insulation weaknesses due to concentrated or improperly distributed electric fields within the high-voltage electrical equipment, and are areas requiring focused attention in subsequent optimization design.

[0024] In some examples, the geometric parameters may include at least one of the following: the radius of curvature of the high-voltage conductive component, the spatial position and tilt angle of the shielding structure, and the profile dimensions of the voltage equalizing element. The electric field distribution can be obtained through numerical simulation.

[0025] For example, in the case where the high-voltage electrical equipment is a high-voltage circuit breaker, a three-dimensional digital model can be established based on the geometric parameters of the high-voltage electrical equipment. Furthermore, electromagnetic field simulation software can be used to apply a specific operating voltage to this three-dimensional digital model and perform numerical simulations to obtain an electric field distribution cloud map. By analyzing this electric field distribution cloud map, it can be found that at the tips, corners, or abrupt changes in surface curvature of certain high-voltage conductive components, the electric field strength is significantly higher than that of the surrounding area, and the electric field lines exhibit a highly concentrated state. These areas are identified as electric field anomaly regions.

[0026] For step S102, for example, the determination of the voltage regulation strategy may include analyzing the causes of the abnormal electric field region, formulating a corresponding potential regulation scheme based on the analysis results, thereby redistributing the potential of the abnormal electric field region to weaken the electric field concentration effect.

[0027] For example, if an abnormal electric field region is located near the tip of a high-voltage conductive component, analysis indicates that the electric field concentration in this region originates from the relative positional relationship between the high-voltage conductive component and the adjacent shielding structure. Based on this analysis, a possible voltage regulation strategy could be to appropriately adjust the spatial position of the shielding structure to increase the distance between it and the tip of the high-voltage conductive component, or to adjust the tilt angle of the shielding structure to improve the electric field line distribution.

[0028] For step S103, for example, determining the design parameters may include converting the voltage regulation strategy into specific engineering design parameters for the voltage equalization element. For instance, through parametric design and analysis, the optimal combination of parameters such as the outline size and spatial location of the voltage equalization element can be determined to make the electric field distribution inside the high-voltage electrical equipment smoother and more uniform. Furthermore, numerical simulation methods can be used to iteratively verify the electric field distribution under different parameter combinations until an optimized scheme that meets the uniformity requirements is obtained.

[0029] For example, if the voltage regulation strategy involves adding an equalizing ring at a specific location, this step requires determining the design parameters of the equalizing ring, such as its outline dimensions, cross-sectional shape, and installation height. By establishing a parametric model and performing numerical simulations, the parameter combination that results in the most uniform electric field distribution can be selected.

[0030] For step S104, for example, the overall insulation reliability level of the high-voltage electrical equipment based on the optimized electric field distribution under simulated operating conditions can be evaluated to obtain the insulation performance evaluation result. It is evident that the insulation performance evaluation result can reflect the overall insulation safety margin of the high-voltage electrical equipment, and may include, but is not limited to, quantitative indicators, such as reliability evaluation indicators for key connection parts or estimated parameters of the overall system withstand voltage level.

[0031] For step S105, for example, if the insulation performance evaluation result does not meet the preset performance target, it indicates that there is room for further design optimization. At this time, collaborative optimization of the insulation structure of the high-voltage electrical equipment can be triggered.

[0032] The collaborative optimization may include comprehensively considering the mutual influence of multiple components in the high-voltage electrical equipment and globally coordinating the adjustment of multiple insulation structure parameters. Through this collaborative optimization, the optimal solution for the overall insulation performance of the high-voltage electrical equipment can be sought, thereby outputting an insulation optimization scheme that meets performance requirements.

[0033] It is understandable that the insulation design process for high-voltage electrical equipment in related technologies has significant limitations. First, the identification of abnormal electric field areas mainly relies on engineers' manual interpretation of simulation cloud maps or simple judgments using fixed thresholds. This method struggles to conduct in-depth, quantitative analysis of the correlation between equipment geometric parameters and complex electric field distributions, potentially leading to inaccurate or missed identification of potential insulation weaknesses. Second, for identified electric field concentration problems, voltage regulation strategies largely depend on the designer's personal experience. Due to the lack of systematic data analysis methods to reveal the intrinsic relationship between protective structure parameters and spatial potential distribution, the resulting optimization directions often lack sufficient scientific basis, affecting the effectiveness of the strategy. Furthermore, the parameter determination of key components such as voltage equalization elements is typically an iterative process of manual adjustment and simulation verification. This approach is not only inefficient but also difficult to perform global optimization within a vast design parameter space, failing to achieve refined and automated optimization of the electric field distribution. More importantly, existing methods usually focus on independent improvements to individual components or local areas, lacking a closed-loop process for system-level integrated verification and coordinated adjustment after component optimization. This can lead to optimizations in one area negatively impacting other parts, making it difficult to achieve the optimal balance and maximum improvement in insulation performance at the system-wide level. These limitations collectively constrain the final effectiveness and efficiency of insulation design for high-voltage electrical equipment.

[0034] It is not difficult to see that, compared with related technologies, the solution provided in this application, by constructing a technical closed loop of electric field anomaly region identification → voltage regulation strategy formulation → voltage equalization element parameter optimization → system insulation performance evaluation → collaborative optimization decision-making, can achieve an organic unity from local problem localization to system performance improvement. Specifically, by identifying abnormal electric field regions based on the geometric parameters and electric field distribution of high-voltage electrical equipment, this method replaces simple judgments based on manual interpretation or fixed thresholds. It achieves quantitative analysis of the correlation between equipment geometric parameters and complex electric field distributions, solving the problem of inaccurate or missed identification of potential insulation weaknesses. By determining voltage regulation strategies based on abnormal electric field regions, it eliminates reliance on designers' personal experience and clarifies optimization directions through systematic correlation analysis, overcoming the limitation of voltage regulation strategies lacking sufficient scientific basis. The method determines the design parameters of voltage equalization components based on voltage regulation strategies, replacing iterative manual adjustments and simulation verifications. This achieves efficient optimization across a vast parameter space, resulting in refined and automated optimization of the electric field. Through insulation performance evaluation and collaborative optimization decisions in a system-integrated state, a closed-loop process of "local optimization - system verification - collaborative adjustment" is constructed, avoiding the adverse effects of independent improvements to single components on other parts and ensuring optimal balance and maximum improvement of insulation performance at the system-wide level. This data-driven, physical mechanism-integrated optimization process can effectively improve the insulation reliability of high-voltage electrical equipment, suppress partial discharge, extend equipment lifespan, and provide a systematic solution for the design and development of high-voltage equipment.

[0035] Second Embodiment

[0036] The second embodiment relates to a method for optimizing the insulation performance of high-voltage electrical equipment. The second embodiment is an improvement upon the first embodiment, specifically in that it provides a method for determining abnormal electric field regions based on the geometric parameters and electric field distribution of the high-voltage electrical equipment.

