Adaptive Support Vector Machine Estimation for Total Transfer Capability of Wind Power Exporting Corridors
Qiu Gao1, Liu Junyong1, Liu Youbo1, Mu Gang2, Liu Tingjian1
1. School of Electrical Engineering and Information Sichuan University Chengdu 610065 China; 2. School of Electrical Engineering Northeast Electric Power University Jilin 132012 China
Abstract：Total transfer capability of a transmission corridor technically changes fast with operation conditions. The conventional worst scenario-based methods hardly compute TTC efficiently that cannot meet online analysis requirement for wind power integration. In this paper, a SVM regression technique was presented to enable estimating TTC online. First, temporal wind power and load were clustered to determine representative scenario, which were used to generate samples by using repeated power flow with transient stability constraints. Second, through maximal information coefficient verification and unsupervised feature selection based on nonparametric mutual information, the most effective attributes were selected. Finally, SVM based on genetic algorithm-grid search was applied to establish regressed fitting model for TTC. Two cases were studied to validate the presented technique. The results indicate that the approach is able to fast and accurately estimate TTC of wind power exporting power systems with powerful fitting and generalization.
邱高, 刘俊勇, 刘友波, 穆钢, 刘挺坚. 风电外送通道极限传输能力的自适应向量机估计[J]. 电工技术学报, 2018, 33(14): 3342-3352.
Qiu Gao, Liu Junyong, Liu Youbo, Mu Gang, Liu Tingjian. Adaptive Support Vector Machine Estimation for Total Transfer Capability of Wind Power Exporting Corridors. Transactions of China Electrotechnical Society, 2018, 33(14): 3342-3352.
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2018, Vol. 33 
