Fault Section Estimation of Power System Using Colored and Timed Petri Nets

A correct and rapid inference is required for practical use of expert system for fault section estimation in power systems. This paper proposes a new model-based expert system for fault section estimation using colored and timed Petri nets. The Petri nets simplify the inference procedure greatly because it can represent the causalities among faults of power system components, protective actions of relays and circuit breakers. They can also represent the operating states of these protective devices explicitly, or, more precisely, whether a relay has been actuated correctly or falsely, etc. In addition, time sequence information on actuated relays and tripped circuit breakers can be handled by using the colored and timed Petri nets. Therefore, the operating states of relays and circuit breakers can be inferred more correctly. Petri nets are suited for parallel processing so well that there is a possibility of reducing the inference time considerably. In the proposed system, the inference is executed by setting the initial states in the Petri nets using the information on the actuated relays and the tripped circuit breakers and then operating the Petri nets. There have been several test cases which used a prototype system on the Macintosh computer employing Object Common Lisp, and good results have been obtained.     

A new approach to fault section estimation in power systems using Ant system

In this paper, a new method to fault section estimation problem in power systems is developed using Ant System. Based on the information from the operated protective relays and tripped circuit breakers, the fault section estimation is first formulated as a combinatorial optimization problem. Then, a new approach known as the Ant System is applied to solve this optimization problem. The Ant System is suitable for solving problems of combinatorial optimization by means of agents that work in parallel, among a population and without a supervisor in a cooperative manner. Finally, a sample power system is used to demonstrate the efficiency of the Ant System based method. From these test results, it appears that the Ant System is of promise for the fault-section estimation problem.     

基于模拟进化理论的电力系统的故障诊断

本文提出了利用基于模拟分子进化（ＳｉｍｕｌａｔｅｄＭｏｌｅｃｕｌａｒＥｖｏｌｕｔｉｏｎ──ＳＭＥ）的优化方法诊断电力系统故障的新方法。电力系统故障诊断问题可表示为０－１整数规划问题，利用ＳＭＥ方法可一次得到几个可能的解，这对于诊断复杂的电力系统故障（尤其是有保护或断路器误动作的情况）具有实际意义。这种方法可以同时利用保护和断路器的信息，改进后也可以只利用断路器的信息，适用范围较广。此外，ＳＭＥ方法在适应能力方面优于现有的基于Ｂｏｌｔｚｍａｎｎ机的方法，可以处理任意多重故障，且适于用并行处理机实现。     

基于小波神经网络的在线警报处理系统

利用小波变换在信号处理方面的时频分析能力和神经网络对任意非线性函数的普通的逼近能力，提出了一个基于小波神经网络的电力系统故障段辨别方法。故障诊断系统依据保护继电器和断路器的采样信息估计电力系统中故障段的位置。仿真结果显示，小波神经网络故障诊断系统能正确估计电力系统单一故障和多重故障的位置，即使在电力系统中存在保护继电器和断路器误动或拒动的情况下，小波神经网络也能给出合理的结果。测试结果表明，小波神经网络在电力系统警报处理系统中有良好的应用前景。     

基于遗传算法和模拟退火算法的电力系统的故障诊断

根据元件故障与保护动作和断路器跳闸之间的逻辑关系，首先把电力系统的故障诊断问题表示为０－１整数规划问题。在此基础上，采用了两种方法求解，第一种方法是遗传算法，第二种方法是模拟退火算法。这两种方法都能以较大的概率收敛到全局最优解，并能够求得多个最优解。提出的方法理论严密，与当前比较流行的以启发式知识为基础的专家系统方法不同。本文的方法可以处理任意复杂的故障，也可以处理有保护和断路器误动作的情况，且计算时间与故障的复杂程度基本无关。此外，提出的方法原则上适合于并行处理。     

Adaptive fault section estimation using matrix representation with fuzzy relations

In this paper, a method for the fault section estimation that considers the network topology under the influence of a circuit breaker tripped by the previous fault is proposed. The proposed method uses a three-dimensional (3-D) matrix to represent the network topology and the protection systems. The proposed network matrix describes the sections that are protected by particular protective devices that can be changed according to the network topology. To deal with the uncertainties imposed on the protection systems, the fuzzy set theory was applied to the network matrix in order to examine the relationships between the operated protective devices and the fault section candidates. A systematic method for the fuzzy membership function for each relation is proposed. In the proposed method, the operated time sequences of the protective devices were employed because either the network matrix or the network topology varied whenever the operations of the circuit breakers occur. The efficiency and adaptivity of the proposed method were demonstrated in experimental studies on real power systems in the Korean electric power system, particularly in case of complicated multiple faults.     

