High-voltage circuit breaker mechanical fault diagnosis method and system based on MIDBO-SVM model

Abstract

The invention discloses a high-voltage circuit breaker mechanical fault diagnosis method and system based on an MIDBO-SVM model, and the method comprises the steps: firstly obtaining a vibration signal of a circuit breaker, and then carrying out the diagnosis of a mechanical fault of the circuit breaker through the obtained vibration signal in combination with a pre-constructed MIDBO-SVM model. In the construction process of the MIDBO-SVM model, a good point set strategy and circle chaotic mapping are used for jointly generating an initial population to improve the uniform ergodicity of the population; a sine and cosine search strategy and a self-adaptive weight factor are introduced in a roll dung beetle individual position updating stage, so that decay oscillation is solved until the solution tends to a global optimal solution; the Cauchy Gaussian mutation strategy is adopted to mutate the individual with the best fitness at present in the mode of being far away from the optimal solution, the space search range is expanded, the problems that a traditional algorithm is low in later search speed and prone to falling into local optimum are further solved, and the fault classification precision of the circuit breaker is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of high-voltage circuit breaker mechanical fault diagnosis, and relates to a high-voltage circuit breaker mechanical fault diagnosis method and system based on a MIDBO-SVM model. Background Art

[0002] As the core component of the power transmission and distribution system, the stable and reliable operation of high-voltage circuit breakers is directly related to the safety and stability of the power grid. In a complex and changeable power environment, circuit breakers must not only quickly and accurately perform opening and closing operations to isolate the fault area, but also have the ability to operate stably for a long time. However, with the accumulation of operating time, the operating mechanism and control circuit of the circuit breaker often cause frequent failures such as refusal to open and refusal to close due to problems such as spring material fatigue, mechanical component wear, and electrical component aging (such as iron core jamming and opening and closing coil failure). If these faults are not identified and handled in time, they will seriously threaten the safety and stable operation of the power grid. To address this problem, vibration signal analysis, as a non-invasive and real-time monitoring method, has gradually shown great potential in the field of mechanical state monitoring and fault diagnosis of high-voltage circuit breakers. The vibration signal contains rich information during the operation of the circuit breaker, such as action time, signal amplitude change, frequency component, etc. These characteristics can directly reflect the mechanical state and potential fault mode inside the circuit breaker. Therefore, by accurately extracting and analyzing the characteristic parameters in the vibration signal, timely warning and accurate diagnosis of circuit breaker mechanical failures can be achieved, providing strong support for the safe operation and maintenance of the power grid.

[0003] With the rapid development of artificial intelligence technology, especially the mature application of machine learning algorithms, it has brought revolutionary changes to high-voltage circuit breaker fault diagnosis. Among them, neural networks and support vector machines (SVM), as the two mainstream technologies, each show unique advantages and limitations. With its powerful self-learning and nonlinear mapping capabilities, neural networks have significant advantages in processing complex and changeable system models, but the complex training process, easy to fall into local optimality, and large sample data requirements limit its extensiveness in practical applications. In contrast, SVM has become another important tool in the field of high-voltage circuit breaker fault diagnosis with its good adaptability to small sample data, effective processing ability of high-dimensional feature space and strong global generalization ability. However, the performance of SVM is highly dependent on its parameter settings, especially the selection of penalty factor C and kernel parameter g, which directly affects the classification accuracy and generalization ability of the model. Therefore, how to optimize SVM parameters efficiently and accurately becomes the key to improving the diagnosis effect.

[0004] In order to solve the problem of SVM parameter optimization, the Dung Beetle Optimization (DBO) algorithm, as a new heuristic optimization algorithm, has been introduced into the field of circuit breaker fault diagnosis due to its strong search ability and fast convergence speed. It is used to automatically search and optimize SVM parameters. The DBO algorithm simulates the behavior of dung beetles in nature rolling dung balls to find food sources. By continuously iteratively updating the population position, it gradually approaches the global optimal solution. However, in practical applications, the DBO algorithm also faces challenges such as decreased population diversity and easy to fall into local optimality. Especially when dealing with complex and changeable circuit breaker fault diagnosis problems, it is necessary to improve and optimize the algorithm for specific problems, such as introducing hybrid strategies, dynamically adjusting search parameters, etc., to balance global exploration and local development capabilities, and further improve the accuracy and efficiency of fault diagnosis. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a high-voltage circuit breaker mechanical fault diagnosis method and system based on the MIDBO-SVM model, thereby solving the technical problem that the dung beetle optimization algorithm in the prior art has an imbalance between global exploration and local development during the iteration process, and still needs to be specifically optimized according to the circuit breaker fault diagnosis problem.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for diagnosing mechanical faults of high-voltage circuit breakers based on a MIDBO-SVM model comprises the following steps:

