配电网故障定位.doc
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配电网故障定位,摘要本文在分析现代配电网拓扑结构以及传统故障定位算法的基础上，采用一种基于改进bp神经网络算法、遗传优化神经网络ga-bp算法以及rbf径向基神经网络算法在中的应用。对三种神经网络在中的应用进行系统比较分析，从而为实现了配电网的故障诊断、隔离故障区域以及恢复非故障区域供电。算法...
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摘 要
本文在分析现代配电网拓扑结构以及传统故障定位算法的基础上，采用一种基于改进BP神经网络算法、遗传优化神经网络GA-BP算法以及RBF径向基神经网络算法在配电网故障定位中的应用。对三种神经网络在配电网故障定位中的应用进行系统比较分析，从而为实现了配电网的故障诊断、隔离故障区域以及恢复非故障区域供电。配电网故障定位算法的研究及其改进成为本论文的工作方向和重点内容。
本文对故障定位算法做了系统深入的研究，主要研究工作如下：
1、深入研究了配电网拓扑结构。算法的实现正是基于配电网的拓扑结构，本文采用了现代电网手拉手的环状结构，具有正常时闭环结构，开环运行，呈辐射状向用户供电的特点。配电网发生故障后，安装在各分段开关处的FTU会检测到故障信息(如故障过电流)，上传到控制中心SCADA系统，系统经过故障诊断算法综合分析，判别故障点位置，实现了故障定位，并下达命令遥控FTU断开故障点两侧的分段开关，进而隔离故障区域。
2、针对BP神经网络未考虑前一次调整时的误差梯度方向以及最佳学习率的问题，使得网络训练过程发生振荡，收敛缓慢，本文采用一种改进BP神经网络算法在配电网故障定位中的应用，解决了上述问题，并通过了算例仿真验证。
3、针对神经网络收敛速度慢和容易陷入局部极小值的问题，本文提出将遗传神经网络算法应用于配电网故障定位。用遗传算法优化BP神经网络解决了BP网络的最优初始权值和阈值问题，加快了网络的收敛。利用遗传算法的全局搜索能力，进一步提高了BP神经网络故障定位的准确性和快速性，并通过了算例仿真验证。
4、针对遗传算法训练时间长、BP神经网络容错性能不佳、BP隐含层神经元个数难以确定、收敛速度慢和容易陷入局部最优的问题，本文将RBF神经网络算法引入配电网故障定位中。RBF网络采用隐含层为高斯函数，是局部逼近网络，有效的加快了收敛速度和避免局部最优。在实际配电网故障诊断中，采用RBF神经网络，实现了故障点的准确定位，并利用Visual C++工具开发故障定位主站，实现了配电网自动化目标，对故障判断准确，反应迅速，完全达到了实时监测的要求。
关键词 配电网；故障定位；神经网络；遗传算法；RBF；C++
Abstract
Based on the analysis of modern distribution network topological structure and traditional fault location algorithm, this paper propose an improved BP neural network algorithm, genetic optimization of neural network GA-BP algorithm and RBF neural network algorithm in the application of distribution network fault section location. Through a comparative analysis of three types of neural network, so as to achieve distribution network fault location, isolation and restoration of power for fault region. Distribution network fault location algorithm and its improved become the main research contents.
In this paper, it studies distribution network fault location algorithm. The main research work is as follows:
1. In-depth study of the topological structure for distribution network. Algorithm is based on the topological structure of distribution network. This paper adopts the modern network hand in hand ring structure, with normal closed-loop structure, open loop operation, radially to user. After Distribution network faults, the FTU which installed in the switch detects the fault information (such as overcurrent), upload to the control center which called SCADA system. Based on fault diagnosis algorithm, the system analysis fault location.Then remote order the FTU disconnection switches on both sides of the fault section, and then isolating the fault area.
2. According to the BP neural network does not take into account the previous adjustment error gradient direction as well as the best learning rate problem, making the network training process occurs oscillation and converges slowly. This paper presents an improved BP neural network algorithm in fault location for distribution network, which solves the problem and through simulation.
3. According to the BP neural network converges slowly and easily falling into local minimum problem, this paper puts forward the genetic neural network algorithm is applied to fault location in distribution network. Using genetic algorithm to optimize BP neural network to solve the BP network optimal initial weights and thresholds, accelerate the network convergence. Using the global search ability of genetic algorithm, further improve the BP neural network fault positioning accuracy and rapidity, and through the example simulation.
4. According to Genetic algorithm training for a long time, the BP neural network fault tolerant performance, BP hidden layer neuron number is difficult to determine, converges slowly and easily to fall into local optimal problem. This paper adopts the RBF neural network algorithm for fault location. RBF networks using implicit layer for the Gauss function, which is local approximation network, effectively accelerates the convergence speed and avoid local optimum. In the practical fault diagnosis of distribution network, the RBF neural network, realizes the accurate fault location, and makes use of Visual C++ development tool for master station in distribution network automation, which achieves the goal..
