Volume 19, Issue 3 (September 2023)                   IJEEE 2023, 19(3): 152-164 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Tiwari S P. An Efficient Protection Scheme for Wind Integrated Microgrid Considering Dissimilar AC Faults and Varying Fault Resistance. IJEEE 2023; 19 (3) :152-164
URL: http://ijeee.iust.ac.ir/article-1-2819-en.html
An Efficient Protection Scheme for Wind Integrated Microgrid Considering Dissimilar AC Faults and Varying Fault Resistance
S. Prasad Tiwari
Abstract:   (1659 Views)
In spite of the numerous benefits over the traditional power distribution system, protection of the microgrid is a challenging and complex task. The varying fault resistances due to dissimilar grounding conditions can affect the performance of the protection scheme. Under such conditions, the magnitude of the fault current can vary from lower to higher level. In addition to the above, the dissimilar magnitude of fault current during grid connected and islanded mode demands a protection scheme that can easily discriminate the mode of operation. The magnitude of fault current in grid-connected and islanded modes needs a robust protection scheme. In this regard, an ensemble of subspace kNN based robust protection scheme has been proposed to detect the faulty conditions of the microgrid. The tasks of the mode detection, fault detection/classification as well as faulty line identification has been carried out in the proposed work. In the proposed protection scheme, discrete wavelet transform (DWT) has been used for processing of the data. After recording the voltage and current signals at bus-1, the protection scheme has been validated. The validation of the protection scheme in Section 6 reveals that the protection scheme is efficiently working.
Keywords: Fault Detection/classification, Grid-connected and Islanded Mode, Ensemble of kNN, Microgrid, Faulty Line Identification.
Full-Text [PDF 588 kb]   (569 Downloads)    
Type of Study: Research Paper | Subject: Artificial Intelligence Techniques
Received: 2023/02/25 | Revised: 2023/11/25 | Accepted: 2023/09/10
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.