[0008] S1: Obtain vibration signal of circuit breaker;

[0009] S2: The mechanical fault of the circuit breaker is diagnosed by using the acquired vibration signal in combination with the pre-built MIDBO-SVM model; during the construction of the MIDBO-SVM model, the initial population is jointly generated by the good point set strategy and the circle chaotic mapping for the DBO algorithm, the sine-cosine search strategy and the adaptive weight factor are introduced in the individual position update stage, and the Cauchy-Gauss mutation strategy is implemented for the individual with the best current fitness to obtain MIDBO, and the penalty factor C and the kernel parameter g of the SVM are optimized by using the MIDBO to complete the model construction of the MIDBO-SVM.

[0010] Preferably, the good point set strategy and circle chaotic mapping are used to jointly generate the initial population, specifically: firstly, the basic framework of the initial population is generated by using the good point set strategy, and then the circle chaotic mapping is applied to fine-tune each individual position.

[0011] Preferably, the sine-cosine search strategy and adaptive weight factor are introduced in the individual position update stage, specifically: in each iteration of DBO, the search direction and step size are adjusted in combination with the sine-cosine function according to the relative relationship between the current individual position and the target position, and the weight factor is dynamically adjusted to adapt to different stages of the search process.

[0012] Preferably, the Cauchy-Gaussian mutation strategy is implemented for the individual with the best current fitness, specifically: for the individual with the highest current fitness, the mutation direction and distance are generated according to the Cauchy distribution, and fine-tuned in combination with the randomness of the Gaussian distribution, so that the mutated individual can move away from the current optimal solution.

[0013] Preferably, the vibration signal of the circuit breaker is acquired by an acceleration sensor.

[0014] Preferably, the vibration signal includes amplitude, frequency and phase.

[0015] A high-voltage circuit breaker mechanical fault diagnosis system based on MIDBO-SVM model, comprising:

[0016] Data acquisition unit: the data acquisition unit is used to acquire the vibration signal of the circuit breaker;

[0017] Fault diagnosis unit: The fault diagnosis unit is used to diagnose the mechanical fault of the circuit breaker by using the acquired vibration signal in combination with the pre-built MIDBO-SVM model; during the construction of the MIDBO-SVM model, the DBO algorithm adopts the best point set strategy and circle chaos mapping to jointly generate the initial population, introduces the sine-cosine search strategy and adaptive weight factor in the individual position update stage, and implements the Cauchy-Gauss mutation strategy for the individual with the best current fitness to obtain MIDBO, and uses the MIDBO to optimize the penalty factor and kernel parameters of the SVM to complete the model construction of the MIDBO-SVM.

[0018] A computer device / equipment / system comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0019] A computer-readable storage medium stores a computer program, wherein the computer program / instructions are executed by a processor to implement the steps of the above method.

[0020] A computer program product comprises a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0021] Compared with the prior art, the present invention has the following beneficial technical effects:

