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Abstract
The integration of renewable energy sources and microgrids has become a key focus in the pursuit of sustainable and resilient power systems. Microgrids, being decentralized and often operating in isolation from the main grid, face unique challenges, including the need for accurate islanding detection and diagnosis to ensure safe and efficient operations. This review article comprehensively investigates and evaluates the application of signal processing and machine learning techniques in the context of islanding detection and diagnosis within microgrids. The significance of islanding detection and diagnosis is highlighted in this review study which emphasizes grid stability, safety risk mitigation, and energy efficiency enhancement during islanding. Further, the study explores the technical aspects, covering signal processing techniques and machine learning approaches used for islanding detection, including harmonic analysis, wavelet transforms, and neural networks. This research explores the most recent developments in the domain, encompassing a variety of strategies that harness sophisticated algorithms and data analysis to improve the dependability and effectiveness of microgrid operations. Subsequently, this review sheds light on the state-of-the-art methodologies, challenges, and promising avenues in islanding detection and diagnosis, ultimately contributing to the advancement of microgrid technology and the broader vision of sustainable and resilient energy systems. As the transition toward renewable energy sources accelerates, microgrids represent a promising solution for enhancing grid resilience and integrating distributed generation. However, ensuring the safety and efficiency of microgrid operations during islanding events is a critical concern. This study explores the intersection of signal processing and machine learning, offering a comprehensive examination of islanding detection and diagnosis techniques in microgrids.
Disclosure Statement
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Sushree Shataroopa Mohapatra
Sushree Shataroopa Mohapatra is an accomplished academic in the field of electrical engineering. She is currently pursuing her Ph.D. at the School of Electrical Engineering, KIIT Deemed to be University, Bhubaneswar, India, under the guidance of Dr. Manoj Kumar Maharana. Her research focuses on islanding detection, a crucial area within electrical power systems that ensures grid stability and safety. In addition to her research, Sushree works as an assistant professor in the Department of Electrical and Electronics Engineering at Gandhi Institute for Technology (GIFT) Autonomous Bhubaneswar. Prior to this she has completed M.Tech in Power & Energy System from KIIT Deemed to be University, Bhubaneswar, India in 2014 and B.Tech in Electrical & Electronics Engineering from BPUT, Odisha in 2012. She is dedicated to teaching and mentoring students while contributing to academic research in her field. Through her work, she aims to advance knowledge and technology in electrical engineering.
Manoj Kumar Maharana
Manoj Kumar Maharana is currently Associate Professor in school of Electrical Engineering, KIIT Deemed to be University, Bhubaneswar, India. Before this he was working as Associate Professor at VIT University Vellore and software developer at GECE Hyderabad. He received his Ph.D in Electrical Engineering from Indian Institute of Technology Madras, India in the year 2010. Prior to this M.Tech in power system from NIT- Warangal(RECW) in 2001 and bachelor degree from The Institution of Engineers, (India), Kolkata, in the year 1997. He published several papers in international and national journal and attained several national and international conferences. His areas of research interest are computer modeling of power systems, Energy management system, Energy storage and monitoring and Smart grid.
Abhilash Pradhan
Abhilash Pradhan is an emerging scholar in the field of electrical engineering. He is currently pursuing his Ph.D. at GIET University, Gunupur, Odisha, under the joint supervision of Dr. Pratap Kumar Panigrahi and Dr. Ramesh Chandra Prusty. Abhilash’s research focuses on innovative approaches in Electrical engineering, contributing valuable insights to the field of Microgrid Islanding. In addition to his doctoral studies, he works as an assistant professor at Maharaja Polytechnic, Khurda, where he imparts knowledge and guides students in their academic pursuits. He has completed his M.Tech and B.Tech in the field of Electrical Engineering from BPUT Odisha in the year 2016 and 2012 respectively. Abhilash is committed to advancing the field of engineering through his research and teaching, and he plays an active role in shaping the next generation of engineers.