Rule extraction for voltage security margin estimation

doi:10.1109/41.807998

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/46223

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題名:	Rule extraction for voltage security margin estimation
作者:	蘇木春;Su, Mu-chun;Liu, Chih-wen;Chang, Chen-sung
貢獻者:	淡江大學電機工程學系
關鍵詞:	Fuzzy systems;neural networks;phasor measurement unit;power systems;voltage security
日期:	1999-12
上傳時間:	2010-03-26 21:20:53 (UTC+8)
出版者:	New York: Institute of Electrical and Electronics Engineers (IEEE)
摘要:	Research efforts have been devoted to estimating voltage security margins which show how close the current operating point of a power system is to a voltage collapse point as assessment of voltage security. One main disadvantage of these techniques is that they require large computations, therefore, they are not efficient for on-line use in power control centers. In this paper, we propose a technique based on hyperrectangular composite neural networks (HRCNNs) and fuzzy hyperrectangular composite neural networks (FHRCNNs) for voltage security margin estimation. The technique provides us with much faster assessments of voltage security than conventional techniques. The two classes of HRCNNs and FHRCNNs integrate the paradigm of neural networks with the rule-based approach, rendering them more useful than either. The values of the network parameters, after sufficient training, can be utilized to generate crisp or fuzzy rules on the basis of preselected meaningful features. Extracted rules are helpful to explain the whole assessment procedure so the assessments are more capable of being trusted. In addition, the power system operators or corresponding experts can delete unimportant features or add some additional features to improve the performance and computational efficiency based on the evaluation of the extracted rules. The proposed technique was tested on 3000 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficiency
關聯:	IEEE Transactions on Industrial Electronics 46(6), pp.1114-1122
DOI:	10.1109/41.807998
顯示於類別:	[電機工程學系暨研究所] 期刊論文

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