A Modified Abductive Inference Model for Fault Section Estimation in Power Systems Using the Tabu Search Approach

A new approach to fault section estimation in power systems is presented based upon a modified abductive inference model and the tabu search method. The developed fault section estimation model can simultaneously take into account the operating reliabilities of protective relays and circuit breakers and the degree of correctness of received and nonreceived alarm data in a formal and systematic manner. In the modified abductive inference model, a criterion for describing the relative plausibility of different diagnosis hypotheses is available. Based on this criterion, the fault section estimation problem is then formulated as a 0-1 integer programming problem, and a tabu search (TS) approach is presented for solving the problem. A sample power system is served for demonstrating the correctness of the developed abductive inference based fault section estimation model and the computational efficiency of the TS-based method.     

Petri网与专家系统结合的输电网络故障诊断方法

介绍了一种应用Petri网数学模型并结合专家系统进行电力系统输电网络故障诊断的方法。根据断路器跳闸信息确定故障区域,通过搜索“六元表”找出故障区域中动作的各类保护及相关的断路器,通过与SCADA系统提供的信息进行比较,把对应各设备的继电保护信息输入Petri网故障诊断模型,最终确定故障元件。这种方法克服了专家系统难于用数学方法描述的缺点,利用Petri网建立数学模型,提高了故障诊断速度。通过实例仿真验证了该方法的有效性、准确性和通用性。     

A new neural networks approach to on-line fault section estimationusing information of protective relays and circuit breakers

This paper proposes a new neural network diagnostic system for online power system fault section estimation using information of relays and circuit breakers. This system has a similar profile of an expert system, but can be constructed much more easily from elemental samples. These samples associate fault section with its primary, local and/or remote protective relays and breakers. The diagnostic system can be applicable to the power system control center for single or multiple fault sections estimation, even in the cases of failure operation of relays and breakers, or error-existent data transmission. The proposed approach has been practically verified by testing on a model power system. The test results, although preliminary, suggest this system can be implemented by various electric utilities with relatively low customization effort     

Fault section estimation in power system using Hebb’s rule and continuous genetic algorithm

In this paper a new approach for fault section estimation (FSE) in electrical power system is presented. We propose a procedure to obtain objective function (required for fault section estimation) using the Hebb’s learning rule. The continuous genetic algorithm (CGA) optimization method is then employed to estimate the fault section making use of the objective function. The Hebb’s learning law used in this paper gives, linear algebraic equations, to represent the targets in terms of the status of relays and circuit breakers (CBs). This gives a simple objective function, which leads to reduction in time required by the CGA to identify fault section. The CGA gives an advantage of requiring less storage than binary genetic algorithm (GA). Also the CGA is inherently faster than binary GA.The proposed approach is tested on various systems, and is found to give correct results in all cases. Simulation results for two illustrations have been presented in this paper. The results show that the proposed approach can find the solution efficiently even in case of multiple faults or in case of failure of relays/circuit breakers. A comparison with artificial neural network (ANN) approach is also presented.     

An Expert System for Fault Section Estimation Using Information from Protective Relays and Circuit Breakers

This paper deals with an expert system which can estimate possible fault sections using information from protective relays and circuit breakers. This system is applicable to dispatching centers and can help dispatchers to judge emergency situations as the first step in restoration procedures. When some faults occur, the system makes inferences based on both knowledge about protection systems and information on the operating protective relays and tripped circuit breakers. The system can give possible answers even in the case of multiple faults and false operations of relays and circuit breakers. This expert system is written in Prolog.     

利用保护和断路器信息的电力系统故障诊断与不可观测的保护的状态识别的模型与 Tabu 搜索方法

根据元件故障与保护动作和断路器跳闸之间的逻辑关系,首先构造了电力系统故障诊断与不可观测的保护的状态识别（ＦＳＥ－ＳＩＵＰＲ）的一种新的０－１整数规划模型。该模型充分利用了断路器信息,在一些保护信息不完整的情况下也能正常工作。之后,提出了用Ｔａｂｕ搜索（ＴＳ）方法来求解这一问题。ＴＳ方法是近年来出现的用于求解组合优化问题的高效的启发式搜索技术。算例计算表明,所发展的ＦＳＥ－ＳＩＵＰＲ的数学模型是正确的,采用的基于ＴＳ的方法相当有效。     

Fault section estimation in power systems by discrimination analysis

Once a fault occurs in a power system, the fault section must be estimated from inadequate information on protective relays and circuit breakers. There have been several studies to develop an automatic estimation method, e.g., knowledge engineering, neural network, etc. However, it was very difficult to obtain a method which is powerful enough and easy to maintain. In this paper, the discrimination analysis theory is applied to the fault section estimation. Using the theory, it is expected that the inference speed can be improved. It is expected also that the knowledge about the fault section estimation can be collected. First, using the small model system, the fault section estimation method with the discrimination analysis theory is described and compared to the method with neural network. Next, it is illustrated that the method can be applied to a large system.     

