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.
Highlights
As the equipment for transferring electric energy, power transformers occupy an important position in the power system
After obtaining the optimal transformer fault model based on particle swarm optimization (PSO)-hypersphere multiclass support vector machine (HMSVM) through training
After obtaining the optimal transformer fault model based on PSO-HMSVM through training samples, 253 testing samples are sent into the model for state classification
Summary
As the equipment for transferring electric energy, power transformers occupy an important position in the power system. Various kinds of intelligent algorithms have been applied to transformer fault diagnosis. Hypersphere support vector machine (HSSVM) is based on the theory of SVM. A new hypersphere multi-class support vector machine was proposed in [15] and applied in text classification, but the parameter setting of the method was not explained further. Among various kinds of artificial intelligent algorithms, hybrid immune algorithm (HIA) [27,28] and kernel extreme learning machine (KELM) [29,30] have attracted widespread attention due to their good performance and have been applied to transformer fault diagnosis in recent years.
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