Neural Network Model Based on The Control for Transient Stability and Low-Frequency Oscillation in Power System Operation

Subject Areas : H.6.2.4. Neural Nets

Amir Bagheran Sharbaf ¹ , Ali Asghar Shojaei ²

1 - Department of Electrical and Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran
2 - Department of Electrical and Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

Received: 2020-05-12 Accepted : 2020-12-20 Published : 2020-05-01

Keywords: STATCOM, power system stability, Recurrent neural network, coordinated control strategy, MB-PSS, MB_RNNPSS,

Abstract :

Flexible AC Transmission System (FACTs) devices are used in power transmission networks to increase maximum power transmission and stability. On the one hand, they help to damp low-frequency oscillations for both local and internal areas. But on the other, the design of these devices with uncoordinated Power System Stability (PSS) may degrade the performance of the power system. In addition, the power systems are vast, complex, and nonlinear. Linear control strategies do not have satisfactory performance for these systems, especially when some disturbances occur. In this study, a nonlinear Coordinated Control Strategy based on Recurrent Neural Network (CCSRNN) is designed to control PSS and Static Synchronous Compensators (STATCOM) coordinates for a standard multi-machine power system and also using the Multi-Bound Power System Stability (MB-PSS) and Multi-Bound Recurrent Neural Network MB-RNN PSS and compare the result of each one for having better stability of the system. The results simulation proves that the controller leads to transient stability and low-frequency oscillation.