A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Bing Zeng,Zhihuai Xiao,Fang Yuan,Sixu Huang,Jiang Guo,Wenqiang Zhu

doi:10.3390/en12214170

Abstract

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

Highlights

Transformer is one of the most critical equipment for power transmission and transformation and its safety and reliability is the basis to ensure continuous operation and power supply of power grid.Failures of transformer may bring huge losses to the power grid, and the repair and maintenance of the transformer is very expensive and difficult
Proposed a transformer fault diagnosis model based on hybrid support vector machine (SVM) and improved evolutionary particle swarm optimization (SVM-MEPSO), which used a stepwise regression approach for data reduction and the results show that the hybrid SVM-MEPSO time-varying acceleration coefficient (TVAC) technology can obtain the highest accuracy compared with other PSO algorithms
The intelligent approaches mentioned above have directly or indirectly improved the accuracy of the transformer fault diagnosis methods based on Dissolved gas analysis (DGA)

Summary

Introduction

Transformer is one of the most critical equipment for power transmission and transformation and its safety and reliability is the basis to ensure continuous operation and power supply of power grid. Failures of transformer may bring huge losses to the power grid, and the repair and maintenance of the transformer is very expensive and difficult. Identifying the incipient faults of the transformer in time becomes very important which may avoid power outages and economic losses. DGA is an important and successful tool to detect incipient faults of oil-filled transformers. Based on the corresponding relationship between the type of dissolved gas in oil and internal fault, the abnormal state of the transformer can be identified by DGA method according to the composition and the content of various gases, and the fault type, severity and development trend of the fault can be determined.

Methods

Results

Conclusion