一种配电网故障区间诊断系统的研究
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摘要
配电网故障区间诊断，故障隔离是配电网自动化的重要方面，当配电网发生故障时要求能够根据配电网的实际拓扑结构，快速地定位故障区间并且通过遥控开关隔离与故障点相连的用户，然后通过转换联络开关和分段开关的状态，恢复对完好区域的供电。
传统的配电网故障区间诊断算法采用统一矩阵法，在实时信息序列中存在畸变信息时有可能错判和误判。本文提出基于改进BP网络的配电网故障区间诊断算法，当实时信息中出现畸变信息时，能自动纠错和进行准确定位，并具有高度的容错性能。
本文对BP网络的理论进行了较为详细的阐述，对于BP网络的层数，隐含层单元数目，初始权值，初始学习率及目标误差的选取进行了分析探讨，对配电网进行了分析，建立了配电网的故障区间诊断的模型，并在对配电网络故障区间诊断问题上进行了运用和仿真分析，证明了该方法具有一定的学习能力和容错能力。
本文用MATLAB作为仿真工具，对故障诊断的改进BP算法进行了实例仿真，分析和算例的结果表明该改进算法是有效的。
Fault Section Detection in distribution network is an important aspect of distribution automation. When there is a fault in the distribution system, it can fast locate the fault and separate the load which connect the fault by operating switches according to thereal-time structure of the distribution network. Then it can find an operating pattern by changing the status of the tie or sectionalizing switches.
The fault sections location in distribution networks based the unified matrix algorithm may result in the false results under the information mutation mode; The method of fault section diagnosis of distribution networks based on new neural network is presented in this paper. The method has high fault-tolerance performance.
This paper comprehensively states the principle and conception of ANN, the author makes a deeper research on such aspects about BP ANN as its algorithmic theory,constructure, initial value, and a model about Fault Section Detection in distribution networks based on the analysis of the distribution network is proposed. This method has high fault-tolerance performance and the test results have verified its correctness.
The author studies the case of BP ANN applying in engineering of Fault Section Detection in Distribution network.
The simulation system is constituted with MATLAB. The results of the analysis and simulation denote that the method proposed in this paper is efficient and reliable.
传统的配电网故障区间诊断算法采用统一矩阵法，在实时信息序列中存在畸变信息时有可能错判和误判。本文提出基于改进BP网络的配电网故障区间诊断算法，当实时信息中出现畸变信息时，能自动纠错和进行准确定位，并具有高度的容错性能。
本文对BP网络的理论进行了较为详细的阐述，对于BP网络的层数，隐含层单元数目，初始权值，初始学习率及目标误差的选取进行了分析探讨，对配电网进行了分析，建立了配电网的故障区间诊断的模型，并在对配电网络故障区间诊断问题上进行了运用和仿真分析，证明了该方法具有一定的学习能力和容错能力。
本文用MATLAB作为仿真工具，对故障诊断的改进BP算法进行了实例仿真，分析和算例的结果表明该改进算法是有效的。
Fault Section Detection in distribution network is an important aspect of distribution automation. When there is a fault in the distribution system, it can fast locate the fault and separate the load which connect the fault by operating switches according to thereal-time structure of the distribution network. Then it can find an operating pattern by changing the status of the tie or sectionalizing switches.
The fault sections location in distribution networks based the unified matrix algorithm may result in the false results under the information mutation mode; The method of fault section diagnosis of distribution networks based on new neural network is presented in this paper. The method has high fault-tolerance performance.
This paper comprehensively states the principle and conception of ANN, the author makes a deeper research on such aspects about BP ANN as its algorithmic theory,constructure, initial value, and a model about Fault Section Detection in distribution networks based on the analysis of the distribution network is proposed. This method has high fault-tolerance performance and the test results have verified its correctness.
The author studies the case of BP ANN applying in engineering of Fault Section Detection in Distribution network.
The simulation system is constituted with MATLAB. The results of the analysis and simulation denote that the method proposed in this paper is efficient and reliable.
