TY  - EJOU
AU  - Jeyaraj, Pandia Rajan 
AU  - Kathiresan, Aravind Chellachi 
AU  - Asokan, Siva Prakash 
AU  - Nadar, Edward Rajan Samuel 
AU  - Rezk, Hegazy 
AU  - Babu, Thanikanti Sudhakar 

TI  - Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm
T2  - Computers, Materials \& Continua

PY  - 2021
VL  - 68
IS  - 1
SN  - 1546-2226

AB  - 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 H<sub>2</sub>. 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.
KW  - Neural network; deep learning algorithm; low-frequency  oscillation; resiliency assessment; smart grid; wide-area control

DO  - 10.32604/cmc.2021.015128