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本文叙述了一个利用继电器和断路器的信息估计故障位置的专家系统。这个系统适用于调度中心,能帮助调度员在故障后采取恢复步骤的第一步时,判断关键位置。当发生故障时,这个系统根据保护系统的知识及继电器和断路器的状态进行推理,推断故障位置。对于多点故障及继电器和断路器的误动作,这个系统也能给出可能的答案。本专家系统是用 prolog 语言构成的。 相似文献
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正1问题的提出断路器是电力系统的重要组成部分,断路器合闸控制电路的完好与否,直接影响断路器操作和保护命令能否正确执行,进而关系到电力系统能否安全稳定运行。断路器合闸控制电路故障大多为外部元件故障,当某一外部合闸条件不满足时,断路器将不能正常合闸,断路器跳位监视回路会出现报警提示。在现有技术中,微机保护测控装置可通过实时采集监视合闸控制电路的跳闸位置继电器和监视分闸控制电路的合闸位置继电器的工作信息,判断并接 相似文献
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断路器在电力系统中肩负着控制和保护的重要任务,加强对断路器机械系统潜伏性故障诊断研究,对提高断路器在运行中的可靠性具有重要意义。文中以ZN98型真空断路器为研究对象,依据其振动强度建立了一套高压断路器振动加速度测试系统,基于MATLAB小波包频带能量分解算法提取故障诊断特征量。以紧固螺栓松动的潜伏性故障为例,分析了不同频段的振动信号能量图谱,提出了一套高压断路器弹簧操动机构螺栓松动潜伏性故障的诊断方法。研究表明,设置单个紧固螺栓故障时,传感器测试位置距离故障螺栓越近所得的特征量阈值越大,应选取特征量阈值的最小值作为测试位置的故障诊断判据;相较于断路器的分闸过程,合闸过程的特征量阈值更能明显准确地反映断路器的机械状态。 相似文献
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故障信息 小波变换与继电保护 总被引:5,自引:0,他引:5
故障信息是继电保护的基础。为研制新型继电保护,需要深入研究和充分利用各种故障信息。本文论述和比较电力系统故障后不同类型的故障信息,指出暂态故障信息和电力系统故障后断路器动作产生的二次优动信息是可利用的;小波变换是分析、提取和计算故障信息的重要手段;以暂态故障信息、断路器动作所产生的二次扰动信息的应用和小波变换为标志的新型行波保护、暂态保护和无通道保护是继电保护研究的重要方向。 相似文献
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通过系统实际故障事例分析,对断路器金属性短接时间——金短保护的作用和实际回路进行阐述,指出金短保护对断路器跳合闸回路以及断路器位置继电器的影响,对于继电保护判断断路器位置的回路采用位置继电器造成误判,提出改正方法。 相似文献
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直流断路器作为电力系统控制和保护的最重要的开关设备之一,其可靠运行关系着电力系统的安全稳定性。直流断路器在分合闸时的振动信号能直接反映断路器的机械状态,因此选取直流断路器的机械振动信号作为研究对象。首先研究了振动传感器选型对振动信号采集的影响并确定了传感器型号,接着研究了振动信号预处理和提取振动信号特征量的方法,最后模拟了几种常见故障并用Elman神经网络对模拟故障进行诊断。处理结果表明,用小波包分解和Elman神经网络实现了直流断路器机械状态监测和诊断。 相似文献
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保护继电器的任务是在电力系统事故时,高速检测事故瞬时的电压、电流信息,并通过断路器切除故障,防止电力设备损伤,维持电力系统的稳定运行。 相似文献
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断路器失灵保护是近后备中防止断路器拒动而扩大事故的一项有效措施,本文介绍断路器失灵故障统计情况,并就断路器失灵保护的设计原则、装设的条件,以及在运行维护中应注意的事项进行分析。能促进运行维护及调试质量的提高,有利于电力系统安全可靠稳定的运行。 相似文献
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<正> 充油或充气类的输变电设备容器由金属或瓷套构成。当设备在动作时或长期使用介质劣化而引起内部压力增大时,会引起设备破坏或爆炸。因此除了继续提高产品性能、质量外,对有容器结构的产品,在压力异常时加强保护,也是提高供电质量的措施。 在电力系统中设备发生故障时,是通过继电器保护系统使断路器将故障设备和电力系统断开。如本系统断路器也发生故障,则需上一级断路器动作。电力系统中设备有很多种类,如变压器,电压互感器、电流互感器、各种断路器、避雷器,电容器等。在电 相似文献
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Hong-Tzer Yang Wen-Yeau Chang Ching-Lien Huang 《Power Delivery, IEEE Transactions on》1994,9(1):220-230
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 相似文献
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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. 相似文献
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高压真空断路器是电力系统开关设备中极其重要的一种高压电器,而高压断路器故障中80%是由于机械特性不良造成,为此通过小波包变换对高压断路器机械振动信号进行了分析,以信号的能谱熵作为特征输入向量,建立了粒子群优化(PSO)径向基函数(RBF)神经网络的高压断路器故障识别系统模型,最后对实际高压断路器振动信号进行获取分析并得到结果。实验结果表明,高压断路器正常信号能谱熵向量各元素分布比较均匀;而故障信号所得能谱熵向量各元素变化较大且有一定变化规律;粒子群优化后的RBF网络模型在正确率、精度等方面高于传统神经网络模型。实验结果表明该方法用于高压断路器的故障诊断是可行的,并且可以为断路器的故障诊断提供更好的理论依据。 相似文献
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基于小波神经网络的可更换电路单元故障诊断 总被引:4,自引:0,他引:4
文章提出了一种基于小波神经网络的模拟电路故障诊断方法。这种方法采用正弦信号作为被测电路的输入激励,在时域中对输出信号采样来构造神经网络的训练和测试样本,将自适应学习率及附加动量BP算法训练后的小波神经网络应用于某装备可更换电路单元故障诊断中。仿真试验表明,该方法减少了故障诊断时间和提高了网络的平均诊断正确率。 相似文献
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Prashant P. Bedekar Sudhir R. Bhide Vijay S. Kale 《International Journal of Electrical Power & Energy Systems》2011,33(3):457-465
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. 相似文献
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ABSTRACT A new method to fault section estimation in power systems by using the time sequence information of tripped circuit breakers is presented. The main contributions of this paper include the following three aspects: a. the mathematical model of the fault section estimation problem by using the time sequence information of the tripped circuit breakers at the actuating time zone of any protective relay is proposed for the first time; b. an efficient method to identify the faulty subnetworks is developed by using the information from circuit breakers. In this way the fault diagnosis can be fulfilled in a very short time for large scale power systems and can be implemented online; c. a new method based upon Refined Genetic Algorithm(RGA) is used to solve the fault section estimation problem. Test results for there sample power systems have shown that the RGA method can find multiple optimal solutions directly and efficiently in a single run. 相似文献
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高压断路器是最重要的电力设备之一,在电力系统中起控制和保护作用。为了提高高压断路器故障诊断的准确率,提出了一种基于概率神经网络(PNN)的高压断路器故障诊断方法。该方法在分析高压断路器的故障特性来确定特征信号的基础上建立了PNN故障诊断模型,该模型将采集的特征数据作为网络的输入,通过Parzen窗估计法得到类条件概率密度,进而按Bayes决策规则对特征数据进行分类。经仿真表明,概率神经网络故障诊断模型具有收敛速度快、故障诊断准确率高、容易训练等特点。因此,该方法是一种有效的故障诊断方法,具有良好的应用前景。 相似文献