A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

Indu Sekhar Samanta, Subhasis Panda, Pravat Kumar Rout, Mohit Bajaj (), Marian Piecha, Vojtech Blazek and Lukas Prokop ()
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Indu Sekhar Samanta: Department of Computer Science Engineering, Siksha ‘O’ Anusandhan University, Odisha 751030, India
Subhasis Panda: Department of Electrical Engineering, Siksha ‘O’ Anusandhan University, Odisha 751030, India
Pravat Kumar Rout: Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan University, Odisha 751030, India
Mohit Bajaj: Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India
Marian Piecha: Ministry of Industry and Trade, 11015 Prague, Czech Republic
Vojtech Blazek: ENET Centre, VSB—Technical University of Ostrava, 70800 Ostrava, Czech Republic
Lukas Prokop: ENET Centre, VSB—Technical University of Ostrava, 70800 Ostrava, Czech Republic

Energies, 2023, vol. 16, issue 11, 1-31

Abstract: Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods.

Keywords: deep-learning (DL); machine learning (ML); artificial intelligence (AI); power quality monitoring and detection; feature extraction; classification of PQ disturbance; artificial neural network (ANN) (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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