引文
[1] 刘健，倪建立，邓永辉编著．配电自动化系统．北京：中国水利电力出版社．
[2] 刘健，倪建立，杜宇(Liu Jian，Ni Jianli．Du Yu)．配电网故障区段判断和隔离的统一矩阵算法(A Unified Matrix Algorithm for Fault Section Detection and Isolation in Distribution system)．电力系统自动化(Automation of Electric Power Systems)， 1999, 23(1)：31-33
[3] 杜红卫等．基于遗传算法的配电网故障定位和隔离[J]．电网技术，2000，24(5)：52-55．
[4] Hideo Tanaka, et al. Research&development on Expert Systems Application to Power Systems in Japan. Proceeding of Third Symposium on Expert Systems Application to Power System.(tokyo),1991
[5] 顾雪平，张文勤，高曙等．东北电网故障实时判断和处理专家系统的在线实现．全国高校电自专业第八届学术年会论文集．南京：1992-10
[6] 岑文辉．戴文祥．仇新宏．基于人工神经网络的电力系统故障诊断全国高校电自专业第八届学术年会论文集．南京 1992．10
[7] 彭晓兰．郑纯．程时杰等．反向传播算法运用于电力系统神经网络的分析研究．全国高校电自专业第八届学术年会论文集．南京：1992．10
[8] 阎平凡．张长水．人工神经网络与模拟进化运算．北京：清华大学出版社．2000：13-15
[9] 周东华．现代故障诊断与容错控制．北京：清华大学出版社，2000：138
[10] 张建文，赵大光，董连文．基于模糊数学的变压器故障诊断专家系统．高电压技术．1998．24(4)：6-9
[11] 杨莉．尚勇．周跃蜂．严璋．基于概率推理和模糊数学的变压器综合故障诊断模型．中国电机工程学报，2000．20(7)：19-23
[12] 张冠军．钱政．严璋．变压器绝缘诊断中的ISODATA法．高电压技术．1999．25(1)：1-3
[13] 杨沁．施文康．变压器故障的模糊检测．变压器．1998，35(2)：32-35
[14] IEEE Working Group on Distribution. Distribution Automation(Au—thorial Course). 1998
[15] IEEE Tutorial Course. Distribution Automation. 1998
[16] 飞思科技产品研发中心．MATLAB6.5辅助神经网络分析与设计 北京：电子工业出版社，2003．1
[17] 杨以涵．唐国庆．高曙．专家系统及其在电力系统中的应用．中国电力出版社，1995
[18] 孙雅明，廖志伟．基于不同RS与NN组合的数据挖掘电网故障诊断模型 电力系统自动化 2003，3 27(6)
[19] 顾雪平 张文勤 人工神经网络和专家系统结合运用的电力系统故障诊断方法 华北电力举院学报 1994 No．2
[20] Raw-Shyang Wu. kan-Lee Liou. Ron F. Chu. A Petri Net Algorithm for Scheduling of Generic Restoration Actions. IEEE Trans. on Power Systmes. 1997. 12(1):69-76
[21] Han-Ching Kuo. Yuan-Yih Hsu. Distribution System Load Estimation and Service Restomtion Using a Fussy Set Approach. IEEE Trans. on Power Delivery. 1993.8(4):1950-1957
[22] Seung-Jae Lee. Seong-Ⅱ Lim, Bok-Shin Ahn. Service Restoration of Primary, Distribution Systems Based on Fuzzy Evaluation of Multi-Criteria. IEEE Trans. on Power Systems. 1998,3(3): 1156- 1161
[23] T.Sakaguchi. K. Matsumoto. Development of A Knowledge Based System for Power System Restoration. IEEE Trans. on Power Apparatus and Systems. 1983. PAS-102(2): 320-329
[24] Danial S.Kirschen, Terry L.Volkman, Guiding A Power System Restoration with An Expert System.IEEE Trans. on Power Systems, 1991, 6(2): 558-566
