A Proposed Methodology for Online Implementation of a Support Vector Machine With Applications in Power Transformers Event Classification

Hagi Jakobson  Dantas da Costa; Bruna Liberato Souza; Lucas Dantas Simões; Matheus Aires; Rodrigo Prado Medeiros; Orivaldo Vieira de Santana Júnior; Flávio Bezerra Costa

doi:10.48011/asba.v2i1.1105

Hagi Jakobson Dantas da Costa Universidade Federal do Rio Grande do Norte
Bruna Liberato Souza Universidade Federal do Rio Grande do Norte
Lucas Dantas Simões Universidade Federal do Rio Grande do Norte
Matheus Aires Universidade Federal do Rio Grande do Norte
Rodrigo Prado Medeiros Universidade Federal Rural do Semi-Árido
Orivaldo Vieira de Santana Júnior Universidade Federal do Rio Grande do Norte
Flávio Bezerra Costa Universidade Federal do Rio Grande do Norte

DOI: https://doi.org/10.48011/asba.v2i1.1105

Keywords: Machine learning, Real-time AI, Electrical power systems, Support vector machine, Methodology

Abstract

The constant evolution of resources in computational processing and machine learning algorithms, combined with the increasing complexity of embedded systems, made the hardware implementation of machine learning models more viable. This paper proposes a methodology for online implementation of a support vector machine classifier through the development of a simple, concise, and easily adapted algorithm for data classification. The system was validated through the development of an application that classifies disturbances in a power transformer, followed by a comparison with the results obtained with the Library for Support Vector Machines (LIBSVM). Besides the very similar results when compared with the LIBSVM, the proposed methodology achieved high overall accuracy and fast classification time.