[0037] Specifically, determining the abnormal electric field region based on the geometric parameters and electric field distribution of the high-voltage electrical equipment, i.e., step S101, may include:

[0038] Step S1011: A classification algorithm is used to process the geometric parameters and the electric field distribution to divide multiple electric field distribution regions;

[0039] Step S1012: Determine whether the electric field strength of each region in the plurality of electric field distribution regions exceeds a preset threshold.

[0040] Step S1013: The region where the electric field strength exceeds the preset threshold is identified as the electric field abnormal region.

[0041] For step S1011, for example, a classification algorithm can be used to extract and compare features of the geometric parameters and the electric field distribution. Based on the analysis results, the internal space of the high-voltage electrical equipment can be divided into regions with different electric field intensity distribution characteristics, achieving a preliminary structured classification of the electric field distribution state.

[0042] The classification algorithm may include, but is not limited to, at least one of: support vector machine, decision tree, random forest or deep neural network.

[0043] For step S1012, for example, for each divided electric field distribution region, the statistical characteristics of the electric field intensity of the region can be extracted. By comparing the statistical characteristics with a preset threshold, it can be determined whether the electric field intensity of the region exceeds the preset threshold.

[0044] The statistical characteristics may include at least one of the maximum electric field strength, the average electric field strength, or the gradient distribution index.

[0045] For step S1013, for example, if the electric field intensity in a certain region exceeds the preset threshold, the region can be marked as an electric field anomaly region. All marking results can be integrated to generate an electric field anomaly region dataset. Based on the electric field anomaly region dataset, the distribution characteristics of the anomaly regions can be output, including the spatial location and electric field intensity distribution characteristics of the anomaly regions.

[0046] For example, classification algorithms such as support vector machines can be used to jointly analyze the geometric parameters and the electric field distribution, thereby dividing the internal space of the high-voltage circuit breaker into multiple electric field distribution regions, such as A, B, and C, with different electric field intensity characteristics. For each divided electric field distribution region, the maximum electric field intensity of that region can be calculated, and the calculation result can be compared with a preset threshold. For example, if the maximum electric field intensity of region A exceeds the preset threshold, then that region can be determined as an electric field anomaly region.

[0047] It is not difficult to see that, in this embodiment of the application, by using a classification algorithm to process the geometric parameters and the electric field distribution to divide multiple electric field distribution regions, the automated and structured identification of the electric field state inside high-voltage electrical equipment can be achieved; by judging whether the electric field intensity of each region exceeds a preset threshold, an objective and quantitative anomaly judgment standard can be established, avoiding the subjectivity and inconsistency of human experience judgment; by identifying the region where the electric field intensity exceeds the preset threshold as the electric field anomaly region, the insulation weak point can be accurately located, providing a clear and reliable data basis for the subsequent formulation of targeted voltage regulation strategies, thereby improving the efficiency and accuracy of the entire insulation optimization process.

[0048] Third Embodiment

[0049] The third embodiment relates to a method for optimizing the insulation performance of high-voltage electrical equipment. The third embodiment is an improvement upon the first embodiment, specifically in that it provides a method for determining a voltage regulation strategy based on the abnormal electric field region.

[0050] Specifically, determining the voltage regulation strategy based on the abnormal electric field region, i.e., step S102, may include:

[0051] Step S1021: Obtain the protective structure parameters and connection potential data associated with the abnormal electric field region;

[0052] Step S1022: A clustering algorithm is used to group and analyze the protective structure parameters and the connection potential data to obtain multiple feature groups;

[0053] Step S1023: Analyze the voltage distribution uniformity within each characteristic group to determine the characteristic group with uneven voltage distribution.

[0054] Step S1024: Extract key nodes from the feature group of uneven voltage distribution as voltage distribution adjustment points;

[0055] Step S1025: Generate a voltage regulation strategy based on the voltage distribution adjustment point.

[0056] For step S1021, for example, the protective structure parameters refer to the geometric and positional parameters of components (such as shields) used to control and improve the electric field distribution, which may include, but are not limited to, the spatial position, tilt angle, and outline dimensions of the shielding structure. The connection potential data refers to the measured or simulated potential values ​​of electrical connection points or specific locations near the abnormal electric field region, which can reflect the potential distribution state of the abnormal electric field region. For example, the coordinates, angles, and dimensions of protective components such as shields that are geometrically adjacent to or electrically related to the abnormal electric field region can be extracted from the three-dimensional model and electric field simulation results of the high-voltage electrical equipment, and the simulated potential values ​​of key test points on or near these components can be obtained simultaneously to form a related dataset.

[0057] Regarding step S1022, for example, the clustering algorithm is an unsupervised machine learning method used to automatically group data points with similar characteristics. It may include, but is not limited to, at least one of the following: K-means algorithm, hierarchical clustering algorithm, or DBSCAN algorithm. For instance, the acquired protective structure parameters and connection potential data can be combined to form a multi-dimensional feature vector, which is then input into a selected clustering algorithm (such as a K-means algorithm with K=3). The clustering algorithm can automatically divide all data points into several feature groups based on the Euclidean distance or density relationship between the data, achieving preliminary classification of the dataset.

[0058] Regarding step S1023, for example, the voltage distribution uniformity is used to characterize the degree of balance in potential distribution among the local regions represented by each data point within the same feature group. This may include calculating at least one of the following indices: variance of the potential gradient within each group, maximum potential difference, or dispersion of the electric field intensity distribution. Specifically, for each obtained feature group, the variance of the local potential gradient corresponding to all data points within that group can be calculated. For example, if the variance of the potential gradient of a certain feature group is significantly higher than that of other groups, then that feature group can be determined to be a feature group with uneven voltage distribution, indicating that the structural region corresponding to that group is the main source of the electric field anomaly.

[0059] For step S1024, for example, the key node refers to a specific spatial location point within a characteristic group of uneven voltage distribution that plays a dominant role in electric field distortion or has significant adjustment potential. This can be represented as a point of abrupt change in potential gradient, a peak point of electric field intensity, or a point where equipotential lines intersect densely. In practice, within a defined characteristic group of uneven voltage distribution, the spatial locations corresponding to all data points can be traversed, and the point with the largest gradient abrupt change can be located by comparing the rate of change of potential gradient between adjacent points; alternatively, the simulated electric field intensity values ​​of each point can be directly compared to find the peak field intensity point. This point is then extracted as the key node requiring potential adjustment, i.e., the voltage distribution adjustment point.

[0060] For step S1025, for example, the voltage regulation strategy may include at least one of the following: a potential redistribution scheme, a shielding structure adjustment scheme, or a voltage equalization element introduction scheme for the adjustment point. In specific implementation, for each extracted voltage distribution adjustment point, specific adjustment measures can be formulated based on its location attributes (e.g., located on a conductor surface or near an insulator) and electric field characteristics (e.g., gradient direction). For example, for the peak electric field point on the conductor surface, the strategy could be "to add a voltage equalization ring above that point"; for potential gradient abrupt change points near an insulator, the strategy could be "to adjust the tilt angle of the adjacent shield to smooth the equipotential lines". The set of all measures for each adjustment point constitutes the complete voltage regulation strategy.