[25] 罗毅，李占柱等．配电网自动化实用技术．北京：中国电力出版社，1999
[26] 王明俊，于尔铿，刘广一．配电系统自动化及其发展．北京：中国电力出版社，1998
[27] 刘有才，刘增良．模糊专家系统原理与设计．北京：北京航空航天大学出版社，1995
[28] 曹存根．面向专家的知识获取．北京：科学出版社，1998
[29] 曹文君．知识库系统原理及其应用．上海：复旦大学出版社，1995
[30] 赵冬梅，郭锐，徐开理，高曙．电网故障诊断专家系统的一种应用．电力自动化设备，2000，20(4)：33-36
[31] 赵德齐．模糊数学．北京：中央民族大学出版社，1995
[32] Young-Moon Park, Kwang-HO Lee. Application of Expert System to Power System Restoration in Sub-control Center. IEEE Trans. on Power Systems. 1997, 12(2): 629-635
[33] 贺家李，宋从矩．电力系统继电保护原理．北京：中国电力出版社，1998：4-6
[34] 毕天妹，倪以信，杨奇逊．人工智能技术在输电网络故障诊断中的应用述评．电力系统自动化．Jan．25．2000：11-16
[35] 夏虹，曹欣荣，王兆样．基于传感器融合的机械设备故障诊断的方法与系统 哈尔滨工程大学学报．1998，(19)：52-57
[36] 周龙．智能方法及其在电力系统故障诊断中的应用．武汉工业学院学报．2000，(1)：69-73
[37] 王道平，张义忠．故障智能诊断系统的理论与方法．北京：冶金工业出版社，2001：95-98
[39] F.Zahra, B.Jeyasurya, J.E.Quaicoe. High-speed Transmission Line Relaying Using Artificial Neural Networks. Electric Power Systems Research. 2000,53(3):173-179
[40] R.K.Aggarwal,Q.YXuan, A.T.Johns, F.Li, A.Bennett. A Novel Approach to Fault Diagnosis in Multicircuit Transmission Lines Using Fuzzy ARTmap Neural Networks. IEEE Transactions on Neural Networks.1999(10)5:1214-1221
[2] 刘健，倪建立，杜宇(Liu Jian，Ni Jianli．Du Yu)．配电网故障区段判断和隔离的统一矩阵算法(A Unified Matrix Algorithm for Fault Section Detection and Isolation in Distribution system)．电力系统自动化(Automation of Electric Power Systems)， 1999, 23(1)：31-33
[3] 杜红卫等．基于遗传算法的配电网故障定位和隔离[J]．电网技术，2000，24(5)：52-55．
[4] Hideo Tanaka, et al. Research&development on Expert Systems Application to Power Systems in Japan. Proceeding of Third Symposium on Expert Systems Application to Power System.(tokyo),1991
[5] 顾雪平，张文勤，高曙等．东北电网故障实时判断和处理专家系统的在线实现．全国高校电自专业第八届学术年会论文集．南京：1992-10
[6] 岑文辉．戴文祥．仇新宏．基于人工神经网络的电力系统故障诊断全国高校电自专业第八届学术年会论文集．南京 1992．10
[7] 彭晓兰．郑纯．程时杰等．反向传播算法运用于电力系统神经网络的分析研究．全国高校电自专业第八届学术年会论文集．南京：1992．10
[8] 阎平凡．张长水．人工神经网络与模拟进化运算．北京：清华大学出版社．2000：13-15
[9] 周东华．现代故障诊断与容错控制．北京：清华大学出版社，2000：138
[10] 张建文，赵大光，董连文．基于模糊数学的变压器故障诊断专家系统．高电压技术．1998．24(4)：6-9
[11] 杨莉．尚勇．周跃蜂．严璋．基于概率推理和模糊数学的变压器综合故障诊断模型．中国电机工程学报，2000．20(7)：19-23
[12] 张冠军．钱政．严璋．变压器绝缘诊断中的ISODATA法．高电压技术．1999．25(1)：1-3
[13] 杨沁．施文康．变压器故障的模糊检测．变压器．1998，35(2)：32-35
[14] IEEE Working Group on Distribution. Distribution Automation(Au—thorial Course). 1998
