A system for modeling and simulating inter-turn short circuit of motor stator winding

Abstract

The application discloses a motor stator winding inter-turn short circuit modeling and simulation system, and relates to the technical field of motor fault simulation.The system comprises a feature extraction module, which is used for calculating and generating a fault excitation index and winding chaos entropy from acquired motor operation data; a fault evolution modeling module, which is used for solving a preset fault evolution model based on the fault excitation index and winding chaos entropy to generate an updated fault resistance; a multi-level parameter dynamic correction module, which is used for correcting parameters of a simulation model according to the winding chaos entropy; and a simulation strategy optimization module, which is used for switching between multiple preset simulation modes according to the winding chaos entropy.The application realizes dynamic simulation of the whole life cycle of motor inter-turn short circuit by constructing a closed-loop adaptive system and combining interdisciplinary theories, and takes into account high fidelity, physical interpretability and computational efficiency through multi-level correction and adaptive strategies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor fault simulation, in particular to a motor stator winding inter-turn short circuit modeling and simulation system. BACKGROUND

[0002] The motor is an indispensable core power equipment in modern industrial production, and its stability and reliability are directly related to the normal operation of the entire production system. Among many motor fault types, the stator winding inter-turn short circuit is of great concern due to its high incidence, strong concealment and rapid destructiveness. This fault usually starts from a small damage to the winding insulation, but can rapidly worsen in a very short time. Its development process has high dynamicity and nonlinearity. If it is not discovered and accurately evaluated in time, it may lead to insulation breakdown, inter-phase short circuit and eventually motor burnout, which not only causes huge direct economic losses, but also may cause a chain of production safety accidents, posing a serious threat to industrial safety. Therefore, it is of great significance to accurately model and simulate the motor stator winding inter-turn short circuit to realize early warning and predictive maintenance, so as to ensure the safety of industrial production.

[0003] The existing motor inter-turn short circuit simulation technology generally relies on electromagnetic field analysis models based on the finite element method or the magnetic circuit method. These traditional models usually solve the problem by equating the inter-turn short circuit to a static problem with fixed short-circuit turn number and constant fault resistance. However, this approach has a fundamental flaw. It greatly simplifies a dynamic and nonlinear physical degradation process into a static and linear problem. In the real physical world, the fault resistance of inter-turn short circuit is not constant. It continuously changes dynamically with the comprehensive action of various factors such as local overheating, electric force impact and insulation material aging caused by short-circuit current. Due to the solidification of its core parameters, the traditional simulation model cannot truly simulate the complete life cycle of the fault from inception, development to final deterioration, resulting in great limitations in fault diagnosis accuracy and predictive maintenance guidance. It is difficult to meet the needs of high-fidelity and prediction ability pursued by modern industry for high-reliability motor digital twin technology. SUMMARY

[0004] The purpose of the present application is to provide a motor stator winding inter-turn short circuit modeling and simulation system to solve the problems in the background art.

[0005] To solve the above technical problems, the present application provides a motor stator winding inter-turn short circuit modeling and simulation system, comprising: a feature extraction module for calculating and generating a fault excitation index and a winding chaos entropy from the acquired motor operation data;

[0006] a fault evolution modeling module configured to solve a preset fault evolution model based on the fault excitation index and the winding chaos entropy to generate an updated fault resistance;

[0007] a multi-level parameter dynamic correction module configured to correct parameters of the simulation model according to the winding chaos entropy;

[0008] a simulation strategy optimization module configured to switch between a plurality of preset simulation modes according to the winding chaos entropy.

[0009] Preferably, the process of calculating the fault excitation index by the feature extraction module includes:

[0010] obtaining the motor speed and the load torque from the motor operation data, and determining a working condition adjustment function based on the motor speed and the load torque;

[0011] obtaining the short-circuit turn number from the motor operation data;

[0012] obtaining the root mean square value of the fault phase current from the motor operation data;

[0013] obtaining the current fault resistance value from the motor operation data;

[0014] combining the working condition adjustment function, the short-circuit turn number, the root mean square value of the fault phase current, and the current fault resistance value to calculate the fault excitation index.

[0015] Preferably, the process of calculating the winding chaos entropy by the feature extraction module includes:

[0016] performing fast Fourier transform on the stator current signal obtained from the motor operation data to obtain a plurality of harmonic components;

[0017] calculating a normalized proportion of energy of each harmonic component based on the plurality of harmonic components;

[0018] calculating the winding chaos entropy based on each normalized proportion by using an information entropy calculation form.

