Transient Stability Assessment Considering Number and Location of PMUs Using CNN-LSTM

Electric power systems in the future will increasingly adopt wide area monitoring systems (WAMS) based on phasor measurement units (PMUs). Conventional methods are not able to utilize the data effectively and efficiently. This research focuses on utilizing PMU data measurement for transient stability detection using convolutional neural network and long short-term memory (CNN-LSTM) by considering the number and location of PMUs. This research aims to detect stable and unstable transient stability based on bus voltage magnitude and angle data. The CNN-LSTM architecture consists of several layers, including the time-distributed layer, two-dimensional convolution layer, batch normalization layer, dropout layer, max-pooling layer, flatten layer, LSTM layer, and dense layer. The case study used in this research is a modified IEEE 39 bus with a PV system. The proposed method produces an accuracy above 99.5% in normal and distorted data quality for all test scenarios. In addition, the results of this study show a trend that the more PMUs used, the better the detection performance, and PMU locations that pay attention to observability and dynamic stability have better detection performance.