CN113379251A - IFOA-SVM-based high-voltage switch cabinet state evaluation method - Google Patents
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Abstract
A high-voltage switch cabinet state assessment method based on an IFOA-SVM comprises the steps of firstly, collecting operation data of a high-voltage switch cabinet, and preprocessing the data by a method of deleting extreme abnormal values and complementing missing values; selecting and evaluating the characteristic quantities of the high-voltage switch cabinet as a partial discharge value, an ultrasonic value, an infrared diagnosis, a cable joint temperature and a busbar temperature according to the on-site investigation result and expert experience; then, combining a polynomial function and a Gaussian radial basis function to construct a mixed kernel function, and mapping an original input space to a high-dimensional feature space; then, optimizing a kernel function proportion coefficient, a kernel function width parameter and a penalty factor by using an improved drosophila optimization algorithm; and finally, training OVO-SVM high-voltage switch cabinet state estimation classifier to realize nonlinear classification of the high-voltage switch cabinet state, namely high-voltage switch cabinet state evaluation. The method utilizes multi-source information to comprehensively evaluate the state of the distribution high-voltage switch cabinet, and has the advantages of simple theoretical framework, easy realization and strong generalization capability.
Description
Technical Field
The invention is applied to the field of high-voltage switch cabinet state evaluation, and relates to a high-voltage switch cabinet state evaluation method based on an improved fruit fly optimization algorithm and an improved support vector machine (IFOA-SVM).
Background
The high-voltage switch cabinet is one of main equipment of a power distribution system and plays a role in controlling and protecting other power equipment in an intelligent power grid. At present, the number of high-voltage switch cabinets installed is huge, the maintenance task is heavy, the fault of the high-voltage switch cabinet becomes a main factor influencing the safe and stable economical operation of the power distribution network, and the health condition assessment of the high-voltage switch cabinet is just an urgent research focus.
Because the internal structure of the high-voltage switch cabinet is very complicated, and the mutual influence among all the components is large, the current state and the future state of the equipment cannot be accurately evaluated through the traditional single analysis on all characteristic quantities influencing the state of the high-voltage switch cabinet. The method has the advantages that the multi-dimensional characteristic quantity of the high-voltage switch cabinet is selected by effectively integrating multiple information sources such as online monitoring, live detection and power failure experiments, and the key for improving the state evaluation precision of the high-voltage switch cabinet is achieved.
The Support Vector Machine (SVM) is a novel machine learning method based on a statistical learning theory, and has a plurality of specific advantages for solving the problems of non-linearity and high-dimensional pattern recognition, so that the method for estimating the state of the high-voltage switch cabinet by using an SVM classification model is an effective method. The traditional method for evaluating the state based on the SVM classification model mainly improves the classification recognition precision and efficiency by searching a better single kernel function or optimizing related parameters of the SVM through a modern optimization algorithm, cannot give consideration to the improvement of the SVM kernel function structure and the parameter optimization algorithm at the same time, and has certain limitation. In order to improve the classification effect of the SVM, the method provided by the invention optimizes the relevant parameters of the SVM by constructing a mixed kernel function and using an improved drosophila optimization algorithm so as to improve the accuracy of state estimation of the high-voltage switch cabinet.
Disclosure of Invention
In order to overcome the defects of the existing high-voltage switch cabinet state evaluation technology, the invention provides a high-voltage switch cabinet state evaluation method based on an improved fruit fly optimization algorithm and an improved support vector machine (IFOA-SVM).