[0019] Preferably, the process of generating the updated fault resistance by the fault evolution modeling module includes:

[0020] obtaining the short-circuit turn number, the root mean square value of the fault phase current, the current fault resistance value, a preset rated current, a preset single-turn winding health resistance, and the working condition adjustment function;

[0021] calculating a dimensionless fault stress factor based on the obtained parameters and functions;

[0022] combining the fault stress factor and the winding chaos entropy to solve a preset fault evolution model to generate the updated fault resistance.

[0023] Preferably, the method is also used for:

[0024] The updated fault resistance is provided to the feature extraction module as a current fault resistance value at a next simulation time step to form a closed-loop feedback.

[0025] Preferably, the process of modifying the parameters of the simulation model by the multi-level parameter dynamic modification module comprises:

[0026] The winding chaos entropy is compared with a preset entropy increase triggering threshold value;

[0027] Based on the comparison result, an adaptive weight is calculated by a preset logic function;

[0028] The adaptive weight is applied to a "turn-phase-machine" three-level progressive architecture to modify the parameters of the simulation model.

[0029] Preferably, the output characteristic of the logic function is that when the winding chaos entropy is less than the entropy increase triggering threshold value, the value of the adaptive weight is close to 0; and when the winding chaos entropy is equal to or greater than the entropy increase triggering threshold value, the value of the adaptive weight is close to 1 to activate the parameter modification.

[0030] Preferably, the process of switching the simulation mode by the simulation strategy optimization module comprises:

[0031] The winding chaos entropy is compared with a preset first threshold value and a second threshold value;

[0032] The second threshold value is greater than the first threshold value.

[0033] Based on the comparison result, switching is performed between a pre-warning mode, a fine diagnosis mode and a failure prediction mode.

[0034] Preferably, the switching of the modes is specifically:

[0035] When the winding chaos entropy is less than the first threshold value, the pre-warning mode is switched to and a lumped parameter model is used for simulation;

[0036] When the winding chaos entropy is greater than or equal to the first threshold value and less than the second threshold value, the fine diagnosis mode is switched to and a field-circuit coupling model is used for simulation and a mesoscopic layer parameter modification is activated;

[0037] When the winding chaos entropy is greater than or equal to the second threshold value, the failure prediction mode is switched to and a multi-physical field coupling simulation is started and a macroscopic layer parameter modification is activated.

[0038] Compared with the prior art, the present application has the following beneficial effects:

[0039] By constructing a closed-loop adaptive system including feature extraction, fault evolution, parameter correction and strategy optimization, the whole life cycle of inter-turn short circuit fault from inception, development to failure can be continuously and highly accurately reproduced dynamically. The fault excitation index of comprehensive quantitative electromagnetic thermal shock strength and winding chaos entropy representing system disorder degree are introduced to provide a solid physical quantitative basis for accurately sensing the instantaneous state and cumulative influence of the fault, and the simulation model has an unprecedented dynamic insight ability.

[0040] Deeply integrated with interdisciplinary theories, the entropy increase theory of physics and the broken window effect of sociology are creatively mathematized into calculable winding chaos entropy and drivable evolution equation, so that the nonlinear deterioration process of damage accelerating damage can be directly coupled with the real-time electromagnetic thermal stress and chaotic state of the system. By feeding back the updated fault resistance to the input end to form a closed loop, the fault evolution is no longer a preset script, but a dynamic process that interacts with the system state in real time and is self-driven, greatly improving the physical authenticity and prediction ability of the simulation.

[0041] A unique "turn-phase-machine" three-level progressive parameter correction architecture is constructed, and a self-adaptive weight is driven by winding chaos entropy to intelligently activate and adjust the amplitude of parameter correction. When the fault is not significant, the correction mechanism remains silent to avoid unnecessary computational overhead; when the fault worsens and the system entropy value exceeds the preset threshold, the correction function is activated and enhanced with the severity of the fault, ensuring that the simulation model maintains high fidelity throughout the fault life cycle, and skillfully optimizing the balance between simulation accuracy and computational cost.

[0042] An adaptive simulation optimization strategy based on entropy threshold is proposed, which can intelligently switch between the lumped parameter model with the highest calculation efficiency, the field-circuit coupled model with high precision, and the most comprehensive multi-physical field coupled model according to the actual severity of the fault. Compared with the limitations of single model that cannot meet all scene requirements, it can dynamically match the most suitable simulation resource configuration for different fault stages throughout the monitoring and prediction task, greatly enhancing the engineering practical value and economy of the system. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, brief descriptions will be given below of the drawings needed in the embodiments or prior art descriptions of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor;

[0044] Figure 1 The logic block diagram of the motor stator winding inter-turn short circuit modeling and simulation system of the present application. Detailed Implementation

[0045] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0046] Please see Figure 1 The present invention provides a modeling and simulation system for inter-turn short circuits in motor stator windings, including: a feature extraction module, used to calculate and generate a fault excitation index and winding chaotic entropy from the acquired motor operating data;

[0047] The fault evolution modeling module is used to solve the preset fault evolution model based on the fault excitation index and the winding chaotic entropy to generate the updated fault resistance.

