考虑时序性出力DG接入的配电网重构

发布时间：2018-02-04 00:04

本文关键词： 分布式电源配电网静态重构改进粒子群算法配电网动态重构 Fisher最优分割法　出处：《郑州大学》2017年硕士论文　论文类型：学位论文

【摘要】：配电网络重构是优化配网运行方式的一种有效方法,可以通过改变网络中各开关的开合状态来改变配网结构,从而达到降低网损、提高供电质量等目标。而随着配电自动化的全面展开和分布式电源(Distributed Generation,DG)在配网中的广泛接入,对配电网重构的要求也越来越高。在实际系统中,负荷与接入的分布式电源出力是时序性变化的,仅针对系统某一时刻的状态进行静态重构不能解决实际中遇到的问题,因此本文在静态重构之外,还将基于系统数据时序性变化的特点对配网动态重构进行研究。配电网络动态重构与静态重构的区别就在于,静态重构是基于时间断面进行的,而动态重构则是基于一个时间段进行的。因此可将动态重构问题等效为含有开关约束条件的多个静态重构优化问题。本文对静态重构提出了基于改进粒子群算法的计算方法。对静态重构进行建模;对粒子群算法作出改进,采用非线性变化的惯性权重及随之非对称异步变化的学习因子并引入高斯白噪声双扰动变异,提高了算法的寻优性能;对于配网重构问题中的不可行解较多的问题,进行了基于环路矩阵位置的十进制编码,并对越限的粒子采用了修复和惩罚的策略。通过两个基本算例进一步验证了该方法的可行性与有效性,并分析了重构与接入分布式电源对配电网的影响。接着在静态重构的基础上,提出了基于Fisher最优分割法的动态重构方法。建立了动态重构的模型;并基于系统数据时序性变化的特点,采用Fisher有序分割法进行时段最优划分;用各段负荷中心依次进行静态重构,若开关动作次数越限,则将最优分段数减一,重新进行分段与重构。该方法无须提前设置分段数与合并时段的阈值,方便快捷。算例结果表明该方法能有效降低重构次数并降低运行总费用。并在对系统多维负荷数据进行分段时,提出了一种获得系统近似负荷曲线的方法,采用主成分分析法拟合多条负荷曲线,并通过计算分段损失函数变化验证了其可行性。
[Abstract]:Distribution network reconfiguration is an effective method to optimize the operation mode of distribution network. It can change the distribution network structure by changing the on-off state of each switch in the network, so as to reduce the network loss. With the development of distribution automation and the wide access of distributed generation DGs in the distribution network. The demand for reconfiguration of distribution network is more and more high. In the actual system, the load and the output of the connected distributed power are time-series changes. Static refactoring only for the state of the system at a certain time can not solve the problems encountered in practice, so this paper is outside of static refactoring. Dynamic reconfiguration of distribution network is also studied based on the characteristics of system data timing. The difference between dynamic reconfiguration of distribution network and static reconfiguration is that static reconfiguration is based on time section. Dynamic reconfiguration is based on a time period, so the dynamic reconfiguration problem can be equivalent to multiple static reconfiguration optimization problems with switching constraints. In this paper, an improved particle swarm optimization algorithm is proposed for static reconfiguration. Modeling static reconfiguration; The particle swarm optimization (PSO) algorithm is improved by using the nonlinear inertia weight and the learning factor of asymmetric asynchronous variation and introducing Gao Si white noise double disturbance mutation to improve the optimization performance of the algorithm. For the infeasible solution of distribution network reconstruction problem, the decimal coding based on the position of loop matrix is carried out. The strategies of repair and punishment are adopted for the particles over the limit, and the feasibility and effectiveness of the method are further verified by two basic examples. The influence of reconfiguration and access to distributed generation on distribution network is analyzed. Based on static reconfiguration, a dynamic reconfiguration method based on Fisher optimal partition method is proposed, and a dynamic reconfiguration model is established. Based on the characteristics of the temporal variation of the system data, the Fisher sequential segmentation method is used for the optimal division of time periods. If the number of switching operations exceeds the limit, the optimal number of segments will be reduced by one, and the segmentation and reconstruction will be carried out again. This method does not need to set the threshold between the number of segments and the combined period in advance. The results show that this method can effectively reduce the number of reconfiguration and reduce the total running cost. When the multi-dimensional load data of the system is segmented, a method to obtain the approximate load curve of the system is proposed. The principal component analysis (PCA) is used to fit multiple load curves, and the feasibility is verified by calculating the variation of piecewise loss function.
【学位授予单位】：郑州大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM711

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