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Refined Transient Stability Evaluation for Power System Based on Ensemble Deep Belief Network

School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China

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This work is supported by National Key R&D Program of China (No. 2018YFB0904500)and National Natural Science Foundation of China (No. 51577009).

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    In order to further improve the prediction accuracy of transient stability for power system and give more refined evaluation results, the deep learning is combined with the transient stability of power system. A refined evaluation model of transient stability for power system based on ensemble deep belief network (DBN) with different structures is proposed based on the characteristics of the generator power angle "trajectory cluster" after fault removal. The base classifier DBN of the model can effectively utilize the feature extraction ability of the deep architecture and fully exploit the nonlinear mapping relationship between the input features and the evaluation results of transient stability. Experimental results on the New England 10-machine 39-node system show that this method is not only superior to the shallow learning framework, but also superior to the partial deep learning model. In addition, the ensemble DBN algorithm not only has higher prediction accuracy, but also can effectively evaluate the stability margin and instability level of the system. It shows strong robustness when some information of phasor measurement unit (PMU) is missing and contains noise.

    表 1 混淆矩阵Table 1 Confusion matrix
    表 3 不同基分类器DBN的结构Table 3 Structure of different base classifier DBNs
    表 8 Table 8
    表 9 Table 9
    表 4 不同分类器性能比较Table 4 Performance comparison of different classifiers
    表 7 Table 7
    表 5 缺失多台发电机信息时的集合组成Table 5 Set composition in absence of multiple generator information
    表 6 暂态稳定多级指标划分Table 6 Multilevel index division of transient stability
    表 2 样本集合的分类Table 2 Classification of sample sets
    图1 DBN的网络结构Fig.1 Network structure of DBN
    图2 集成DBN预测模型Fig.2 Prediction model of ensemble DBN
    图3 集成DBN暂态稳定评估流程Fig.3 Evaluation process of transient stability for ensemble DBN
    图4 发电机信息缺失时模型的预测结果Fig.4 Prediction results of model when generator information is missing
    图5 含噪声时浅层学习性能比较Fig.5 Comparison of shallow learning performance with noise
    图6 测试集中失稳样本的失稳时间分布直方图Fig.6 Histograms of instable time distribution of instable samples in test set
    图 特征之间相关性热力图Fig. Correlation thermal maps between features
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LI Baoqin,WU Junyong,SHAO Meiyang,et al.Refined Transient Stability Evaluation for Power System Based on Ensemble Deep Belief Network[J].Automation of Electric Power Systems,2020,44(6):17-26.DOI:10.7500/AEPS20190528009

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  • Received:May 28,2019
  • Revised:August 26,2019
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  • Online: March 21,2020
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