[0022] The present invention discloses a mechanical fault diagnosis method for a high-voltage circuit breaker based on a MIDBO-SVM model. The method adopts the MIDBO-SVM model, that is, a multi-strategy fusion improved adaptive dung beetle optimization algorithm to optimize a support vector machine parameter model. During the construction process of the model, firstly, for a DBO algorithm, a good point set strategy and a circle chaos map are adopted to jointly generate an initial population. This method helps to increase the diversity and distribution uniformity of the population, thereby improving the global search capability of the algorithm. Secondly, a sine-cosine search strategy and an adaptive weight factor are introduced in the individual position update stage. The sine-cosine search strategy simulates the optimization behavior of certain organisms in nature and can avoid falling into a local optimum to a certain extent. The adaptive weight factor is dynamically adjusted according to feedback information in the search process to balance the capabilities of the global search and the local search. In addition, during the construction process, a Cauchy-Gaussian mutation strategy is implemented for the individual with the best current fitness. The Cauchy distribution has a heavy-tail characteristic. , so that the mutated individuals can move away from the current optimal solution and explore a wider solution space. At the same time, fine-tuning combined with the randomness of Gaussian distribution can ensure that the mutation process is both directional and random, which is helpful to find a better solution; and through the optimized DBO, that is, the MIDBO algorithm, the optimized SVM parameters, namely the penalty factor C and the kernel parameter g, are obtained. These two parameters have a crucial impact on the performance of the SVM classifier. The penalty factor C controls the degree of penalty for misclassified samples, while the kernel parameter g determines the width of the radial basis function (RBF) in the SVM, which in turn affects the complexity and generalization ability of the model. Therefore, the mechanical fault diagnosis method of high-voltage circuit breakers based on the MIDBO-SVM model effectively improves the performance of the SVM classifier by introducing a series of optimization measures such as the good point set strategy, circle chaos mapping, sine and cosine search strategy, adaptive weight factor and Cauchy-Gauss mutation strategy, making fault diagnosis more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a method for diagnosing mechanical faults of high-voltage circuit breakers based on a MIDBO-SVM model in the present invention;

[0025] Figure 2 This is a flow chart of high-voltage circuit breaker fault diagnosis based on the MIDBO-SVM model in Example 3 of the present invention;

[0026] Figure 3 Actual photos of the vibration signal acquisition platform for high-voltage circuit breakers constructed by the present invention, wherein: (a) is a photo of the vibration signal acquisition platform; (b) is a photo of the high-voltage circuit breaker;

[0027] Figure 4 This is a schematic diagram of a circuit breaker mechanical fault data acquisition system in Embodiment 3 of the present invention;

[0028] Figure 5 It is a structural schematic diagram of a high-voltage circuit breaker mechanical fault diagnosis system based on the MIDBO-SVM model in the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0030] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0032] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0033] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0034] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0035] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0036] Example 1

[0037] like Figure 1 As shown, the present invention discloses a high-voltage circuit breaker mechanical fault diagnosis method based on the MIDBO-SVM model, comprising the following steps:

[0038] S1: Acquire a vibration signal of a circuit breaker; wherein the vibration signal includes amplitude, frequency and phase, and an acceleration sensor may be used to acquire the vibration signal of the circuit breaker.

[0039] S2: The mechanical fault of the circuit breaker is diagnosed by using the acquired vibration signal in combination with the pre-built MIDBO-SVM model; during the construction of the MIDBO-SVM model, the initial population is jointly generated by the good point set strategy and the circle chaotic mapping for the DBO algorithm, the sine-cosine search strategy and the adaptive weight factor are introduced in the individual position update stage, and the Cauchy-Gauss mutation strategy is implemented for the individual with the best current fitness to obtain MIDBO, and the penalty factor C and the kernel parameter g of the SVM are optimized by using the MIDBO to complete the model construction of the MIDBO-SVM.

[0040] Among them, the good point set strategy and circle chaotic mapping are used to jointly generate the initial population, specifically: first, the basic framework of the initial population is generated by using the good point set strategy, and then the circle chaotic mapping is applied to each individual position for fine-tuning; the sine-cosine search strategy and adaptive weight factor are introduced in the individual position update stage, specifically: in each iteration of DBO, according to the relative relationship between the current individual position and the target position, the search direction and step size are adjusted in combination with the sine-cosine function, and the weight factor is dynamically adjusted to adapt to the different stages of the search process; the Cauchy-Gaussian mutation strategy is implemented for the individual with the best current fitness, specifically: for the individual with the highest current fitness, the mutation direction and distance are generated according to the Cauchy distribution, and the randomness of the Gaussian distribution is combined for fine-tuning, so that the mutated individual can be away from the current optimal solution.