本文在分析现代配电网拓扑结构以及传统故障定位算法的基础上，采用一种基于改进BP神经网络算法、遗传优化神经网络GA-BP算法以及RBF径向基神经网络算法在配电网故障定位中的应用。对三种神经网络在配电网故障定位中的应用进行系统比较分析，从而为实现了配电网的故障诊断、隔离故障区域以及恢复非故障区域供电。配电网故障定位算法的研究及其改进成为本论文的工作方向和重点内容。
本文对故障定位算法做了系统深入的研究，主要研究工作如下：
1、深入研究了配电网拓扑结构。算法的实现正是基于配电网的拓扑结构，本文采用了现代电网手拉手的环状结构，具有正常时闭环结构，开环运行，呈辐射状向用户供电的特点。配电网发生故障后，安装在各分段开关处的FTU会检测到故障信息(如故障过电流)，上传到控制中心SCADA系统，系统经过故障诊断算法综合分析，判别故障点位置，实现了故障定位，并下达命令遥控FTU断开故障点两侧的分段开关，进而隔离故障区域。
2、针对BP神经网络未考虑前一次调整时的误差梯度方向以及最佳学习率的问题，使得网络训练过程发生振荡，收敛缓慢，本文采用一种改进BP神经网络算法在配电网故障定位中的应用，解决了上述问题，并通过了算例仿真验证。
3、针对神经网络收敛速度慢和容易陷入局部极小值的问题，本文提出将遗传神经网络算法应用于配电网故障定位。用遗传算法优化BP神经网络解决了BP网络的最优初始权值和阈值问题，加快了网络的收敛。利用遗传算法的全局搜索能力，进一步提高了BP神经网络故障定位的准确性和快速性，并通过了算例仿真验证。
4、针对遗传算法训练时间长、BP神经网络容错性能不佳、BP隐含层神经元个数难以确定、收敛速度慢和容易陷入局部最优的问题，本文将RBF神经网络算法引入配电网故障定位中。RBF网络采用隐含层为高斯函数，是局部逼近网络，有效的加快了收敛速度和避免局部最优。在实际配电网故障诊断中，采用RBF神经网络，实现了故障点的准确定位，并利用Visual C++工具开发故障定位主站，实现了配电网自动化目标，对故障判断准确，反应迅速，完全达到了实时监测的要求。
关键词 配电网；故障定位；神经网络；遗传算法；RBF；C++
Abstract
Based on the analysis of modern distribution network topological structure and traditional fault location algorithm, this paper propose an improved BP neural network algorithm, genetic optimization of neural network GA-BP algorithm and RBF neural network algorithm in the application of distribution network fault section location. Through a comparative analysis of three types of neural network, so as to achieve distribution network fault location, isolation and restoration of power for fault region. Distribution network fault location algorithm and its improved become the main research contents.
In this paper, it studies distribution network fault location algorithm. The main research work is as follows:
1. In-depth study of the topological structure for distribution network. Algorithm is based on the topological structure of distribution network. This paper adopts the modern network hand in hand ring structure, with normal closed-loop structure, open loop operation, radially to user. After Distribution network faults, the FTU which installed in the switch detects the fault information (such as overcurrent), upload to the control center which called SCADA system. Based on fault diagnosis algorithm, the system analysis fault location.Then remote order the FTU disconnection switches on both sides of the fault section, and then isolating the fault area.
2. According to the BP neural network does not take into account the previous adjustment error gradient direction as well as the best learning rate problem, making the network training process occurs oscillation and converges slowly. This paper presents an improved BP neural network algorithm in fault location for distribution network, which solves the problem and through simulation.
3. According to the BP neural network converges slowly and easily falling into local minimum problem, this paper puts forward the genetic neural network algorithm is applied to fault location in distribution network. Using genetic algorithm to optimize BP neural network to solve the BP network optimal initial weights and thresholds, accelerate the network convergence. Using the global search ability of genetic algorithm, further improve the BP neural network fault positioning accuracy and rapidity, and through the example simulation.
4. According to Genetic algorithm training for a long time, the BP neural network fault tolerant performance, BP hidden layer neuron number is difficult to determine, converges slowly and easily to fall into local optimal problem. This paper adopts the RBF neural network algorithm for fault location. RBF networks using implicit layer for the Gauss function, which is local approximation network, effectively accelerates the convergence speed and avoid local optimum. In the practical fault diagnosis of distribution network, the RBF neural network, realizes the accurate fault location, and makes use of Visual C++ development tool for master station in distribution network automation, which achieves the goal..