[0048] A multi-level parameter dynamic correction module is used to correct the parameters of the simulation model based on the winding chaotic entropy.

[0049] The simulation strategy optimization module switches between multiple preset simulation modes based on the winding chaotic entropy. This system aims to overcome the shortcomings of existing technologies, such as fixed simulation model parameters and the inability to simulate the dynamic evolution of faults. By constructing a closed-loop, adaptive simulation system, the system accurately depicts the complete lifecycle of inter-turn short-circuit faults from initiation to development and deterioration. The system includes a feature extraction module, a fault evolution modeling module, a multi-level parameter dynamic correction module, and a simulation strategy optimization module.

[0050] The feature extraction module aims to calculate core indicators that can quantify the fault state from motor operation data. This module is configured to calculate and generate the fault excitation index and winding chaos entropy from the acquired real-time or historical motor operation data. These two indicators jointly characterize the instantaneous state and cumulative impact of the fault from two dimensions: electromagnetic thermal shock intensity and system spectrum disorder, and serve as the core input for subsequent modules to perform dynamic modeling and adaptive control.

[0051] The fault evolution modeling module aims to build and solve a mathematical model capable of describing the dynamic changes of the fault core physical parameters; this module is configured to solve a pre-set fault evolution model driven by the broken window effect based on the fault excitation index and winding chaos entropy output by the aforementioned feature extraction module; the solution output of this model is an updated fault resistance value, which reflects the degradation of the insulation state of the short-circuit point at the next time step under the combined action of the current electromagnetic thermal stress and the system chaos state, thereby closely coupling the evolution process of the fault with the real-time running state of the system;

[0052] The multi-level parameter dynamic correction module aims to ensure that the simulation model maintains high fidelity at different stages of fault development; this module is configured to real-time correct the electrical, magnetic field and thermal parameters in the simulation model under a three-level progressive architecture of "turn-phase-machine" according to the winding chaos entropy calculated by the feature extraction module; by introducing an adaptive weight driven by winding chaos entropy, this module can intelligently control the triggering and amplitude of parameter correction, mapping the dynamic changes of microscopic physical parameters to macroscopic performance indicators;

[0053] The simulation strategy optimization module aims to balance the accuracy and efficiency of the simulation to adapt to the analysis needs of different fault stages; this module is configured to adaptively switch between multiple pre-set simulation modes according to the size of the winding chaos entropy; these modes correspond to different fault severity levels and match simulation models of different complexity and computational resource consumption, thereby providing technical support for the predictive maintenance of high-reliability motors and high-fidelity digital twins;

[0054] This embodiment realizes the modeling paradigm innovation from static parameter setting to dynamic evolution through the close coupling and closed-loop feedback of the data flow of the above-mentioned modules; it deeply integrates cross-disciplinary theories, mathematically transforming the ideas of entropy increase theory in physics and broken window effect in sociology into computable indicators and drivable simulation evolution equations, not only realizing dynamic simulation of the whole life cycle of inter-turn short circuit fault, but also considering multi-scale fidelity, simulation accuracy and computational efficiency through multi-level correction and adaptive strategy optimization, enhancing the physical interpretability and engineering practical value of the model.

[0055] The process of the feature extraction module calculating and generating the fault excitation index includes:

[0056] Obtain the motor speed and load torque from the motor running data, and determine the working condition adjustment function accordingly;

[0057] Obtain the number of short-circuit turns from the motor running data;

[0058] Obtain the root mean square value of the fault phase current from the motor running data;

[0059] obtaining a current fault resistance value from motor operation data;

[0060] The fault excitation index is calculated by combining the working condition adjustment function, the short-circuit turn number, the root mean square value of the fault phase current, and the current fault resistance value.

[0061] The process of calculating the fault excitation index by the feature extraction module is specifically described in this embodiment. The fault excitation index is a composite index for quantifying the comprehensive electromagnetic thermal shock intensity of the short-circuit fault on the system under a specific working condition. The function of the fault excitation index is to provide a quantitative physical quantity for driving the fault evolution. The fault excitation index is calculated from a plurality of real-time operation parameters;

[0062] To calculate the index, the feature extraction module obtains the motor speed and load torque from the motor operation data, and determines a preset working condition adjustment function according to the motor speed and load torque. The function is a dimensionless function, and the determination method is to fit and calibrate the aging test data of a specific motor model under different working conditions to simulate the aggravating effect of different speed and load combinations on the insulation aging rate. The module also obtains the short-circuit turn number from the motor operation data. The parameter represents the spatial extent of the fault. The root mean square value of the fault phase current is obtained, which represents the current electric stress level. The current fault resistance value is obtained, which represents the instantaneous severity of the fault.

