Deep Learning Based Effective Technique for Smart Grid Contingency Analysis Using RNN with LSTM

Abstract

Smart grids play a crucial role in modernizing power systems, yet their susceptibility to disruptions necessitates effective contingency analysis for reliable operation. Traditional algorithms lack prediction accuracy for mitigation. To address this, a proposed method employs LSTM networks within a DL RNN framework, utilizing a logical collection of pertinent data—historical smart grid operation data, sensor measurements, weather conditions, and contingency events. This data undergoes preprocessing to train an LSTM-based DL RNN model capable of capturing temporal dependencies and complex dynamics within the smart grid system. The trained model yields a substantial improvement in prediction accuracy, increasing from 88% to 93%, while decreasing false positive and false negative rates from 3.2% to 1.25% with optimal time detection. Leveraging deep learning and recurrent neural networks, this approach offers accurate predictions, proactive decision support, and efficient restoration strategies, ultimately elevating the resilience and reliability of smart grid systems. The solution’s dataset encompasses a comprehensive array of relevant information. The neural network architecture integrates DL RNN with LSTM networks, showcasing its efficacy in enhancing prediction accuracy for smart grid contingency analysis. Evaluation metrics such as RMSE and MAPE providing a reliable foundation for proactive decision-making and fortifying the smart grid’s resilience against various contingencies.

Keywords:

• Smart grids (SG)
• contingency analysis
• rehabilitation
• reliability
• long short-term memory (LSTM,)
• deep learning
• recurrent neural networks (RNN)

Authorship Contributions

All authors are contributed equally to this work.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Disclosure Statement

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

Ethics Approval and Consent to Participate

No participation of humans takes place in this implementation process.

Human and Animal Rights

No violation of Human and Animal Rights is involved.

Additional information

Notes on contributors

C. Udhaya Shankar

C. Udhaya Shankar Associate Professor, SNS College of Engineering, Coimbatore, India completed his Ph.D. degree at Anna University, Chennai, India in October 2015. He has more than 20 years of teaching experience. His main research interests are Solar Photo Voltaic, Wind Energy Conversion systems, optimization techniques and their application to Power Electronics, Power Quality, FACTS devices and their control. He has published more than 20 research papers in reputed journals to his credit.

Sai Ram Inkollu

Sai Ram Inkollu is a dynamic individual with a strong academic background in Electrical Engineering. He obtained his Bachelor’s degree in Electrical and Electronics engineering from Jawaharlal Nehru Technological University, Hyderabad, Andhra Pradesh, India in the year 1995. After that he obtained his Master’s degree in Advanced power systems from Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh in 2005. In 2017 he was awarded Ph.D. with a title of “Allocation of FACT devices in Power systems using Novel algorithms”, from Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh. He got 24 years teaching experience and currently working as Professor in the Department of Electrical and Electronics engineering and also Vice principal of R.K. College of Engineering, Kethanakonda, Vijayawada, Andhra Pradesh, India. He is passionate about research, believes in learning, striving for enhancing skills. Currently, his research area includes Power systems operation and control, FACT devices, Renewable energy systems and Smart Grids. His research findings have been published in many SCIE/SCOPUS/Google-indexed international journals. He has attended and presented many research articles at international conferences. He is a Member of Institute of Engineers, India and Life Member of Indian Society of Technical Education (M.I.S.T.E), India. He has been as selected as best mentor for online certification course under NPTEL. He is also a part of funded projects.

N. Nithyadevi

N. Nithyadevi Assistant Professor, Department of Applied Mathematics, Bharathiar University, has 15 years of teaching experience and has guided 8 research associates in the field of Computational Fluid Dynamics. She also has 20 publications in internationally reputed journals with the SCI index to her credit. Her research interest includes numerical and theoretical fluid dynamics and control systems. She is interested in more foreign collaborations and in conducting experimental studies in the above research areas.