基于改进状态空间分割法的继电保护设备拒动风险评估

继电保护设备隐性故障已经成为导致电力系统连锁故障的重要因素.因此,在电力系统的风险分析中,需要突破传统N-1故障设置假设,考虑受继电保护设备隐性故障影响的N-k连锁故障.针对继电保护设备拒动引发连锁故障的问题,首先根据继电保护设备的功能模块建立继电保护设备拒动故障树模型;然后综合考虑元件固有失效率和运行信息,建立继电保护设备拒动概率动态评估模型.基于此,提出一种改进的状态空间分割法对电力系统面临的连锁故障风险进行分析,实现了在枚举空间和抽样空间中进行连锁故障搜索,可提高连锁故障搜索的完备性和风险分析的计算效率.最后,在仿真中验证了所提方法的有效性.     

一种基于搜索树的配电网故障处理新算法

针对配电网络结构特点,提出了一种基于树形搜索的配电网故障区域检测、隔离及恢复算法。该算法基于各开关联络关系构造出配电网树形模型结构,利用配电自动化终端采集的故障信息搜索出故障区域,并找出多重供电恢复方案。算例表明,该算法不仅对单重故障有效,而且对多重故障能有效判别,同时对配电自动化终端误信号有一定的抑制能力,具有较高的实用性。     

基于配电网故障区域辨识的定位算法

提出了配电网故障区域辨识的定位算法。该算法根据馈线终端单元FTU(Feeder Terminal Unit）上传的电流信息有效地推导出配电网归一化区域负荷电量,从而进行过热区域搜索判别出故障区域。给出了算法原理及实例推导,结果表明该算法有效。该算法亦可适用于末梢馈线段故障及并列多重故障的定位场合。     

基于故障群-征兆群对子的变电站故障诊断

为处理好变电站故障诊断问题中故障源和故障征兆间因果关系的不确定性,本文提出了基于故障群组合优化的故障诊断方法.在建立保护配合的Petri网模型的基础上,用状态转移方程分析模型的动态特性,从中获取所有故障群-征兆群对子.创建故障群组合匹配已知故障征兆的适应度函数,并用遗传算法搜索出最优诊断解.实验证明:该方法不仅能有效地辨识出故障源,而且能提供保护装置拒动、断路器装置拒动、信号传输错误等信息,对现场人员迅速确认故障设备非常有利.     

基于过热区域搜索的多电源复杂配电网故障定位方法

对基于过热弧搜索的配电网故障定位方法进行了多方面分析,讨论了其不足之处。在此基础上提出基于过热区域搜索的多电源复杂配电网故障定位新方法。通过判断区域故障以及引入顶点对弧的负荷和区域负荷,给出简便的最小配电区域分离算法,并详细论述了过热区域的搜索步骤;同时对馈线测控终端故障信息不完备的情况给出了相应对策。算例分析表明,新方法推理简单,容错性好,且可精确衡量故障的程度,具有较高的实用价值。     

基于有向分层模型的配电网故障区域判断

基于配电网分层模型,根据故障节点过电流的方向与潮流方向的关系,将故障信息矩阵与网基结构矩阵相结合改进了故障判断矩阵,通过搜索查找发生故障最小配电区域的父节点,可以快速、直观、准确地找到故障最小配电区域,从而确定故障点.     

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。     

含光伏电源配电网的改进矩阵保护算法

为了解决分布式电源并网给配电网保护带来的问题,针对含光伏电源的配电网,给出一种基于广域保护的故障区域定位矩阵算法。利用智能电子设备(IED)收集节点电流信息,根据电流综合幅值判定故障搜索方向,形成故障区域搜索正方向矩阵和反方向矩阵。保护算法在矩阵修订过程中对网络拓扑结构的中间节点和边缘节点不作区分,使用同样的修订方法,简化了算法,对不断变化的网络拓扑结构有适应性,并且在判据中能清楚分辨这两类节点。算例分析结果表明,改进的矩阵保护算法对故障的区域定位是有效的。     

广域后备保护故障识别方案

在广域信息基础上,提出了一种基于集中式结构的故障识别方案,根据各序分量排序情况确定故障区域,利用方向和纵差原理来判别故障元件。启动方式保证了经高阻短路故障情况下保护启动的灵敏性;故障区域划分方法既能缩小故障搜索范围,又能准确涵盖各故障元件;故障判别算法能够快速、准确地识别出线路及母线的简单、复合故障。提出了在断路器拒动、一点或一个站信息丢失情况下的故障识别策略。基于河北南网系统实际参数和运行断面进行了大量仿真,仿真结果验证了该方案的合理性和有效性。     

融合遥信报警和电气量分析的电网故障诊断方法

随着广域测量系统和故障录波信息网的广泛应用,系统的电气量信息获取也越来越方便可靠。利用融合开关量和电气量信息的综合故障诊断方法,首先通过分析报警信息中的开关量信息、搜索停电区域,得到可疑故障设备集;然后对可疑设备进行模糊解析模型求解分析以及相关电气量的希尔伯特黄变换分析;最后,定义元件的解析故障度、频率畸变度和能量变化度三个故障测度,并将这三者作为证据体采用改进的D-S证据理论进行融合得到最终诊断结果。算例表明,该方法能有效降低保护和断路器拒动、误动的影响,提高故障诊断结果的准确度。     

基于凸优化理论的多传感器故障诊断技术

针对DS证据理论基本概率赋值函数难以获取、证据问要求相互独立等缺点,提出了利用凸优化理论来建立满足需要的多传感器故障诊断模型。本文首先分析了传感器故障诊断报告的形式,构造了多传感器故障融合的代价函数。通过分解代价函数,将多传感器故障诊断问题转换为凸优化问题。同时利用对数罚函数内点算法求解凸优化模型,该算法结构简单,计算量小,易于实现。理论分析和仿真结果表明,基于凸优化模型的多传感器故障诊断方法较之传统的DS证据方法具有更好的识别能力、更强的鲁棒性和更广的适用范围。     

改进Tabu优化算法在地区电网故障诊断的应用

基于优化算法的电网故障诊断是将电力系统故障诊断描述为优化问题后用优化方法求解。提出了基于改进Tabu搜索算法的电力系统故障诊断的新方法。以覆盖集理论为基础, 把电力系统警报处理问题表示为0-1整数规划问题, 并引入了一种新的评估指标,确保了全局最优解,并极大地满足了电网实时性的要求。最后用一个全新算例来证明计算结果的可靠性与快速性, 结果表明以改进TS为基础的方法能够满足地区电网故障诊断的要求。     

基于交叉随机粒子群优化算法的机组组合优化模型

针对机组运行时故障的不确定性,利用威布尔失效概率函数来详细描述机组的故障概率,并以此为基础提出了兼顾机组故障率的机组组合优化模型。根据所建模型的特点,提出了带有随机权重和带有异步变化学习因子的粒子群算法,将机组组合问题划分为离散量和连续量两部分,通过在机组编码矩阵中进行交叉计算来解决机组组合问题。以5台机组24 h的机组组合优化问题为例进行计算,验证了所建模型的正确性及所提算法在求解机组组合优化模型时的有效性。     

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.     

Fault Location Observability Using Phasor Measurement Units in a Power Network Through Deterministic and Stochastic Algorithms

This article studies deterministic and stochastic algorithms for placing minimum number of phasor measurement units (PMUs) in a power system in order to locate any fault in the power system. The optimization problem is initially formulated in a mixed integer linear programing framework with binary-valued variables as well as in a binary integer linear programing model. Then, the optimization problem is formulated as an equivalent non-linear programing model, minimizing a quadratic objective function subject to equality non-linear constraints defined over a bounded and closed set. The problem is solved by using a Sequential Quadratic Programming algorithm. The non-linear program is illustrated with a 7-bus test system. Also, stochastic algorithms such as binary-coded genetic algorithm and particle swarm optimization have been implemented in solving the optimal PMU placement under fault condition. The accuracy of suggested algorithms is independent from the fault type and its resistance. The optimization models are applied to the IEEE systems. The numerical results indicate that the proposed algorithms locate minimizers at the optimal objective function value in complete agreement with those obtained by branch-and-bound algorithms.     

