Abstract In the operational process of power grid, transient stable samples and transient unstable samples are obviously imbalanced, and the cost of misclassifying stable samples is unequal to that of unstable samples. The existing transient stability assessment methods using data mining techniques are mostly based on shallow models, which pay little attention to the situation of misclassifying transient unstable samples. Moreover, the evaluation accuracy needs to be further improved. This paper proposes a power system transient stability assessment method integrating neighborhood rough reduction and deep forest. By using neighborhood rough sets at different granularity levels, several optimal feature subsets can be obtained to re-represent the original feature space. The cascade structure of deep forest can further strength the representation learning ability, which can reinforce the nonlinear mapping relation between features and transient stability state. The employment of weighted voting mechanism can make the learning process pay more attention to transient unstable samples. The experimental results on IEEE 10 machine 39 bus system show that the proposed method can effectively improve the evaluation accuracy and reduce the misclassification rate of transient unstable samples. Moreover, it also has a good performance on data sets with different scale and imbalance degrees, which is robust and applicable.
Li Bingyang,Xiao Jianmei,Wang Xihuai. Power System Transient Stability Assessment Based on Hybrid Neighborhood Rough Reduction and Deep Forest[J]. Transactions of China Electrotechnical Society, 2020, 35(15): 3245-3257.
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2020, Vol. 35 