The technical scheme adopted by the invention for solving the technical problems is as follows:
a high-voltage switch cabinet state evaluation method based on an improved fruit fly optimization algorithm and a support vector machine (IFOA-SVM), comprising the following steps:
1) the method comprises the following steps of (1) preprocessing the state evaluation data acquisition of the high-voltage switch cabinet, wherein the process comprises the following steps:
data acquisition is carried out through an online monitoring device and various sensors which are equipped with a high-voltage switch cabinet, firstly, the mean value of the same type of data and the mean value of measured values before and after the missing position data are used for completing the missing value, and secondly, the extreme abnormal value is defined through the comparison of the data of the abnormal condition and the normal condition, so that the extreme abnormality is eliminated;
2) the method comprises the following steps of (1) normalizing the state evaluation data of the high-voltage switch cabinet, wherein the process comprises the following steps:
carrying out normalization processing on the data of the high-voltage switch cabinet processed by the abnormal values and the missing values by adopting a formula (1);
in the formula, xiThe data of the high-voltage switch cabinet are not normalized; x is the number ofmaxThe maximum value is the maximum value in the data of the same group of the high-voltage switch cabinets which are not normalized; x is the number ofminThe minimum value in the high-voltage switch cabinet data which are not in the same group and are not normalized;
the data are the high-voltage switch cabinet data after normalization;
3) the inputs and outputs of the model are determined as follows:
selecting high-voltage switch cabinet characteristic quantities from multiple information sources for on-line monitoring, live detection and power failure experiments, wherein the selected characteristic quantities are an partial discharge value, an ultrasonic value, infrared diagnosis, cable joint temperature and busbar temperature, the sample data characteristics of the preprocessed high-voltage switch cabinet are used as the input of a model, and the state of the high-voltage switch cabinet is used as the output;
4) determining the SVM multi-classification strategy, wherein the process is as follows:
the OVO-SVM high-voltage switch cabinet state estimation classifier is formed by combining a one-to-one strategy and an SVM (support vector machine), when the K-type high-voltage switch cabinet state multi-classification problem is solved, a two-classification SVM sub-classifier is constructed between any two classification samples, K (K-1)/2 sub-classifiers are required to be constructed in total, and only two types of data in training samples are required to be used for generating each sub-classifier, so that the two types of data in the K-type multi-classification problem are distinguished; after constructing all sub-classifiers, combining the sub-classifiers to make decisions on the classes of new samples, and judging the classes by adopting a 'voting method', wherein each sub-classifier performs 'voting' on samples to be classified, only 1 vote can be cast for a certain sample, after traversing all the sub-classifiers, the samples are divided into the classes with the highest 'number of votes', a sub-classifier is constructed between any two classes of states based on a 'one-to-one' classification strategy state estimation classifier, and different sub-classifiers are combined together to form a final classifier;
5) dividing the state evaluation sample data of the high-voltage switch cabinet, wherein the process is as follows:
immediately dividing sample data of the high-voltage switch cabinet into a training data set and a testing data set, wherein the training data set accounts for 60% -80% and is used for supervised training learning of the model, and the rest data is used as the testing data set and is used for testing the trained classification model;
6) constructing a mixed kernel function by the following process:
for multi-dimensional features selected by the high-voltage switch cabinet, introducing a kernel function into an SVM classification model, and mapping original data features of the nonlinear high-voltage switch cabinet into a high-dimensional linear separable feature space so as to enable the high-dimensional linear separable feature space to be linearly separable; according to the characteristics of each kernel function, a mixed kernel function is constructed to replace a single kernel function, so that the state estimation accuracy is improved, namely
K1(x,z)＝((xTz)+1)
0≤λ≤1
In the formula, K1(x, z) is a polynomial kernel function; k2(x, z) is a radial basis kernel function; lambda is a kernel function proportionality coefficient, sigma is a radial basis kernel function width parameter, and x and z respectively correspond to original data characteristics and characteristics in a mapping space; k (x, z) is a final mixed kernel function expression;
7) substituting the new mixed kernel function obtained in the step 6) into the SVM classification model to obtain a state estimation model of the high-voltage switch cabinet, wherein the state estimation model comprises the following steps:
in the formula, yiIs the output of the ith example, αiIs Lagrange multiplier, b is bias, m is characteristic quantity, f (x) represents the final classification result of the classifier;