[0061] For steps S1021-S1025, for example, in the design of a high-voltage circuit breaker, protective structure parameters (such as parameter sets A, B, and C) and connection potential data (such as datasets 1, 2, and 3) related to the abnormal electric field region can be obtained. A clustering algorithm is used to group and analyze the protective structure parameters and connection potential data, resulting in feature groups G1, G2, and G3. Analyzing the voltage distribution uniformity within each feature group, feature group G2 is determined to be a feature group with uneven voltage distribution. From feature group G2, key nodes P1 and P2 can be extracted as voltage distribution adjustment points. Based on the voltage distribution adjustment points P1 and P2, corresponding voltage regulation strategies can be generated, such as adjusting the tilt angle of the shielding structure associated with P1 or introducing a voltage equalization element at position P2.

[0062] Optionally, in some embodiments, the step of extracting key nodes from the feature group with uneven voltage distribution as voltage distribution adjustment points, i.e., step S1024, may further include:

[0063] Step S10241: Based on the electric field distortion characteristics of the electric field anomaly region, perform physical mechanism mapping on the cluster analysis results;

[0064] Step S10242: Based on the physical mechanism mapping results, determine the voltage distribution adjustment point with a clear adjustment target.

[0065] For step S10241, for example, the electric field distortion features may include, but are not limited to: the degree of distortion in the distribution of equipotential lines, the directional convergence or divergence of the electric field gradient vector, or suspected concentrated regions of space charge. By performing correlation analysis between the feature groups obtained by the clustering algorithm and the physical field features, the grouping results based solely on data similarity can be mapped to specific, interpretable physical phenomena or structural causes.

[0066] Regarding step S10242, for example, if a certain feature group is mapped to "edge electric field concentration caused by the lack of shielding structure", then the adjustment point can be determined as the key potential support point of that edge region; if it is mapped to "gap electric field distortion caused by the excessively close spacing of conductors with different potentials", then the adjustment point can be determined as the specific equipotential surface control points on both sides of the gap. Through this step, points with clear physical adjustment paths and obvious expected improvement effects can be selected from several candidate points that meet the data characteristics, thereby generating a highly targeted voltage regulation strategy.

[0067] It should be noted that this embodiment can also be an improvement based on the second embodiment.

[0068] It is not difficult to see that, in this embodiment of the application, by acquiring the protective structure parameters and connection potential data associated with the electric field anomaly region, a quantitative correlation between electric field anomalies and specific structural parameters can be established; by using a clustering algorithm to group and analyze the protective structure parameters and connection potential data to obtain multiple feature groups, structured classification and pattern recognition of multi-source heterogeneous data can be achieved; by analyzing the voltage distribution uniformity within each feature group and determining the feature groups with uneven voltage distribution, the parameter combinations causing electric field distortion can be accurately located; by extracting key nodes from the feature groups with uneven voltage distribution as voltage distribution adjustment points, regional problems can be transformed into specific and operable point adjustment targets, and then voltage regulation strategies can be generated based on voltage distribution adjustment points to form an optimization scheme with clear physical meaning and implementation path, providing accurate input for subsequent voltage equalization element design, thereby systematically improving the electric field distribution uniformity and insulation reliability of high-voltage electrical equipment.

[0069] Fourth embodiment

[0070] The fourth embodiment relates to a method for optimizing the insulation performance of high-voltage electrical equipment. The fourth embodiment is an improvement upon the first embodiment, specifically in that it provides a concrete implementation method for determining the design parameters of the voltage equalization element based on the voltage regulation strategy.

[0071] Specifically, determining the design parameters of the voltage equalization element according to the voltage regulation strategy, i.e., step S103, may include:

[0072] Step S1031: For the voltage distribution adjustment point determined in the voltage regulation strategy, obtain the initial size data of the voltage equalization element:

[0073] Step S1032: The initial size data is modeled using a numerical simulation method to simulate the changing trend of the electric field distribution;

[0074] Step S1033: Adjust the size parameters of the equalizing element according to the simulation results;

[0075] Step S1034: Determine the parameter combination that makes the electric field distribution reach a uniform state through iterative simulation;

[0076] Step S1035: Based on the parameter combination, determine the target design parameters of the equalizing element.

[0077] For step S1031, for example, the initial dimensional data may include, but is not limited to, the outline dimensions, cross-sectional shape, and spatial location parameters of the voltage equalization element. In some examples, a set of feasible dimensional parameters, such as the diameter, cross-sectional radius, and installation height of the voltage equalization ring, can be initially calculated from a standard component library or using empirical formulas based on the spatial coordinates of the voltage distribution adjustment point, potential distribution requirements, and geometric constraints of adjacent structures.

[0078] For step S1032, for example, the numerical simulation method may include at least one of the finite element method, boundary element method, or finite difference method. Specifically, a refined three-dimensional electromagnetic field simulation model of the voltage equalizing element and its surrounding insulation structure containing the initial size data can be established, and numerical calculations can be performed under typical operating conditions to obtain the changing trend of the electric field distribution.

[0079] For step S1033, for example, if the simulation shows that the improvement in electric field uniformity is not as expected, such as insufficient reduction in maximum field strength or the appearance of new local high points, the dimensional parameters can be adjusted in a targeted manner. The adjustment method may include increasing or decreasing the key profile dimensions of the voltage equalizing element by a preset step size, changing its cross-sectional shape, or fine-tuning its spatial position.

[0080] For step S1034, for example, the iterative simulation can be performed automatically based on an optimization algorithm (such as gradient descent or genetic algorithm). Each iteration generates a new parameter combination based on the simulation results of the current parameter combination and calculates the corresponding electric field uniformity index. The iterative process continues until the electric field uniformity index is lower than a preset threshold or the convergence condition is met. At this point, the corresponding parameter combination is determined as the optimal solution.

[0081] For step S1035, for example, the target design parameters are the optimal set of size parameters obtained through iterative simulation optimization that enables the electric field distribution to reach a uniform state. It can be understood that determining the target design parameters means completing the specific design of the voltage equalization element for the voltage regulation strategy.

[0082] For steps S1031-S1035, for example, in the optimized design of a high-voltage circuit breaker, if the voltage regulation strategy determines that an equalizing ring needs to be added at location P to improve the electric field, a set of initial size data for the equalizing ring (e.g., diameter 300mm, cross-sectional radius 15mm, installation height 200mm) can be initially selected based on the spatial coordinates of point P and the constraints of adjacent structures. Further, a three-dimensional simulation model containing the equalizing ring of this size can be established using the finite element method to calculate the initial electric field distribution cloud map. The results show that the maximum field strength is 12kV / mm, still higher than the target value. Based on this, the diameter parameter of the equalizing ring can be adjusted to 320mm and the simulation can be repeated. Through multiple iterations (e.g., adjusting the diameter to 330mm and simultaneously increasing the cross-sectional radius to 18mm), a set of parameter combinations (diameter 335mm, cross-sectional radius 17mm, installation height 205mm) is finally found, reducing the maximum field strength to 8kV / mm and meeting the uniformity index. This parameter combination can then be determined as the target design parameters for the equalizing ring, completing the component design for point P.

