本文選題：光伏發(fā)電 + 神經(jīng)網(wǎng)絡(luò)��； 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：隨著化石燃料導(dǎo)致的環(huán)境問題與能源緊缺問題的出現(xiàn),太陽能光伏發(fā)電系統(tǒng)在世界各國的能源結(jié)構(gòu)的轉(zhuǎn)變中已成為重要部分。但是,分布式光伏發(fā)電系統(tǒng)對于外界環(huán)境的依賴性較高,造成了其輸出功率隨著環(huán)境因素的變化而變化,其接入主電網(wǎng)后將對電網(wǎng)造成不確定性影響,且分布式光伏發(fā)電系統(tǒng)的滲透率越高,電力系統(tǒng)的復(fù)雜性與風(fēng)險也越大。因此,分布式光伏發(fā)電系統(tǒng)的功率預(yù)測與并網(wǎng)后的配網(wǎng)潮流計算,有助于調(diào)度部門提前做好調(diào)度計劃和風(fēng)險規(guī)避,以提高電力系統(tǒng)的安全性。本文的具體工作如下:(1)將太陽輻射強度、環(huán)境溫度、天氣類別作為光伏系統(tǒng)輸出不穩(wěn)定的主要影響因素,分別基于動量自適應(yīng)學(xué)習(xí)速率法的BP神經(jīng)網(wǎng)絡(luò)、RBF神經(jīng)網(wǎng)絡(luò)以及GRNN神經(jīng)網(wǎng)絡(luò)建立了光伏發(fā)電系統(tǒng)輸出功率的短期預(yù)測模型。(2)提出了一種基于決策論極大極小準(zhǔn)則的組合預(yù)測模型,該組合預(yù)測模型由BP神經(jīng)網(wǎng)絡(luò)、RBF神經(jīng)網(wǎng)絡(luò)、GRNN神經(jīng)網(wǎng)絡(luò)組成,對光伏發(fā)電系統(tǒng)輸出功率進(jìn)行預(yù)測,探索提高輸出功率的預(yù)測精度。(3)提出了一種改進(jìn)型的前推回代法,設(shè)計不同的接入方案,利用光伏系統(tǒng)輸出功率的預(yù)測值進(jìn)行仿真實驗,通過前推回代方法計算了含分布式的配網(wǎng)潮流,研究了分布式光伏發(fā)電系統(tǒng)接入電網(wǎng)后對配電網(wǎng)絡(luò)的影響。研究結(jié)果表明:將太陽輻射強度、環(huán)境溫度、天氣類別作為影響光伏發(fā)電系統(tǒng)輸出功率的主要因素能夠較好地適用于人工神經(jīng)網(wǎng)絡(luò)建立的預(yù)測模型,在三種單一預(yù)測模型中,GRNN神經(jīng)網(wǎng)絡(luò)的輸出功率預(yù)測精度較高;而基于決策論極大極小準(zhǔn)則的組合預(yù)測模型的能夠進(jìn)一步提高預(yù)測精度;分布式光伏發(fā)電系統(tǒng)接入電網(wǎng)后對配電網(wǎng)絡(luò)的影響為:分布式電源的接入提高了饋線電壓的分布,且都能在一定程度上減少系統(tǒng)的網(wǎng)損。
[Abstract]:With the emergence of environmental problems and energy shortages caused by fossil fuels, solar photovoltaic power generation systems have become an important part in the transformation of energy structure in the world. However, the distributed photovoltaic power generation system is highly dependent on the external environment, which causes its output power to change with the change of environmental factors. And the higher the permeability of distributed photovoltaic generation system, the greater the complexity and risk of power system. Therefore, the power prediction of the distributed photovoltaic power generation system and the distribution power flow calculation after the grid connection can help the dispatching department to prepare the dispatch plan and avoid the risk in advance, so as to improve the security of the power system. The main work of this paper is as follows: (1) the solar radiation intensity, ambient temperature and weather type are the main factors that affect the output instability of photovoltaic system. BP neural network based on momentum adaptive learning rate method and GRNN neural network are used to establish short-term prediction models of output power of photovoltaic power generation system. (2) A combined prediction model based on decision theory minimax criterion is proposed. The combined prediction model is composed of BP neural network and RBF neural network and GRNN neural network. The output power of photovoltaic power generation system is predicted, and the prediction accuracy of output power is explored. (3) an improved forward push-back method is proposed. Different access schemes are designed, and the simulation experiments are carried out by using the predicted output power of photovoltaic system. The distribution power flow with distributed distribution network is calculated by the method of forward push back substitution. The influence of distributed photovoltaic system on distribution network is studied. The results show that solar radiation intensity, ambient temperature and weather type are the main factors affecting the output power of photovoltaic power generation system, which can be applied to the prediction model established by artificial neural network. Among the three single prediction models, the output power prediction accuracy of GRNN neural network is higher, while the combined prediction model based on the decision theory minimax criterion can further improve the prediction accuracy. The influence of distributed photovoltaic system on distribution network is as follows: the distribution of feeder voltage is improved by the access of distributed generation, and the network loss can be reduced to a certain extent.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM615

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