8) the parameter optimization is carried out on the SVM based on the improved drosophila optimization algorithm IFOA algorithm, and the process is as follows:
8.1) initializing IFOA parameters: defining a fruit fly population in a plane coordinate system, initializing population scale s and maximum iteration times maxgenDetermining the position coordinates of the individual drosophila; the kernel function proportionality coefficient lambda of the parameter to be optimized, the radial basis kernel function width parameter sigma and a penalty factor C, and the random initialization population position is (lambda)0，σ0，C0) And endowing the fruit fly with random movement direction and distance, thereby generating seedsThe individual positions of the fruit flies in the population are defined as l, the coordinates of the jth fruit fly are,
in the formula, R is a random search distance;
8.2) calculating the fitness value of the individual fruit fly optimization: substituting the position coordinates of the fruit flies into a fitness function F, evaluating the taste concentration of the individual positions of the fruit flies according to fitness values F (lambda, sigma, C), wherein the fitness values are determined according to the accuracy A of an SVM model, solving the fruit Fly closest to the food position in a population, namely solving the maximum value max F of the fitness corresponding to the individual BI of the fruit flies,
in the formula, DTFor all samples with correct prediction, D is a total sample, BS is an optimal concentration value, and BI is a drosophila individual;
8.3) preservation of optimal taste concentration values: storing the best fitness value BS executed for the first time in Sb, and simultaneously storing the coordinates of lambda, sigma and C to obtain
8.4) determining the average position coordinates of the induced learning fruit flies: sorting the fruit fly taste fitness values searched by the generation, taking two fruit fly individuals subBI1 and subBI2 next to the optimal fruit fly as induced learning individuals, respectively calculating the positions of coordinates of the induced learning individuals, and calculating an arithmetic mean value to obtain lambdae、σe、CeI.e. by
8.5) calculating the target position of the convergence direction of the fruit fly colony: updating the position of the fruit fly population by using the formula (8);
in the formula, eta is a learning factor, lambdaj′，σj′，Cj' is a new drosophila location coordinate;
in the fruit fly optimizing process, a learning factor is introduced to promote a fruit fly population to learn to the elaiopsis eligua individuals, so that the algorithm is ensured to be finally converged at the global optimal position, in the fruit fly population iterative optimizing process, the direction and the distance of population movement are guided by the elaiopsis eligua individuals, so that the fruit fly population is continuously close to the global optimal position, the influence of the elaiopsis eligua individuals of the previous iteration on the population is gradually reduced along with the increase of the iteration times T, and a fractional attenuation mechanism is adopted to set a learning factor eta and a learning factor attenuation amplitude control parameter cf；
η＝η0/(1+cf(T-1)) (9)
In the formula eta0As an initial learning factor, cfAttenuating the amplitude control parameter for the learning factor;
8.6) carrying out iterative optimization: the BS of the present generation is given to the preBS and then enters an iterative loop, the steps are repeated, the Sb value of each generation is stored, and when the algorithm reaches maxgenThe loop is ended when the set value is reached, and the position coordinates (lambda, sigma, C) of the best fruit fly individual, namely the optimizing result, are obtained
Sb＝max{BS,preBS} (10)
9) Obtaining optimal parameter combinations according to IFOA algorithm
Therefore, an SVM optimal model is determined, test set data are used for verifying the optimal model, and new state data of the high-voltage switch cabinet are input into the SVM classification model after data preprocessing, so that the SVM optimal model can be obtained
Compared with the prior art, the invention has the advantages that: 1) the method is different from the traditional state single information evaluation, and multi-state comprehensive evaluation is carried out on the high-voltage switch cabinet by utilizing multi-source information. 2) The method is different from the traditional SVM which only uses a single kernel function, and the accuracy of state estimation is further improved by constructing a mixed kernel function to replace the single kernel function and applying the mixed kernel function to a classifier; 3) the improved fruit fly optimization algorithm IFOA is provided, the defect that the algorithm is easy to fall into local optimum when the initial value selection of the fruit fly optimization algorithm is inappropriate is overcome, and the algorithm is guaranteed to be finally converged to the global optimum position. 4) The detection method has a simple theoretical framework, is easy to realize and has good generalization capability.
Drawings
FIG. 1 is a detailed flow chart of a method for estimating the state of a high-voltage switch cabinet based on an IFOA-SVM.
FIG. 2 is a diagram of OVO-SVM state estimation multi-classifier.
