本文選題：模糊神經(jīng)網(wǎng)絡(luò) + 剩余容量��； 參考：《湖南科技大學(xué)》2015年碩士論文

【摘要】：太陽(yáng)能光伏系統(tǒng)具有綠色無(wú)污染、安全便捷、不受地域限制和儲(chǔ)量無(wú)限等優(yōu)點(diǎn)。鉛酸蓄電池是太陽(yáng)能光伏系統(tǒng)中的關(guān)鍵儲(chǔ)能裝置,因此對(duì)蓄電池剩余容量高精度預(yù)測(cè)的研究是非常有必要的。實(shí)現(xiàn)剩余容量的精準(zhǔn)預(yù)測(cè)不僅有利于提高蓄電池的工作效率,有效延長(zhǎng)工作壽命,并且能恰當(dāng)?shù)姆乐蛊溥^充過放。但是鉛酸蓄電池內(nèi)部復(fù)雜的電化學(xué)特性導(dǎo)致剩余容量的預(yù)測(cè)問題再業(yè)界一直是一項(xiàng)較難攻克的問題,對(duì)于容量預(yù)測(cè)方面也沒有統(tǒng)一的研究標(biāo)準(zhǔn)。本文通過翻閱大量相關(guān)文獻(xiàn)和學(xué)術(shù)期刊,在學(xué)校實(shí)驗(yàn)室已有的研究基礎(chǔ)上,在現(xiàn)有的模糊神經(jīng)網(wǎng)絡(luò)基礎(chǔ)上進(jìn)行改進(jìn),將其運(yùn)用于太陽(yáng)能光伏照明系統(tǒng)的剩余容量預(yù)測(cè)。本論文首先敘述了鉛酸蓄電池的主要特性參數(shù)等理論知識(shí),主要包括有電池電壓、蓄電池容量、荷電狀態(tài)、蓄電池內(nèi)阻。其次詳細(xì)分析了鉛酸蓄電池的放電特性和影響鉛酸蓄電池壽命的四大因素,并討論了蓄電池的工作原理及等效電路模型。接著對(duì)目前國(guó)內(nèi)外鉛酸蓄電池剩余容量的預(yù)測(cè)方法進(jìn)行了詳細(xì)總結(jié),并比較分析各類方法的優(yōu)缺點(diǎn)。然后考慮到太陽(yáng)能光伏照明系統(tǒng)中鉛酸蓄電池的特性,選擇以模糊神經(jīng)網(wǎng)絡(luò)算法為基礎(chǔ)的改進(jìn)模型進(jìn)行蓄電池剩余容量預(yù)測(cè)。設(shè)計(jì)輸入層、隱含層、推理層、求和層以及輸出層。其主要特點(diǎn)是在于對(duì)輸入數(shù)據(jù)選用7個(gè)語(yǔ)言變量來定義模糊集合,隸屬度函數(shù)選用高斯函數(shù),推理層采用兩個(gè)模糊規(guī)則分別為乘積和求和。并提出對(duì)自學(xué)習(xí)算法的改進(jìn),通過與另外三種常用算法進(jìn)行實(shí)驗(yàn)對(duì)比分析,可知改進(jìn)算法針對(duì)于改進(jìn)的模型在預(yù)測(cè)精度和匹配度上起到優(yōu)化作用。最后通過在MATLAB軟件中進(jìn)行仿真實(shí)驗(yàn),從仿真數(shù)據(jù)和實(shí)驗(yàn)數(shù)據(jù)對(duì)比的曲線觀察可知,改進(jìn)的模糊神經(jīng)網(wǎng)絡(luò)模型和算法能在不影響電池正常工作的情況下對(duì)光伏蓄電池剩余容量預(yù)測(cè)精度和穩(wěn)定性上有所提高,具有一定的應(yīng)用價(jià)值。
[Abstract]:Solar photovoltaic system has the advantages of green pollution, safe and convenient, free from geographical restrictions and unlimited reserves. Lead acid battery is the key energy storage device in solar photovoltaic system, so it is necessary to study the high precision prediction of battery residual capacity. The accurate prediction of residual capacity can not only improve the working efficiency and prolong the working life of the battery, but also prevent its overcharging and overloading properly. However, the complex electrochemical characteristics of lead-acid batteries lead to the prediction of residual capacity, which is always a difficult problem in the industry, and there is no uniform research standard for capacity prediction. In this paper, a large number of related literatures and academic journals are reviewed, and based on the existing research in the school laboratory, the fuzzy neural network is improved and applied to the prediction of the residual capacity of solar photovoltaic lighting system. In this paper, the main characteristic parameters of lead-acid battery are described, including battery voltage, battery capacity, charge state, battery internal resistance and so on. Secondly, the discharge characteristics of lead-acid battery and the four factors affecting the life of lead-acid battery are analyzed in detail, and the working principle and equivalent circuit model of the battery are discussed. Then, the prediction methods of residual capacity of lead-acid batteries at home and abroad are summarized in detail, and the advantages and disadvantages of various methods are compared and analyzed. Then, considering the characteristics of lead-acid battery in solar photovoltaic lighting system, an improved model based on fuzzy neural network algorithm is selected to predict the battery residual capacity. Design input layer, hidden layer, inference layer, summation layer and output layer. The main features are that the fuzzy set is defined by seven language variables for input data, Gao Si function is used for membership function, and two fuzzy rules are used in inference layer for product and summation respectively. The improvement of self-learning algorithm is put forward. By comparing and analyzing with other three common algorithms, we can see that the improved algorithm plays an optimization role in prediction accuracy and matching degree for the improved model. Finally, through the simulation experiment in the MATLAB software, from the curve observation of the contrast between the simulation data and the experimental data, we can know, The improved fuzzy neural network model and algorithm can improve the prediction accuracy and stability of the residual capacity of photovoltaic battery without affecting the normal operation of the battery.
【學(xué)位授予單位】：湖南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM912

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 高鵬;馬江泓;崔君瑩;白瑞雪;;閥控密封式鉛酸蓄電池的原理及其運(yùn)行維護(hù)[J];電源技術(shù)應(yīng)用;2009年04期

2 夏超英;張術(shù);孫宏濤;;基于推廣卡爾曼濾波算法的SOC估算策略[J];電源技術(shù);2007年05期

3 崔義霞;;延長(zhǎng)閥控鉛酸蓄電池使用壽命措施探討[J];硅谷;2014年15期

4 馬文偉;鄭建勇;尤捚;張先飛;;應(yīng)用Kalman濾波法估計(jì)光伏發(fā)電系統(tǒng)鉛酸蓄電池SOC[J];通信電源技術(shù);2009年05期

相關(guān)博士學(xué)位論文 前1條

1 歐陽(yáng)名三;獨(dú)立光伏系統(tǒng)中蓄電池管理的研究[D];合肥工業(yè)大學(xué);2004年

相關(guān)碩士學(xué)位論文 前1條

1 張商州;蓄電池儲(chǔ)能效率的測(cè)試與分析[D];陜西科技大學(xué);2014年

，

本文編號(hào)：1807161