Convolution Neural Network-Based Load Model Parameter Selection Considering Short-Term Voltage Stability

The recently proposed ambient signal-based load modeling approach offers an important and effective idea to study the time-varying and distributed characteristics of power loads. Meanwhile, it also brings new problems. Since the load model parameters of power loads can be obtained in real-time for each load bus, the numerous identified parameters make parameter application difficult. In order to obtain the parameters suitable for off-line applications, load model parameter selection (LMPS) is first introduced in this paper. Meanwhile, the convolution neural network (CNN) is adopted to achieve the selection purpose from the perspective of short-term voltage stability. To begin with, the field phasor measurement unit (PMU) data from China Southern Power Grid are obtained for load model parameter identification, and the identification results of different substations during different times indicate the necessity of LMPS. Meanwhile, the simulation case of Guangdong Power Grid shows the process of LMPS, and the results from the CNN-based LMPS confirm its effectiveness.