Open Access

ARTICLE

Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

Pandia Rajan Jeyaraj¹, Aravind Chellachi Kathiresan¹, Siva Prakash Asokan¹, Edward Rajan Samuel Nadar¹, Hegazy Rezk^2,3,*, Thanikanti Sudhakar Babu⁴

1 Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu, India
2 College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser, 11991, Saudi Arabia
3 Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61517, Egypt
4 Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, 500075, India

* Corresponding Author: Hegazy Rezk. Email:

Computers, Materials & Continua 2021, 68(1), 553-567. https://doi.org/10.32604/cmc.2021.015128

Received 07 November 2020; Accepted 05 February 2021; Issue published 22 March 2021

Abstract

The power transfer capability of the smart transmission grid-connected networks needs to be reduced by inter-area oscillations. Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks. This fact is more noticeable in smart grid-connected systems. The smart grid infrastructure has more renewable energy resources installed for its operation. To overcome this problem, a deep learning wide-area controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes. The proposed Deep Wide Area Controller (DWAC) uses the Deep Belief Network (DBN). The network weights are updated based on real-time data from Phasor measurement units. Resilience assessment based on failure probability, financial impact, and time-series data in grid failure management determine the norm H₂. To demonstrate the effectiveness of the proposed framework, a time-domain simulation case study based on the IEEE-39 bus system was performed. For a one-channel attack on the test system, the resiliency index increased to 0.962, and inter-area damping ξ was reduced to 0.005. The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well. Results also offer robust management of power system resilience and timely control of the operating conditions.

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Citations