Transient Stability Assessment of Electric Power System based on Voltage Phasor and CNN-LSTM

Increasing interconnections to fulfill the rising electrical demand and wide application of power electronics devices has led to an increase in transient stability criterion. This leads to the requirement of high ended computational features for Transient Stability Assessment (TSA). On failing to assess of transient stability may cause severe failures as blackouts and some other limitations in the power system. The deployment of different sensors, PMUs, and other measuring devices has increased for its advanced features and performance capabilities with time synchronism. The conventional methods of TSA are unable to process huge data from these devices. Therefore, requires deep learning methods for data mining and feature extraction for transient stability assessment. This paper proposes the hybrid CNNLSTM model for feature extraction of voltage phasor for transient stability assessment. CNN-LSTM hybrid model improves accuracy mapping the non-linear relationship between input and output data. This model possesses the features of both CNN and LSTM, therefore, improves the TSA ability with enhanced performance. The proposed hybrid model is applied to the IEEE 39-bus New England Power System to authenticate its accuracy. And also, a comparison between the proposed hybrid model and the other one is included demonstrating the efficacy of the proposed model.