本文選題：光伏組件　切入點(diǎn)：在線診斷　出處：《中國(guó)電機(jī)工程學(xué)報(bào)》2014年13期 　論文類型：期刊論文

【摘要】：分析光伏組件在短路、異常老化狀態(tài)下的輸出特性,提出一種基于開路電壓、短路電流、最大功率點(diǎn)電壓和電流四參數(shù)的光伏組件在線診斷短路及異常老化故障的方法。建立了故障類型因子K,通過(guò)比較K與標(biāo)準(zhǔn)值的差異判斷組件是否存在短路和異常老化故障。發(fā)生故障即可進(jìn)行在線故障程度分析和預(yù)警:短路故障時(shí),利用神經(jīng)網(wǎng)絡(luò)方法診斷組件中電池短路的塊數(shù);異常老化故障時(shí),利用填充因子值獲得組件老化程度。仿真及實(shí)驗(yàn)結(jié)果顯示該方法具有較高的準(zhǔn)確率,證明了方法的可行性和有效性。
[Abstract]:Analysis of PV modules in short circuit, the output characteristics of abnormal aging condition, put forward a kind of based on open circuit voltage, short-circuit current, PV module online diagnosis of short circuit voltage and current maximum power point four parameters and method of abnormal aging fault. The fault type of factor K, the difference judgment component through comparison with the standard value is K short circuit fault and abnormal aging. Failure can be carried out online fault analysis and early warning: when the fault, the number of battery short circuit method in neural network diagnostic module; abnormal aging fault, the fill factor value for component aging. Simulation and experimental results show that the method has high accuracy, proves the method the feasibility and effectiveness.

【作者單位】： 上海市電站自動(dòng)化技術(shù)重點(diǎn)實(shí)驗(yàn)室(上海大學(xué));
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(51107079)~~
【分類號(hào)】：TM914

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陳昌松;段善旭;殷進(jìn)軍;;基于神經(jīng)網(wǎng)絡(luò)的光伏陣列發(fā)電預(yù)測(cè)模型的設(shè)計(jì)[J];電工技術(shù)學(xué)報(bào);2009年09期

2 丁金磊;程曉舫;翟載騰;查s，

本文編號(hào)：1640554