Suzhou Electric Appliance Research Institute
期刊號(hào): CN32-1800/TM| ISSN1007-3175

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基于IBQPSO的含DG配電網(wǎng)故障區(qū)段定位

來(lái)源:電工電氣發(fā)布時(shí)間:2020-05-14 14:14 瀏覽次數(shù):994
基于IBQPSO的含DG配電網(wǎng)故障區(qū)段定位
 
趙敏1,田書(shū)2
(1 鶴壁汽車(chē)工程職業(yè)學(xué)院 電子工程系,河南 鶴壁 458030;2 河南理工大學(xué) 電氣工程與自動(dòng)化學(xué)院,河南 焦作 454000)
 
    摘 要:為適應(yīng)含分布式電源(DG)智能配網(wǎng)的建設(shè)和發(fā)展對(duì)其故障區(qū)段定位高效性和準(zhǔn)確性的要求,構(gòu)建了可自適應(yīng)多DG 投切的開(kāi)關(guān)函數(shù)模型,并結(jié)合網(wǎng)絡(luò)分區(qū)處理解決方案,提出一種基于改進(jìn)二進(jìn)制量子粒子群算法(IBQPSO)的含DG配電網(wǎng)故障區(qū)段定位方法,有效克服了二進(jìn)制粒子群算法(BPSO)固有的全局和局部搜索能力不平衡問(wèn)題。通過(guò)含DG的IEEE 33節(jié)點(diǎn)系統(tǒng)仿真分析,驗(yàn)證了該方法的容錯(cuò)性、快速性和準(zhǔn)確性。
    關(guān)鍵詞:含DG配電網(wǎng);故障區(qū)段定位;開(kāi)關(guān)函數(shù);分區(qū)處理;改進(jìn)二進(jìn)制量子粒子群算法(IBQPSO)
    中圖分類(lèi)號(hào):TM711     文獻(xiàn)標(biāo)識(shí)碼:A      文章編號(hào):1007-3175(2020)05-0012-05
 
Fault Section Location for Distribution Network Containing DG Based on IBQPSO
 
ZHAO Min1, TIAN Shu2
(1 Department of Electronic Engineering, Hebi Automotive Engineering Professional College, Hebi 458030, China;
2 School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China)
 
    Abstract: To meet the requirements of the efficiency and accuracy for fault section location introduced by the construction and development of smart distribution network containing DG, a switch function model with adaptive multi-DG switching is constructed.Combined with the solution of network partition processing a fault section location for distribution network containing DG based on improved binary quantum particle swarm optimization (IBQPSO) , which can effectively overcome the problem of global and local search capability imbalance in binary particle swarm optimization (BPSO) is proposed.The fault tolerance, rapidity and accuracy of this method are verified by simulation analysis of IEEE 33 node system containing DG.
    Key words: distribution network containing DG; fault section location; switching function; regional processing; improved binary quantum particle swarm optimization (IBQPSO)
 
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