[15] IEEE Tutorial Course. Distribution Automation. 1998
[16] 飞思科技产品研发中心．MATLAB6.5辅助神经网络分析与设计 北京：电子工业出版社，2003．1
[17] 杨以涵．唐国庆．高曙．专家系统及其在电力系统中的应用．中国电力出版社，1995
[18] 孙雅明，廖志伟．基于不同RS与NN组合的数据挖掘电网故障诊断模型 电力系统自动化 2003，3 27(6)
[19] 顾雪平 张文勤 人工神经网络和专家系统结合运用的电力系统故障诊断方法 华北电力举院学报 1994 No．2
[20] Raw-Shyang Wu. kan-Lee Liou. Ron F. Chu. A Petri Net Algorithm for Scheduling of Generic Restoration Actions. IEEE Trans. on Power Systmes. 1997. 12(1):69-76
[21] Han-Ching Kuo. Yuan-Yih Hsu. Distribution System Load Estimation and Service Restomtion Using a Fussy Set Approach. IEEE Trans. on Power Delivery. 1993.8(4):1950-1957
[22] Seung-Jae Lee. Seong-Ⅱ Lim, Bok-Shin Ahn. Service Restoration of Primary, Distribution Systems Based on Fuzzy Evaluation of Multi-Criteria. IEEE Trans. on Power Systems. 1998,3(3): 1156- 1161
[23] T.Sakaguchi. K. Matsumoto. Development of A Knowledge Based System for Power System Restoration. IEEE Trans. on Power Apparatus and Systems. 1983. PAS-102(2): 320-329
[24] Danial S.Kirschen, Terry L.Volkman, Guiding A Power System Restoration with An Expert System.IEEE Trans. on Power Systems, 1991, 6(2): 558-566
[25] 罗毅，李占柱等．配电网自动化实用技术．北京：中国电力出版社，1999
[26] 王明俊，于尔铿，刘广一．配电系统自动化及其发展．北京：中国电力出版社，1998
[27] 刘有才，刘增良．模糊专家系统原理与设计．北京：北京航空航天大学出版社，1995
[28] 曹存根．面向专家的知识获取．北京：科学出版社，1998
[29] 曹文君．知识库系统原理及其应用．上海：复旦大学出版社，1995
[30] 赵冬梅，郭锐，徐开理，高曙．电网故障诊断专家系统的一种应用．电力自动化设备，2000，20(4)：33-36
[31] 赵德齐．模糊数学．北京：中央民族大学出版社，1995
[32] Young-Moon Park, Kwang-HO Lee. Application of Expert System to Power System Restoration in Sub-control Center. IEEE Trans. on Power Systems. 1997, 12(2): 629-635
[33] 贺家李，宋从矩．电力系统继电保护原理．北京：中国电力出版社，1998：4-6
[34] 毕天妹，倪以信，杨奇逊．人工智能技术在输电网络故障诊断中的应用述评．电力系统自动化．Jan．25．2000：11-16
[35] 夏虹，曹欣荣，王兆样．基于传感器融合的机械设备故障诊断的方法与系统 哈尔滨工程大学学报．1998，(19)：52-57
[36] 周龙．智能方法及其在电力系统故障诊断中的应用．武汉工业学院学报．2000，(1)：69-73
[37] 王道平，张义忠．故障智能诊断系统的理论与方法．北京：冶金工业出版社，2001：95-98
[39] F.Zahra, B.Jeyasurya, J.E.Quaicoe. High-speed Transmission Line Relaying Using Artificial Neural Networks. Electric Power Systems Research. 2000,53(3):173-179
[40] R.K.Aggarwal,Q.YXuan, A.T.Johns, F.Li, A.Bennett. A Novel Approach to Fault Diagnosis in Multicircuit Transmission Lines Using Fuzzy ARTmap Neural Networks. IEEE Transactions on Neural Networks.1999(10)5:1214-1221