Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm
Microgrids are the future version of advanced distribution networks due to the fast growth of renewable energy resources near consumers’ side. The microgrids are operated in on-grid mode (OGM) with the utility grid, and isolation mode (IM) without the utility grid. This dual operational m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10401231/ |
| _version_ | 1797346272872497152 |
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| author | Faisal Mumtaz Kashif Imran Habibur Rehman Hammad Ali Qureshi |
| author_facet | Faisal Mumtaz Kashif Imran Habibur Rehman Hammad Ali Qureshi |
| author_sort | Faisal Mumtaz |
| collection | DOAJ |
| description | Microgrids are the future version of advanced distribution networks due to the fast growth of renewable energy resources near consumers’ side. The microgrids are operated in on-grid mode (OGM) with the utility grid, and isolation mode (IM) without the utility grid. This dual operational mode causes protection and control challenges in the microgrids. This research paper suggests an advanced hardware-supported fault detection, phase identification & localization method for AC microgrids. The scheme deploys a Discrete Kalman Filter (DKF) for state estimation of voltage and current signals. Then, a Mathematical Morphology (MM) is engaged for generating a novel fault detection/classification index named segregated energy signature (SES) from estimated voltage and current signals. The system is considered to be faulty if the SES is higher than a predefined threshold setting, while phase identification is achieved by default because of the per-phase implementation of DKF&MM. Moreover, the directional features of the cumulative energy signature (CES) are also computed from MM-based non-fundamental current and voltage to localize the faulty section. The established scheme is tested on the CIGRE microgrid benchmark test bed on Matlab-Simulink software. In addition, the suggested method is also examined on the dSPACE MicroLab testing hardware setup in the Smart grid lab at USPCAS-E, NUST, Pakistan. The result illustrates that the proposed scheme successfully detects, classifies, and localizes the low impedance fault (LIF) as well as high impedance fault (HIF) in both operational modes and topological structures with 96.6% accuracy. |
| first_indexed | 2024-03-08T11:30:41Z |
| format | Article |
| id | doaj.art-08d4afa6165a4b77b0ed348f6e3ac4ac |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T11:30:41Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-08d4afa6165a4b77b0ed348f6e3ac4ac2024-01-26T00:01:25ZengIEEEIEEE Access2169-35362024-01-0112124461245710.1109/ACCESS.2024.335479010401231Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer AlgorithmFaisal Mumtaz0https://orcid.org/0000-0002-6945-9819Kashif Imran1https://orcid.org/0000-0002-9926-297XHabibur Rehman2https://orcid.org/0000-0002-8251-654XHammad Ali Qureshi3U.S. Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, PakistanU.S. Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, PakistanDepartment of Electrical Engineering, American University of Sharjah, Sharjah, United Arab EmiratesU.S. Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, PakistanMicrogrids are the future version of advanced distribution networks due to the fast growth of renewable energy resources near consumers’ side. The microgrids are operated in on-grid mode (OGM) with the utility grid, and isolation mode (IM) without the utility grid. This dual operational mode causes protection and control challenges in the microgrids. This research paper suggests an advanced hardware-supported fault detection, phase identification & localization method for AC microgrids. The scheme deploys a Discrete Kalman Filter (DKF) for state estimation of voltage and current signals. Then, a Mathematical Morphology (MM) is engaged for generating a novel fault detection/classification index named segregated energy signature (SES) from estimated voltage and current signals. The system is considered to be faulty if the SES is higher than a predefined threshold setting, while phase identification is achieved by default because of the per-phase implementation of DKF&MM. Moreover, the directional features of the cumulative energy signature (CES) are also computed from MM-based non-fundamental current and voltage to localize the faulty section. The established scheme is tested on the CIGRE microgrid benchmark test bed on Matlab-Simulink software. In addition, the suggested method is also examined on the dSPACE MicroLab testing hardware setup in the Smart grid lab at USPCAS-E, NUST, Pakistan. The result illustrates that the proposed scheme successfully detects, classifies, and localizes the low impedance fault (LIF) as well as high impedance fault (HIF) in both operational modes and topological structures with 96.6% accuracy.https://ieeexplore.ieee.org/document/10401231/Distributed generationdiscrete Kalman filterfault detectionhardware in the loopmicrogrids protectionmathematical morphology |
| spellingShingle | Faisal Mumtaz Kashif Imran Habibur Rehman Hammad Ali Qureshi Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm IEEE Access Distributed generation discrete Kalman filter fault detection hardware in the loop microgrids protection mathematical morphology |
| title | Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm |
| title_full | Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm |
| title_fullStr | Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm |
| title_full_unstemmed | Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm |
| title_short | Hardware Supported Fault Detection and Localization Method for AC Microgrids Using Mathematical Morphology with State Observer Algorithm |
| title_sort | hardware supported fault detection and localization method for ac microgrids using mathematical morphology with state observer algorithm |
| topic | Distributed generation discrete Kalman filter fault detection hardware in the loop microgrids protection mathematical morphology |
| url | https://ieeexplore.ieee.org/document/10401231/ |
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