Probabilistic modeling system and method for insulator discharge initiation and progression mechanism

Abstract

This invention discloses a probabilistic modeling system and method for the discharge triggering-development mechanism of insulators, belonging to the technical field of high-voltage discharge and external insulation reliability assessment. The system includes: a discharge triggering stochastic modeling module, a discharge development physical modeling module, a statistical parameter inversion module, a Monte Carlo simulation module, and a probability distribution generation module. The method includes: acquiring experimental data; fitting statistical distribution parameters and inverting physical parameters; generating simulated samples through Monte Carlo simulation based on the physical parameters; and constructing a breakdown probability distribution. This invention achieves explicit coupling between statistical distribution and physical mechanism by splitting the breakdown process into two physical stages: triggering and development. This allows for the quantification of the contribution of different stages to voltage dispersion, providing a physical basis for insulator reliability assessment and operating condition extrapolation.

Description

Technical Field

[0001] This invention relates to the field of high-voltage discharge and external insulation reliability assessment technology, and in particular to a probabilistic modeling system and method for insulator discharge triggering-development mechanism. Background Technology

[0002] Under lightning impulse conditions, the flashover process of insulators typically exhibits significant randomness, and their breakdown voltage shows a clear statistical dispersion in repeated tests. Existing research and engineering practices mostly use statistical models such as the Weibull distribution to fit the breakdown voltage, but these methods are essentially empirical statistical descriptions and are difficult to reveal the physical reasons behind the changes in distribution parameters.

[0003] The limitations of traditional statistical methods become increasingly apparent, especially in the following scenarios: at high altitudes, reduced air pressure leads to significant changes in the leader development process; under non-standard lightning waveforms such as short tail waves, the dominant mechanisms of discharge triggering and development stages change; and under different insulator materials and structures, the breakdown voltage dispersion varies significantly. Existing methods typically use statistical distribution parameters directly as results, lacking modeling and interpretation of their physical origins, and mainly suffer from the following shortcomings: 1. It is impossible to distinguish the contribution of the discharge triggering stage and the development stage to dispersion; 2. Statistical parameters lack physical meaning and are difficult to use for extrapolation of operating conditions or engineering corrections; 3. It is impossible to predict the evolution trend of the distribution pattern through changes in physical parameters; 4. It is difficult to form a closed-loop verification between experimental statistical results and discharge mechanism models.

[0004] Therefore, it is necessary to propose a system and method that explicitly incorporates the discharge physics process into the probabilistic modeling framework and explains the breakdown voltage distribution generation mechanism through physical parameter inversion and stochastic simulation. Summary of the Invention

[0005] The purpose of this invention is to provide a probabilistic modeling system and method for the insulator discharge triggering-development mechanism, in order to solve the problems in the prior art where statistical evaluation of distribution parameters lacks physical interpretation, cannot quantify the sources of discharge dispersion, and is difficult to perform operating condition sensitivity analysis.

[0006] To achieve the above objectives, this invention provides a probabilistic modeling system for the insulator discharge triggering-progression mechanism, comprising: The discharge triggering stochastic modeling module is used to construct a trigger threshold model that describes the stochastic evolution process of the insulator discharge channel trigger threshold. The trigger threshold model includes model parameters that characterize random perturbations introduced by environmental or material uncertainties. The discharge development physics modeling module is used to construct a development physics model that describes the lead channel expansion process after discharge triggering. The development physics model includes lead development characteristic parameters that determine the conditions required for discharge completion. The statistical parameter inversion module is used to map the statistical distribution parameters of the insulator impact test data to the model parameters of the trigger threshold model and the leading development characteristic parameters of the development physical model. The Monte Carlo simulation module is used to generate a simulated breakdown voltage sample set based on the model parameters and leader development characteristic parameters output by the statistical parameter inversion module through random sampling and numerical simulation. The probability distribution generation module is used to perform statistical analysis on the simulated breakdown voltage sample set and generate a probability distribution of the breakdown voltage.

[0007] Preferably, the trigger threshold model constructed in the discharge triggering stochastic modeling module is a stochastic differential equation containing a stochastic perturbation term, a discrete stochastic process, a random walk model, or an equivalent stochastic mapping model.

[0008] Preferably, the development physics model constructed in the discharge development physics modeling module is a leader development rate model, which is used to estimate the voltage consumption or equivalent energy conditions required for the discharge channel to complete.

[0009] Preferably, in the statistical parameter inversion module, the statistical distribution parameters are the scale parameter and shape parameter of the Weibull distribution.

[0010] Preferably, the system further includes a model consistency verification and engineering correction module, which is used to perform consistency verification between the breakdown probability distribution generated by the probability distribution generation module and the survival function corresponding to the experimental data, and to correct the model parameters of the trigger threshold model or the leading development feature parameters of the development physics model according to the verification results.

