Advanced Machine Learning-Driven Security and Anomaly Identification in Inverter-Based Cyber-Physical Microgrids

Abstract

In a landscape dominated by the digitalization of energy networks, safeguarding cyber-physical microgrids and against breaches anomalies emerges as a critical imperative. This study pioneers inventive methodologies for Intelligent Detection of Intrusions and Anomalies in Inverter-centric Cyber-Physical Microgrids, leveraging state-of-the-art machine learning paradigms. Using the combined power of LSTM and Convolutional Neural Networks, our suggested approach finds breaches and aberrations in microgrid structures with remarkable accuracy and efficiency. Additionally, we integrate the effectiveness of Gradient Boosting Machines to enhance the overall detection capabilities. Experimental findings underscore the efficacy of our machine learning-driven approach, with CNN achieving a precision of 95%, recall of 92%, and F1-score of 93% in intrusion detection, while LSTM attains a precision of 93%, recall of 91%, and F1-score of 92% in anomaly identification. Furthermore, GBM contributes to achieving an overall efficiency of 96%. Our approach not only bolsters microgrid resilience but also lays the foundation for a secure energy future. In an increasingly interconnected world, the integration of these sophisticated techniques not only bolsters microgrid resilience but also lays the foundation for a secure energy future. By embracing innovation at the convergence of machine learning and energy systems, we stride toward fortified and dependable cyber-physical microgrids.

Keywords:

• cyber-physical microgrids
• intrusion detection
• fault identification
• machine learning
• energy management systems
• convolutional neural networks (CNN)
• long Short-Term memory (LSTM)
• gradient boosting machines
• resilient energy systems
• grid stability and sustainability

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

K. Gokulraj

K. Gokulraj received his B.E. degree in Electrical and Electronics Engineering from E.G.S. Pillay engineering College, Nagapattinam, Tamil Nadu, India in 2008 and M.E. degree in Power Electronics and Drives from Hindustan University, Chennai, Tamil Nadu, India in 2012. He is currently pursuing Ph.D., at Anna University Chennai under the faculty of Electrical Engineering through the department of Electrical and Electronics Engineering of Sona college of technology, Salem, Tamilnadu, India. He is currently working as an Assistant Professor in the Department of Electrical and Electronics Engineering at E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India. He has published 5 articles in peer reviewed international journals and presented 6 papers in international conferences. His areas of interest are power electronics, Cyber physical grids and renewable energy systems.

C. B. Venkatramanan

C. B. Venkatramanan received his AMIE degree from the Institution of Engineers India in 2001 and Master of Engineering in power electronics and drives from Anna University Chennai, India, in 2007, and a PhD from Anna University Chennai, in April 2014. He is an associate professor in the Department of Electrical and Electronics Engineering, having 9 years of industrial and 27 years of teaching experience. He has published 15 papers in international journals and 15 papers in national journals. He has reviewed textbooks on Control Systems. His area of specialization is power electronics and drives.