风电外送通道极限传输能力的自适应向量机估计

doi:10.19595/j.cnki.1000-6753.tces.170766

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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.

Key words： Wind power total transfer capability rule extraction support vector machine

Received: 01 June 2017 Published: 27 July 2018

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TM732

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	Qiu Gao
	Liu Junyong
	Liu Youbo
	Mu Gang
	Liu Tingjian

Cite this article:

Qiu Gao,Liu Junyong,Liu Youbo等. Adaptive Support Vector Machine Estimation for Total Transfer Capability of Wind Power Exporting Corridors[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3342-3352.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.170766 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I14/3342

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