Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study

Juan Carlos Bravo-Rodríguez, Francisco J. Torres and María D. Borrás
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Juan Carlos Bravo-Rodríguez: Escuela Politécnica Superior, Universidad de Sevilla, c/ Virgen de África 9, 41011 Sevilla, Spain
Francisco J. Torres: Escuela Politécnica Superior, Universidad de Sevilla, c/ Virgen de África 9, 41011 Sevilla, Spain
María D. Borrás: Escuela Politécnica Superior, Universidad de Sevilla, c/ Virgen de África 9, 41011 Sevilla, Spain

Energies, 2020, vol. 13, issue 11, 1-20

Abstract: The economic impact associated with power quality (PQ) problems in electrical systems is increasing, so PQ improvement research becomes a key task. In this paper, a Stockwell transform (ST)-based hybrid machine learning approach was used for the recognition and classification of power quality disturbances (PQDs). The ST of the PQDs was used to extract significant waveform features which constitute the input vectors for different machine learning approaches, including the K-nearest neighbors’ algorithm (K-NN), decision tree (DT), and support vector machine (SVM) used for classifying the PQDs. The procedure was optimized by using the genetic algorithm (GA) and the competitive swarm optimization algorithm (CSO). To test the proposed methodology, synthetic PQD waveforms were generated. Typical single disturbances for the voltage signal, as well as complex disturbances resulting from possible combinations of them, were considered. Furthermore, different levels of white Gaussian noise were added to the PQD waveforms while maintaining the desired accuracy level of the proposed classification methods. Finally, all the hybrid classification proposals were evaluated and the best one was compared with some others present in the literature. The proposed ST-based CSO-SVM method provides good results in terms of classification accuracy and noise immunity.

Keywords: power quality disturbances; classification; feature selection; swarm optimization; support vector machine; genetic algorithm; K-NN algorithm; decision tree; S-transform (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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