LSTM based deep neural network model of power system stabilizer for power oscillation damping in multimachine system

Power system oscillation is an unavoidable threat to the stability of an interconnected modern power system. The reliable operation of a modern power system is widely related to the dampening of electromechanical low-frequency oscillations (ELFOs). These ELFOs must be dampened appropriately to maintain the stability and reliability of the system. However, it is relatively difficult to resolve the problem of ELFOs completely with traditional power system stabilizers (PSSs). Consequently, research should be directed towards the development of efficient damping controllers, or PSSs, for power oscillation damping. Motivated from the aforementioned fact, this article presents the design of a proportional integral derivative power system stabilizer (PID-PSS) via a long short-term memory neural network (LSTM) based deep neural approach for damping power system oscillations in interconnected power systems. LSTM is used to train the parameters of PID-PSS. To evaluate the performance of the proposed LSTM based PID-PSS, diverse test cases under different operating conditions are examined. Further, the performance of the proposed LSTM based PID-PSS is compared with traditional PSSs through time-domain simulations. The test cases reveal the desired efficiency achieved by the proposed LSTM based PID-PSS under diverse loading conditions.