[0063] Based on the above parameters, the module combines the working condition adjustment function, the short-circuit turn number, the root mean square value of the fault phase current, and the current fault resistance value to calculate the fault excitation index by the following preset formula:

[0064]

[0065] The fault excitation index (unit: A / Ω) is The short-circuit turn number (dimensionless) is

[0066] The root mean square value of the fault phase current (unit: A) obtained from the operation data is

[0067] The current fault resistance value (unit: Ω) is

[0068] The small normal number (unit: Ω) is set to prevent the denominator from being zero.

[0069] The working condition adjustment function (dimensionless) is inputted with the motor speed and the load torque obtained from the operation data.

[0070] To enable the skilled person to implement the working condition adjustment function, the working condition adjustment function can adopt a specific function form, for example, a quadratic polynomial function:

[0071]

[0072] is the motor speed;

[0073] is the load torque;

[0074] , , , , is a fitting coefficient with a specific dimension;

[0075] is a dimensionless fitting coefficient;

[0076] These coefficients are determined for a specific motor model by least square fitting of the insulation accelerated aging experimental data under multiple stable speed and load torque combinations; this form can effectively represent the coupling effect of speed and load on the insulation aging rate;

[0077] The index is recalculated at each simulation time step and passed to the fault evolution modeling module;

[0078] The technical effect brought by this embodiment is that by constructing a composite index that can comprehensively reflect the current load, fault severity, fault breadth, and motor operating conditions, the fault evolution rate is directly related to the actual pressure the motor is subjected to; for example, when the motor is under heavy load, the index will significantly increase, thereby accelerating the deterioration of the fault in the model, which is highly consistent with the physical reality, greatly improving the authenticity of the fault dynamic evolution simulation.

[0079] The process of calculating and generating the winding chaos entropy by the feature extraction module includes:

[0080] Performing fast Fourier transform on the stator current signal obtained from the motor operating data to obtain multiple harmonic components;

[0081] Based on the multiple harmonic components, calculate the normalized proportion of the energy of each harmonic component in the total harmonic energy;

[0082] Based on each normalized proportion, calculate and generate the winding chaos entropy using the information entropy calculation form.

[0083] The embodiment specifically describes the process of calculating the winding chaos entropy by the feature extraction module; the winding chaos entropy refers to an objective index for quantifying the degree of spectrum confusion caused by faults from the overall level of the system, which aims to quantify the physical process of increasing system disorder degree caused by faults into an objective and calculable decision index, and the source is the spectrum analysis result of the stator current signal;

[0084] To calculate the entropy value, the module performs fast Fourier transform (FFT) on the stator current signal obtained from the motor operation data to obtain a series of harmonic components and their amplitudes in the frequency domain;

[0085] Based on the plurality of harmonic components, the module calculates the normalized proportion of the energy of each harmonic component to the total harmonic energy; specifically, if the amplitude of the mth harmonic is , then the normalized proportion corresponding to the mth harmonic is calculated as:

[0086]

[0087] is the upper limit of the number of harmonics, and its value is determined by covering the frequency range of all main harmonics caused by inter-turn short circuit, which is determined in advance by analyzing the fault experiment spectrum of the sample motor;

[0088] Finally, the module calculates the winding chaos entropy based on each normalized proportion using the information entropy calculation form:

[0089]

[0090] is the dimensionless winding chaos entropy;

[0091] is the normalized proportion of the energy of the mth harmonic calculated in the previous step;

[0092] The entropy value is simultaneously transmitted to the fault evolution modeling module, the multi-level parameter dynamic correction module and the simulation strategy optimization module as a macroscopic and system-level state index;

[0093] The technical effect brought by the embodiment is that the physical nature of the fault (i.e. the increase of system disorder degree) is converted into a robust quantitative index; compared with the traditional method which relies on a single physical quantity, the winding chaos entropy can more comprehensively and stably reflect the overall health status of the system, providing a global and reliable decision basis for the adaptive correction of the subsequent modules and the intelligent switching of the simulation strategy.