[0011] This invention also provides a probabilistic modeling method for the insulator discharge triggering-development mechanism, applied to the above method, comprising the following steps: S1. Obtain impact test data for insulators; S2. Based on the experimental data, the statistical distribution parameters of the breakdown voltage are fitted, and the model parameters in the discharge triggering stochastic model and the leader development characteristic parameters in the discharge development physical model are obtained by inversion based on the statistical distribution parameters. The discharge triggering stochastic model is used to describe the stochastic evolution process of the discharge channel triggering threshold, and the discharge development physical model is used to describe the expansion process of the leader channel after discharge triggering. S3. Based on the model parameters obtained from the inversion and the aforementioned leader development characteristic parameters, a simulated breakdown sample set is generated using the Monte Carlo method; S4. Perform statistical analysis on the simulated breakdown sample set to construct the probability distribution of breakdown voltage.

[0012] Preferably, in step S2, the model parameters in the inverted discharge triggering stochastic model include a disturbance intensity parameter and a recovery rate parameter that characterize the random fluctuation of the triggering threshold caused by environmental disturbances or material micro-inhomogeneities.

[0013] Preferably, in step S3, the process of generating a simulated breakdown sample set includes: simulating whether a triggering event occurs under each impact based on a discharge triggering random model, and after the triggering event occurs, determining whether the development is complete based on a discharge development physical model, and jointly determining a complete breakdown event and its corresponding breakdown voltage.

[0014] Preferably, after step S4, step S5 is further included: verifying the consistency between the breakdown probability distribution generated by simulation and the statistical distribution corresponding to the experimental data, and correcting the parameters of the discharge triggering stochastic model and / or the discharge development physical model based on the verification results.

[0015] Preferably, the impact test data includes the applied impact voltage amplitude, breakdown or withstand result identifier, and operating condition information including altitude, air pressure, waveform type, insulator type, or material parameters.

[0016] Therefore, the insulator discharge triggering-propagation mechanism probabilistic modeling system and method using the above-described structure has the following beneficial effects: (1) This invention achieves explicit coupling between the statistical distribution of breakdown voltage and the physical process of discharge. By inverting statistical parameters into physically meaningful triggering model parameters and evolution model parameters, the description of breakdown voltage dispersion is based on the real physical mechanism, which significantly improves the theoretical depth of the model.

[0017] (2) This invention can quantitatively distinguish the contribution of the discharge triggering stage and the development stage to voltage dispersion. By modeling the breakdown process as two independent physical stages, the dominant sources of dispersion can be analyzed in depth, providing precise guidance for the optimized design of insulators.

[0018] (3) This invention significantly improves the interpretability and extrapolation capability of statistical models. The parameterized model with physical meaning can be used to predict the evolution of breakdown voltage distribution under operating conditions such as altitude and waveform changes, reducing the reliance on a large number of expensive full-scale tests.

[0019] (4) This invention provides a physical basis for engineering criterion correction and reliability assessment. The specific quantile voltage values ​​and correction criteria output by the model are derived from physical mechanism analysis, which can provide more scientific decision support for engineering applications such as insulation coordination and line reliability assessment.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of a probabilistic modeling system for the insulator discharge triggering-development mechanism according to the present invention; Figure 2 This is a schematic diagram of a probabilistic modeling method for the insulator discharge triggering-development mechanism according to the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0024] Example 1 like Figure 1 As shown, this invention provides an insulator impulse withstand voltage probability modeling system based on the discharge triggering-development mechanism, which can be implemented as a software system, embedded computing system, engineering analysis platform, or cloud computing service. The system includes: The data input module is used to receive lightning impulse test data for insulators. The data includes at least the applied impulse voltage amplitude and a breakdown or withstand result identifier, and may also include operating condition information such as altitude, air pressure, waveform type, insulator type, and material parameters. In one embodiment, the data is derived from a standard lightning impulse test; in another embodiment, it may also be derived from field operational statistics or a historical test database.

[0025] The discharge triggering stochastic modeling module is used to construct a trigger threshold model describing the stochastic evolution of the trigger threshold in an insulator discharge channel. Unlike traditional methods that treat the trigger threshold as a fixed parameter, this module treats it as a stochastic evolution process, with model parameters containing random perturbations introduced by environmental or material uncertainties. In one embodiment, the module uses a stochastic differential equation with random perturbation terms to describe the time evolution of the trigger threshold, where the random perturbation terms characterize uncertainties caused by environmental factors, electric field fluctuations, and material micro-inhomogeneities. In other embodiments, the trigger threshold model can also be implemented using a discrete stochastic process, a random walk model, or an equivalent stochastic mapping model.