一种新型故障定位方法的研究

论述了利用因果网技术进行故障定位的方法。因果网描述了故障元件、继电器、开关之间内在的动作关系,利用了比传统专家系统更深的知识、简单、明确。提出了利用面向对象技术形成因果网的方法,所形成的因果网能适应各种不同的网络,通用性强,且能考虑故障扩大的情形。     

基于加权模糊时序Petri网络的电网故障诊断模型

提出了一种基于加权模糊时序Petri网络(FTPN)的电网故障智能诊断模型,该模型主要包括停电区域识别、可能故障元件加权FTPN建模、故障元件集求解、保护/断路器动作情况判定4个部分。介绍了故障差错告警差错信息的分类以及故障告警信息时序特性,研究了断路器动作信息及网络拓扑关系,结合广度优先搜索,得到了停电区域识别方法。最后,以新英格兰10机39节点系统为例,建立了母线和线路加权FTPN故障诊断数学模型,并给出该系统的总体结构,结果表明该模型能够快速故障定位,准确提供保护和断路器动作评价信息。     

电力系统故障诊断的量子粒子群优化算法

电力系统故障诊断是利用保护和断路器的动作信息来推断可能的故障位置.其中故障元件的识别是故障诊断实现的关键.文中应用量子粒子群优化算法研究故障元件的识别方法,先根据保护动作原理将故障诊断问题表示为0-1整数规划问题,然后用量子粒子群优化算法求解.与标准PSO算法和传统遗传算法比较,文中采用的量子粒子群优化算法具有稳定性高...     

基于开关变位信息的电网可疑故障元件集识别方法

根据现有的电网故障诊断过程，提出在建立系统元件的开关变位次序图的基础上，采用变位开关附近的改进深度优先搜索法确定停电区域。通过计算变位开关的拒动级别将可疑故障元件依故障概率的大小按顺序排列，进一步提高了故障诊断速度。对开关变位信息在传输过程中发生畸变的情况进行了分析，给出了相应的停电区域搜索步骤。对于电网的单一故障、多重故障以及存在保护和开关多次不正确动作的严重故障，该方法均能快速准确地定位停电区域，并给出精简后的可疑故障元件集。     

基于遗传算法的电力系统故障诊断的解析模型与方法──第一部分 : 模型与方法

本文共分三部分。第一部分发展了电力系统故障诊断的解析模型与基于遗传算法的方法;第二部分首先提出了用计算机自动形成故障诊断的目标函数的方法,这是实现在线故障诊断所必需的。之后介绍了为浙江省电力局开发的在线故障诊断实用软件的组成与功能;第三部分介绍了将该软件应用于浙江省２２０ｋＶ以上电力系统时其ＥＭＳ信息的获取方法及模拟在线测试结果。这里是第一部分,着重介绍了所发展的同时利用保护和断路器信息的故障诊断的解析模型。其在数学上是一个无约束０－１整数规划模型,借助于充分利用断路器的跳阐信息。这种模型在一定程度上解决了保护信息不完整时的电力系统的故障诊断问题。之后,概述了利用实时网络拓扑分析方法识别故障平息后的停电（故障）区域的方法,从而可将故障诊断局限于这些小的停电区域之中,以大大节省计算时间。此外,还简要介绍了用遗传算法求解故障诊断问题的基本步骤。     

基于贝叶斯网络的电网复杂故障推演新方法

为了分析电网复杂故障过程,基于保护装置与断路器的动作信息构建贝叶斯网络,提出了电网复杂故障推演新方法。应用电网实际拓扑结构信息、继电保护装置和断路器的动作信息构建贝叶斯网络。基于贝叶斯网络进行故障元件诊断,针对远后备保护、近后备保护误判的两种特殊情况,制定了专家系统规则实现保护拒动、误动的识别。将保信系统上送信息与拒动误动分析结果按主保护层-近后备保护-远后备保护-断路器层顺序进行信息整合,推演出发生故障时各层保护装置以及断路器动作先后顺序,实现了电网复杂故障推演。     

基于正反向推理的电力系统故障诊断专家系统

介绍了一种在电力系统故障诊断专家系统中采用的基于正反向推理的电力系统故障诊断方法。该方法首先根据跳闸断路器提出故障假说,然后根据断路器和继电保护动作的信息逐一检验假说的正确性,最后给出故障设备或故障范围。该方法可以诊断输电线路、母线、变压器和发电机设备的故障及继电保护和断路器的非正确动作。对山西电网实际故障事例的测试结果表明,该方法是有效的。