Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm

Kari, Tusongjiang; Gao, Wensheng; Zhao, Dongbo; Abiderexiti, Kaherjiang; Mo, Wenxiong; Wang, Yong; Luan, Lan

doi:10.1049/iet-gtd.2018.5482

Cited by 83 publications

(40 citation statements)

References 27 publications

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“…In addition, embedded methods cost more in terms of computation than filter ones. Popular embedded methods are Recursive Feature Elimination for Support Vector Machines (SVM-RFE) [68]- [70] and Feature Selection-Perceptron (FS-P) [71]- [73].…”

Section: ) Embedded Techniquesmentioning

confidence: 99%

“…a small number of parameters to be tuned that are easy to implement and independent of the gradient of an optimization objective, more and more studies have been focused on utilizing these heuristic algorithms to deal with feature selection problems. Representative heuristic algorithms include genetic algorithms [68], [81], [97] [98], differential evolutional algorithms [99], [100], simulated annealing [14], particle swarm optimization [101]- [103], tabu search [104]- [106],and Fisher score algorithms [87], etc. These methods can generally achieve a good feature subset with a fast speed, making the study of feature selection incorporated with search strategies a new trend.…”

Section: B Feature Selection Based On Heuristic Algorithmsmentioning

confidence: 99%

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Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

2019

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Feature selection has been an important research area in data mining, which chooses a subset of relevant features for use in the model building. This paper aims to provide an overview of feature selection methods for big data mining. First, it discusses the current challenges and difficulties faced when mining valuable information from big data. A comprehensive review of existing feature selection methods in big data is then presented. Herein, we approach the review from two aspects: methods specific to a particular kind of big data with certain characteristics and applications of methods in classification analysis, which are significantly different to the existing review work. This paper also highlights the current issues of feature selection in big data and suggests the future research directions. INDEX TERMS Feature selection, big data, data mining, applications.

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Section: ) Embedded Techniquesmentioning

confidence: 99%

Section: B Feature Selection Based On Heuristic Algorithmsmentioning

confidence: 99%

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

2019

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“…The generalization error indicates the prediction precision of the prediction model for different data sets. The generalization error formula is shown in formulas (20) and (21).…”

Section: ) Generalization Ability Of Predictive Modelmentioning

confidence: 99%

“…Compared with the traditional neural network model, the catenary CPCM status prediction model has a hidden layer of nonlinear transformation, which can handle more complex functional relationships. The global optimization ability of genetic algorithms is better than other evolutionary algorithms [20]- [22]. Therefore, in order to avoid the prediction model falling into local optimum, the global search method of genetic algorithm is used to optimize the catenary CPCM status prediction model to achieve higher prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Genetic Optimization Method of Pantograph and Catenary Comprehensive Monitor Status Prediction Model Based on Adadelta Deep Neural Network

Yuan

Chi

et al. 2019

IEEE Access

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The status of the Pantograph and Catenary is the guarantee for the safe operation of the railway. However, the traditional Pantograph and Catenary status judgment efficiency is not satisfactory, and it is impossible to timely repair the catenary, which may lead to the greater economic loss. In this paper, a new GA-ADNN-based (genetic algorithm-Adadelta deep neural network-based) optimization method for the prediction model for catenary comprehensive pantograph and catenary monitor (CPCM) status is proposed. According to the status values of the CPCM parameters such as height, stagger, hard point, contact force, and height difference within span, the status value of the pillars in the catenary has been calculated by the analytic hierarchy process, and then the prediction model for predicting catenary CPCM status has been established and then optimized by genetic algorithm to avoid prediction model falling into local optimum. Finally, the CPCM test parameters of each pillar of the catenary in the actual example are input and the CPCM status value of the corresponding pillar is predicted. With the smallest prediction error found, the genetic algorithm is used for optimization, the optimal learning rate of the prediction model is 0.0559, and the optimal number of the hidden layer of the CPCM status prediction model is determined to be 14. The experimental results show the feasibility of GA-ADNN-based prediction model for predicting the catenary CPCM status, and that compared with the support vector machine and traditional artificial neural network prediction methods, the GA-ADNN-based prediction model has higher prediction precision and better generalization ability.

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“…Binary particle swarm algorithm is a population-based optimizer similar to GA. BPSO algorithm has the strong global search ability, but it cannot converge to the global optimal position of particles [15]. Moreover, with the iteration of the algorithm, the randomness of BPSO becomes stronger, but it lacks the local search ability in the later period [16], [17]. In the iterative process of the imperialist competitive algorithm, the number of empire is continuously reduced, resulting in a decrease in population diversity, which is unfavorable for solving high-dimensional multi-mode optimization problems, and the algorithm is easy to fall into the local optimal solution [18].…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Model of Power Transformers Based on Dissolved Gas Analysis Features Selection and Improved Krill Herd Algorithm Optimized Support Vector Machine

Zhang

Zheng

et al. 2019

IEEE Access

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In this paper, a set of dissolved gas analysis (DGA) new feature combinations is selected as input from the mixed DGA feature quantity, and an improved krill herd (IKH) algorithm optimized support vector machine (SVM) transformer fault diagnosis model is established to solve the problem that the single characteristic gas or characteristic gas ratio, which are utilized as the DGA feature quantity cannot fully reflect the transformer fault classification. The following work has been done in this paper: 1) IEC TC 10 fault data and other 117 sets of fault data in China are preprocessed in order to reduce the influence on the diagnosis results causing by the edge data in the fuzzy area; 2) the SVM parameters and 11 features are encoded by a binary code technique; 3) a preferred DGA feature set for fault diagnosis of power transformers is selected by genetic algorithm (GA) and SVM, and; 4) IKH is utilized to optimize the parameters of SVM. Combining with cross-validation principle, a transformer fault diagnosis model based on IKH algorithm to optimize SVM is established. The fault diagnosis results based on the new fault sample show that the proposed DGA feature set to increase the accuracy by 26.78% and 10.83% over the DGA full data and IEC ratios. Moreover, the accuracy of IKHSVM is better than the GASVM, back-propagation neural network (BPNN), and particle swarm optimization optimized support vector machine (PSOSVM), the accuracy rates are 85.71%, 75%, 64.29%, and 71.43%, which proves the validity of the proposed fault diagnosis model. INDEX TERMS Power transformers, fault diagnosis, support vector machine, improved krill herd algorithm, DGA feature.

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Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm

Cited by 83 publications

References 27 publications

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Genetic Optimization Method of Pantograph and Catenary Comprehensive Monitor Status Prediction Model Based on Adadelta Deep Neural Network

A Fault Diagnosis Model of Power Transformers Based on Dissolved Gas Analysis Features Selection and Improved Krill Herd Algorithm Optimized Support Vector Machine

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