Wavelet group method of data handling for fault prediction in electrical power insulators. (December 2020)

• Record Type: Journal Article
• Authors: Stefenon, Stéfano Frizzo; Dal Molin Ribeiro, Matheus Henrique; Nied, Ademir; Mariani, Viviana Cocco; Coelho, Leandro dos Santos; Menegat da Rocha, Diovana Fátima; Grebogi, Rafael Bartnik; Ruano, António Eduardo de Barros
• Is Part Of: International journal of electrical power & energy systems. Volume 123(2020)
• Publication Date: 2020-12
• Subjects: Group method of data handling -- Wavelet transform -- Electric power system; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2020.106269
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Hybrid methods are evaluated to predict time series. GMDH is suitable to fault prediction in electrical power system. Wavelet is a suitable tool for extracting features and reducing signal noise. Insulators of the medium voltage distribution network are evaluated in the laboratory. GMDH proves to be faster than LSTM and ANFIS for predicting chaotic time series. Abstract: Electric power is increasingly being used in the globalized day-to-day and keeping the electric power system running is necessary. Insulators are important components of the electric power system. In case of failure in these components, there may be disconnections and, consequently, no electricity. Contaminated insulators can develop irreversible failures if they are not inspected. One equipment used for the inspection of the electric power system is the ultrasound, which generates an audible noise based on a time series that is used to identify possible failures. The time series forecast can be used for possible prediction of the development of failure. In this paper, a hybrid method that uses Wavelet Energy Coefficient (WEC) for feature extraction and Group Method of Data Handling (GMDH) for time series prediction is proposed, being defined as Wavelet GMDH. For comparison and validation of the proposed method, a benchmark is made with well-established algorithms such as Long Short-Term Memory (LSTM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). For a fairer analysis, these algorithms are also evaluated based on the same data extraction with WEC. The proposed method proved to have good accuracy comparing with LSTM and ANFIS, and is much faster than the compared methods.