Vol.68, No.1, 2021, pp.553-567, doi:10.32604/cmc.2021.015128
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 Rezk2,3,*, 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:
Received 07 November 2020; Accepted 05 February 2021; Issue published 22 March 2021
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 . 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 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.
Neural network; deep learning algorithm; low-frequency oscillation; resiliency assessment; smart grid; wide-area control
Cite This Article
P. R. Jeyaraj, A. C. Kathiresan, S. P. Asokan, E. Rajan, H. Rezk et al., "Power system resiliency and wide area control employing deep learning algorithm," Computers, Materials & Continua, vol. 68, no.1, pp. 553–567, 2021.
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