[0083] Optionally, in some embodiments, the step of determining the parameter combination that makes the electric field distribution uniform through iterative simulation, i.e., step S1034, may include:

[0084] Step S10341: Establish a proxy model with the size of the voltage equalization element as the design variable and electric field homogenization as the optimization objective;

[0085] Step S10342: Optimize the proxy model using an optimization algorithm to determine the parameter combination.

[0086] For step S10341, for example, within the design space of the equalizing element, multiple representative combinations of size parameters can be selected using experimental design methods; high-precision numerical simulations can be performed on each set of parameters to calculate the corresponding electric field uniformity index (such as the field strength non-uniformity coefficient); based on these sample data, an approximate mathematical model capable of quickly predicting the electric field uniformity index under arbitrary size parameters can be trained, i.e., the surrogate model. For example, Gaussian process regression or neural networks can be used to construct this surrogate model, thereby simulating the complex nonlinear relationship between size parameters and electric field performance with lower computational cost.

[0087] For step S10342, for example, the optimization algorithm refers to a mathematical method used to automatically find the optimal combination of parameters in the design space constructed by the surrogate model, which enables the objective function (electric field uniformity index) to reach its optimal value. The optimization algorithm may include Bayesian optimization, genetic algorithms, or gradient descent.

[0088] Specifically, a trained surrogate model (e.g., a model built based on Gaussian process regression) can be used as the objective function. Within a predefined design space of equalizing element size parameters, a selected optimization algorithm (e.g., a Bayesian optimization algorithm) is run. This algorithm intelligently selects the next set of size parameters most likely to improve performance for virtual evaluation, based on the surrogate model's performance prediction of the currently evaluated parameter combinations and the model's own uncertainty estimation. Through multiple iterations, the optimization algorithm efficiently explores the design space, continuously updating the estimate of the optimal solution until a preset convergence condition is met (e.g., performance improvement is less than a certain threshold or the maximum number of iterations is reached). Finally, the optimization algorithm outputs a set of size parameter combinations predicted to optimize the electric field uniformity index. For example, after Bayesian optimization iterations, an optimal set of parameters might be output: equalizing ring diameter 335mm, cross-sectional radius 17mm, and installation height 205mm.

[0089] It should be noted that this embodiment may also be an improvement based on the second embodiment and / or the third embodiment.

[0090] It is not difficult to see that in this embodiment, firstly, by obtaining the initial size data of the voltage equalization element, a clear design starting point and parameter adjustment benchmark can be provided for the subsequent optimization process. Secondly, by using numerical simulation methods to model and analyze the initial design, the actual impact of different size parameters on the electric field distribution can be accurately predicted, establishing a quantitative relationship between geometric parameters and electric field performance. Thirdly, targeted adjustments to the size parameters based on the simulation results can ensure the correctness of the optimization direction and the efficiency of the adjustment. Through iterative simulation, the influence of various parameter combinations on the uniformity of the electric field is systematically explored, ultimately determining the optimal parameter combination that enables the electric field distribution to reach the optimal uniform state. Finally, based on this optimized parameter combination, the target design parameters are determined, forming a fully validated design scheme that can be directly applied to engineering practice. This phased and systematic design method can ensure the scientificity and reliability of the voltage equalization element design, providing effective technical support for achieving uniform electric field distribution in high-voltage electrical equipment.

[0091] Fifth embodiment

[0092] The fifth embodiment relates to a method for optimizing the insulation performance of high-voltage electrical equipment. The fifth embodiment is an improvement upon the first embodiment, specifically in that it provides a method for determining the insulation performance evaluation result of the high-voltage electrical equipment in a system integration state based on the homogenized electric field distribution.

[0093] Specifically, determining the insulation performance evaluation result of the high-voltage electrical equipment in the system integration state based on the homogenized electric field distribution, i.e., step S104, may include:

[0094] Step S1041: Extract the electric field intensity distribution data of the connection part based on the homogenized electric field distribution;

[0095] Step S1042: Based on the electric field intensity distribution data, obtain the surface treatment features and adhesive layer parameters of the connection part;

[0096] Step S1043: Determine whether the surface treatment features and adhesive layer parameters meet the preset adhesive standards.

[0097] Step S1044: Based on the judgment result, generate reliability index data; wherein, the reliability index data serves as the first part of the insulation performance evaluation result.

[0098] For step S1041, exemplarily, the electric field intensity distribution data is used to characterize a set of data extracted from the homogenized electric field distribution to characterize the electrical state of the connection points. This set of data may include electric field intensity values, electric field gradient distribution, and electric field vector directions. Specifically, key mechanical connection interfaces can be located in the three-dimensional digital model of the high-voltage electrical equipment. From the simulation results of the homogenized electric field distribution, the electric field intensity values ​​of these interface regions, the electric field gradient distribution along the interface normal and tangential directions, and the spatial direction data of the electric field vector are extracted.

[0099] Specifically, regarding step S1042, the surface treatment features refer to the surface preparation process parameters implemented at the connection site to achieve reliable adhesion, which may include surface roughness, cleanliness indicators, and processing type; the adhesive layer parameters refer to the performance parameters of the adhesive material itself, which may include adhesive layer thickness, material dielectric constant, and mechanical strength. In practice, the surface treatment features can be obtained based on the electric field intensity distribution data extracted in step S1041, in conjunction with the design drawings and process documents of the connection site, and the adhesive layer parameters can be obtained based on the material data of the adhesive used.

[0100] Regarding step S1043, for example, the preset bonding standard refers to the comprehensive technical requirements established to ensure the long-term reliability of the connection part under an electric field environment, specifying the threshold range that the surface treatment characteristics and adhesive layer parameters must meet. In some examples, the obtained surface treatment characteristics and adhesive layer parameters can be compared and calculated one by one with the corresponding requirements in the preset bonding standard to evaluate whether the interface characteristics of the connection part simultaneously meet the comprehensive requirements of electrical insulation and mechanical fixation under the homogenized electric field distribution conditions.

[0101] For step S1044, for example, the reliability index data refers to the evaluation data characterizing the interface reliability generated after quantifying the compliance judgment result of the connection part. This reliability index data constitutes the first part of the insulation performance evaluation result. In specific implementation, based on the judgment result of step S1043, reliability index data can be generated by using quantitative scoring, grading, or calculating a safety factor to reflect the expected reliability level of the connection part under the optimized electric field environment.