[0041] The present invention proposes an adaptive dung beetle optimization algorithm based on multi-strategy fusion improvement to optimize the support vector machine parameters, thereby solving the problem of identifying different types of faults of high-voltage circuit breakers. Firstly, a method of jointly generating the initial population by the good point set strategy and the circle chaotic map is proposed to improve the uniform ergodicity of the population; secondly, a sine-cosine search strategy and an adaptive weight factor are introduced in the individual position update stage of the rolling ball dung beetle, so that the solution attenuation oscillates until it tends to the global optimal solution, avoiding falling into the local optimum; finally, the Cauchy-Gauss mutation strategy is adopted to mutate the individual with the best current fitness in a way away from the optimal solution, thereby expanding the spatial search range. By combining the improved DBO algorithm with the support vector machine, the MIDBO-SVM method not only maintains a fast convergence speed, but also solves the problem of slow search speed and easy to fall into the local optimum in the later stage of the traditional algorithm, improves the classification accuracy of circuit breaker faults, and provides a more practical solution for circuit breaker fault diagnosis technology.

[0042] Example 2

[0043] In optimization algorithms, the distribution uniformity and diversity of the initial population have a significant impact on the overall performance of the algorithm, including convergence speed and accuracy. Traditional Logistic chaotic mapping, due to its uneven distribution, often causes the algorithm to fall into local optimality during the search process, thus limiting the algorithm's search ability and the quality of the final solution. In order to solve this problem, researchers are constantly exploring more stable and evenly distributed chaotic mapping methods.

[0044] As an improvement of the Logistic mapping, the Tent mapping improves the ergodic uniformity to a certain extent and has a more consistent distribution density for different parameters, but its inherent unstable periodicity and the defect of being easily trapped in fixed points still limit its application in complex optimization problems. This prompts us to find a more stable and reliable chaotic mapping mechanism.

[0045] This application innovatively proposes a population initialization method that combines Circle chaotic mapping with the good point set strategy. Circle chaotic mapping is known for its stable dynamic characteristics and uniform distribution characteristics in phase space, which makes it an ideal choice for initializing the population. The initial population generated by Circle chaotic mapping can be evenly distributed in the solution space, effectively avoiding the risk of falling into a local optimum at the beginning of the algorithm.

[0046] At the same time, in order to further enhance the diversity of the population and the robustness of initialization, this application introduces the good point set strategy. The good point set theory is a mathematical method for evenly distributing points in multidimensional space. It can ensure that the distribution of point sets in space is as even as possible under a limited number of points. Applying the good point set strategy to population initialization can ensure that the initial population is not only evenly distributed in a single dimension, but also maintains good distribution characteristics in multidimensional space, thereby improving the global search ability of the algorithm.

[0047] Therefore, this application proposes to use a relatively stable and evenly distributed Circle chaotic mapping and a good point set to initialize the population group, while making up for the singleness of the group initialization method. The population mapping method is as follows:

[0048]

[0049] Among them, mod is the remainder function, x i represents the group position, a is 0.2, b is 0.5; o is a random number uniformly distributed between [0,1]; r k represents the optimal point; ub and lb represent the upper and lower limits of the group position respectively.

[0050] In order to overcome the premature convergence and local optimal trap problems of the dung beetle search algorithm caused by the too fast search speed, the present invention proposes an innovative improvement strategy, which combines the sine-cosine search strategy with the dynamic inertia weight mechanism to optimize the producer's position update process.

[0051] Among them, the sine-cosine search strategy is a mechanism that guides the search direction through the periodic changes of sine and cosine functions. These functions can produce oscillating behavior within their period, allowing the algorithm to achieve a balance between exploration and exploitation. In the present invention, the sine-cosine search strategy is integrated into the producer's position update formula, so that the algorithm does not approach the potential optimal solution in a linear manner during the search process, but gradually approaches it in an oscillating manner, which helps the algorithm to jump out of the local optimal area and explore a wider solution space.

[0052] In addition, the inertia weight ω plays an important role in the optimization algorithm, which determines the degree to which the algorithm maintains or changes the current motion state during the search process. In the present invention, by introducing random inertia weights into the individual position update equation, the algorithm can dynamically adjust its search step size and direction according to different stages of the search process. In particular, randomness ensures that the algorithm has different exploration capabilities in different iterations, which helps to avoid falling into local optimality.

[0053] However, the traditional linear decreasing weight strategy (i.e., the weight gradually decreases with the number of iterations) helps the algorithm converge in the later stage to a certain extent, but if a good solution is not found in the early stage of the algorithm, it may cause the weight to decay prematurely, limiting the global search ability of the algorithm. Therefore, the present invention proposes further optimization strategies for inertia weights, such as nonlinear decreasing, dynamic weights based on fitness adjustment, etc., to ensure that the algorithm can maintain high search efficiency and flexibility throughout the search process.

[0054] By combining the sine and cosine search strategy with the dynamic inertia weight mechanism, in the improved algorithm proposed by the present invention, the oscillation characteristics of the sine and cosine functions enable the algorithm to more effectively jump out of the local optimum, while the dynamic inertia weight ensures that the algorithm can maintain appropriate exploration capabilities at different stages, thereby enhancing the global search capability; the randomness and dynamically adjusted weight strategy reduce the risk of the algorithm falling into the local optimum in the early stage of the search, thereby avoiding premature convergence; by finely adjusting the search direction and step size, the algorithm can approach the global optimal solution more quickly, and maintain high precision after reaching stability, thereby improving the convergence speed and precision; this strategy has good adaptability to different types of optimization problems, and can adapt to different search needs and scenarios by adjusting parameters.

[0055] Therefore, in order to prevent the dung beetle from searching too fast, causing the algorithm to converge prematurely and fall into a local optimum, this patent proposes to introduce a sine-cosine search strategy to update the producer's position update formula, using the sine-cosine function to make the solution oscillate toward the global optimal solution, and at the same time introduce a random inertia weight into the individual position update equation. The size of the inertia weight ω clarifies the level of the algorithm's search ability and can reflect the individual's ability to maintain a historical state. When a linear decreasing weight is used to optimize the optimization process, if the algorithm cannot find the best point in the early stage, the weight will gradually decay as the iteration increases, and the population optimization will be more likely to fall into local convergence. The improved formula is as follows:

[0056]

[0057] ω=ω max +(ω max -ω min )rand()+δ·randn(); (3)

[0058] In the formula, t represents the current iteration number, X i (t) represents the position information of the dung beetle during the t-th iteration; k∈(0,0.2] represents the deflection coefficient; b represents a constant in the range of (0,1); α is the natural coefficient; ω max is the maximum value of random inertia weight, which is 0.9; r1 and r2 are proportional coefficients; ω minis the minimum value of the random inertia weight, which is 0.4; rand() is a random number uniformly distributed in [0,1]; randn() is a random number with a standard normal distribution; σ is used to measure the deviation between the random inertia weight and the mathematical expectation, which is 0.3.

[0059] In the stage of updating the individual positions of the rolling dung beetles, a sine-cosine search strategy and an adaptive weight factor are introduced to make the solution attenuate and oscillate until it approaches the global optimal solution. In this description, the rolling dung beetle refers to an algorithm role that simulates the behavior of dung beetles (commonly known as dung beetles) rolling dung balls in nature. The Dung Beetle Optimizer (DBO) is a swarm intelligence optimization algorithm inspired by the biological behavior of dung beetles. In the algorithm, the rolling dung beetles represent the individuals or solutions in the algorithm, and they move in the search space by simulating the behavior of dung beetles rolling dung balls to find the optimal solution.

[0060] In the later iteration of DBO, as the dung beetle population gradually converges, that is, the differences between individuals decrease, the algorithm tends to fall into the local optimal solution, resulting in update stagnation and a significant decrease in search efficiency. In order to effectively deal with this problem, the present invention innovatively introduces the Cauchy-Gauss mutation strategy, which aims to enhance the global search capability of the algorithm by increasing the diversity of the population, thereby avoiding the local optimal stagnation phenomenon.

[0061] First, the Cauchy-Gaussian mutation strategy combines the characteristics of the Cauchy distribution and the Gaussian distribution, providing a powerful mutation mechanism for the algorithm. The Cauchy distribution is known for its long tail characteristics, which can produce large mutation lengths far away from the current position, helping the algorithm to jump out of the local optimal area; while the Gaussian distribution is characterized by its symmetry and concentration, which can produce smaller mutations near the current position, which helps the algorithm to search finely.