[0094] ​​The process of generating the updated fault resistance by the fault evolution modeling module includes:

[0095] obtaining the short-circuit turn number, the root mean square value of the fault phase current, the current fault resistance value, the preset rated current, the preset single-turn winding health resistance, and the working condition adjustment function;

[0096] Based on the aforementioned obtained parameters and functions, a dimensionless fault stress factor is calculated and generated;

[0097] The preset fault evolution model is solved in combination with the fault stress factor and the winding chaotic entropy to generate the updated fault resistance;

[0098] The updated fault resistance is provided to the feature extraction module as the current fault resistance value in the next simulation time step to form a closed-loop feedback.

[0099] The process of generating the updated fault resistance by the fault evolution modeling module and the closed-loop feedback formed with the feature extraction module are specifically described in this embodiment;

[0100] The workflow of the fault evolution modeling module starts with obtaining a series of parameters, including the short-circuit turn number, the root mean square value of the fault phase current, and the current fault resistance value provided by the feature extraction module at the current time step, and a plurality of preset values, including the rated current and the single-turn winding health resistance determined according to the motor model, and the aforementioned working condition adjustment function;

[0101] Based on the aforementioned obtained parameters and functions, the module calculates and generates a dimensionless fault stress factor; the factor aims to normalize the comprehensive electrical stress level of the current fault point, and its role is to enhance the generality of the model and the clarity of the physical meaning; its calculation formula is as follows:

[0102]

[0103] The dimensionless fault stress factor is

[0104] The preset rated current (unit: A) is

[0105] The preset single-turn winding health resistance at the reference temperature (unit: Ω) is

[0106] , The definitions and sources of the remaining symbols are the same as before;

[0107] By introducing the reference value and for normalization, the fault stress factor can more universally reflect the relative severity of the fault.

[0108] In the computation flow of this module, the computed fault stress factor is taken as an input together with the winding chaos entropy from the feature extraction module to solve a pre-defined fault resistance evolution model to generate the updated fault resistance; this model is a fault resistance evolution differential equation driven by the broken-window effect to describe the non-linear deterioration process of damage accelerating damage; this equation is constructed as:

[0109]

[0110] is the change rate of the fault resistance over time (unit: Ω / s);

[0111] is the base feature degradation rate (unit: Ω / s), which represents the base degradation rate under the reference stress and without considering the system entropy effect, which is calibrated by accelerated aging experiments on the motor winding insulation materials;

[0112] is a dimensionless entropy amplification coefficient, which is also calibrated by experimental data;

[0113] is a random noise term (unit: Ω / s) to represent random shocks; by solving this differential equation, the fault resistance value at the next time step can be obtained;

[0114] To enable those skilled in the art to implement it, the random noise term can be set as a zero-mean Gaussian white noise, and the variance of which reflects the average strength of random shocks, which is also calibrated by statistical analysis of the random fluctuation component of resistance change in experimental data; for the solution of this differential equation, a discretized numerical method such as the first-order forward Euler method can be used to update the fault resistance within each simulation time step :

[0115]

[0116] is the fault resistance at the current time step;

[0117] is the updated fault resistance at the next time step;

[0118] is the simulation time step, which should be small enough to ensure the stability and accuracy of numerical calculation;

[0119] The system thereby realizes a key closed-loop feedback: the updated fault resistance is provided to the feature extraction module as the current fault resistance value in the next simulation time step; this design forms a core feedback loop, so that the updated fault resistance is used as the initial value of the next simulation time step, and the simulation process is repeated until the fault resistance converges. Changes the electrical characteristics of the motor, thereby affecting the phase current at the next time The winding chaos entropy , and changes the fault evolution rate again; this closed-loop mechanism makes the fault evolution a self-consistent and tightly coupled dynamic process with the system state;

[0120] The synergistic technical effect of the embodiment is that it first realizes dynamic, closed-loop, and self-consistent modeling of the inter-turn short circuit core parameter (fault resistance); by introducing a differential equation based on the broken window effect and deeply coupling its real-time solution with the system operating state, the simulation is no longer a splicing of static snapshots, but a continuous and high-fidelity reproduction of the fault physical degradation process, fundamentally improving the prediction ability of the simulation.

[0121] The process of the multi-level parameter dynamic correction module correcting the parameters of the simulation model includes:

[0122] Comparing the winding chaos entropy with a preset entropy increase trigger threshold;

[0123] Based on the comparison result, an adaptive weight is calculated and generated through a preset logic function;

[0124] The adaptive weight is applied to the "turn-phase-machine" three-level progressive architecture to correct the parameters of the simulation model;

[0125] The output characteristics of the logic function are: when the winding chaos entropy is less than the entropy increase trigger threshold, the value of the adaptive weight is close to 0; when the winding chaos entropy is equal to or greater than the entropy increase trigger threshold, the value of the adaptive weight is close to 1, to activate the parameter correction.

