Study on power transformer protection based on chaos particle swarm optimization

Failure of power transformer is very complex, so that it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on ant colony-support vector machine (AC-SVM) approach is presented for fault diagnosis of power transformer. The AC-SVM selects kernel function parameter and soft margin constant C penalty parameter of support vector machine (SVM) classifier. The performance of the AC-SVM system proposed in this study is evaluated by cases in China. The test results show that this AC-SVM model is effective to detect failure of power transformer.

In order to improve the performance of Support Vector Machine (SVM) classifier for imbalanced data, this paper proposes dynamic cost-sensitive SVM classifier based on chaos particle swarm optimization (CPDC_SVM). Firstly, this paper introduces dynamic cost-sensitive thought to SVM classifier, and gives the method for structuring dynamic cost and cost-sensitive SVM model. Secondly, we propose the evaluation methodology performance for classifier, and adopts decimal base to code the particles. At last, chaos thought is introduced in particle swarm optimization algorithm, and the Algorithm of the dynamic cost-sensitive SVM classifier is given, which improves convergent speed and accuracy of particle swarm optimization, and can optimize dynamic cost-sensitive SVM well, so CPDC_SVM adds effectively the convergence speed and accuracy for the particle swarm optimization algorithm. Experimental results show CPDC_SVM has higher precision than traditional SVM classifier, and dynamic cost and chaos particle swarm optimization can improve the performance for classifier.

Hyperspectral remote sensing imagery, due to its rich source of spectral information, provides an efficient tool for land cover classifications in complex geographical areas. However, the high-dimensional space of this imagery poses two important challenges in the classification process: the Hughes phenomena and the existence of relevant and redundant features. The robustness of Support Vector Machines (SVM) in high-dimensional space makes them an efficient tool for classifying hyperspectral imagery. However, optimum SVM parameter determination and optimum feature selection are the two optimization issues that strongly effect SVM performance. Traditional optimization algorithms can discover optimum solutions in a limited search space with one local optimum. Nevertheless, in high-dimensional space traditional optimization algorithms usually get trapped in a local optimum, therefore it is necessary to apply meta-heuristic optimization algorithms to obtain near-global optimum solutions. This study evaluates the potential of Ant Colony Optimization (ACO) for determining SVM parameters and selecting features. Results obtained from AVIRIS and ROSIS hyperspectral datasets demonstrate the superior performance of SVM, achieved by simultaneously optimizing SVM parameters and subsets of the input feature. For comparison, the evaluation is also performed by applying it to other meta-heuristic optimization algorithms such as simulated annealing, tabu search, and genetic algorithm. The results demonstrate a better performance of the ACO-based algorithm in regards to improving the classification accuracy and decreasing the size of selected feature subsets.

This article focuses on classifying a variety of faults in power transformers with high precision, using an Improved Particle Swarm Optimization-Support Vector Machine (IPSO-SVM) system designed for fault diagnosis. The process begins with the identification of five distinct gases dissolved in oil, serving as diagnostic features. Minimal output encoding is then used to construct multiple binary support vector machine (SVM), facilitating a multi-class classification of transformer faults. While other studies often combine traditional Particle Swarm Optimization (PSO) algorithms with SVMs, our approach employs an enhanced PSO algorithm. This improved algorithm allows for the optimization of inertia and learning factors, values of which adapt based on iteration counts. The PSO is then leveraged on the optimization of the penalty factor and the radial basis function of SVM, thereby improving its classification performance. Simulation results indicate that our IPSO-SVM methodology achieves 90% and 92% accuracy in training and testing sets, respectively. This method significantly enhances the accurateness oftransformer malfunction diagnosis, exhibiting superior diagnostic precision compared to traditional power transformer malfunction diagnosis methods.

PurposePower transformers are vital components of an electrical network. A defective transformer can cause instability and blackouts in parts of the network. An accurate classification of different transformer faults results in a relatively accurate fault diagnosis and timely corrective actions. It is possible to increase productivity and reduce costs by using fault detection of power transformers through the analysis of gases dissolved in oil. The proposed technique is a suitable tool to help the utilities and engineers in charge of preventive maintenance by reducing the costs of different fault diagnosis tests for power transformers.Design/methodology/approachIn this paper, the IEC 60599 standard along with clustering and classification methods are used to classify power transformer’s fault types. K-means and Fuzzy C-means clustering methods are used for clustering, and the support vector machine (SVM) method is used for classification of different types of faults in ‎power ‎transformers. The performance of K-means and SVM methods is improved by using the Grasshopper Optimization Algorithm (GOA). The efficiency of the proposed methods is evaluated using real field data of power transformers. The purpose of this study is to propose hybrid methods including K-means-GOA clustering and SVM-GOA classification for accurate fault diagnosis. These methods have been used for the first time in fault diagnosis determination of power transformers through gas analysis. The Silhouette criteria is used in this paper to compare the efficiency of different clustering methods.FindingsSimulation results of the paper are based on the gas chromatography data related to 266 different real power transformers. They show the high accuracy and high-performance speed of intelligent clustering and classification methods compared to conventional ones. This analysis would be helpful in performing the required maintenance check and plan for repairs.Originality/valueThe applicability and efficiency of the proposed hybrid K-means-GOA and SVM-GOA models are verified for transformer fault detection using the experimental diverse data set including 266 set of real field test parameters of power transformers.