[0062] In the present invention, the Cauchy-Gaussian mutation strategy is applied to the individuals with the best current fitness. This is because, although these individuals perform well in the current search space, they may only be local optimal solutions, not global optimal solutions. By mutating them, they can be forced to stay away from the current optimal area and explore a wider solution space, thereby increasing the possibility of finding the global optimal solution. In the mutation process, the mutated individuals are first selected, that is, in each iteration, the individuals with the best current performance are selected as mutation objects according to the fitness value. Then, the variable step length is generated, that is, the variable step length is generated using a mixed strategy of Cauchy distribution and Gaussian distribution. Specifically, the Cauchy distribution or Gaussian distribution can be selected to generate the step length according to a certain probability, or the results of the two can be combined in some way. Then, the position is updated, that is, the generated variable step length is applied to the selected individual, and its position in the solution space is updated. This step is critical because it directly determines the search direction and range of the mutated individual. Finally, the mutated individual is evaluated, and it is determined whether to accept the mutation according to its fitness value. If the mutated individual performs better, the original individual is replaced; otherwise, it can be decided whether to retain the mutation result according to certain rules (such as acceptance probability).

[0063] The method uses the Cauchy-Gaussian mutation strategy, and the algorithm can still maintain a high global search capability in the later stage of iteration, effectively avoiding the local optimal stagnation phenomenon and enhancing the global search capability; the mutation operation enables some individuals in the population to update their positions in a way away from the optimal solution, thereby increasing the diversity of the population, helping the algorithm to search in a wider solution space and improving the population diversity; the parameters in the Cauchy-Gaussian mutation strategy (such as the ratio of Cauchy distribution and Gaussian distribution, mutation probability, etc.) can be adjusted according to the characteristics of the specific problem, making the algorithm more flexible and adaptable, and improving the flexibility and adaptability of the algorithm. Therefore, the Cauchy-Gaussian mutation strategy proposed in the present invention provides an effective solution to the local optimal stagnation problem that may occur in the DBO algorithm in the later stage of iteration. By increasing the diversity of the population and the global search capability, the strategy can significantly improve the performance and stability of the algorithm.

[0064] That is, in the later stage of DBO iteration, due to the rapid assimilation of the dung beetle population, the local optimal stagnation update phenomenon is prone to occur. In order to solve this problem, this patent proposes to use the Cauchy-Gauss mutation strategy for optimization, select the individuals with the best current fitness for mutation, and make some individuals in the population update their positions in a way away from the optimal solution to increase the diversity of exploring the global space.

[0065] Gaussian mutation is to add a random vector that obeys Gaussian distribution to the state of the original individual. The one-dimensional Gaussian density function with mean μ and variance σ is:

[0066]

[0067] In the formula, x is a random variable, which indicates the value for which the probability density needs to be calculated; f G (x) represents the probability density of the random variable x at a specific value; μ is the mean of the distribution, which determines the symmetric center of the distribution; σ is the standard deviation of the distribution. The larger the standard deviation, the wider the data distribution.

[0068] The probability density function of the one-dimensional Cauchy distribution is:

[0069]

[0070] Where x is a random variable; f(x) represents the probability density of the random variable x at a specific value; t represents the scale parameter, which indicates the width of the distribution. The larger the value, the wider the distribution, and the smaller the value, the narrower the distribution.

[0071] The Cauchy-Gauss mutation strategy is used in the iterative process to compare the population positions before and after the mutation, and a competition mechanism is used to select the solution with better fitness to enter the next iteration, as follows:

[0072] P gbest =X gbest [1+λ1Cauchy(0,σ 2 )+λ2Gauss(0,σ 2 )] (6)

[0073]

[0074]

[0075] Where P gbest represents the position of the optimal individual after mutation; X gbest represents the optimal individual; X newbest is the optimal individual after individual mutation; σ 2 represents the standard deviation of the Cauchy-Gaussian mutation strategy; Cauchy(0,σ 2 ) is a random variable that satisfies the Cauchy distribution; Gauss(0,σ 2 ) is a random variable that satisfies the Gaussian distribution; t represents the current number of iterations; T is the total number of iterations; λ1 and λ2 are dynamic parameters that are adaptively adjusted with the number of iterations; in the early stage of iteration, the Cauchy distribution has a strong perturbation ability, which can improve the global search ability of the algorithm. As the number of iterations increases, λ1 gradually decreases, and the influence of the Cauchy mutation perturbation decreases. When entering the late stage of iteration, λ1 decreases and λ2 increases, and Gaussian perturbations are mainly performed to enhance the local development ability of the algorithm and improve the convergence accuracy. The application of adaptive parameters shows the advantages of the Cauchy distribution and the Gaussian distribution, respectively, and balances the global exploration and local search capabilities of the algorithm.

