改進粒子群優化神經網絡的變壓器故障診斷
喬維德
無錫開放大學，江蘇 無錫 214011
摘 要：在分析傳統誤差反向傳播(BP) 算法和標準粒子群優化(PSO) 算法的特征及其問題基礎上，提出一種改進粒子群優化(IPSO) 算法和改進BP(IBP) 算法，建立基于IPSO-IBP 混合算法的電力變壓器神經網絡故障診斷模型。通過85 組訓練樣本和16 組測試樣本的仿真對比分析，該方法能夠實現電力變壓器不同故障的有效診斷，提高了電力變壓器故障模式的識別能力及故障診斷準確率。
關鍵詞：電力變壓器；IPSO-IBP；故障診斷
中圖分類號：TM407 文獻標識碼：A 文章編號：1007-3175(2015)12-0024-04
Transformer Fault Diagnosis Based on Neural Network with Improved Particle Swarm Optimization
QIAO Wei-de
Wuxi Open University, Wuxi 214011, China
Abstract: Based on analysis characteristics and problems of traditional error back propagation (BP) algorithm and standard particle swarm optimization (PSO) algorithm, this paper proposed an improved particle swarm optimization (IPSO) algorithm and an improved BP (IBP) algorithm, and established a model of neural network for power transformer fault diagnosis based on IPSO-IBP hybrid algorithm. By simulation comparison and analysis of 85 groups training samples and 16 groups test samples, this method can realize the effective diagnosis for different power transformer faults and improve the recognition ability of power transformer fault mode with high accuracy
Key words: power transformer; improved particle swarm optimization-improved back propagation; fault diagnosis
參考文獻
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