本文關鍵詞：基于復合神經網絡的電力系統(tǒng)暫態(tài)穩(wěn)定評估和裕度預測，由筆耕文化傳播整理發(fā)布。

基于復合神經網絡的電力系統(tǒng)暫態(tài)穩(wěn)定評估和裕度預測

Power System Transient Stability Assessment and Stability Margin Prediction Based on Compound Neural Network

[1] [2] [3]

YAO Dequan, JIA Hongjie, ZHAO Shuai （Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China）

智能電網教育部重點實驗室,天津大學,天津市300072

文章摘要：提出一種基于復合神經網絡的暫態(tài)穩(wěn)定評估與故障臨界切除時間（CCT）裕度預測新方法，它將概率神經網絡（PNN）和徑向基函數（RBF）網絡組合使用，充分利用兩者各自的優(yōu)點，以提高暫態(tài)穩(wěn)定評估能力和CCT裕度預測能力。該方法首先利用PNN進行暫態(tài)事故場景分類，分類時充分考慮了相鄰故障樣本類型重疊的影響；進一步采用RBF網絡對分類結果進行裕度預測；最后，通過自檢和校正以提高預測精度。利用NewEngland39節(jié)點系統(tǒng)，通過與反向傳播（BP）神經網絡、RBF神經網絡等方法的比較，證明了本文方法的優(yōu)越性。

Abstr：A method based on compound artificial neural network （ANN） to predict critical clearing time （CCT） margin and to do transient stability assessment is derived in this paper. It consists of probabilistic neural network （PNN） and radial basic function （RBF） network to integrate their advantages. PNN network is first used to classify sampling data and it can consider the overlapping influence in adjacent samples in the classification. Then RBF network is used to forecast the CCT margin based on the classification results. A self-checking procedure and an amending procedure are added to improve the prediction accuracy. New England 39 bus system is used to do the validation. Numerical studies reveal that the proposed method is better than the traditional BP and RBF methods not only on predictive accuracy but also on calculation speed. So the given method is helpful to power system transient stability assessment.

文章關鍵詞：

Keyword：:compound artificial neural network transient stability assessment critical clearing time margin prediction classification overlapping

課題項目：國家重點基礎研究發(fā)展計劃（973計劃）資助項目（2009CB219701）;國家自然科學基金資助項目（51277128）;國家電網公司大電網重大專項資助項目（SGCC-MPLG028-2012）.

  本文關鍵詞：基于復合神經網絡的電力系統(tǒng)暫態(tài)穩(wěn)定評估和裕度預測，，由筆耕文化傳播整理發(fā)布。