本文選題：光伏發(fā)電功率 + 粒子群優(yōu)化算法��； 參考：《沈陽(yáng)工程學(xué)院》2017年碩士論文

【摘要】：隨著人類對(duì)能源需求的增加,太陽(yáng)能的利用受到了越來越廣泛的關(guān)注,光伏發(fā)電因其無(wú)污染、可再生的特點(diǎn),作為一種可持續(xù)的能源,近年來受到了國(guó)內(nèi)外學(xué)者們的廣泛關(guān)注。但光伏發(fā)電具有間歇性、隨機(jī)性,光伏發(fā)電功率的波動(dòng)會(huì)影響電網(wǎng)的穩(wěn)定性及電能質(zhì)量,因此光伏發(fā)電預(yù)測(cè)技術(shù)是一項(xiàng)亟需深入研究的工作,準(zhǔn)確預(yù)測(cè)光伏發(fā)電功率對(duì)電網(wǎng)的優(yōu)化調(diào)度、電能質(zhì)量均有較好影響。本文首先通過華能營(yíng)口熱電有限責(zé)任公司光伏觀測(cè)站數(shù)據(jù)分析了光伏發(fā)電功率的波動(dòng)特性及其影響因素,通過分析表明,季節(jié)、天氣類型對(duì)光伏發(fā)電功率的波動(dòng)特性具有重要影響�；趥鹘y(tǒng)粒子群算法存在粒子“早熟”的問題,本文通過對(duì)粒子群算法改進(jìn),刪除了慣性權(quán)重并增加了隨機(jī)因子,提高了粒子群算法的全局收斂性。用粒子群算法優(yōu)化了神經(jīng)網(wǎng)絡(luò)光伏發(fā)電預(yù)測(cè)模型,通過預(yù)測(cè)數(shù)據(jù)與實(shí)測(cè)數(shù)據(jù)的比較,驗(yàn)證了本文所提出方法的有效性�；谝陨侠碚撗芯�,本文設(shè)計(jì)了光伏發(fā)電功率預(yù)測(cè)系統(tǒng),可以將本文所提出的算法應(yīng)用到實(shí)際中。本文設(shè)計(jì)的光伏發(fā)電功率預(yù)測(cè)系統(tǒng),按照電力二次系統(tǒng)安全防護(hù)規(guī)定,配備了反向物理隔離裝置以保障數(shù)據(jù)傳輸?shù)陌踩�。此�?還設(shè)計(jì)了該系統(tǒng)的軟件結(jié)構(gòu)與硬件結(jié)構(gòu),并對(duì)該系統(tǒng)的功能加以展示。通過本文的分析可以得出結(jié)論:季節(jié)、天氣類型對(duì)光伏發(fā)電功率具有較大影響,在預(yù)測(cè)光伏發(fā)電功率時(shí)應(yīng)將同季節(jié)的天氣類型相似日功率作為神經(jīng)網(wǎng)絡(luò)模型的輸入層;刪除了慣性權(quán)重并增加了隨機(jī)搜索因子可以提高粒子群算法的全局搜索能力,從而得到更好的神經(jīng)網(wǎng)絡(luò)模型以準(zhǔn)確預(yù)測(cè)光伏發(fā)電功率;晴天的預(yù)測(cè)效果要明顯優(yōu)于云天和雨雪天,因此云天和雨雪天氣的光伏發(fā)電功率波動(dòng)規(guī)律還有待更深入研究。
[Abstract]:With the increasing demand for energy, the use of solar energy has attracted more and more attention. Photovoltaic power generation, as a kind of sustainable energy, has been widely concerned by scholars at home and abroad in recent years because of its pollution-free and renewable characteristics.However, photovoltaic generation is intermittent, random, and the fluctuation of photovoltaic power will affect the stability and power quality of power grid. Therefore, photovoltaic generation prediction technology is a work that needs to be studied deeply.Accurate prediction of photovoltaic power generation has a good impact on power quality.In this paper, firstly, the fluctuation characteristics of photovoltaic power and its influencing factors are analyzed through the data of photovoltaic observation station of Huaneng Yingkou Thermal Power Co., Ltd.The weather type has an important influence on the fluctuation characteristics of photovoltaic power generation.Based on the problem of precocity of particles in the traditional particle swarm optimization algorithm, the inertia weight is removed and the random factor is added to improve the global convergence of the particle swarm optimization algorithm.The prediction model of photovoltaic generation based on neural network is optimized by particle swarm optimization. The validity of the proposed method is verified by comparing the predicted data with the measured data.Based on the above theoretical research, a photovoltaic power prediction system is designed, which can be applied to practice.The photovoltaic power prediction system designed in this paper is equipped with reverse physical isolation device to ensure the safety of data transmission according to the safety protection regulations of the secondary power system.In addition, the software structure and hardware structure of the system are designed, and the functions of the system are demonstrated.Through the analysis of this paper, we can draw a conclusion: season, weather type has a great influence on photovoltaic power generation, in the prediction of photovoltaic power generation, we should take the similar daily power of the same season weather type as the input layer of the neural network model;Removing inertial weight and adding random search factor can improve the global search ability of PSO and obtain a better neural network model to accurately predict photovoltaic power generation.The forecasting effect of sunny weather is obviously better than that of cloud and rain, so the fluctuation of photovoltaic power in cloudy and rainy weather still needs to be further studied.
【學(xué)位授予單位】：沈陽(yáng)工程學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM615

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