[0102] Specifically, taking a high-voltage circuit breaker as an example, steps S1041-S1044 involve extracting the electric field intensity distribution data of the connection between the moving and stationary contacts from the homogenized electric field distribution. For instance, the maximum electric field intensity in this area is 2.8 kV / mm, and the average electric field gradient is 0.15 kV / mm². Based on this electric field data and in conjunction with the product process specifications, the surface treatment characteristics of the connection (e.g., surface roughness Ra = 0.8 μm after sandblasting) and adhesive layer parameters (e.g., epoxy adhesive thickness 150 μm, dielectric constant 3.5) can be obtained. Subsequently, the obtained roughness, adhesive layer thickness, and dielectric constant parameters, along with the 2.8 kV / mm electric field strength data for this area, are included in the evaluation to determine whether they meet the preset standard requirement that "at an electric field strength of 2.5 kV / mm, the roughness Ra must be ≥ 0.6 μm and the adhesive layer thickness should be 100-200 μm". Based on the judgment result, a quantitative connection interface reliability score (e.g., 92 / 100) can be generated. This reliability score serves as the reliability index data (Part 1) of the insulation performance evaluation result of the high-voltage circuit breaker under system integration status.

[0103] Optionally, in some embodiments, the step of determining whether the surface treatment features and adhesive layer parameters meet the preset adhesion criteria, i.e., step S1043, may include:

[0104] Step S10431: Calculate the electro-mechanical coupling tolerance coefficient η of the connection part;

[0105] Step S10432: If the electro-mechanical coupling tolerance coefficient η is greater than or equal to the preset safety factor, then it is determined that the bonding standard is met.

[0106] The electro-mechanical coupling tolerance coefficient η is calculated as follows: η = k × (σ_m / σ_e) × (E_b / E_max); k is the safety factor, σ_m is the mechanical strength parameter of the interface material, σ_e is the electrical stress parameter calculated from the local electric field, E_b is the intrinsic breakdown field strength of the interface material, and E_max is the maximum electric field strength at the interface.

[0107] Specifically, a quantitative evaluation method can be used to determine whether the connection meets the preset bonding standards. Taking the aforementioned connection between the moving and stationary contacts of a high-voltage circuit breaker as an example, the electro-mechanical coupling withstand coefficient η of this part can be calculated first. According to the formula η=k×(σ_m / σ_e)×(E_b / E_max), the following parameters need to be obtained: assuming the safety factor k is 1.5; the mechanical strength parameter σ_m of the epoxy material used at this interface is 80MPa; the electrical stress parameter σ_e calculated from the electric field distribution data of this part is 40MPa; the intrinsic breakdown field strength E_b of the epoxy material is 25kV / mm; and the maximum electric field strength E_max of this interface extracted from the homogenized electric field is 2.8kV / mm.

[0108] Substituting the above data into the formula, we calculate: η = 1.5 × (80 / 40) × (25 / 2.8) ≈ 1.5 × 2 × 8.93 ≈ 26.8. The calculated result is compared with a preset safety factor (e.g., η ≥ 2.0). Since the calculated η value (26.8) is much larger than the preset safety factor (2.0), the connection is determined to meet the adhesion standard. Therefore, this quantitative judgment result can provide a direct basis for generating reliability index data.

[0109] Optionally, in some embodiments, determining the insulation performance evaluation result of the high-voltage electrical equipment in the system integration state based on the homogenized electric field distribution, i.e., step S104, may further include:

[0110] Step S1041': Integrate all optimized component parameters and perform system-level electric field simulation calibration;

[0111] Step S1042': Obtain the coordination parameters of the discharge control device, and perform data fusion processing on the coordination parameters using an integration algorithm;

[0112] Step S1043': Based on the fusion results, adjust the electric field intensity distribution parameters to calibrate the electric field intensity distribution;

[0113] Step S1044': Determine the partial discharge initiation voltage level based on the calibrated electric field distribution; wherein the partial discharge initiation voltage level serves as the second part of the insulation performance evaluation result.

[0114] Specifically, regarding step S1041', the system-level electric field simulation calibration refers to the process of reassembling all component models into a complete system model after the parameters of all components of the high-voltage electrical equipment have been optimized and determined through steps S101 to S103, and then verifying and fine-tuning the overall electric field distribution of the integrated system under rated operating conditions through high-precision numerical simulation. In practice, based on the final design parameters of optimized components such as voltage equalization elements and shielding structures, a complete three-dimensional assembly model of the high-voltage electrical equipment can be reconstructed in electromagnetic field simulation software. A full-system simulation is then performed by applying the rated operating voltage to obtain a global electric field distribution consistent with the actual assembly state.

[0115] For step S1042', exemplarily, the coordination parameters of the discharge control device refer to the relative positions, potential coupling relationships, and geometric coordination parameters among multiple devices (such as shields and equalizing rings at different locations) used to suppress partial discharge in the high-voltage electrical equipment. The integration algorithm characterizes the algorithm used to normalize and correlate these multi-source parameters.

[0116] In practical implementation, the spatial coordinates of each shield, the potential data of the equalizing rings, and their capacitive coupling coefficients can be extracted from the system integration model to form a multi-parameter matrix. Principal component analysis or weighted fusion algorithms are then used to process this matrix to extract key coordination features affecting the system's electric field distribution.

[0117] For step S1043', for example, based on the key features of the coordination relationship obtained after the fusion processing in step S1042', a few parameters of relevant components in the system model (such as slightly moving the position of a shield or adjusting the reference potential of the equalizing ring) can be fine-tuned, and a rapid simulation can be performed again to reduce the maximum electric field strength of the system or make its distribution more reasonable, thereby achieving fine calibration of the electric field distribution of the entire system. The electric field strength distribution parameters refer to key variables used to describe and control the overall electric field distribution pattern of the system, such as the maximum field strength setpoint on the critical path, the field strength gradient control target for a specific region, etc.

[0118] For step S1044', for example, based on the calibrated final system electric field distribution, a criterion combining electric field strength and the insulation material's withstand characteristics can be used (e.g., when the maximum electric field strength on the surface of a solid insulating medium reaches 80% of its withstand strength, partial discharge is considered to be likely to begin). An estimated partial discharge initiation voltage value can be determined through simulation extrapolation or semi-empirical formula calculation. This value serves as a key indicator for quantitatively evaluating the overall insulation withstand strength of the system, constituting the second part of the insulation performance evaluation result. In this step, the partial discharge initiation voltage level refers to the lowest applied voltage value expected to occur when detectable partial discharge occurs in the high-voltage electrical equipment, predicted through simulation or calculated based on standard empirical formulas after the system-level electric field simulation calibration.

[0119] For steps S1041'-S1044', in some application examples, the optimized parameters of components such as the equalizing ring (diameter φ350mm) and the shield (tilt angle 15°) can be integrated to establish a complete three-dimensional assembly model. System-level electric field simulation calibration is then performed with a mesh accuracy of 0.1mm. Subsequently, the coordination parameters between the three equalizing rings and their corresponding shields (relative distances of 45mm, 50mm, and 48mm, and potential differences of 12kV, 10kV, and 11kV, respectively) are obtained, and principal component analysis is used to analyze the data of this parameter set. The system underwent fusion processing to extract principal component features that accounted for 85% of the weight. Subsequently, based on the fusion analysis results, the tilt angle of the first shield was finely adjusted from 15° to 18°, reducing the maximum electric field strength in the critical area from 3.2kV / mm to 2.9kV / mm, thereby calibrating the electric field strength distribution of the system. Finally, based on this calibrated electric field distribution, and according to the criterion that the surface electric field strength of the solid insulation does not exceed 80% of its withstand strength (20kV / mm), the estimated partial discharge initiation voltage level of the high-voltage circuit breaker was determined to be 185kV through simulation extrapolation. This value is used as the second part of the insulation performance evaluation result.

