Anomaly Detection of Smart Grid Equipment Using Machine Learning Applications

Authors

  • Arun Sekar Rajasekaran Department of Electronics and Communication Engineering, GMR Institute of Technology, GMR Nagar, Rajam – 532 127, Andhra Pradesh, India
  • P. Kalyanchakravarthi Department of Electronics and Communication Engineering, GMR Institute of Technology, GMR Nagar, Rajam – 532 127, Andhra Pradesh, India
  • Partha Sarathi Subudhi 2)Department of Electrical Engineering, Bajaj Institute of Technology (BIT), Wardha, Maharashtra, India 3)Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nisantasi University, Istanbul, Turkey

DOI:

https://doi.org/10.13052/dgaej2156-3306.37518

Keywords:

Anomaly, multivariate statistical analysis, principal component analysis, condition monitoring

Abstract

Many application systems in today’s smart grid network comprise a variety of middleware components, such as vibrating or spinning electrical equipment, data bases, storage, caches, and identification services, among others. Each component is a discrete bundle of physical or virtual computers that will generate a large amount of data in the form of logs and metrics, and failure of these high-vibrating machines will result in the system’s entire shutdown. As a result, the condition monitoring system for this smart grid equipment is more dependable and efficient in predicting the machine’s health ahead of time. To analyze the data and derive any inferences for future analysis and anomaly detection, we’ll need a separate system, which will take longer to handle data given by each component and will demand additional processing resources. As a result, in this chapter, machine learning methods are used to identify abnormalities or condition monitoring for smart grid equipment and machines. Here, we used two separate methods: multivariate statistical analysis by calculating Mob distance and auto encoders, which is an artificial neural network approach. Furthermore, the findings demonstrate that these apps are effective in identifying anomaly ahead of time, i.e. before a few days.

Author Biographies

Arun Sekar Rajasekaran, Department of Electronics and Communication Engineering, GMR Institute of Technology, GMR Nagar, Rajam – 532 127, Andhra Pradesh, India

Arun Sekar Rajasekaran received his Bachelor’s degree in Electronics and Communication engineering from Sri Ramakrishna Engineering College in 2008 and his Master’s degree in VLSI Design in 2013 and his Doctorate of philosophy in Low Power VLSI design from Anna University, Chennai in 2019. He is currently working as an Assistant professor in the Department of Electronics and Communication Engineering at GMR Institute of Technology, Rajam, Andhra pradesh. He has nearly 12 years of Teaching experience. He had published more than 24 papers in International conferences and 23 reputed Indexed Journals namely, IEEE Transactions on Industrial informatics, Springer, (Microprocessor and microsystems, Computers and electrical engineering) Elsevier, IET communications, IEEE Access and Concurrency and computation (Wiley) publications. His areas of interest are Low power VLSI design, Network security, Blockchain, Body area networks and Image processing. He is a life member of ISTE, IETE, ISRD and IEANG.

P. Kalyanchakravarthi, Department of Electronics and Communication Engineering, GMR Institute of Technology, GMR Nagar, Rajam – 532 127, Andhra Pradesh, India

P. Kalyanchakravarthi received the bachelor’s degree in Electronics and communication engineering from JNTU Hyderabad in 2007, the master’s degree in Electronics and communication engineering from NIT Rourkela in 2011, and currently pursuing the philosophy of doctorate degree in Electrical Engineering , NIT Rourkela. He is currently working as an Assistant Professor at the Department of Electronics and Communication Engineering, GMR Institute of Technology, Rajam, Andhrapradesh,India. His research areas include mobile security, deep learning, and social network analysis.

Partha Sarathi Subudhi, 2)Department of Electrical Engineering, Bajaj Institute of Technology (BIT), Wardha, Maharashtra, India 3)Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nisantasi University, Istanbul, Turkey

Partha Sarathi Subudhi, (M’15–SM’21, IEEE) completed his Bachelor of Technology in “Electrical Engineering” from Biju Patnaik University of Technology, Odisha, India in 2012 and Master of Technology in “Power Electronics and Drives” from Vellore Institute of Technology, Chennai, India in 2015. He then went on to join for a full-time Ph.D. with the School of Electrical Engineering (SELECT), Vellore Institute of Technology (VIT) from 2015 to 2020. Currently, he is an Adjunct Professor with the Department of Electrical and Electronics, Faculty of Engineering and Architectures, Nisantasi University, Istanbul, Turkey. He has been working as an Assistant Professor with the Department of Electrical Engineering, Bajaj Institute of Technology (BIT), Wardha, Maharashtra, India since 2021. He is an Editor of International Journal of Power and Energy Systems, Acta Press, and a Guest Editor of River Publisher. He is also managing editor of IEEE Educational Videos on Power Electronics. He is an active member of IEEE PELS. Dr. Subudhi is a member of the IEEE Technical Committee 9 (TC 9) on Wireless Power Transfer Systems. Recently, he got selected as a member of publication committee of IEEE PELS TC9. He is also a member of IEEE Technical Committee 12 (TC 12) on Energy Access and off Grid System. He is also a member of the IEEE PELS Educational Videos Committee. He is a life member of IEANG. He is also selected as a life member of ISTE, India. He had received best paper award in International Conference on Innovations & Discoveries in Science, Engineering and Technology 2018 (ICIDSET-18). His field of interest includes Power Electronics Converters, Wireless Power Transfer, Electric Vehicle Charger, Residential Nano Grid, Solar Power Generation System, Hybrid Converters, and their applications.

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Published

2022-07-01

How to Cite

Rajasekaran, A. S. ., Kalyanchakravarthi, P. ., & Subudhi, P. S. . (2022). Anomaly Detection of Smart Grid Equipment Using Machine Learning Applications. Distributed Generation &Amp; Alternative Energy Journal, 37(05), 1721–1738. https://doi.org/10.13052/dgaej2156-3306.37518

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Section

Articles