配电网理论线损率的分析与预测
发布时间：2021-02-24 10:58
【摘要】：近年来,随着我国经济的快速发展,人们对电网运行水平提出了更高的要求,大规模的降损措施被应用到电网,对于改善电源浪费起到了积极作用。再者,随着全球能源危机的加剧,十二五计划提出坚持把建设资源节约型、环境友好型社会作为加快转变经济发展方式的重要着力点。为了积极响应党中央的政策,建立节约型社会,最大限度地降低电能在传输过程中产生的损耗,开展线损预测研究变得尤为重要。配电网连接着发电系统、输电系统和用户,随着电力系统的快速发展,配电网线损的不可避免性、复杂性和不确定性给电力系统的安全运行及电能质量带来了严峻的挑战。因此,准确地对线损率进行预测,帮助相关工作人员制定符合现实情况的考核指标和规划,有效发挥电能价值,已经成为了当前亟需分析和解决的实际课题。深入研究线损率预测,对提高电力系统协调运行能力,促进电能持续健康发展具有十分重要的意义。基于历史理论线损率数据,本文对理论线损率展开了以下研究：1.神经网络预测模型能较好的应对序列的波动性且具有良好的精准度,因此该非线性模型被广泛的应用。利用神经网络预测模型对理论线损率进行预测,利用马尔可夫对理论线损率预测误差进行修正处理,建立RBF-马尔可夫模型,预测理论线损率。2.SVM(支持向量机)收敛速度快,学习能力强,泛化能力好,能有效预测序列的变化趋势。以支持向量机原理为基础,建立支持向量机回归模型,实现理论线损率直接预测。3.对支持向量机的模型参数优化问题进行研究。分析惩罚因子C和核参数σ的作用及对支持向量机性能的影响。运用遗传优化算法,提出基于遗传算法优化支持向量机预测模型(GA-SVM),解决SVM建模时存在的弊端,并利用GA-SVM预测模型实现理论线损率预测。4.结合RBF-Markov模型和遗GA-SVM模型,研究理论线损率的概率预测模型。针对概率预测模型中求解概率密度难这一关键问题,采用非参数核密度估计方法估计理论线损率的概率密度函数,最终建立概率预测模型求得置信区间。
[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.
【学位授予单位】：安徽工程大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM732
本文编号：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.
【学位授予单位】：安徽工程大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM732
文章目录
摘要
ABSTRACT
第1章 绪论
1.1 研究背景及意义
1.1.1 研究背景
1.1.2 研究的目的和意义
1.2 研究现状
1.3 本文工作
第2章 计算和分析配电网理论线损
2.1 配电网线损率的基本概念及组成
2.1.1 线损定义和组成
2.1.2 线损率相关概念
2.2 配电网理论线损的计算方法比较分析
2.2.1 均方根电流法
2.2.2 最大电流法（损失因数法）
2.2.3 平均电流法
2.2.4 等值电阻法
2.2.5 回归分析法
2.2.6 前推回代法
2.2.7 动态潮流法
2.2.8 智能算法
2.3 配电网线损率影响因数分析
2.3.1 配电网运行电压对线损率影响
2.3.2 功率因数对线损率影响
2.3.3 导线对线损率影响
2.3.4 变压器对线损率影响
2.3.5 三相负荷不平衡对线损率影响
2.3.6 管理措施对线损率影响
2.4 小结
第3章 基于RBF神经网络马尔可夫模型理论线损率预测
3.1 引言
3.2 神经网络模型
3.2.1 人工神经网络模型
3.2.2 RBF神经网络模型
3.3 马尔可夫理论
3.3.1 马尔可夫链
3.3.2 马尔可夫的性质
3.3.3 马尔可夫模型
3.4 基于RBF神经网络-马尔可夫模型的理论线损率预测
3.4.1 RBF-马尔可夫模型构建
3.4.2 算例分析
3.5 小结
第4章 基于遗传优化的支持向量机理论线损率预测
4.1 引言
4.2 统计学习理论基础
4.2.1 VC维和推广性的界
4.2.2 结构风险最小化
4.3 支持向量机模型
4.3.1 支持向量回归原理
4.3.2 核函数
4.3.3 支持向量机模型参数
4.4 遗传算法优化支持向量机建模
4.4.1 遗传算法原理
4.4.2 遗传优化支持向量机模型构建
4.5 算例分析
4.5.1 GA-SVM模型预测分析
4.5.2 仿真误差对比
4.6 小结
第5章 理论线损率预测结果不确定性研究
5.1 引言
5.2 非参数估计理论介绍
5.2.1 直方图方法
5.2.2 Rosenblatt估计
5.2.3 非参数核密度估计概念
5.2.4 密度估计优良性标准及性质
5.2.5 核函数的选择
5.2.6 窗宽的选择
5.3 置信区间非参数估计
5.3.1 理论线损率预测误差概率分布
5.3.2 置信区间估计
5.4 实例分析
5.4.1 确定性预测结果
5.4.2 求取置信区间
5.5 小结
第6章 总结与展望
6.1 论文工作结论
6.2 论文工作展望
参考文献
攻读硕士期间研究成果
致谢
本文编号：2298688
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