Detailed Description
In order to make the technical scheme and the design idea of the present invention clearer, the following detailed description is made with reference to the accompanying drawings.
Referring to fig. 1 and 2, a high-voltage switchgear state evaluation method based on an improved drosophila optimization algorithm and a support vector machine (IFOA-SVM), the method comprising the steps of:
1) the method comprises the following steps of (1) preprocessing the state evaluation data acquisition of the high-voltage switch cabinet, wherein the process comprises the following steps:
data acquisition is carried out through an online monitoring device and various sensors which are equipped in a high-voltage switch cabinet, and the possibility that the obtained data has a missing value and an abnormal value is considered, firstly, the missing value is completed through the mean value of the same type of data and the mean value of the measured values before and after the missing position data, and secondly, the extreme abnormal value is defined through the comparison of the data of the abnormal condition and the normal condition, so that the extreme abnormality is deleted;
2) the method comprises the following steps of (1) normalizing the state evaluation data of the high-voltage switch cabinet, wherein the process comprises the following steps:
the data normalization processing can reduce the influence of dimensions on the algorithm precision, simultaneously reduce the algorithm calculation complexity, accelerate the calculation speed and improve the high-voltage switch cabinet evaluation performance, and the high-voltage switch cabinet data processed by abnormal values and missing values is normalized by adopting the formula (1);
in the formula, xiThe data are the high-voltage switch cabinet data which are not normalized; x is the number ofmaxThe maximum value is the maximum value in the data of the same group of high-voltage switch cabinets which are not normalized; x is the number ofminA minimum value in the high voltage switchgear data that is not in the same group without normalization;
the data are the high-voltage switch cabinet data after normalization;
3) the inputs and outputs of the model are determined as follows:
in consideration of the practical feasibility of characteristic quantity acquisition, selecting and evaluating the characteristic quantity of the high-voltage switch cabinet according to on-site investigation results and expert experiences, selecting the characteristic quantity of the high-voltage switch cabinet from multiple information sources such as online monitoring, live detection, power failure experiments and the like, wherein the selected characteristic quantity is a partial discharge value, an ultrasonic wave value, infrared diagnosis, cable joint temperature and busbar temperature, the sample data characteristics of the preprocessed high-voltage switch cabinet are used as the input of a model, and the state of the high-voltage switch cabinet is used as the output;
4) determining the SVM multi-classification strategy, wherein the process is as follows:
referring to fig. 2, a OVO-SVM high-voltage switch cabinet state estimation classifier is formed by combining a one-to-one (OVO) strategy and SVM, and when the problem of multi-classification of K-class high-voltage switch cabinet states is solved, a two-classification SVM sub-classifier is constructed between any two classes of samples, and K (K-1)/2 sub-classifiers are required to be constructed. Generating each sub-classifier only needs two types of data in the training sample, so that two types of differentiation in the K-type multi-classification problem is realized; after all the sub-classifiers are constructed, the classification of a new sample is determined by combining the sub-classifiers, the classification is judged by adopting a 'voting method', each sub-classifier carries out 'voting' on the sample to be classified, only 1 ticket can be cast for a certain sample, after all the sub-classifiers are traversed, the sample is divided into the class with the highest 'number of votes', a sub-classifier is constructed between any two classes of states based on the state estimation classifier of the 'one-to-one' classification strategy, and different sub-classifiers are combined to form a final classifier;
5) dividing the state evaluation sample data of the high-voltage switch cabinet, wherein the process is as follows:
in order to give consideration to the training accuracy and the generalization capability of the verification model, sample data of the high-voltage switch cabinet is immediately divided into a training data set and a test data set, wherein the training data set accounts for 60% -80% and is used for supervised training and learning of the model, and the rest data is used as the test data set for testing the trained classification model;
6) constructing a mixed kernel function:
for multi-dimensional features selected by a high-voltage switch cabinet, a kernel function is introduced into an SVM classification model, original data features of the nonlinear high-voltage switch cabinet are mapped into a high-dimensional linear separable feature space, so that the high-dimensional linear separable feature space is linearly separable, common kernel functions comprise polynomial kernel functions and Gaussian radial basis kernel functions
K1(x,z)＝((xTz)+1)
0≤λ≤1
In the formula, K1(x, z) is a polynomial kernel function; k2(x, z) is a radial basis kernel function; lambda is a kernel function proportionality coefficient, sigma is a radial basis kernel function width parameter, and x and z respectively correspond to original data characteristics and characteristics in a mapping space; k (x, z) is a final mixed kernel function expression;
7) substituting the new mixed kernel function obtained in the step 6) into the SVM classification model to obtain a state estimation model of the high-voltage switch cabinet, wherein the state estimation model comprises the following steps:
in the formula, yiIs the output of the ith example, αiIs lagrange multiplier, b is bias, m is feature quantity, f (x) represents the result of the final classification of the classifier.