[0026] The discharge development physics modeling module is used to construct a development physics model describing the leader channel expansion process after discharge triggering. This model includes leader development characteristic parameters that determine the conditions required for discharge completion. The output of this module, together with the output of the discharge triggering module, determines a complete breakdown event. In one embodiment, this module estimates the voltage consumption or equivalent energy conditions required for discharge channel completion based on a leader development rate model. In other embodiments, empirical or semi-empirical development models may be used for approximate description.

[0027] The statistical parameter inversion module maps the statistical distribution parameters obtained by fitting insulator impact test data to the model parameters of the trigger threshold model and the leading development characteristic parameters of the development physical model. This module enables the inversion and identification from statistical results to physical parameters, ensuring a clear physical correspondence between statistical parameters such as the Weibull distribution. In one embodiment, the statistical distribution parameters are the scale and shape parameters of the Weibull distribution. In other embodiments, other probability distribution forms suitable for breakdown statistics may also be used.

[0028] The Monte Carlo simulation module generates a large set of simulated breakdown voltage samples based on the model parameters and leading evolution characteristic parameters output by the statistical parameter inversion module, through random sampling and numerical simulation. This method generates a probability distribution in the physical parameter space, rather than directly fitting it to the voltage space. In one embodiment, the module constructs the simulated breakdown voltage set by repeatedly generating a large number of random breakdown samples. In other embodiments, adaptive sampling or importance sampling strategies may be employed depending on computational resource constraints.

[0029] The probability distribution generation module is used to perform statistical analysis on the simulated breakdown voltage sample set generated by the Monte Carlo simulation module to generate the probability distribution of the breakdown voltage, such as the breakdown probability distribution or survival function.

[0030] The model consistency verification and engineering correction module is used to verify the consistency between the breakdown probability distribution generated by the probability distribution generation module and the survival function corresponding to the experimental data, and to correct the model parameters of the trigger threshold model or the leading development characteristic parameters of the development physical model based on the verification results. Based on changes in physical parameters, this module can also propose distribution correction strategies for different operating conditions.

[0031] The results output and interface module is used to output probability distribution parameters, quantile voltage values, or engineering correction results, and to provide interfaces to engineering design software, standardization platforms, or external systems.

[0032] like Figure 2 As shown, the present invention also provides a probabilistic modeling method for insulator impulse withstand voltage based on the discharge triggering-development mechanism, comprising the following steps: Step S1: Data Acquisition. Acquire lightning impulse test data for insulators, including breakdown samples and withstand samples. The data includes at least the applied impulse voltage amplitude, breakdown or withstand result indicators, and optional operating condition information, such as altitude, air pressure, waveform type, insulator type, and material parameters.

[0033] Step S2: Initial Statistical Distribution Fitting and Parameter Inversion. Based on the experimental data, the statistical distribution parameters of the breakdown voltage are fitted, such as the scale and shape parameters of the Weibull distribution. Based on these statistical distribution parameters, the model parameters (such as disturbance intensity parameters and recovery rate parameters) in the discharge triggering stochastic model and the leader development characteristic parameters in the discharge development physical model are inverted. The discharge triggering stochastic model describes the stochastic evolution process of the discharge channel trigger threshold, and the discharge development physical model describes the expansion process of the leader channel after discharge triggering.

[0034] Step S3: Random discharge simulation. Based on the physical model parameters obtained from the inversion, a large number of simulated breakdown samples are generated using the Monte Carlo method. The generation process includes: simulating whether a triggering event occurs under each impact based on the discharge triggering random model; if a triggering event occurs, determining whether the development is complete based on the discharge development physical model, and the two together determine a complete breakdown event and its corresponding breakdown voltage.

[0035] Step S4: Constructing the probability distribution. Statistical analysis is performed on a large number of simulated breakdown samples generated by Monte Carlo simulation to construct the corresponding breakdown probability distribution or survival curve.

[0036] Step S5, Consistency Verification and Correction: The breakdown probability distribution generated by the simulation is compared and verified with the statistical distribution corresponding to the experimental data. If there is a significant difference between the two, the parameters of the discharge triggering stochastic model and / or the discharge development physical model are corrected, and the process returns to step S3 for a new round of simulation until the model output and experimental results meet the consistency requirements.

[0037] Step S6: Engineering Output. Output the probability distribution results, specific quantile voltage values, or engineering correction criteria based on physical mechanisms that are ultimately used for engineering design or evaluation.

[0038] Example 2 Various modifications can be made to this invention without departing from its spirit. The discharge triggering model can employ different forms of stochastic processes, such as the Itō process, Poisson process, etc. The discharge development model can be parameterized according to different insulator types (such as glass insulators, composite insulators) and structures. The number of Monte Carlo simulations can be set according to the required accuracy and computational resources. The system can be implemented as a standalone software system, an embedded module, or a cloud service.

