Imbalance Correction Based on the Ratio of Loss Function Values for Transient Stability Assessment

Accurate and fast transient stability assessment is helpful for grid operators to take effective emergency control actions after faults. The gradual deployment of the wide-area measurement system provides a basis for introducing machine learning into transient stability assessment (TSA). However, the application of the machine learning model is restricted by the imbalance of samples in actual systems. In this paper, a time-adaptive assessment model with imbalance correction based on the ratio of loss function values is built to realize accurate and fast TSA. First, a long short-term memory (LSTM)-based model whose optimization goal is to reduce the prediction loss at a fixed time step is established. Several LSTM-based models with different decision time values are integrated as a multiple LSTM (MLSTM) TSA model. It is found that the effect of imbalanced samples on model parameters can be quantified by the loss function values. Then, the ratio of loss function values of two classes is obtained by pre-training, by which the imbalance degree of data can be quantified. The correction coefficient is determined and used to retrain LSTMs to solve the evaluation tendency problem. Simulation results in an IEEE 39-bus system and an actual power system show the excellent performance of the proposed imbalance correction scheme and evaluation scheme. Compared with traditional methods, imbalance correction can be achieved with better results.