National Natural Science,
In order to further improve the accuracy of transient stability assessment (TSA), a TSA model based on the bi-directional long-short-term memory (Bi-LSTM) network is established according to the sequential characteristics of data in the power system transient process. This method uses the Bi-LSTM network to establish a non-linear mapping relationship between the underlying measurement data and the transient stability category of power system. The performance of Bi-LSTM network model is evaluated by accuracy, F1-measure (F1) index and false positive rate (FPR). On this basis, the t-distribution stochastic neighbor embedding (t-SNE) dimension-reduction method and k-nearest neighbor (KNN) classifier are used to further improve the accuracy of TSA. The example based on the New England 10-generator 39-bus system show that the proposed method has better performance than conventional machine learning models and some deep learning models. The assessment model is analyzed by visualization methods and network prediction scores. The result shows that the Bi-LSTM network has a strong ability to extract the characteristics of power system transient process, which is suitable for the TSA of power system. Further, the influence of the normalization mode and method of the underlying input data on the TSA model is studies. The result show that z-score normalization method is better than min-max normalization method, and the assessment performance of the model using the total dimension normalization mode is better.
SUN Lixia,BAI Jingtao,ZHOU Zhaoyu,et al.Transient Stability Assessment of Power System Based on Bi-directional Long-short-term Memory Network[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20191225003.