[0120] Optionally, in some embodiments, the goal of the system-level electric field simulation calibration may include: guiding the point of maximum electric field intensity of the system from the surface of the solid dielectric to the vacuum gap by adjusting the coordination parameters of the shielding structure and the equalizing element.

[0121] Specifically, this embodiment aims to change the spatial distribution pattern of the electric field strength inside the device through fine-tuning, and transfer the highest risk field strength extreme point in the insulation system from the surface of the weaker solid insulating medium (such as epoxy support) to the vacuum gap (such as the fracture gap) with stronger electrical tolerance.

[0122] For example, during the system calibration of a high-voltage circuit breaker, the initial simulation might show the point of maximum electric field strength (e.g., 28 kV / mm) located at the interface between an epoxy insulator and a conductor. By fine-tuning parameters such as the tilt angles of multiple shields and the installation height of the equalizing rings within the circuit breaker, and by re-simulating the entire system, the location of the maximum electric field strength point (which might decrease to 25 kV / mm) could eventually shift to the vacuum gap between the moving and stationary contacts. This change is electrically safer because the insulation recovery strength in a vacuum is much higher than that on a solid dielectric surface, thus fundamentally improving the overall insulation reliability of the equipment.

[0123] Optionally, in some embodiments, the insulation performance evaluation result determined in step S104 can comprehensively include quantitative data from two dimensions: microscopic interface reliability and macroscopic system tolerance. Specifically, the first part of the evaluation result may be reliability index data generated by evaluating the state of key connection parts under the homogenized electric field, reflecting the reliability level of the interface under electro-mechanical coupling; the second part of the evaluation result may be the partial discharge initiation voltage level determined after system-level electric field simulation calibration, reflecting the overall insulation tolerance of the high-voltage electrical equipment. The evaluation of the first and second parts can together constitute a complete, multi-level insulation performance evaluation system, providing a comprehensive decision-making basis for subsequent collaborative optimization.

[0124] It should be noted that this embodiment may also be an improvement based on any one or more of the second to fourth embodiments.

[0125] It is easy to see that in this embodiment, by focusing the homogenized electric field distribution data onto a specific mechanical connection point, precise electric field information at that location is obtained. Based on this, combined with actual surface treatment process parameters and adhesive material characteristics, a complete interface state dataset can be formed. Subsequently, by comparing the measured data with preset process standards, an objective assessment of the reliability of the connection point under a specific electric field environment can be achieved. Furthermore, a quantitative reliability index can be generated based on this assessment result. This index directly reflects the safety level of critical connection points and serves as an important component of the overall insulation performance evaluation. This process ensures that the optimized design not only stays at the macroscopic electric field level but also delves into the microscopic interface that determines the long-term operational reliability of the equipment, providing solid local reliability data support for subsequent overall performance judgment.

[0126] Sixth Embodiment

[0127] The sixth embodiment relates to a method for optimizing the insulation performance of high-voltage electrical equipment. The sixth embodiment is an improvement upon the fifth embodiment, specifically in that it provides a concrete implementation method for determining a collaborative optimization scheme for the insulation structure of the high-voltage electrical equipment based on the insulation performance evaluation results.

[0128] Specifically, step S105, which involves determining a collaborative optimization scheme for the insulation structure of the high-voltage electrical equipment based on the insulation performance evaluation results, may include:

[0129] Step S1051: When the insulation performance evaluation result does not reach the preset performance threshold, the incremental adjustment and optimization of the insulation structure of the high-voltage electrical equipment is triggered.

[0130] Step S1052: Obtain adjustment data for the calibrated starting voltage level, and use a regression analysis algorithm to extract features and analyze trends in the adjustment data;

[0131] Step S1053: Based on the analysis results, determine the correlation between voltage level and insulation performance, and extract key influencing factors;

[0132] Step S1054: By integrating the key influencing factors, an insulation performance improvement factor is generated;

[0133] Step S1055: Based on the insulation performance improvement factor, generate an adjustment scheme for the insulation structure.

[0134] For step S1051, for example, the system can automatically compare the insulation performance evaluation results (such as the first part of the reliability index and the second part of the partial discharge initiation voltage) with the preset threshold. If the partial discharge initiation voltage is lower than the design requirement of 200kV, or the reliability score of a certain connection part is lower than 90 points, an optimization instruction is generated to trigger subsequent steps.

[0135] The preset performance threshold refers to the minimum acceptable standard for insulation performance set to ensure the safe operation of high-voltage electrical equipment. This threshold may include quantitative requirements such as the lower limit of partial discharge initiation voltage and critical values ​​for reliability indicators. Furthermore, it should be noted that the triggering of incremental adjustments and optimizations to the insulation structure of the high-voltage electrical equipment in this step refers to automatically initiating a refined improvement process based on the existing design when the evaluation results fail to meet the standards, rather than redesigning the entire system.

[0136] For step S1052, for example, after trigger optimization, small-scale parameter adjustments can be made to 2-3 key insulation components (such as the most sensitive equalizing ring), establishing an adjustment database containing 10-15 sets of parameter combinations and their corresponding starting voltages. Algorithms such as multiple linear regression are used to analyze the influence trend of parameter changes on the voltage level. In some examples, the calibrated starting voltage level refers to the partial discharge starting voltage value determined after system-level electric field simulation calibration in step S104'. The adjustment data refers to the dataset of starting voltage test values ​​or simulation values ​​corresponding to each adjustment scheme during the process of fine-tuning the insulation structure parameters (such as ±5% change in equalizing ring size, ±3° adjustment of shield tilt angle).

[0137] Regarding step S1053, for example, the key influencing factors refer to a few design parameters that play a dominant role in improving insulation performance. These parameters typically have high regression coefficients and statistical significance. In some cases, key parameters affecting the change in initiation voltage can be identified by performing analysis of variance and ranking the importance of features on the regression model. For example, the analysis might show that the change in the diameter of the equalizing ring accounts for 65%, the change in the tilt angle of the shield accounts for 25%, and the remaining parameters account for 10% in total; the first two are then the key influencing factors.

[0138] For step S1054, for example, the insulation performance improvement factor refers to a coefficient that quantifies the effect of adjusting key parameters on improving insulation performance, typically expressed as the performance improvement per unit change in parameter. For instance, the performance improvement factor can be calculated based on the key influencing factors and their regression coefficients extracted in step S1053. For example, if the regression model shows that an increase of 1 mm in the diameter of the equalizing ring can increase the starting voltage by 0.5 kV, then the improvement factor can be quantified as 0.5 kV / mm, forming a complete improvement factor matrix.