[0039] Finally, it should be noted that the above embodiments are merely preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A probabilistic modeling system for the triggering and propagation mechanism of insulator discharge, characterized in that, include: The discharge triggering stochastic modeling module is used to construct a trigger threshold model that describes the stochastic evolution process of the insulator discharge channel trigger threshold. The trigger threshold model includes model parameters that characterize random perturbations introduced by environmental or material uncertainties. The discharge development physics modeling module is used to construct a development physics model that describes the lead channel expansion process after discharge triggering. The development physics model includes lead development characteristic parameters that determine the conditions required for discharge completion. The statistical parameter inversion module is used to map the statistical distribution parameters of the insulator impact test data to the model parameters of the trigger threshold model and the leading development characteristic parameters of the development physical model. The Monte Carlo simulation module is used to generate a simulated breakdown voltage sample set based on the model parameters and leader development characteristic parameters output by the statistical parameter inversion module through random sampling and numerical simulation. The probability distribution generation module is used to perform statistical analysis on the simulated breakdown voltage sample set and generate a probability distribution of the breakdown voltage.

2. The insulator discharge triggering-development mechanism probabilistic modeling system according to claim 1, characterized in that, The trigger threshold model constructed in the discharge triggering stochastic modeling module is a stochastic differential equation containing a stochastic perturbation term, a discrete stochastic process, a random walk model, or an equivalent stochastic mapping model.

3. The insulator discharge triggering-development mechanism probabilistic modeling system according to claim 1, characterized in that, The development physics model constructed in the discharge development physics modeling module is a leader development speed model, which is used to estimate the voltage consumption or equivalent energy conditions required for the discharge channel to complete.

4. The insulator discharge triggering-development mechanism probabilistic modeling system according to claim 1, characterized in that, In the statistical parameter inversion module, the statistical distribution parameters are the scale parameter and shape parameter of the Weibull distribution.

5. The insulator discharge triggering-development mechanism probabilistic modeling system according to claim 1, characterized in that, It also includes a model consistency verification and engineering correction module, which is used to verify the consistency between the breakdown probability distribution generated by the probability distribution generation module and the survival function corresponding to the experimental data, and to correct the model parameters of the trigger threshold model or the leading development feature parameters of the development physical model based on the verification results.

6. A probabilistic modeling method for insulator discharge triggering-development mechanism, applied to the probabilistic modeling system for insulator discharge triggering-development mechanism as described in any one of claims 1-5, characterized in that, Includes the following steps: S1. Obtain impact test data for insulators; S2. Based on the experimental data, the statistical distribution parameters of the breakdown voltage are fitted, and the model parameters in the discharge triggering stochastic model and the leader development characteristic parameters in the discharge development physical model are obtained by inversion based on the statistical distribution parameters. The discharge triggering stochastic model is used to describe the stochastic evolution process of the discharge channel triggering threshold, and the discharge development physical model is used to describe the expansion process of the leader channel after discharge triggering. S3. Based on the model parameters obtained from the inversion and the aforementioned leader development characteristic parameters, a simulated breakdown sample set is generated using the Monte Carlo method; S4. Perform statistical analysis on the simulated breakdown sample set to construct the probability distribution of breakdown voltage.

7. The probabilistic modeling method for insulator discharge triggering-development mechanism according to claim 6, characterized in that, In step S2, the model parameters in the inverted discharge triggering stochastic model include the disturbance intensity parameter and the recovery rate parameter, which characterize the random fluctuation of the triggering threshold caused by environmental disturbances or material micro-inhomogeneities.

8. The probabilistic modeling method for insulator discharge triggering-development mechanism according to claim 6, characterized in that, In step S3, the process of generating a simulated breakdown sample set includes: simulating whether a triggering event occurs under each impact based on a discharge triggering random model, and after the triggering event occurs, determining whether the development is complete based on a discharge development physical model, and jointly determining a complete breakdown event and its corresponding breakdown voltage.

9. The probabilistic modeling method for insulator discharge triggering-development mechanism according to claim 6, characterized in that, After step S4, step S5 is also included: verifying the consistency between the breakdown probability distribution generated by simulation and the statistical distribution corresponding to the experimental data, and correcting the parameters of the discharge triggering stochastic model and / or the discharge development physical model based on the verification results.

10. The probabilistic modeling method for insulator discharge triggering-development mechanism according to claim 6, characterized in that, The impact test data includes the applied impact voltage amplitude, breakdown or withstand result indicators, and operating condition information including altitude, air pressure, waveform type, insulator type, or material parameters.

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