配電網理論線損率的分析與預測
發(fā)布時間：2021-02-24 10:58
【摘要】：近年來,隨著我國經濟的快速發(fā)展,人們對電網運行水平提出了更高的要求,大規(guī)模的降損措施被應用到電網,對于改善電源浪費起到了積極作用。再者,隨著全球能源危機的加劇,十二五計劃提出堅持把建設資源節(jié)約型、環(huán)境友好型社會作為加快轉變經濟發(fā)展方式的重要著力點。為了積極響應黨中央的政策,建立節(jié)約型社會,最大限度地降低電能在傳輸過程中產生的損耗,開展線損預測研究變得尤為重要。配電網連接著發(fā)電系統(tǒng)、輸電系統(tǒng)和用戶,隨著電力系統(tǒng)的快速發(fā)展,配電網線損的不可避免性、復雜性和不確定性給電力系統(tǒng)的安全運行及電能質量帶來了嚴峻的挑戰(zhàn)。因此,準確地對線損率進行預測,幫助相關工作人員制定符合現實情況的考核指標和規(guī)劃,有效發(fā)揮電能價值,已經成為了當前亟需分析和解決的實際課題。深入研究線損率預測,對提高電力系統(tǒng)協調運行能力,促進電能持續(xù)健康發(fā)展具有十分重要的意義�；跉v史理論線損率數據,本文對理論線損率展開了以下研究：1.神經網絡預測模型能較好的應對序列的波動性且具有良好的精準度,因此該非線性模型被廣泛的應用。利用神經網絡預測模型對理論線損率進行預測,利用馬爾可夫對理論線損率預測誤差進行修正處理,建立RBF-馬爾可夫模型,預測理論線損率。2.SVM(支持向量機)收斂速度快,學習能力強,泛化能力好,能有效預測序列的變化趨勢。以支持向量機原理為基礎,建立支持向量機回歸模型,實現理論線損率直接預測。3.對支持向量機的模型參數優(yōu)化問題進行研究。分析懲罰因子C和核參數σ的作用及對支持向量機性能的影響。運用遺傳優(yōu)化算法,提出基于遺傳算法優(yōu)化支持向量機預測模型(GA-SVM),解決SVM建模時存在的弊端,并利用GA-SVM預測模型實現理論線損率預測。4.結合RBF-Markov模型和遺GA-SVM模型,研究理論線損率的概率預測模型。針對概率預測模型中求解概率密度難這一關鍵問題,采用非參數核密度估計方法估計理論線損率的概率密度函數,最終建立概率預測模型求得置信區(qū)間。
[Abstract]:In recent years, with the rapid development of China's economy, people put forward higher requirements for the level of power grid operation. Large-scale loss reduction measures have been applied to the power grid, which has played a positive role in improving power waste. Furthermore, with the aggravation of the global energy crisis, the 12th Five-Year Plan puts forward that the construction of resource-saving and environment-friendly society should be regarded as an important point to accelerate the transformation of economic development mode. In order to respond positively to the policies of the CPC Central Committee, establish a conservation-oriented society and minimize the loss of electric energy in the transmission process, it is particularly important to carry out the research on line loss prediction. Distribution network is connected with generation system, transmission system and users. With the rapid development of power system, the inevitable, complexity and uncertainty of distribution network line loss bring severe challenges to the safe operation and power quality of power system. Therefore, the accurate prediction of line loss rate, the help of relevant staff to formulate assessment indicators and plans in line with the actual situation, and the effective use of electric energy value, has become a practical issue that needs to be analyzed and solved. It is very important to study the prediction of line loss rate for improving the coordinated operation ability of power system and promoting the sustainable and healthy development of electric energy. Based on the historical theory line loss rate data, this paper carries out the following research on the theoretical line loss rate: 1. Neural network prediction model can deal with the volatility of the sequence and has a good accuracy, so the nonlinear model is widely used. Neural network prediction model is used to predict the theoretical line loss rate and Markov model is used to correct the theoretical line loss rate prediction error. The RBF- Markov model is established. 2.SVM (support Vector Machine) converges fast, has strong learning ability and good generalization ability, and can effectively predict the change trend of the sequence. Based on the principle of support vector machine, the regression model of support vector machine is established, and the direct prediction of theoretical line loss rate is realized. The optimization of support vector machine (SVM) model parameters is studied. The effects of penalty factor C and kernel parameter 蟽 on the performance of support vector machines are analyzed. By using genetic optimization algorithm, the support vector machine prediction model (GA-SVM) based on genetic algorithm is proposed to solve the disadvantages of SVM modeling, and the theoretical line loss rate prediction is realized by using GA-SVM prediction model. 4. Combined with RBF-Markov model and posthumous GA-SVM model, the probabilistic prediction model of theoretical line loss rate is studied. In order to solve the problem that probability density is difficult to solve in probabilistic prediction model, the nonparametric kernel density estimation method is used to estimate the probability density function of theoretical linear loss rate, and the confidence interval is obtained by establishing the probabilistic prediction model.
