【摘要】：光伏發(fā)電因其清潔無污染受到了各國的青睞。但同時因其輸出功率的隨機性和波動性,使得當光伏電站并網(wǎng)后會對公用電網(wǎng)造成一定的沖擊。這對電網(wǎng)的正常運行極其不利。因此對并網(wǎng)型光伏電站的發(fā)電功率進行準確的短期預測,有利于電力調(diào)度部門合理分配和規(guī)劃光伏能源和常規(guī)能源所占的比重,有利于及時調(diào)整調(diào)度計劃,使得電力系統(tǒng)以安全、穩(wěn)定、經(jīng)濟的方式運行。本文在大量閱讀國內(nèi)外相關文獻的基礎上,采用甘肅某光伏電站的歷史數(shù)據(jù)作為研究對象,并利用HS-ESN模型建立了預測模型。然后對該電站進行了光伏發(fā)電功率的短期預測分析。最后,通過C#與MATLAB的混編,介紹了光伏發(fā)電功率短期預測系統(tǒng)的設計�；谏衔拿枋�,本文的研究內(nèi)容主要包括以下幾個方面:首先,利用MATLAB建立的光伏電池仿真模型梳理了光伏電池的輸出特性,分析了太陽輻照強度和溫度對光伏發(fā)電功率的影響,進一步確定了影響光伏發(fā)電功率的主要因素。然后將各影響因素組成特征向量,利用相似日選擇算法提取相似日和訓練樣本。其次,在深入研究了回聲狀態(tài)網(wǎng)絡(Echo State Network,ESN)算法的前提下,提出了和聲搜索(Harmony Search,HS)算法優(yōu)化回聲狀態(tài)網(wǎng)絡算法的混合算法。本文利用HS算法優(yōu)化ESN算法的儲備池參數(shù),有效提高了ESN算法的精度,并將HS-ESN算法應用到光伏發(fā)電功率短期預測問題中,通過不同預測模型分別預測不同天氣類型下的光伏發(fā)電功率,驗證了引入相似日選擇算法的有效性,同時表明了HS-ESN模型的性能更優(yōu)于單一的ESN模型和目前常用的其他幾種模型。最后,根據(jù)本文提出的預測模型和方法,設計出了一個光伏發(fā)電功率短期預測系統(tǒng),并對該系統(tǒng)進行了需求分析和結構設計。該預測系統(tǒng)具備光伏發(fā)電功率短期預測的基本模塊,包括登錄界面及數(shù)據(jù)導入模塊、發(fā)電功率短期預測模塊、報表統(tǒng)計模塊、數(shù)據(jù)查詢及輸出模塊,因此具有一定的實用價值。
[Abstract]:Photovoltaic power generation is favored by many countries because of its clean and pollution-free. However, because of the randomness and fluctuation of the output power, the photovoltaic power station will have a certain impact on the public power grid when it is connected to the grid. This is extremely detrimental to the normal operation of the power grid. Therefore, the accurate short-term prediction of the generation power of grid-connected photovoltaic power stations is beneficial to the rational distribution and planning of the proportion of photovoltaic and conventional energy in the power dispatching department, and to the timely adjustment of the dispatching plan. Make the power system run in a safe, stable and economical way. Based on a large number of domestic and foreign literatures, this paper takes the historical data of a photovoltaic power plant in Gansu as the research object, and establishes a prediction model by using the HS-ESN model. Then the short-term prediction analysis of PV power generation is carried out. Finally, by mixing C # and MATLAB, the design of PV power short-term prediction system is introduced. Based on the above description, the research contents of this paper mainly include the following aspects: firstly, the output characteristics of photovoltaic cells are combed by using the simulation model established by MATLAB. The influence of solar radiation intensity and temperature on photovoltaic power generation is analyzed, and the main factors affecting photovoltaic power generation are determined. Then each factor is composed of feature vector and the similar day selection algorithm is used to extract the similar day and training sample. Secondly, based on the in-depth study of echo state network (Echo State Network) algorithm, a hybrid algorithm is proposed to optimize echo state network algorithm based on Harmony search (HS) algorithm. In this paper, HS algorithm is used to optimize the reserve cell parameters of ESN algorithm, and the precision of ESN algorithm is improved effectively. The HS-ESN algorithm is applied to the short-term prediction of photovoltaic power generation. The effectiveness of introducing the similar day selection algorithm is verified by forecasting PV power under different weather types by different prediction models. The performance of HS-ESN model is better than that of single ESN model and other models commonly used at present at the same time the performance of HS-ESN model is better than that of single ESN model and other commonly used models. Finally, according to the prediction model and method proposed in this paper, a PV power short-term forecasting system is designed, and the demand analysis and structure design of the system are carried out. The prediction system has the basic modules of short-term prediction of photovoltaic power generation, including login interface and data import module, short-term power generation prediction module, report statistics module, data query and output module, so it has certain practical value.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM615

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