Accurate faults diagnosis in power transformers is important for utilities to schedule maintenance and minimises the operation cost. Dissolved gas analysis (DGA) is one of the proven and widely accepted tools for incipient fault diagnosis in power transformers. To improve the accuracy and solve the cases that cannot be classified using Rogers’ Ratios, IEC ratios and Duval triangles methods, a novel DGA technique based on Parzen window estimation have been presented in this study. The model uses the concentrations of five combustible hydrocarbon gases: methane, ethane, ethylene, acetylene and hydrogen to compute the probability of transformers fault categories. Performance of the proposed method has been evaluated against different conventional techniques and artificial intelligence‐based approaches such as support vector machines, artificial neural networks, rough sets analysis and extreme learning machines for the same set of transformers. A comparison with other soft computing approaches shows that the proposed method is reliable and effective for incipient fault diagnosis in power transformers.

Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine

Automatic recognition of skin symptom plays an importance role in the skin diagnosis and treatment. Feature selection is to increase the classification performance of skin symptom. In this paper, the effects of feature selection on the classification of 4-class skin symptoms (chloasma, blackhead, freckled and comedone) are analyzed. Support vector machine (SVM) is employed to construct classifier, the parameters setting is very crucial to the learning results and generalization ability of SVM, and many irrelevant and redundant features degrade the performance of classification, furthermore, the choice of SVM parameters is influenced by the feature subset taken into account and vice versa. So a joint optimization method for selecting features and SVM parameters is proposed. Genetic algorithms, which have a lot of improvements about fitness, crossover and mutation in comparison with simple genetic algorithms, is to achieve a balance between the classification accuracy and the size of the feature subsets selected and have a fast convergence rate. Furthermore, ‘one-against-one’ approach is used to solve multi-class classification problems and a comparison of the model performances is made among the results of the joint optimization and the single optimization of features or SVM parameters in the experiments. The simulation result shows that our approach can find subsets of features for 4-class skin symptoms classification with higher average cross validation accuracy and a higher rate of convergence compared with single optimization of features or SVM parameters.

The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.

For the voltage sag caused by short-circuit fault, transformer switching and induction motor starting in distribution network, an optimized support vector machine method based on Gray Wolf algorithm is proposed for voltage sag identification. The empirical mode decomposition method is used to analyze the voltage sag signal, obtain an inherent mode function set (IMFs), and calculate the energy entropy of each order IMF as the eigenvector. In order to solve the problem that the traditional support vector machine is easy to fall into local optimization in the process of optimization, a method of optimizing the penalty factor and kernel function parameters of support vector machine (SVM) through gray wolf algorithm (GWO) is proposed, a GWO-SVM classifier is constructed, and then the extracted feature vector is input into the GWO-SVM classifier to train and recognize the samples, So as to realize the automatic classification and identification of different types of voltage sag sources. The simulation results show the effectiveness of the extracted feature vector and GWO-SVM classifier. Compared with other five traditional methods, it is verified that it has fast speed and high precision.

Dissolved gas analysis (DGA) is one of the most useful techniques to detect incipient faults in power transformers. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational variables. A novel extension method, which is based on the matter-element model and extended relation functions, is presented for fault diagnosis of power transformers. Thus, incipient faults in power transformers can be identified directly by the degree of relation. The application of this new method to some transformers has given promising results.

This paper presents a novel fault diagnosis model for oil-immersed power transformers based on dissolved gas analysis. The model is rooted on the theories of rough set and support vector machine. A fitness function based on attribute dependence is developed to identify fault features to improve classification accuracy of transformer fault samples by using Genetic Algorithm. To get improved classification performance, grid search, genetic algorithm and particle swarm optimization are applied to search parameters of support vector machine. Compared with modified Rogers and back propagation neural network, the superiority of the established model is verified.

In order to obtain the higher classification accuracy in specific categories for the different feature subset, a hierarchical classification algorithm based on Feature Selection is proposed, and is used for synthetic aperture radar (SAR) image classification, and feature selection is achieved by Genetic algorithm. The algorithm has two main characteristics: one is hierarchical classification which consists of many two-class classifier, and the two-class classifier is trained by the optimal feature subset which is selected according to different categories; the second is the classifier of support vector machine SVM (Support Vector Machine); the two is Genetic algorithm which can search out the optimal feature subset and parameters of support vector machine that is most suitable for the category, by unified coding the feature set and the parameters of SVM to constitute the Chromosome. The experiment on the first polarimetric SAR data show that the algorithm can obtain higher classification accuracy rate.

Fault diagnosis of power transformer based on multi-layer SVM classifier

An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis