Wide Area Measurement Based Online Monitoring and Event Detection Using Convolutional Neural Networks

Online monitoring of the power system is a vital application for enhancing the situational awareness capabilities of the system. Rapid integration of phasor measurement units in the network enables transmission system operators to analyze the events in real time due to their high reporting rates. Real-time detection and classification of the fault related events as no-fault, fault-incidence, fault-on and post-fault stage with no further disturbance, is an important requirement in order to decide on the control actions to protect the system from any instability. In this paper, a sliding window based continuous online monitoring method of the power system using convolutional neural networks is proposed. The effectiveness of the proposed method is validated on the 127-bus Western Systems Coordinating Council test system.