A two‐stage transformer fault diagnosis method based multi‐filter interactive feature selection integrated adaptive sparrow algorithm optimised support vector machine. Issue 3 (4th December 2022)

• Record Type: Journal Article
• Authors: Shi, Hanyu; Chen, Mingxia
• Is Part Of: IET electric power applications. Volume 17:Issue 3(2023)
• Page Start: 341
• Page End: 357
• Publication Date: 2022-12-04
• Subjects: feature selection -- sparrow algorithm -- support vector machine -- transformer fault diagnosis; Electric power -- Periodicals; Electric power systems -- Periodicals; 621.305
• DOI: 10.1049/elp2.12270
• Languages: English
• ISSNs: 1751-8660

Abstract

Abstract: The scarcity of samples and disunity of feature inputs hinder the enhancement of transformer fault diagnosis performance, and there are mutual influences between model construction and feature selection, which cannot only consider a single process. Therefore, this study proposes a novel two‐stage transformer fault diagnosis strategy, which includes a multi‐filter interactive feature selection method (MIFS) constructed, and a diagnosis model ASSA‐SVM based on the adaptive sparrow algorithm (ASSA) optimised support vector machine (SVM). Firstly, the proposed MIFS incorporates ReliefF and mRMR to establish a comprehensive criterion ReliefF‐mRMR for feature importance ranking, and then performs dimension‐by‐dimension input classifier interaction selection based on the ranking results to obtain the optimal feature subset. Secondly, ASSA was proposed to optimise the kernel parameters of SVM. A two‐stage integration model MIFS‐ASSA‐SVM was developed. Finally, Experiments were conducted using real fault data, and the diagnostic performance of different feature inputs, optimisation algorithms and classifiers were compared. The results show that the proposed method performs well on parameter optimisation, can dynamically and interactively select feature subsets with few dimensions and good generalisation performance, its overall diagnosis accuracy reached 92.47%, and the diagnosis performance of each fault type has good performance in multiple evaluation metrics. Abstract : A two‐stage transformer fault diagnosis model which can conduct the feature selection and fault diagnosis was proposed, ReliefF and mRMR filtering criteria were combined to evaluate the comprehensive importance of features, adaptive sparrow algorithm (ASSA) was proposed to optimise the kernel parameters of SVM. The integration method has more fault feature mining potential, has outstanding performance on various evaluation metrics, which provides a new and useful tool for subsequent research and practical operation and maintenance work.