Development of PMU-Based Transient Stability Detection Methods Using CNN-LSTM Considering Time Series Data Measurement

Abstract

The development of electric power systems has become more complex. Consequently, electric power systems are operating closer to their limits and are more susceptible to instability when a disturbance occurs. Transient stability problems are especially prevalent. In addition, the identification of transient stability is difficult to achieve in real time using the current measurement data. This research focuses on developing a convolutional neural network—long short-term memory (CNN-LSTM) model using historical data events to detect transient stability considering time-series measurement data. The model was developed by considering noise, delay, and loss in measurement data, line outage and variable renewable energy (VRE) integration scenarios. The model requires PMU measurements to provide high sampling rate time-series information. In addition, the effects of different numbers of PMUs were also simulated. The CNN-LSTM method was trained using a synthetic dataset produced using the DigSILENT PowerFactory simulation to represent the PMU measurement data. The IEEE 39 bus test system was used to simulate the model under different loading conditions. On the basis of the research results, the proposed CNN-LSTM model is able to detect stable and unstable conditions of transient stability only from the magnitude and angle of the bus voltage, without considering system parameter information on the network. The accuracy of transient stability detection reached above 99% in all scenarios. The CNN-LSTM method also required less computation time compared to CNN and conventional LSTM with the average computation times of 190.4, 4001.8 and 229.8 s, respectively.