A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis
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- Artvin-Darien Gonzalez-Abreu & Miguel Delgado-Prieto & Roque-Alfredo Osornio-Rios & Juan-Jose Saucedo-Dorantes & Rene-de-Jesus Romero-Troncoso, 2021. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances," Energies, MDPI, vol. 14(10), pages 1-17, May.
- Zakarya Oubrahim & Yassine Amirat & Mohamed Benbouzid & Mohammed Ouassaid, 2023. "Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review," Energies, MDPI, vol. 16(6), pages 1-41, March.
- Kavaskar Sekar & Karthick Kanagarathinam & Sendilkumar Subramanian & Ellappan Venugopal & C. Udayakumar & Ravi Samikannu, 2022. "An Improved Power Quality Disturbance Detection Using Deep Learning Approach," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, May.
- Dash, P.K. & Prasad, Eluri N.V.D.V. & Jalli, Ravi Kumar & Mishra, S.P., 2022. "Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm," Applied Energy, Elsevier, vol. 309(C).
- Fei Mei & Yong Ren & Qingliang Wu & Chenyu Zhang & Yi Pan & Haoyuan Sha & Jianyong Zheng, 2018. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network," Energies, MDPI, vol. 12(1), pages 1-16, December.
- Yue Shen & Muhammad Abubakar & Hui Liu & Fida Hussain, 2019. "Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems," Energies, MDPI, vol. 12(7), pages 1-26, April.
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Cited by:
- Paweł Pijarski & Adrian Belowski, 2024. "Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 17(2), pages 1-42, January.
- Osman Akbulut & Muhammed Cavus & Mehmet Cengiz & Adib Allahham & Damian Giaouris & Matthew Forshaw, 2024. "Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques," Energies, MDPI, vol. 17(10), pages 1-23, May.
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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);All these keywords.
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