[0126] The embodiment specifically describes the process of the multi-level parameter dynamic correction module correcting the parameters of the simulation model;

[0127] The technical core of the module is to introduce an adaptive weight based on the cumulative effect to control the correction amplitude of the mesoscopic and macroscopic level parameters; the correction process compares the winding chaos entropy from the feature extraction module with a preset entropy increase trigger threshold; the threshold is a preset dimensionless value, and its setting basis is to statistically analyze a large amount of experimental data from healthy to fault of the motor, and find the entropy value that best distinguishes the fault initiation and fault development stage;

[0128] Based on the comparison result, an adaptive weight is calculated by a pre-defined logic function. The logic function is a standard Sigmoid function, which is used to smoothly control the triggering and amplitude of the model parameter correction. The calculation formula is:

[0129]

[0130] is a dimensionless adaptive weight ranging between (0, 1);

[0131] is the winding chaos entropy;

[0132] is the aforementioned entropy increase triggering threshold;

[0133] is the dimensionless slope of the function, which is determined by experimental data analysis to determine the severity of state transition;

[0134] The output characteristics of the function are clearly defined: when the winding chaos entropy is less than the entropy increase triggering threshold ( ), the value of is close to 0; when the winding chaos entropy is equal to or greater than the entropy increase triggering threshold ( ), the value of rapidly increases and approaches 1 to activate parameter correction;

[0135] The module applies the adaptive weight to a "turn-phase-machine" three-level progressive architecture to correct the parameters of the simulation model. For example, in the mesoscopic layer (phase winding), the correction amount of the equivalent inductance or resistance of the phase winding can be represented as a function related to and ; in the early stage of failure, is close to 0, and parameter correction hardly occurs; when the fault develops and exceeds the threshold , is activated, making the amplitude of parameter correction significantly increase with ;

[0136] To enable those skilled in the art to implement it, the adaptive weight can be used to correct the parameters of the mesoscopic layer (phase winding level) and the macroscopic layer (machine level); for example, the correction of the equivalent resistance and the equivalent inductance of the mesoscopic layer phase winding is realized by the following formula:

[0137]

[0138]

[0139] and are the phase resistance and phase inductance in the initial healthy state, respectively;

[0140] and is a dimensionless correction factor calibrated by experimental data, used to adjust the sensitivity of chaotic entropy to the parameter;

[0141] The correction is activated when the simulation strategy enters the fine diagnosis mode;

[0142] For the macroscopic layer parameters, such as the equivalent moment of inertia of the whole machine The correction can be expressed as:

[0143]

[0144] is the initial moment of inertia;

[0145] is the macroscopic correction factor;

[0146] The correction reflects the possible aggravation of mechanical vibration or imbalance caused by fault development, and is activated when the simulation strategy enters the failure prediction mode;

[0147] The technical effect brought by the embodiment is that an intelligent multi-scale parameter correction mechanism is constructed; through an adaptive weighting mechanism controlled by a system-level index (chaotic entropy), necessary corrections are introduced at the most needed time (the stage of significant fault development), so that the fidelity of the model throughout the life cycle is ensured, unnecessary complex calculations in the early stage of fault are avoided, and an optimal balance between fidelity and calculation cost is achieved.

[0148] The process of the simulation strategy optimization module switching the simulation mode includes:

[0149] Comparing the winding chaotic entropy with the first threshold value and the second threshold value;

[0150] The second threshold value is greater than the first threshold value;

[0151] Based on the comparison result, switching between the early warning mode, the fine diagnosis mode and the failure prediction mode;

[0152] The switching of the mode is specifically:

[0153] When the winding chaotic entropy is less than the first threshold value, switching to the early warning mode and using the lumped parameter model for simulation;

[0154] When the winding chaos entropy is greater than or equal to the first threshold value and less than the second threshold value, switching to a fine diagnosis mode, and adopting a field-circuit coupling model for simulation and activating a mesoscopic layer parameter correction;

[0155] When the winding chaos entropy is greater than or equal to the second threshold value, switching to a failure prediction mode, and starting a multi-physical field coupling simulation and activating a macroscopic layer parameter correction.

