改進(jìn)粒子群優(yōu)化神經(jīng)網(wǎng)絡(luò)的變壓器故障診斷
喬維德
無(wú)錫開(kāi)放大學(xué)，江蘇 無(wú)錫 214011
摘 要：在分析傳統(tǒng)誤差反向傳播(BP) 算法和標(biāo)準(zhǔn)粒子群優(yōu)化(PSO) 算法的特征及其問(wèn)題基礎(chǔ)上，提出一種改進(jìn)粒子群優(yōu)化(IPSO) 算法和改進(jìn)BP(IBP) 算法，建立基于IPSO-IBP 混合算法的電力變壓器神經(jīng)網(wǎng)絡(luò)故障診斷模型。通過(guò)85 組訓(xùn)練樣本和16 組測(cè)試樣本的仿真對(duì)比分析，該方法能夠?qū)崿F(xiàn)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(shí)別能力及故障診斷準(zhǔn)確率。
關(guān)鍵詞：電力變壓器；IPSO-IBP；故障診斷
中圖分類(lèi)號(hào)：TM407 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：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(xiàn)
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[2] 程加堂，熊偉，徐紹坤，艾莉. 基于改進(jìn)粒子群優(yōu)化神經(jīng)網(wǎng)絡(luò)的電力變壓器故障診斷[J]. 高壓電器，2012，48(2)：42-45.
[3] 付寶英，王啟志. 自適應(yīng)粒子群優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的變壓器故障診斷[J]. 華僑大學(xué)學(xué)報(bào)：自然科學(xué)版，2013，34(3)：262-266.