[0139] Regarding step S1055, for example, the insulation structure adjustment scheme refers to specific parameter modification suggestions formulated based on quantitative improvement factors, including the adjustment object, adjustment direction, and adjustment magnitude. In specific implementation, the optimal adjustment amount of each key parameter can be calculated by combining the target performance gap and the improvement factor. For example, if it is necessary to increase the starting voltage from 185kV to 200kV, a scheme can be formulated according to the improvement factor matrix: increase the diameter of the equalizing ring by 30mm (contributing a 15kV increase), and at the same time increase the tilt angle of the shield by 2° (contributing a 5kV increase). The final adjustment scheme is formed after comprehensively considering the feasibility of the process.

[0140] Optionally, in some embodiments, generating an adjustment scheme for the insulation structure based on the insulation performance improvement factor, i.e., step S1055, may include:

[0141] Step S10551: Extract electric field concentration data and system extended information based on the insulation performance improvement factor;

[0142] Step S10552: Determine the matching degree between the electric field concentration data and the system extended information and the system;

[0143] Step S10553: Generate an optimized suppression configuration scheme based on the matching degree results.

[0144] For step S10551, for example, the electric field concentration data refers to the characteristic data of high field strength regions identified from the electric field distribution, which may include, but is not limited to, the peak position of the field strength, the field strength gradient distribution, and the range of electric field concentration. The system extended information refers to extended parameters that affect the insulation structure design in addition to the electric field data, including information such as installation space limitations, material cost constraints, heat dissipation requirements, and manufacturing tolerances.

[0145] In some cases, the most effective adjustment area for performance improvement can be located based on the insulation performance improvement factor. From this area, concentrated data such as maximum electric field strength and equipotential line density can be extracted, while extended information such as available installation space size and optional material cost range can be obtained from the design database.

[0146] Regarding step S10552, for example, the matching degree is used to characterize the degree of fit between the electric field concentration characteristics and the system's extended constraints, reflecting the balance between the technical feasibility and engineering feasibility of the adjustment scheme. For example, a correlation matrix can be established between electric field concentration data (such as the required equalizing ring size) and system extended information (such as available installation space), and calculated using a weighted scoring method or constraint satisfaction algorithm. For instance, when an adjustment scheme requires an equalizing ring with a diameter of 350mm, but the actual installation space only allows 320mm, the matching degree will be significantly reduced.

[0147] Regarding step S10553, for example, the optimized suppression configuration scheme refers to the best parameter configuration scheme formed after comprehensively considering the insulation performance improvement factor, electric field concentration characteristics, and system constraints, under the premise of meeting the matching degree requirements. In specific implementation, feasible schemes can be screened based on the matching degree evaluation results, and high-matching degree schemes can be prioritized. For example, if multiple adjustment schemes all meet the matching degree standard, the scheme with the highest product of the improvement factor and the matching degree is selected as the final output, forming a complete configuration scheme that includes specific parameter values, implementation steps, and expected effects.

[0148] Optionally, in some embodiments, determining the matching degree between the electric field concentration data and the system extended information and the system, i.e., step S10552, may include the following steps:

[0149] Step A: Calculate the system matching index M; wherein, the calculation formula for the system matching index M is: M=Σ[w_i×f(R_protect_i,d_i)], w_i is the weighting coefficient, and f(R_protect_i,d_i) is a function characterizing the matching relationship between the protection range of the i-th equalizing or shielding element and the distance to the i-th electric field concentration point;

[0150] Step B: If the system matching degree index M is greater than or equal to the preset matching threshold, then the matching degree is determined to meet the requirements.

[0151] For example, in the implementation of step A, suppose a high-voltage circuit breaker has three electric field concentration points that require attention, and two equalizing rings and a shield are configured as suppression elements. When calculating the system matching index M, the weight coefficient w_i of each element can be determined first (for example, the weight is allocated to each concentration point based on the peak electric field strength as 0.4, 0.3, 0.3), and then the matching function value of the protection range R_protect_i of each element and the distance d_i of the corresponding electric field concentration point can be calculated. Taking the first equalizing ring as an example, its effective protection radius R_protect_1 is 120mm, and the distance d_1 from the first electric field concentration point is 100mm. If the matching function f is designed as f(R,d)=R / d (when R≥d), then the matching contribution value of this ring is f(120,100)=1.2, which is 0.4×1.2=0.48 after weighting. Similarly, calculate the contribution values ​​of the remaining components, and finally sum them to obtain the system matching index M, for example, M=0.48+0.36+0.33=1.17.

[0152] In step B, the calculated system matching index M (1.17) can be compared with a preset matching threshold (e.g., 1.0). Since M = 1.17 is greater than the threshold 1.0, the system can determine that the current configuration of the suppression element (including its location and protection range) matches the distribution of the electric field concentration point, thus providing a direct basis for generating an optimized suppression configuration scheme: if M does not meet the standard, the location or size of the suppression element needs to be adjusted to improve the matching degree; if M meets the standard, the current configuration can be included as an effective scheme in the final optimization output.

[0153] It should be noted that this embodiment may also be an improvement based on any one or more embodiments in the first to fourth embodiments.

[0154] It is easy to see that a complete "evaluation-feedback-optimization" decision-making closed loop has been established in this embodiment. When the insulation performance evaluation results are unsatisfactory, the system can automatically trigger targeted structural parameter fine-tuning, rather than redesigning. By collecting performance test data under different parameter adjustment schemes and using regression analysis, it is possible to clearly identify which structural parameter changes are most critical to improving insulation performance (such as starting voltage) and their specific quantitative relationships. Based on these key parameters and their influence coefficients (i.e., insulation performance improvement factors), precise and efficient adjustment schemes can be formulated. For example, it can be clearly known how many kilovolts the starting voltage will be increased by increasing the diameter of a certain equalizing ring by a specific value. This makes the final insulation structure optimization scheme no longer based on trial and error based on experience, but on decisions based on quantitative data and clear causal relationships, which is conducive to significantly improving optimization efficiency and the reliability of results.

[0155] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this application. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this application.

[0156] Furthermore, some embodiments of this application also provide an electronic device. The electronic device can be various forms of digital computer, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, mainframe computers, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0157] The electronic device includes: one or more processors; and a memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the methods provided in any one or more of the above embodiments.

[0158] Figure 2An exemplary structural diagram of the electronic device is disclosed. The electronic device includes one or more processors 1101, a memory 1102, an input device 1103, and an output device 1104. The various components are interconnected via a bus or other means (the diagram shows an example of bus connection). The processor 1101 can be used to execute instructions stored in the memory 1102 to control the overall operation of the electronic device. The memory 1102 may include a program storage area and a data storage area, wherein the program storage area stores the operating system and applications required for at least one function; the data storage area stores data created according to the use of the electronic device, etc. The memory 1102 may include high-speed random access memory and may also include non-transitory memory, such as disk storage devices, flash memory devices, or other non-transitory solid-state storage devices. In some embodiments, the memory 1102 may also include storage resources located remotely to the processor and accessible via a network.

[0159] Input device 1103 can be used to receive input numerical or character information or user operation signals, such as a touch screen, keypad, mouse, trackpad, touchpad, indicator, one or more mouse buttons, trackball, joystick, etc. Output device 1104 may include display devices (such as liquid crystal displays, light-emitting diode displays, plasma displays, and optional touch screens), auxiliary lighting devices (such as LEDs), and haptic feedback devices (such as vibration motors), etc.