[0076] Example 3

[0077] In addition, if Figure 2 As shown, in order to fully verify the effectiveness of the proposed method (i.e., the MIDBO-SVM model combining MIDBO (improved dung beetle behavior optimization algorithm) and SVM (support vector machine)) in high-voltage circuit breaker fault diagnosis, this embodiment designs an experimental scheme, selects the VS1-ZN63A high-voltage vacuum circuit breaker as the experimental object, and constructs a fault simulation system. The following is the experimental process, data acquisition, signal processing, model training and optimization, and the final verification steps.

[0078] (1) Experimental design and fault simulation

[0079] Selection of experimental objects: VS1-ZN63A high-voltage vacuum circuit breaker was selected as the experimental object because of its wide application and typical failure mode.

[0080] Construction of fault simulation system: In order to simulate mechanical failures in actual operation, a fault simulation system was designed and built, which can simulate various mechanical failure conditions such as low operating voltage, high operating voltage, loose base bolts, and operating conditions under normal working conditions.

[0081] (2) Data collection and preprocessing

[0082] Signal acquisition platform: Figure 3 As shown in the figure, the signal acquisition operation platform integrates an acceleration sensor, a high-speed acquisition card and an industrial computer. The acceleration sensor is responsible for accurately capturing the vibration signal of the circuit breaker during the closing process. These signals are transmitted to the industrial computer in real time through the high-speed acquisition card for storage and display.

[0083] Data collection: When the circuit breaker is in the no-load state, the closing vibration signals under four working conditions are collected: normal working condition, low operating voltage, high operating voltage, and loose base bolts. Under each working condition, ensure that a sufficient number of samples are collected to ensure the representativeness and reliability of the data.

[0084] Preprocessing: Perform denoising, filtering and other preprocessing operations on the collected raw vibration signals to improve signal quality and lay a good foundation for subsequent feature extraction and model training.

[0085] (3) Feature extraction

[0086] FFT feature extraction: The fast Fourier transform (FFT) method is used to perform frequency domain analysis on the preprocessed vibration signal to extract key features including frequency, amplitude, phase, etc. as input features of the support vector machine. The FFT method can effectively reveal the characteristics of the signal in the frequency domain and help distinguish the vibration modes under different working conditions.

[0087] (4) Model training and optimization

[0088] MIDBO-SVM model construction: Figure 4 As shown in Figure 1, the core of the MIDBO-SVM model is to use the MIDBO algorithm to optimize the parameters of the SVM. The MIDBO algorithm uses its unique search mechanism to iteratively optimize the penalty factor C and the kernel parameter g of the SVM to find the parameter combination that optimizes the model performance.

[0089] Parameter optimization: The training set data after feature extraction is input into the MIDBO-SVM model. Through the iterative optimization process of the MIDBO algorithm, the parameters of the SVM are continuously adjusted until the preset stopping conditions (such as the maximum number of iterations, convergence of the objective function, etc.) are reached.

[0090] Model testing: Use the test set data to perform performance testing on the optimized MIDBO-SVM model to evaluate key indicators such as diagnostic accuracy and robustness under different working conditions.

[0091] (5) Results verification and discussion

[0092] Result analysis: The performance of the MIDBO-SVM model is compared with that of the traditional SVM model or other fault diagnosis methods on the same data set to verify the effectiveness of the MIDBO algorithm in optimizing SVM parameters and the superior performance of the MIDBO-SVM model in high-voltage circuit breaker fault diagnosis.

