A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Abstract

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

“…To simplify the assessment of hundreds of power transformer data, a Machine Learning-based DPM has been developed using the tool provided by MATLAB. The use of machine learning in power transformer assessment has also been reported by several studies [17,[37][38][39][40][41][42][43][44][45].…”

An Inspired Machine-Learning Algorithm with a Hybrid Whale Optimization for Power Transformer PHM

“…In addition, MDE-WOA-PNN, as a novel power transformer fault diagnosis method also had some defects, that is, the parameter setting had a greater impact on the performance of the model. Compared with other methods of similar publications, such as the power transformer fault diagnosis method combining hypersphere multiclass SVM and improved D-S evidence theory proposed by Shang, H. et al [34] and the traditional diagnosis method of BA-BP, the MDE-WOA-PNN not only had a higher accuracy and faster convergence speed, but also improved the defect whereby traditional fault diagnosis method is easy to fall into local optimum.…”

Improved Genetic Algorithm and XGBoost Classifier for Power Transformer Fault Diagnosis

“…There are three main kinds of chromatographic analysis method of dissolved gases namely the characteristic gas method (Fu et al, 2012), the gas production rate method (Nogami et al, 1995;Xi Chen et al, 2010;Zeng et al, 2011), and the three-ratio method (Jiang et al, 2014;Dhote and Helonde, 2012;Liu et al, 2020). The above methods generally utilize the concentration of a specific gaseous molecule or the ratios of several different molecules indicate the state of a power transformer (Shang et al, 2019). In addition, several improved methods have been proposed and applied for transformer fault diagnosis, including the Roger method (Ghoneim et al, 2016), the basic triangular diagram (Singh and Bandyopadhyay, 2010), the dual triangle method (Shang et al, 2019), etc.…”

“…The above methods generally utilize the concentration of a specific gaseous molecule or the ratios of several different molecules indicate the state of a power transformer (Shang et al, 2019). In addition, several improved methods have been proposed and applied for transformer fault diagnosis, including the Roger method (Ghoneim et al, 2016), the basic triangular diagram (Singh and Bandyopadhyay, 2010), the dual triangle method (Shang et al, 2019), etc. However, these methods have their inherent shortcomings.…”