Intelligent wide area monitoring of power system oscillatory dynamics in real time

The oscillatory stability of the system is becoming a vital problem to be taken into consideration in modern interconnected power systems operation. The information about poorly damped modes and their source should be known to the operator in real time to apply any control actions. But the conventional methods are usually time consuming and offline. In this paper, an intelligent Wide Area Measurement System (WAMS) based method employing Phasor Measurement Unit and Artificial Neural Network (PMU-ANN) is proposed, capable of identifying poorly damped low frequency oscillations and the responsible critical generator in real time under varying system operating conditions. The optimal PMU Placement is obtained using Integer Linear Programming (ILP). The input to ANN is the real time data obtained from PMU and output is the online mode related information like participation factor, damping of modes, and the most critical generator. The effectiveness of the proposed approach is investigated on IEEE 39-bus test system. The test results obtained for unseen operating conditions proves that the proposed method effectively monitors the power system oscillatory dynamics and identify the poorly damped modes and their source in real time with very less computational burden.