【摘要】：針對(duì)光伏發(fā)電系統(tǒng)在不同天氣狀況下發(fā)電功率預(yù)測(cè)精度不高的問(wèn)題,在分析傳統(tǒng)方法的基礎(chǔ)上,提出一種無(wú)跡卡爾曼濾波神經(jīng)網(wǎng)絡(luò)光伏發(fā)電預(yù)測(cè)方法。該方法利用無(wú)跡卡爾曼濾波實(shí)時(shí)更新神經(jīng)網(wǎng)絡(luò)模型的權(quán)重,以直流電壓和電流作為系統(tǒng)的輸入,以有功功率和無(wú)功功率作為系統(tǒng)的輸出,分別建立兩個(gè)獨(dú)立的雙輸入單輸出功率預(yù)測(cè)模型。實(shí)驗(yàn)結(jié)果表明:所提出的方法對(duì)有功功率和無(wú)功功率的預(yù)測(cè)精度分別為97.3%和94.2%,并且對(duì)天氣具有良好的魯棒性。
[Abstract]:In order to solve the problem that the prediction accuracy of photovoltaic power generation system under different weather conditions is not high, based on the analysis of the traditional method, an unscented Kalman filter neural network photovoltaic power generation prediction method is proposed. In this method, the unscented Kalman filter is used to update the weight of the neural network model in real time, DC voltage and current are used as the inputs of the system, and the active power and reactive power are taken as the outputs of the system. Two independent double input and single output power prediction models are established respectively. The experimental results show that the prediction accuracy of the proposed method for active power and reactive power is 97.3% and 94.2% respectively, and it is robust to weather.
【作者單位】： 國(guó)網(wǎng)青海省電力公司電力科學(xué)研究院(青海省光伏發(fā)電并網(wǎng)技術(shù)重點(diǎn)實(shí)驗(yàn)室);重慶大學(xué)輸配電裝備及系統(tǒng)安全與新技術(shù)國(guó)家重點(diǎn)實(shí)驗(yàn)室;
【基金】：青海省光伏發(fā)電并網(wǎng)技術(shù)重點(diǎn)實(shí)驗(yàn)室項(xiàng)目(2014-Z-Y34A)~~
【分類(lèi)號(hào)】：TM615;TP183

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