基于仿电磁学算法的辐射状配电网故障定位

A typical 0-1 programming problem in distribution network is described for the fault location problem.To solve the problem effectively,a new approach named Electromagnetism-like Mechanism algorithm(ELM)is proposed.The simulation for fault location with 8 distribution nodes is executed through comparing with the Genetic Algorithm.The correctness of the fault location model and the effectiveness of ELM are testified in solving 0-1 optimization problem.     

电网故障诊断解析模型的改进二进制增益共享知识算法求解

针对现有智能优化算法在求解电网故障诊断解析模型时存在的易于陷入局部最优和种群质量低等问题,提出一种改进二进制增益共享知识算法(improved binary gaining-sharing knowledge-based algorithm, IBGSK)。首先,根据故障诊断规则,构建一种包含完备故障信息的完全解析模型。其次,将离散工作机制融入改进算法的种群更迭中,以避免发生空间脱节。然后,结合进化种群动力学思想(evolutionary population dynamics, EPD),引入一种自适应交叉算子,以提高种群质量和增强算法的全局寻优能力。最后,通过特征选择和故障诊断仿真实验对算法性能进行评估。结果表明：IBGSK算法相较于其他优化算法,在特征选择问题上具有更高的计算效率、更强的全局寻优能力和泛化能力;在求解电网故障诊断解析模型上具有更优的诊断可靠性、时效性和收敛性。     

基于蚁群算法的配电网故障定位与隔离

蚁群算法是一种求解组合最优化问题的新型通用启发式方法,该方法具有正反馈、分布式计算和富于建设性的贪婪启发式搜索的特点。通过建立适当的数学模型,基于故障过电流的配电网故障定位变为一种非线性全局寻优问题。该文将蚁群算法用于配电网故障定位方面的研究,并通过实例证明了该算法的可行性和高效性。     

基于贝叶斯优化随机森林的变压器故障诊断

针对集成学习参数众多,缺乏高效准确的参数寻优方法的问题,文章提出了基于贝叶斯优化随机森林(RF)的变压器故障诊断方法.该方法采用了多个决策树构成RF故障诊断模型,然后将高斯过程(GP)作为概率代理模型、提升策略(PI)作为采集函数,构建贝叶斯优化(B0)算法,进行RF模型参数寻优.此外,还对支持向量机(SVM)和K最近邻(KNN)两种模型进行贝叶斯优化并对比.在RF模型上,将贝叶斯优化与随机搜索优化进行性能对比.实验结果表明:RF模型经贝叶斯参数寻优后,诊断准确率有明显提高;与随机搜索优化方法相比,贝叶斯优化搜索的模型参数更优,寻优效率更高.     

Optimizing service restoration in distribution systems based on scheduling of crews considering priorities of loads

The service restoration to sound sections within a short period as much as possible, performed after the permanent fault occurs in a distribution system, is significant for maintaining power supply reliability. This paper proposes an efficient method to find service restoration procedures based on fault searching and the optimal work scheduling of crews, considering the power supply priorities of loads and the probability of occurrence of faults. The proposed method is composed of two-stage problems to reduce the outage sections efficiently: (i) the problem to find a series of searching procedures for unknown fault locations in the first stage and (ii) the optimization problem of work schedules of crews in the second stage. In the first stage, the order of operation of switches opened for searching for the fault direction is determined, constituting the searching tree. After that, the proposed optimization method can efficiently solve the work scheduling problem by formulating it as a mixed-integer linear programming problem in the second stage. The computational experiments using a large-scale distribution system model with many remote and manual switches show that the proposed method can provide efficient service restoration procedures within a reasonable computational time.