[0093] Example 4

[0094] In addition, if Figure 5 As shown, the present invention also discloses a high-voltage circuit breaker mechanical fault diagnosis system based on the MIDBO-SVM model, comprising:

[0095] Data acquisition unit: the data acquisition unit is used to acquire the vibration signal of the circuit breaker;

[0096] Fault diagnosis unit: The fault diagnosis unit is used to diagnose the mechanical fault of the circuit breaker using the acquired vibration signal in combination with the pre-built MIDBO-SVM model; during the construction of the MIDBO-SVM model, the DBO algorithm adopts the best point set strategy and circle chaotic mapping to jointly generate the initial population, introduces the sine-cosine search strategy and adaptive weight factor in the individual position update stage, and implements the Cauchy-Gauss mutation strategy for the individual with the best current fitness to obtain MIDBO, and uses the MIDBO to optimize the penalty factor C and kernel parameter g of the SVM to complete the model construction of the MIDBO-SVM.

[0097] In addition, a schematic diagram of a terminal device is provided in an embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0098] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0099] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0100] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0101] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0102] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals. The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for diagnosing mechanical faults of high-voltage circuit breakers based on the MIDBO-SVM model, characterized in that: The following steps are involved: S1: Obtain vibration signal of circuit breaker; S2: The mechanical fault of the circuit breaker is diagnosed by using the acquired vibration signal in combination with the pre-built MIDBO-SVM model; during the construction of the MIDBO-SVM model, the initial population is jointly generated by the good point set strategy and the circle chaotic mapping for the DBO algorithm, the sine-cosine search strategy and the adaptive weight factor are introduced in the individual position update stage, and the Cauchy-Gauss mutation strategy is implemented for the individual with the best current fitness to obtain MIDBO, and the penalty factor C and the kernel parameter g of the SVM are optimized by using the MIDBO to complete the model construction of the MIDBO-SVM.

2. A high-voltage circuit breaker mechanical fault diagnosis method based on MIDBO-SVM model according to claim 1, characterized in that: The method of jointly generating the initial population by using the good point set strategy and the circle chaotic map is as follows: firstly, the basic framework of the initial population is generated by using the good point set strategy, and then the circle chaotic map is applied to fine-tune each individual position.

3. A high-voltage circuit breaker mechanical fault diagnosis method based on MIDBO-SVM model according to claim 1, characterized in that: The sine-cosine search strategy and adaptive weight factor are introduced in the individual position update stage. Specifically, in each iteration of DBO, the search direction and step size are adjusted in combination with the sine-cosine function according to the relative relationship between the current individual position and the target position, and the weight factor is dynamically adjusted to adapt to different stages of the search process.

4. A method for diagnosing mechanical faults of high-voltage circuit breakers based on the MIDBO-SVM model according to claim 1, characterized in that: The Cauchy-Gaussian mutation strategy is implemented for the individual with the best current fitness. Specifically, for the individual with the highest current fitness, the mutation direction and distance are generated according to the Cauchy distribution, and fine-tuned in combination with the randomness of the Gaussian distribution so that the mutated individual can stay away from the current optimal solution.

5. The method for diagnosing mechanical faults of high-voltage circuit breakers based on the MIDBO-SVM model according to claim 1 is characterized in that: The vibration signal of the circuit breaker is obtained by an acceleration sensor.

6. A method for diagnosing mechanical faults of high-voltage circuit breakers based on the MIDBO-SVM model according to claim 1, characterized in that: The vibration signal includes amplitude, frequency and phase.

7. A high-voltage circuit breaker mechanical fault diagnosis system based on MIDBO-SVM model, characterized in that: include: Data acquisition unit: the data acquisition unit is used to acquire the vibration signal of the circuit breaker; Fault diagnosis unit: The fault diagnosis unit is used to diagnose the mechanical fault of the circuit breaker by using the acquired vibration signal in combination with the pre-built MIDBO-SVM model; during the construction of the MIDBO-SVM model, the DBO algorithm adopts the best point set strategy and circle chaos mapping to jointly generate the initial population, introduces the sine-cosine search strategy and adaptive weight factor in the individual position update stage, and implements the Cauchy-Gauss mutation strategy for the individual with the best current fitness to obtain MIDBO, and uses the MIDBO to optimize the penalty factor and kernel parameters of the SVM to complete the model construction of the MIDBO-SVM.

8. A computer device / equipment / system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images