8) The parameter optimization is carried out on the SVM based on an improved fruit fly optimization algorithm (IFOA) algorithm, and the process is as follows:
8.1) initializing IFOA parameters: defining a fruit fly population in a plane coordinate system, initializing the population size s as 100 and maximum iteration number maxgenDetermining the position coordinates of individual drosophila 200, a kernel function proportionality coefficient lambda of a parameter to be optimized, a radial basis kernel function width parameter sigma and a penalty factor C, and randomly initializing the population position to be (lambda)0，σ0，C0) Giving the fruit fly individual random movement direction and distance to generate each fruit fly individual position in the population, defining the number of the fruit flies in the population as l, then the coordinates of the jth fruit fly as,
in the formula, R is a random search distance;
8.2) calculating the fitness value of the individual fruit fly optimization: and substituting the position coordinates of the fruit flies into a fitness function F, and evaluating the taste concentration of the individual positions of the fruit flies according to fitness values F (lambda, sigma, C), wherein the fitness values are determined according to the accuracy A of the SVM model. Obtaining the fruit Fly nearest to the food position in the population, namely obtaining the maximum value max F of the fitness corresponding to the fruit Fly individual BI,
in the formula, DTFor all samples with correct prediction, D is a total sample, BS is an optimal concentration value, and BI is a drosophila individual;
8.3) preservation of optimal taste concentration values: storing the best fitness value BS executed for the first time in Sb, and simultaneously storing the coordinates of lambda, sigma and C to obtain
8.4) determining the average position coordinates of the induced learning fruit flies: sorting the fruit fly taste fitness values searched by the generation, taking two fruit fly individuals subBI1 and subBI2 next to the optimal fruit fly as induced learning individuals, respectively calculating the positions of coordinates of the induced learning individuals, and calculating an arithmetic mean value to obtain lambdae、σe、CeI.e. by
8.5) calculating the target position of the convergence direction of the fruit fly colony: updating the position of the fruit fly population by using the formula (8);
in the formula, eta is a learning factor, lambdaj′，σj′，Cj' is a new drosophila location coordinate;
in the fruit fly optimizing process, a learning factor is introduced to promote a fruit fly population to learn to the elaiopsis eligua individuals, so that the algorithm is ensured to be finally converged at the global optimal position, in the fruit fly population iterative optimizing process, the direction and the distance of population movement are guided by the elaiopsis eligua individuals, so that the fruit fly population is continuously close to the global optimal position, the influence of the elaiopsis eligua individuals of the previous iteration on the population is gradually reduced along with the increase of the iteration times T, and a fractional attenuation mechanism is adopted to set a learning factor eta and a learning factor attenuation amplitude control parameter cf；
η＝η0/(1+cf(T-1)) (9)
In the formula eta0In order to be the initial learning factor,cfattenuating the amplitude control parameter for the learning factor; setting an initial learning factor η00.2, learning factor decay control parameter cf＝0.4；
8.6) carrying out iterative optimization: the BS of the present generation is given to the preBS and then enters an iterative loop, the steps are repeated, the Sb value of each generation is stored, and when the algorithm reaches maxgenThe loop is ended when the set value is reached, and the position coordinates (lambda, sigma, C) of the best fruit fly individual, namely the optimizing result, are obtained
Sb＝max{BS,preBS} (10)
9) Obtaining optimal parameter combinations according to IFOA algorithm
Therefore, an SVM optimal model is determined, test set data are used for verifying the optimal model, and the new state data of the high-voltage switch cabinet are input into the SVM classification model after data preprocessing, so that accurate state evaluation can be performed on the high-voltage switch cabinet.