[0156] The embodiment specifically describes the process of switching the simulation strategy optimization module to the simulation mode;

[0157] The decision basis of the module is the winding chaos entropy calculated by the feature extraction module ; the module compares the winding chaos entropy with preset first and second threshold values The two threshold values are dimensionless preset values, and the setting logic is based on statistical analysis of a motor fault database, corresponding to a typical entropy value at which the fault can be preliminarily detected, corresponding to a typical entropy value at which the fault has significantly affected the performance of the motor, and ;

[0158] Based on the above comparison result, the system automatically switches between the early warning mode, the fine diagnosis mode and the failure prediction mode; the switching rule of the modes is specifically:

[0159] When the winding chaos entropy is less than the first threshold value ( ), the system switches to the early warning mode; this state is interpreted as the system being in a healthy or extremely early fault state, and the system adopts a lumped parameter model with the highest calculation efficiency for rapid simulation to realize real-time monitoring of potential abnormalities;

[0160] When the winding chaos entropy is greater than or equal to the first threshold value and less than the second threshold value ( ), the system switches to the fine diagnosis mode; this state is interpreted as the fault having been confirmed to form and start to develop, and the system automatically adopts a high-precision field-circuit coupling model for simulation and activates a parameter correction mechanism of the mesoscopic layer (the phase winding level);

[0161] When the winding chaos entropy is greater than or equal to the second threshold value ( ), the system switches to the failure prediction mode; this state is interpreted as the fault having entered a serious deterioration stage, and the system starts a multi-physical field coupling simulation with a thermal field and a stress field and activates a macroscopic layer (the whole machine level) parameter correction to perform comprehensive performance evaluation and life prediction;

[0162] ​The technical effect brought by the embodiment is that an adaptive simulation strategy based on actual severity of faults is provided; the strategy can intelligently match the most suitable simulation model and resource configuration for different fault stages, avoids using an excessively complex model in early faults or using an excessively simplified model in serious faults, and thus, in the whole fault monitoring and prediction task, the precision and efficiency of simulation are considered, and the practicability and economy of the system are greatly improved.

[0163] Compared with the technical scheme of the prior art simulation model using parameter solidification, which cannot simulate the dynamic evolution process of faults, the present application has the following beneficial effects:

[0164] The present application provides a motor stator winding inter-turn short circuit modeling and simulation system, which fundamentally overcomes the defects of static and isolated simulation model of the prior art; by constructing a closed-loop adaptive system including feature extraction, fault evolution modeling, multi-level parameter dynamic correction and simulation strategy optimization, the present scheme realizes continuous and high-fidelity dynamic reproduction of the whole life cycle of inter-turn short circuit faults from inception, development to failure;

[0165] The feature extraction module of the system can calculate and generate a fault excitation index and winding chaos entropy from motor operation data; the fault excitation index comprehensively quantifies the combined impact strength of the short circuit point by the real-time working condition, fault space breadth and electric stress level of the motor; and the winding chaos entropy objectively measures the degree of system disorder caused by faults from the global perspective of system frequency spectrum; the introduction of these two core indexes enables the model to accurately perceive the instantaneous state and cumulative influence of faults for the first time, providing a solid physical quantitative basis for subsequent dynamic evolution;

[0166] The fault evolution modeling module of the system is based on the above-mentioned fault excitation index and winding chaos entropy, and solves a preset fault evolution model to generate an updated fault resistance; this design reflects the nonlinear deterioration process of damage acceleration damage, and is directly coupled with the real-time electromagnetic thermal stress and chaotic state of the system; more importantly, the updated fault resistance is fed back to the feature extraction module at the next simulation time step, forming a self-consistent core loop; this mechanism makes the fault evolution no longer a preset script, but a dynamic process that interacts with the system state in real time and is self-driven, fundamentally improving the physical authenticity and prediction ability of the simulation;

[0167] The multi-level parameter dynamic correction module of the system corrects the simulation model parameters in a three-level progressive architecture of "turn-phase-entire machine" according to the winding chaos entropy; the module intelligently activates and adjusts the amplitude of parameter correction through an adaptive weight driven by winding chaos entropy; when the fault is not significant, the correction almost does not occur, avoiding unnecessary calculation overhead; when the fault deteriorates and causes the system entropy value to exceed the preset threshold, the correction is activated and is enhanced with the increase of the entropy value; this design ensures that the simulation model can maintain high fidelity in the whole life cycle of the fault, and optimizes the balance between simulation accuracy and calculation cost;

[0168] The simulation strategy optimization module of the present application is also based on winding chaos entropy, and adaptively switches between multiple modes such as early warning, fine diagnosis and failure prediction; it can intelligently match the lumped parameter model with the highest calculation efficiency, the field-circuit coupled model with high precision, or the most comprehensive multi-physical field coupled model according to the actual severity of the fault; compared with the single model of the prior art which cannot meet the needs of all scenarios, this strategy can dynamically and intelligently balance the accuracy and efficiency of simulation in the whole monitoring and prediction task, greatly enhancing the engineering practical value of the system.

