Research on Frequency Indicators Evaluation of Disturbance Events Based on Improved Stacked Denoising Autoencoders

Since the massive integration of renewable generation reduced the system inertia level, frequency stability issues occurred in the events of sudden power imbalances. Due to the excessive computation burden of the time-domain simulation method to simulate credible disturbances, it is difficult to meet the rapid assessment requirement of frequency stability under uncertain operational scenarios and massive credible contingencies. In order to realize rapid assessment of multiple frequency metrics of the center of inertia (i.e., frequency nadir, maximum rate-of-change of frequency, quasi-steady state frequency), a deep learning method based on improved stacked denoising autoencoders (ISDAE) was applied to frequency stability assessment. First, the random forest algorithm was used to screen the data. The importance feature variables were used as the inputs to achieve data dimensionality reduction and reduce model complexity. Second, multiple denoising autoencoders were stacked, the pre-training, fine-tuning method was used to train network parameters, and the nonlinear complex mapping between input data and output data was established. In the pre-training process, the Dropout method was used to improve algorithm generalization ability and prevent over-fitting. Then, the root mean square back propagation (RMSprop) optimization was deployed to fine-tune network parameters so as to reduce the possibility of falling into local optimums. Finally, according to the established ISDAE network, online assessment of multi-frequency indicators was realized. Case studies on the modified IEEE RTS-79 system demonstrate the rapidity, high accuracy and well generalization ability of the proposed method. © 2019 Chin. Soc. for Elec. Eng.