Claims (1)
1. A high-voltage switch cabinet state evaluation method based on an IFOA-SVM is characterized by comprising the following steps:
1) the method comprises the following steps of (1) preprocessing the state evaluation data acquisition of the high-voltage switch cabinet, wherein the process comprises the following steps:
data acquisition is carried out through an online monitoring device and various sensors which are equipped in a high-voltage switch cabinet, firstly, the mean value of the same type of data and the mean value of the measured values before and after the data of the missing position are used for completing the missing value, and secondly, the data of the abnormal condition and the data of the normal condition are compared to define the extreme abnormal value, so that the extreme abnormality is eliminated;
2) the method comprises the following steps of (1) normalizing the state evaluation data of the high-voltage switch cabinet, wherein the process comprises the following steps:
carrying out normalization processing on the data of the high-voltage switch cabinet processed by the abnormal values and the missing values by adopting a formula (1);
in the formula, xiThe data of the high-voltage switch cabinet are not normalized; x is the number ofmaxThe maximum value is the maximum value in the data of the same group of the high-voltage switch cabinets which are not normalized; x is the number ofminThe minimum value in the high-voltage switch cabinet data which are not in the same group and are not normalized;
the data are the high-voltage switch cabinet data after normalization;
3) the inputs and outputs of the model are determined as follows:
selecting high-voltage switch cabinet characteristic quantities from multiple information sources for on-line monitoring, live detection and power failure experiments, wherein the selected characteristic quantities are an partial discharge value, an ultrasonic value, infrared diagnosis, cable joint temperature and busbar temperature, the sample data characteristics of the preprocessed high-voltage switch cabinet are used as the input of a model, and the state of the high-voltage switch cabinet is used as the output;
4) determining the SVM multi-classification strategy, wherein the process is as follows:
the OVO-SVM high-voltage switch cabinet state estimation classifier is formed by combining a one-to-one strategy and an SVM (support vector machine), when the K-type high-voltage switch cabinet state multi-classification problem is solved, a two-classification SVM sub-classifier is constructed between any two classification samples, K (K-1)/2 sub-classifiers are required to be constructed in total, and only two types of data in training samples are required to be used for generating each sub-classifier, so that the two types of data in the K-type multi-classification problem are distinguished; after constructing all sub-classifiers, combining the sub-classifiers to make decisions on the classes of new samples, and judging the classes by adopting a 'voting method', wherein each sub-classifier carries out 'voting' on samples to be classified, only 1 vote can be cast for a certain sample, after traversing all the sub-classifiers, the samples are divided into the classes with the highest 'number of votes', a sub-classifier is constructed between any two classes of states based on a state estimation classifier of a 'one-to-one' classification strategy, and different sub-classifiers are combined together to form a final classifier;
5) dividing the state evaluation sample data of the high-voltage switch cabinet, wherein the process is as follows:
immediately dividing sample data of the high-voltage switch cabinet into a training data set and a testing data set, wherein the training data set accounts for 60% -80% and is used for supervised training learning of the model, and the rest data is used as the testing data set and is used for testing the trained classification model;
6) constructing a mixed kernel function by the following process:
for multi-dimensional features selected by the high-voltage switch cabinet, introducing a kernel function into an SVM classification model, and mapping original data features of the nonlinear high-voltage switch cabinet into a high-dimensional linear separable feature space so as to enable the high-dimensional linear separable feature space to be linearly separable; according to the characteristics of each kernel function, a mixed kernel function is constructed to replace a single kernel function, so that the state estimation accuracy is improved, namely
In the formula, K1(x, z) is a polynomial kernel function; k2(x, z) is a radial basis kernel function; lambda is a kernel function proportionality coefficient, sigma is a radial basis kernel function width parameter, and x and z respectively correspond to the original data characteristic and the characteristic in a mapping space; k (x, z) is a final mixed kernel function expression;