[0160] To facilitate user interaction, the electronic device may be configured to include a display device (such as an LCD or CRT monitor) and input devices such as a keyboard and pointing devices (e.g., a mouse or touchpad). Feedback can be any form of sensory feedback (e.g., visual feedback, auditory feedback); input may also be received via voice, touch, or other means.

[0161] This application also relates to a computer-readable medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the methods provided in any one or more of the above embodiments. This computer-readable medium may be a memory included in an electronic device, or it may be a standalone storage medium not assembled into the device.

[0162] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium, a computer-readable storage medium, or a combination of both. Examples include, but are not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. Specific examples of storage media may include, but are not limited to, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory, optical fibers, portable CD-ROMs, optical storage devices, magnetic storage devices, etc., or any suitable combination thereof.

[0163] Computer-readable media may store one or more programs that can be used by or in conjunction with an instruction execution system. The media may be permanent or non-permanent, removable or non-removable, and may store information by any method or technology, including computer-readable instructions, data structures, program modules, or other data.

[0164] The computer program code used to implement the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Java, Smalltalk, and C++) and conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. The remote computer can be connected to the user's computer via any network (including a local area network or a wide area network) or can be connected to an external computer.

[0165] In the above embodiments, the functions can be implemented in whole or in part by software, hardware, firmware, or any combination thereof, for example, by using application-specific integrated circuits, general-purpose computers, or other similar hardware devices. In some embodiments, the software program of this application can be executed by a processor to implement the steps or functions; it can also be implemented by hardware, for example, as a circuit that works in conjunction with the processor to execute the steps or functions.

[0166] This application also provides a computer program product, including one or more computer programs / instructions, which, when executed by a processor, generate all or part of the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one storage medium to another via wired (e.g., DSL) or wireless (e.g., wireless, microwave) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive).

[0167] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0168] The scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to encompass all variations falling within the meaning and scope of equivalents of the claims. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device in software or hardware. Terms such as "first," "second," etc., are used only for distinguishing descriptions and do not indicate any particular order, nor should they be construed as indicating or implying relative importance.

[0169] 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 made 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, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A method for optimizing the insulation performance of high-voltage electrical equipment, characterized in that, The method includes: Based on the geometric parameters and electric field distribution of the high-voltage electrical equipment, the abnormal electric field region is determined; Based on the described abnormal electric field region, determine the voltage regulation strategy; Based on the voltage regulation strategy, the design parameters of the voltage equalization element are determined to obtain a uniform electric field distribution; Based on the homogenized electric field distribution, the insulation performance evaluation result of the high-voltage electrical equipment in the system integration state is determined; Based on the insulation performance evaluation results, a collaborative optimization scheme for the insulation structure of the high-voltage electrical equipment is determined.

2. The method according to claim 1, characterized in that, The step of determining the voltage regulation strategy based on the abnormal electric field region includes: Obtain the protective structure parameters and connection potential data associated with the abnormal electric field region: Clustering algorithms are used to group and analyze the protective structure parameters and the connection potential data to obtain multiple feature groups; Analyze the voltage distribution uniformity within each characteristic group to identify the characteristic groups with uneven voltage distribution; Key nodes are extracted from the feature group of uneven voltage distribution as voltage distribution adjustment points; A voltage regulation strategy is generated based on the voltage distribution adjustment point.

3. The method according to claim 1, characterized in that, The step of determining the design parameters of the voltage equalization element according to the voltage regulation strategy includes: For the voltage distribution adjustment point determined in the voltage regulation strategy, obtain the initial size data of the voltage equalization element: The initial size data were modeled using numerical simulation methods to simulate the changing trend of the electric field distribution; Adjust the dimensional parameters of the equalizing element based on the simulation results; Through iterative simulation, the combination of parameters that makes the electric field distribution uniform was determined; Based on the parameter combination, the target design parameters of the equalizing element are determined.

4. The method according to claim 3, characterized in that, The parameter combination determined through iterative simulation to achieve a uniform electric field distribution includes: Establish a proxy model with the size of the voltage equalizing element as the design variable and electric field homogenization as the optimization objective; The parameter combination is determined by optimizing the proxy model using an optimization algorithm.

5. The method according to claim 1, characterized in that, The determination of the insulation performance evaluation result of the high-voltage electrical equipment in the system integration state based on the homogenized electric field distribution includes: Based on the homogenized electric field distribution, extract the electric field intensity distribution data of the connection part; Based on the electric field intensity distribution data, the surface treatment features and adhesive layer parameters of the connection part are obtained; Based on the surface treatment features and adhesive layer parameters, determine whether they meet the preset adhesive standards; Based on the judgment results, reliability index data is generated; wherein, the reliability index data serves as the first part of the insulation performance evaluation results.

6. The method according to claim 5, characterized in that, The step of determining whether the surface treatment features and adhesive layer parameters meet the preset adhesive standards includes: Calculate the electro-mechanical coupling tolerance coefficient η of the connection point; If the electro-mechanical coupling tolerance coefficient η is greater than or equal to the preset safety factor, then it is determined that the adhesion standard is met; The formula for calculating the electro-mechanical coupling tolerance coefficient η is: η = k × (σ_m / σ_e) × (E_b / E_max); k is the safety factor, σ_m is the mechanical strength parameter of the interface material, σ_e is the electrical stress parameter calculated from the local electric field, E_b is the intrinsic breakdown field strength of the interface material, and E_max is the maximum electric field strength at the interface.

7. The method according to claim 1, characterized in that, The determination of the insulation performance evaluation result of the high-voltage electrical equipment in the system integration state based on the homogenized electric field distribution further includes: Integrate all optimized component parameters and perform system-level electric field simulation calibration; The coordination parameters of the discharge control device are obtained, and the coordination parameters are fused using an integration algorithm. Based on the fusion results, the electric field intensity distribution parameters are adjusted to calibrate the electric field intensity distribution; Based on the calibrated electric field distribution, the partial discharge initiation voltage level is determined; wherein, the partial discharge initiation voltage level serves as the second part of the insulation performance evaluation result.

8. The method according to any one of claims 1 to 7, characterized in that, The step of determining a collaborative optimization scheme for the insulation structure of the high-voltage electrical equipment based on the insulation performance evaluation results includes: When the insulation performance evaluation result fails to reach the preset performance threshold, incremental adjustment and optimization of the insulation structure of the high-voltage electrical equipment is triggered. Adjustment data is obtained for the calibrated starting voltage level, and regression analysis algorithm is used to extract features and analyze trends in the adjustment data; Based on the analysis results, the correlation between voltage level and insulation performance was determined, and key influencing factors were extracted. By integrating the aforementioned key influencing factors, an insulation performance improvement factor is generated; Based on the insulation performance improvement factor, an adjustment scheme for the insulation structure is generated.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as described in any one of claims 1 to 8.

10. A computer-readable medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 8.

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