【學位授予單位】：安徽工程大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM732
本文編號：2298688
[Abstract]:In recent years, with the rapid development of China's economy, people put forward higher requirements for the level of power grid operation. Large-scale loss reduction measures have been applied to the power grid, which has played a positive role in improving power waste. Furthermore, with the aggravation of the global energy crisis, the 12th Five-Year Plan puts forward that the construction of resource-saving and environment-friendly society should be regarded as an important point to accelerate the transformation of economic development mode. In order to respond positively to the policies of the CPC Central Committee, establish a conservation-oriented society and minimize the loss of electric energy in the transmission process, it is particularly important to carry out the research on line loss prediction. Distribution network is connected with generation system, transmission system and users. With the rapid development of power system, the inevitable, complexity and uncertainty of distribution network line loss bring severe challenges to the safe operation and power quality of power system. Therefore, the accurate prediction of line loss rate, the help of relevant staff to formulate assessment indicators and plans in line with the actual situation, and the effective use of electric energy value, has become a practical issue that needs to be analyzed and solved. It is very important to study the prediction of line loss rate for improving the coordinated operation ability of power system and promoting the sustainable and healthy development of electric energy. Based on the historical theory line loss rate data, this paper carries out the following research on the theoretical line loss rate: 1. Neural network prediction model can deal with the volatility of the sequence and has a good accuracy, so the nonlinear model is widely used. Neural network prediction model is used to predict the theoretical line loss rate and Markov model is used to correct the theoretical line loss rate prediction error. The RBF- Markov model is established. 2.SVM (support Vector Machine) converges fast, has strong learning ability and good generalization ability, and can effectively predict the change trend of the sequence. Based on the principle of support vector machine, the regression model of support vector machine is established, and the direct prediction of theoretical line loss rate is realized. The optimization of support vector machine (SVM) model parameters is studied. The effects of penalty factor C and kernel parameter 蟽 on the performance of support vector machines are analyzed. By using genetic optimization algorithm, the support vector machine prediction model (GA-SVM) based on genetic algorithm is proposed to solve the disadvantages of SVM modeling, and the theoretical line loss rate prediction is realized by using GA-SVM prediction model. 4. Combined with RBF-Markov model and posthumous GA-SVM model, the probabilistic prediction model of theoretical line loss rate is studied. In order to solve the problem that probability density is difficult to solve in probabilistic prediction model, the nonparametric kernel density estimation method is used to estimate the probability density function of theoretical linear loss rate, and the confidence interval is obtained by establishing the probabilistic prediction model.
【學位授予單位】：安徽工程大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM732
文章目錄
摘要
ABSTRACT
第1章 緒論
1.1 研究背景及意義
1.1.1 研究背景
1.1.2 研究的目的和意義
1.2 研究現狀
1.3 本文工作
第2章 計算和分析配電網理論線損
2.1 配電網線損率的基本概念及組成
2.1.1 線損定義和組成
2.1.2 線損率相關概念
2.2 配電網理論線損的計算方法比較分析
2.2.1 均方根電流法
2.2.2 最大電流法（損失因數法）
2.2.3 平均電流法
2.2.4 等值電阻法
2.2.5 回歸分析法
2.2.6 前推回代法
2.2.7 動態(tài)潮流法
2.2.8 智能算法
2.3 配電網線損率影響因數分析
2.3.1 配電網運行電壓對線損率影響
2.3.2 功率因數對線損率影響
2.3.3 導線對線損率影響
2.3.4 變壓器對線損率影響
2.3.5 三相負荷不平衡對線損率影響
2.3.6 管理措施對線損率影響
2.4 小結
第3章 基于RBF神經網絡馬爾可夫模型理論線損率預測
3.1 引言
3.2 神經網絡模型
3.2.1 人工神經網絡模型
3.2.2 RBF神經網絡模型
3.3 馬爾可夫理論
3.3.1 馬爾可夫鏈
3.3.2 馬爾可夫的性質
3.3.3 馬爾可夫模型
3.4 基于RBF神經網絡-馬爾可夫模型的理論線損率預測
3.4.1 RBF-馬爾可夫模型構建
3.4.2 算例分析
3.5 小結
第4章 基于遺傳優(yōu)化的支持向量機理論線損率預測
4.1 引言
4.2 統(tǒng)計學習理論基礎
4.2.1 VC維和推廣性的界
4.2.2 結構風險最小化
4.3 支持向量機模型
4.3.1 支持向量回歸原理
4.3.2 核函數
4.3.3 支持向量機模型參數
4.4 遺傳算法優(yōu)化支持向量機建模
4.4.1 遺傳算法原理
4.4.2 遺傳優(yōu)化支持向量機模型構建
4.5 算例分析
4.5.1 GA-SVM模型預測分析
4.5.2 仿真誤差對比
4.6 小結
第5章 理論線損率預測結果不確定性研究
5.1 引言
5.2 非參數估計理論介紹
5.2.1 直方圖方法
5.2.2 Rosenblatt估計
5.2.3 非參數核密度估計概念
5.2.4 密度估計優(yōu)良性標準及性質
5.2.5 核函數的選擇
5.2.6 窗寬的選擇
5.3 置信區(qū)間非參數估計
5.3.1 理論線損率預測誤差概率分布
5.3.2 置信區(qū)間估計
5.4 實例分析
5.4.1 確定性預測結果
5.4.2 求取置信區(qū)間
5.5 小結
第6章 總結與展望
6.1 論文工作結論
6.2 論文工作展望
參考文獻
攻讀碩士期間研究成果
致謝
本文編號：2298688
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