[0169] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any skilled person in the art can use the disclosed technical content to make changes or modifications to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application, according to the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A modeling and simulation system for inter-turn short circuits in motor stator windings, characterized in that, include: The feature extraction module is used to calculate and generate the fault excitation index and winding chaotic entropy from the acquired motor operation data. The fault evolution modeling module is used to solve the preset fault evolution model based on the fault excitation index and the winding chaotic entropy to generate the updated fault resistance. A multi-level parameter dynamic correction module is used to correct the parameters of the simulation model based on the winding chaotic entropy. The simulation strategy optimization module is used to switch between multiple preset simulation modes based on the winding chaotic entropy. The process by which the feature extraction module calculates and generates the winding chaotic entropy includes: A fast Fourier transform is performed on the stator current signal obtained from the motor operation data to obtain multiple harmonic components; Based on multiple harmonic components, calculate the normalized proportion of the energy of each harmonic component to the total harmonic energy; If the first The amplitude of the subharmonic is Then its corresponding normalized weight Calculated as: The upper limit of the harmonic analysis is determined by covering all major harmonic frequency ranges caused by inter-turn short circuits. This value is predetermined by analyzing the fault test spectrum of the sample motor. Based on each normalized weight, the chaotic entropy of the generated winding is calculated using the information entropy calculation method: The dimensionless entropy of the winding chaos; The first step calculated Normalized proportion of subharmonic energy; This entropy value, as a macroscopic, system-level state indicator, is simultaneously transmitted to the fault evolution modeling module, the multi-level parameter dynamic correction module, and the simulation strategy optimization module. The process by which the fault evolution modeling module generates the updated fault resistance includes: Obtain the number of short-circuit turns, the root mean square value of the fault phase current, the current fault resistance value, the preset rated current, the preset single-turn winding health resistance, and the operating condition adjustment function. Based on the parameters and functions obtained above, the dimensionless fault stress factor is calculated and generated; the calculation formula is as follows: The fault stress factor is a dimensionless factor. The preset rated current (unit: A); The preset health resistance of a single-turn winding at the reference temperature (unit: Ω); , The definitions and origins of the remaining symbols are the same as before; By combining the fault stress factor and the winding chaotic entropy, the preset fault evolution model is solved to generate the updated fault resistance. The process by which the multi-level parameter dynamic correction module corrects the parameters of the simulation model includes: The winding chaotic entropy is compared with the preset entropy increase trigger threshold; Based on the comparison results, adaptive weights are generated by calculating using a preset logical function; Adaptive weights are applied to the three-level progressive architecture of "turn-phase-system" to correct the parameters of the simulation model; The output characteristics of the logic function are as follows: when the winding chaotic entropy is less than the entropy increase trigger threshold, the value of the adaptive weight is close to 0; when the winding chaotic entropy is equal to or greater than the entropy increase trigger threshold, the value of the adaptive weight is close to 1, so as to activate parameter correction.

2. The system for modeling and simulating inter-turn short circuits in motor stator windings according to claim 1, characterized in that, The process by which the feature extraction module calculates and generates the fault excitation index includes: The motor speed and load torque are obtained from the motor operating data, and the operating condition adjustment function is determined accordingly. Obtain the number of short-circuited turns from the motor operating data; Obtain the root mean square value of the fault phase current from the motor operating data; Obtain the current fault resistance value from the motor operating data; The fault excitation index is calculated by combining the operating condition adjustment function, the number of short-circuit turns, the root mean square value of the fault phase current, and the current fault resistance value.

3. The system for modeling and simulating inter-turn short circuits in motor stator windings according to claim 2, characterized in that, Also used for: The updated fault resistance is used as the current fault resistance value in the next simulation time step and provided to the feature extraction module to form a closed-loop feedback.

4. The system for modeling and simulating inter-turn short circuits in motor stator windings according to claim 1, characterized in that, The process of switching simulation modes by the simulation strategy optimization module includes: The winding chaotic entropy is compared with the preset first threshold and second threshold; The second threshold is greater than the first threshold; Based on the comparison results, the system switches between early warning mode, fine diagnosis mode, and failure prediction mode.

5. The system for modeling and simulating inter-turn short circuits in motor stator windings according to claim 4, characterized in that, The specific mode switching is as follows: When the winding chaotic entropy is less than the first threshold, switch to early warning mode and use the lumped parameter model for simulation; When the winding chaotic entropy is greater than or equal to the first threshold and less than the second threshold, switch to fine diagnosis mode, use field-circuit coupling model for simulation and activate meso-level parameter correction. When the winding chaotic entropy is greater than or equal to the second threshold, switch to failure prediction mode, start multiphysics coupling simulation and activate macroscopic layer parameter correction.

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