Fault Prognosis in Smart Distribution System with Distributed Generation Using Fast Fourier Transform Assisted Machine Learning

Abstract

The affordability and accessibility of electrical data related to microgrids and active distribution networks (ADN) have significantly improved due to smart digital relays with advanced communication capabilities. These relays can communicate with each other and central protection systems. Due to data accessibility a machine learning (ML) based module is proposed. This module predicts both fault type and location within an IEEE 34 bus active distribution system featuring distributed generations. To design this module, current measurements are retrieved from five specified bus locations and further Fast Fourier Transform is applied for feature extraction. The feature creation method is much simpler without any complexity. These features are further used for ML module’s training. Practical ADN operating conditions are considered when curating the training dataset. Weighted K-nearest neighbors are used for fault type prediction and linear regression is applied for fault location estimation and they are proven to be superior to other ML approaches. The obtained results show high accuracy and reliability in predictions, making these modules suitable for ADN protection. They also exhibit robustness against input measurement noise, ensuring consistent protection in various operating scenarios. The ADN is simulated in SIMULINK 2020a, and ML modeling is done using MATLAB 2020a.

KEYWORDS:

• Distributed generation
• Fast Fourier transform
• Fault location
• K-Nearest neighbor
• Machine learning
• Microgrid

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Adhishree Srivastava

Adhishree Srivastava received the BE degree in electrical and electronics engineering from the Birla Institute of Technology Mesra, Ranchi, India, in 2012, and the MTech degree with a gold medal in power system engineering from the Motilal Nehru National Institute of Technology, Allahabad, India, in 2014. She received her PhD degree from Indian Institute of Technology, Patna in Feb, 2022. She has worked as an edison engineer in General Electric Private Ltd. from 2014 to 2016. Currently, she is working as an assistant professor in BIT Mesra, Patna, Bihar, India. She has authored various papers in international journals and conferences such as IEEE transactions, Electric power system research, etc. Her research interests include optimal relay coordination, distributed generation, and micro grid protection using machine learning and deep learning. Corresponding author. Email: [email protected]

Shivani Jha

Shivani Jha received her BTech degree in electrical and electronics engineering from Birla Institute of Technology, MESRA, India in 2022. Her areas of interest are artificial intelligence, machine learning, data science, and data analysis using MATLAB. Email: [email protected]

Shriya Mishra

Shriya Mishra has received his BTech degree in electrical and electronics engineering from Birla Institute of Technology Patna, Bihar, India in 2022. She is currently working as cyber security consultant in one of the big 4, Ernst and Young, Mumbai, India. Her areas of interest are nonlinear control systems, artificial intelligence, operational technology, and industrial automation systems. Email: [email protected]

S.K Parida

S K Parida (SMIEEE, 2016) is currently an associate professor with the Department of Electrical Engineering, IIT Patna. He was awarded the Senior Research Fellowship (SRF) by the Power Management Institute, NTPC Ltd., Noida, in 2007, during his PhD program. He has received Bhaskar Advanced Solar Energy (BASE- 2015) Research Fellowship award by Indo US Science and Technology Forum (IUSSTF), DST, Govt. of India, in 2015 and Young Faculty Research Fellowship (YFRF) award by Digital India Ltd., Ministry of Electronics and Information Technology (MeitY), Govt. of India in the year 2019. His research interest includes power system protection, control and stability. Email: [email protected]

Himanshu Priyadarshi

Himanshu Priyadarshi received his BTech degree in electrical and electronics engineering from SASTRA University, Thanjavur, India in 2010 and MTech degree in solid state electronic materials from Indian Institute of Technology, Roorkee, India in 2012. He is currently pursuing PhD at the Department of Electrical Engineering, Manipal University Jaipur, Jaipur, India. His areas of interest are artificial intelligence,machine learning, distributed energy resources, semiconductor technology, and energy storage devices. Email: [email protected]