A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/11/4406 |
| _version_ | 1827739622179864576 |
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| author | Indu Sekhar Samanta Subhasis Panda Pravat Kumar Rout Mohit Bajaj Marian Piecha Vojtech Blazek Lukas Prokop |
| author_facet | Indu Sekhar Samanta Subhasis Panda Pravat Kumar Rout Mohit Bajaj Marian Piecha Vojtech Blazek Lukas Prokop |
| author_sort | Indu Sekhar Samanta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:08:51Z |
| format | Article |
| id | doaj.art-ad3e02ff7e4b463aa07e6522ffee0eb5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T03:08:51Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ad3e02ff7e4b463aa07e6522ffee0eb52023-11-18T07:48:26ZengMDPI AGEnergies1996-10732023-05-011611440610.3390/en16114406A Comprehensive Review of Deep-Learning Applications to Power Quality AnalysisIndu Sekhar Samanta0Subhasis Panda1Pravat Kumar Rout2Mohit Bajaj3Marian Piecha4Vojtech Blazek5Lukas Prokop6Department of Computer Science Engineering, Siksha ‘O’ Anusandhan University, Odisha 751030, IndiaDepartment of Electrical Engineering, Siksha ‘O’ Anusandhan University, Odisha 751030, IndiaDepartment of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan University, Odisha 751030, IndiaDepartment of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, IndiaMinistry of Industry and Trade, 11015 Prague, Czech RepublicENET Centre, VSB—Technical University of Ostrava, 70800 Ostrava, Czech RepublicENET Centre, VSB—Technical University of Ostrava, 70800 Ostrava, Czech RepublicPower 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.https://www.mdpi.com/1996-1073/16/11/4406deep-learning (DL)machine learning (ML)artificial intelligence (AI)power quality monitoring and detectionfeature extractionclassification of PQ disturbance |
| spellingShingle | Indu Sekhar Samanta Subhasis Panda Pravat Kumar Rout Mohit Bajaj Marian Piecha Vojtech Blazek Lukas Prokop A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis Energies deep-learning (DL) machine learning (ML) artificial intelligence (AI) power quality monitoring and detection feature extraction classification of PQ disturbance |
| title | A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis |
| title_full | A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis |
| title_fullStr | A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis |
| title_full_unstemmed | A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis |
| title_short | A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis |
| title_sort | comprehensive review of deep learning applications to power quality analysis |
| topic | deep-learning (DL) machine learning (ML) artificial intelligence (AI) power quality monitoring and detection feature extraction classification of PQ disturbance |
| url | https://www.mdpi.com/1996-1073/16/11/4406 |
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