7) substituting the new mixed kernel function obtained in the step 6) into the SVM classification model to obtain a state estimation model of the high-voltage switch cabinet, wherein the state estimation model comprises the following steps:
in the formula, yiIs the output of the ith example, αiIs Lagrange multiplier, b is bias, m is characteristic quantity, f (x) represents the final classification result of the classifier;
8) the parameter optimization is carried out on the SVM based on the improved drosophila optimization algorithm IFOA algorithm, and the process is as follows:
8.1) initializing IFOA parameters: defining a fruit fly population in a plane coordinate system, initializing population scale s, and maximizing overlappingGeneration times maxgenDetermining the position coordinates of the individual drosophila; the kernel function proportionality coefficient lambda of the parameter to be optimized, the radial basis kernel function width parameter sigma and a penalty factor C, and the random initialization population position is (lambda)0，σ0，C0) Giving the fruit flies random movement direction and distance to generate the positions of the individual fruit flies in the population, defining the number of the fruit flies in the population as l, and then the coordinates of the jth fruit fly as,
in the formula, R is a random search distance;
8.2) calculating the fitness value of the individual fruit fly optimization: substituting the position coordinates of the fruit flies into a fitness function F, evaluating the taste concentration of the individual positions of the fruit flies according to fitness values F (lambda, sigma, C), wherein the fitness values are determined according to the accuracy A of an SVM model, solving the fruit Fly closest to the food position in a population, namely solving the maximum value maxF of the fitness corresponding to the individual BI of the fruit flies,
in the formula, DTFor all samples with correct prediction, D is a total sample, BS is an optimal concentration value, and BI is a drosophila individual;
8.3) preservation of optimal taste concentration values: storing the best fitness value BS executed for the first time in Sb, and simultaneously storing the coordinates of lambda, sigma and C to obtain
8.4) determining the average position coordinates of the induced learning fruit flies: sorting the fruit fly taste fitness values searched by the generation, taking two fruit fly individuals subBI1 and subBI2 next to the optimal fruit fly as induced learning individuals, respectively calculating the positions of coordinates of the induced learning individuals, and calculating the arithmetic mean to obtain lambdae、σe、CeI.e. by
8.5) calculating the target position of the convergence direction of the fruit fly colony: updating the position of the fruit fly population by using the formula (8);
in the formula, eta is a learning factor, lambdaj′，σj′，Cj' is a new drosophila location coordinate;
in the fruit fly optimizing process, a learning factor is introduced to promote a fruit fly population to learn to the elaiopsis elite fruit fly individual, so that the algorithm is ensured to be finally converged at the global optimal position, in the fruit fly population iterative optimizing process, the direction and distance of population movement are guided by the elaiopsis elite individual, so that the fruit fly population is continuously close to the global optimal position, along with the increase of the iteration times T, the influence of the elaiopsis elite fruit fly individual of the last iteration on the population is gradually reduced, and a fractional attenuation mechanism is adopted to set a learning factor eta and a learning factor attenuation amplitude control parameter cf；
η＝η0/(1+cf(T-1)) (9)
In the formula eta0As an initial learning factor, cfAttenuating the amplitude control parameter for the learning factor;
8.6) carrying out iterative optimization: the BS of the present generation is given to preBS, then an iterative loop is entered, the steps are repeated, the Sb value of each generation is stored, and when the algorithm reaches maxgenThe loop is ended when the set value is reached, and the position coordinates (lambda, sigma, C) of the best fruit fly individual, namely the optimizing result, are obtained
Sb＝max{BS,preBS} (10)
9) Obtaining optimal parameter combinations according to IFOA algorithm
Therefore, an SVM optimal model is determined, test set data are used for verifying the optimal model, and the new state data of the high-voltage switch cabinet are input into the SVM classification model after data preprocessing, so that accurate state evaluation can be performed on the high-voltage switch cabinet.
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