Regulated Two-Dimensional Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Cheng-I Chen & Yeong-Chin Chen & Chung-Hsien Chen & Yung-Ruei Chang, 2020. "Voltage Regulation Using Recurrent Wavelet Fuzzy Neural Network-Based Dynamic Voltage Restorer," Energies, MDPI, vol. 13(23), pages 1-19, November.
- Cheng-I Chen & Chien-Kai Lan & Yeong-Chin Chen & Chung-Hsien Chen & Yung-Ruei Chang, 2020. "Wavelet Energy Fuzzy Neural Network-Based Fault Protection System for Microgrid," Energies, MDPI, vol. 13(4), pages 1-13, February.
- Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
- 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.
- Cheng-I Chen & Chien-Kai Lan & Yeong-Chin Chen & Chung-Hsien Chen, 2019. "Adaptive Frequency-Based Reference Compensation Current Control Strategy of Shunt Active Power Filter for Unbalanced Nonlinear Loads," Energies, MDPI, vol. 12(16), pages 1-14, August.
- Ruijin Zhu & Xuejiao Gong & Shifeng Hu & Yusen Wang, 2019. "Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor," Energies, MDPI, vol. 12(24), pages 1-12, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Artvin-Darien Gonzalez-Abreu & Roque-Alfredo Osornio-Rios & Arturo-Yosimar Jaen-Cuellar & Miguel Delgado-Prieto & Jose-Alfonso Antonino-Daviu & Athanasios Karlis, 2022. "Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review," Energies, MDPI, vol. 15(5), pages 1-26, March.
- 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.
- Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
- Shao, Han & Henriques, Rui & Morais, Hugo & Tedeschi, Elisabetta, 2024. "Power quality monitoring in electric grid integrating offshore wind energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
- Jiajun Cai & Kai Zhang & Hui Jiang, 2023. "Power Quality Disturbance Classification Based on Parallel Fusion of CNN and GRU," Energies, MDPI, vol. 16(10), pages 1-12, 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.
- Cheng-I Chen & Yeong-Chin Chen & Chung-Hsien Chen, 2022. "Recurrent Wavelet Fuzzy Neural Network-Based Reference Compensation Current Control Strategy for Shunt Active Power Filter," Energies, MDPI, vol. 15(22), pages 1-23, November.
- Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
- Paolo Castello & Carlo Muscas & Paolo Attilio Pegoraro & Sara Sulis, 2019. "PMU’s Behavior with Flicker-Generating Voltage Fluctuations: An Experimental Analysis," Energies, MDPI, vol. 12(17), pages 1-14, August.
- Enrique Reyes-Archundia & Wuqiang Yang & Jose A. Gutiérrez Gnecchi & Javier Rodríguez-Herrejón & Juan C. Olivares-Rojas & Aldo V. Rico-Medina, 2024. "Effect of Phase Shifting on Real-Time Detection and Classification of Power Quality Disturbances," Energies, MDPI, vol. 17(10), pages 1-14, May.
- Paula Remigio-Carmona & Juan-José González-de-la-Rosa & Olivia Florencias-Oliveros & José-María Sierra-Fernández & Javier Fernández-Morales & Manuel-Jesús Espinosa-Gavira & Agustín Agüera-Pérez & José, 2022. "Current Status and Future Trends of Power Quality Analysis," Energies, MDPI, vol. 15(7), pages 1-18, March.
- Azam Bagheri & Roger Alves de Oliveira & Math H. J. Bollen & Irene Y. H. Gu, 2022. "A Framework Based on Machine Learning for Analytics of Voltage Quality Disturbances," Energies, MDPI, vol. 15(4), pages 1-14, February.
- Ruijin Zhu & Xuejiao Gong & Shifeng Hu & Yusen Wang, 2019. "Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor," Energies, MDPI, vol. 12(24), pages 1-12, December.
- Juan-José González de-la-Rosa & Manuel Pérez-Donsión, 2020. "Special Issue “Analysis for Power Quality Monitoring”," Energies, MDPI, vol. 13(3), pages 1-6, January.
- Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
- Alexandre Serrano-Fontova & Pablo Casals Torrens & Ricard Bosch, 2019. "Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies," Energies, MDPI, vol. 12(16), pages 1-21, August.
- Do-In Kim, 2021. "Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network," Energies, MDPI, vol. 14(15), pages 1-15, July.
- Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
- Karol Jakub Listewnik, 2022. "A Method for the Evaluation of Power-Generating Sets Based on the Assessment of Power Quality Parameters," Energies, MDPI, vol. 15(14), pages 1-24, July.
- Pandelara, Diego & Kristjanpoller, Werner & Michell, Kevin & Minutolo, Marcel C., 2022. "A fuzzy regression causality approach to analyze relationship between electrical consumption and GDP," Energy, Elsevier, vol. 239(PE).
More about this item
Keywords
power quality disturbances; signal synchronization; regulated two-dimensional deep convolutional neural network; microgrid; power quality classifier; IEEE Std. 1159;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2532-:d:783195. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.