Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

• Published in: Computers, materials & continua Vol. 68; no. 1; pp. 553 - 567
• Main Authors: Rajan Jeyaraj, Pandia, Chellachi Kathiresan, Aravind, Prakash Asokan, Siva, Rajan Samuel Nadar, Edward, Rezk, Hegazy, Sudhakar Babu, Thanikanti
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 01.01.2021
• Subjects: Algorithms; Belief networks; Control systems; Controllers; Damping; Deep learning; Electric power grids; Electricity distribution; Energy sources; Machine learning; Measuring instruments; Oscillations; Phasors; Power transfer; Real time; Resilience; Smart grid
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2021.015128

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.