Six Phase Transmission Line Protection Using Bat Algorithm Tuned Stacked Sparse Autoencoder

Abstract

Six-phase transmission lines have the capability to address the continually evolving power demand. It allows upgrading the power transfer capability of the prevailing three-phase double circuit line without major changes in the transmission corridors. However, the operational performance of any six-phase system is highly dependent on its protection scheme. The possibility of larger number of faults in six-phase system complicates the protection task. Furthermore, the harmonics intrusion arising because of nonlinear loading compromises the reliability of the conventional threshold-based protection schemes. In this regard, this article addresses the above-mentioned challenges by developing a protection scheme based on the hybrid frameworks of bat algorithm and stacked sparse autoencoder-deep neural network (SAE-DNN). To overcome the limitation of SAE-DNN regarding optimal selection of architecture and tuning parameters, the selection task has been formulated as an optimization problem and solved using Bat algorithm. The use of raw voltage and current signals as input to the SAE-DNN reduces the overall complexity of the protection scheme. The efficacy of the proposed scheme has been validated for all 120 types of faults under varying operating, loading and fault scenarios. Furthermore, the proposed scheme has been validated for practical settings by performing real-time simulations on OPAL-RT digital simulator.

Keywords:

• bat algorithm
• deep neural network
• six phase transmission line protection
• stacked sparse autoencoder
• real time validation

Additional information

Funding

The authors acknowledge the support received from the Chhattisgarh Council of Science & Technology, Government of Chhattisgarh, under Research Grant 2229/CCOST/MRP/2015.

Notes on contributors

Tirupathi Rao Althi

Tirupathi Rao Althi received the B.Tech. degree in electrical and electronics engineering from J.N.T.U Hyderabad, India, in 2007 and the M.Tech. degree in power systems engineering from Acharya Nagarjuna University Guntur, India, in 2010. He is currently working toward the Ph.D. degree from Electrical Engineering Department, National Institute of Technology, Raipur, India. His current research interests include application of machine learning techniques for power system protection and optimization.

Ebha Koley

Ebha Koley received the Ph.D. degree from National Institute of Technology, Raipur, India, in 2015. She is currently working as Associate Professor in the Department of Electrical Engineering at National Institute of Technology, Raipur, India. She has more than fourteen years of teaching and industrial experience. Her research interests include power system protection, microgrid and soft-computing techniques.

Subhojit Ghosh

Subhojit Ghosh received the Ph.D. degree from Indian Institute of Technology Kharagpur, India, in 2010. He is currently working as Associate Professor in the Department of Electrical Engineering at National Institute of Technology, Raipur, India. He has more than nineteen years of teaching and research experience. His research interests include optimization techniques, renewable energy and cyber security of power networks.

Sunil Kumar Shukla

Sunil Kumar Shukla received the Ph.D. degree in electrical engineering from National Institute of Technology Raipur, India, in 2020. He is currently working as Assistant Professor in the Department of Information Technology at MITS Gwalior, India. He has more than six years of teaching experience. His research interests include optimization techniques, application of soft computing, and data mining techniques in power system protection.