改进的配电网故障定位、隔离与恢复算法

提出了一种改进的配电网故障定位,隔离与恢复方法,考虑到配电自动化水平的提高,采用基于SCADA信息的故障定位方法,算法简法,实用,能够屏蔽SCADA数据的错误和畸宽,得出故障隔离与恢复需要的故障位置的,为了实现故障处理的闭环控制,提出了在开关柜动的情况下,对故障隔离和恢复算法的改进,并结合扬州城区配电网实际线路给出计算结果,显示了算法的准确性和有效性。     

综合智能式的故障恢复专家系统：与故障恢复算法集拿相结合？…

提出了一种新的用于配电网故障后恢复供电的综合智能专家系统。该系统包括用于故障恢复的算法集（ＳＲＡＳ）、配电管理系统（ＤＭＳ）的软模型、邦联模式的识别和自学习的模糊恢复专家系统（ＳＥＲＦＥＳ）。基于以上技术实现的故障后的恢复供电方案,对配电网络结构无信赖性,使范围较广,能满足实时要求,可适用于大规模网络和多邦联条件下的恢复供电。     

电力系统负荷恢复问题的混合遗传算法求解

对电力恢复过程中最后一个阶段的负荷恢复问题进行了研究,把电力系统的负荷恢复问题建模为带众多约束条件的0?1背包问题,并设计了一种将贪心算法与改进遗传算法相结合的改进混合遗传算法来对问题进行求解.在遗传算法之前,先用贪心算法生成该问题的贪心解,然后让每一代中有着最差适应度的个体无条件的变为此贪心解,使最终结果至少不会比贪心法差.解决了系统在负荷恢复过程中的潮流计算问题,采用先求系统的频率变化,然后再计算潮流分布的方法,将约束条件和目标函数融合在一起,通过建立一种偏序关系,避免了罚函数选择参数的困难.利用贪心算法求解背包问题的快速性和多父体杂交的非凸组合技术,使算法具有求解的快速性和在解空间内搜索的遍历性.算例求解结果表明了该算法在负荷恢复问题中的有效性.     

基于小波神经网络的电力电子电路故障模式识别

提出了基于两种不同小波神经网络的电力电子电路故障模式识别方法.针对电力电子电路故障,构造了激活函数型和权值型两种不同的三层小波神经网络,给出了相应的数学模型和学习算法.以三相整流桥电路为例,建立了小波神经网络的输出与故障元之间的对应关系,实现了电路故障的模式识别,并与用普通BP网络识别的结果进行了比较.仿真结果验证了两种故障识别方法的正确性和较好的准确性.     

综合智能式的故障恢复专家系统--与故障恢复算法集相结合的自学习模糊专家系统

提出了一种新的用于配电网故障后恢复供电的综合智能专家系统.该系统包括用于故障恢复的算法集(SRAS)、配电管理系统(DMS)的软模型、故障模式的识别和自学习的模糊恢复专家系统(SERFES).基于以上技术实现的故障后的恢复供电方案,对配电网络结构无依赖性,使用范围较广,能满足实时要求,可适用于大规模网络和多故障条件下的恢复供电.     

基于免疫原理的蚁群算法在配电网恢复中的应用

对配电网的故障恢复问题进行了描述,提出了结合实际的配电网故障恢复目标函数,并将蚁群优化算法引入其中,用来解决这个复杂的、多目标、多约束的组合优化问题.针对蚁群算法存在的易陷入局部最优和收敛难的问题,引入免疫机制,通过基于抗体浓度的选择机制和多样性策略来提高蚁群的全局搜索能力和停滞现象,并通过配电网恢复的具体实例验证该算法的快速有效性.     

蚁群算法在配电网冷负荷恢复中的应用

配电网长时间停电后,负荷会失去多样性,引起冷负荷故障恢复问题.为防止变压器过热,通过隔离开关实现配电网负荷的逐步恢复.在电力市场新规则下,电力公司将会因为供电质量不符合要求,而受到严重的惩罚.该文的目标函数考虑了电力市场的影响,采用蚁群算法,选取最优的负荷恢复次序,实现对顾客的公平和供电公司的经济利益.实例计算表明该方法可行、有效.     

基于DPSO算法的黑启动负荷恢复方案

为了科学、合理制定负荷恢复方案,通过研究负荷恢复特性,提出了一种基于DPSO算法的负荷恢复算法.该方法对指定的目标节点进行负荷恢复计算,使得负荷恢复具有更强的针对性、适应性和可操作性.通过经典电网模型和实际运行电网的验证,结果表明了算法有效性.     

基于克隆算法的舰船电力系统故障恢复

为了解决舰船电力系统的故障恢复问题,在综合考虑多电站环形供电网络的拓扑结构、发电机的容量限制、负载的优先级以及最大限度恢复负荷供电等约束条件下,将供电网络的故障恢复看作是一个多目标、非线性优化问题.给出了节点电压约束的供电恢复模型,采用负载支路相关矩阵法对容量约束进行检验,减小了分析、计算难度.对负载进行0、1、2编码,每一位编码对应一个负载,有效地缩短了编码长度.最后应用克隆算法得出了优化的故障恢复策略,选择亲和力高的抗体并进行克隆.仿真结果表明,该算法比简单遗传算法、启发式遗传算法以及混沌遗传算法等其他故障恢复优化算法具有更快的收敛速度,更高的获得最优解的概率,能够有效提高故障恢复的速度和准确性.     

基于蚁群算法的配电网故障检测定位与恢复研究

根据当前配电网故障检测定位与恢复的实际需求,介绍一种基于蚁群算法的配电网故障检测定位方法,该方法可实现多电源条件下配电网故障检测定位,并具有较高精准性。另外,将蚁群算法与启发式规则相结合,共同实现配电网故障恢复和供电恢复,达成配电网故障自动恢复效果。     

Network switching and voltage evaluation during power system restoration

In this work, voltage evaluation after power components energization such as transmission line, transformer and shunt reactor is analyzed using artificial neural network (ANN)-based approach. Throughout the initial phase of system restoration, unexpected overvoltage may happen due to nonlinear interaction between the unloaded transformer and the transmission system. Such an overvoltage might damage some equipment and delay power system restoration. In the cases of transformer and shunt reactor energization, ANN is trained with the worst case scenario of switching angle and remanent flux which reduce the number of required simulations for training ANN. Moreover, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of overvoltages during network switching with good accuracy.     

基于改进遗传算法的舰船电力系统网络重构

为了解决舰船电力系统发生多重故障时供电恢复问题,本文改进了遗传算法,通过采用最优保留的选择机制和自适应交叉、变异概率,有效地加快了算法的收敛速度.而综合考虑适应值和相似度的适应度选取,保证了种群选择的多样性,和现有的算法相比,改进效果是明显的.为检验重构方案是否满足约束条件限制,本文提出了更为简便、快捷的基于线路、负荷相关性的方法.舰船电力系统典型故障恢复算例表明,改进的遗传算法有效提高了供电恢复的速度及精度,避免了不成熟收敛.     

CP组合神经网络在基于DGA的变压器绝缘故障诊断中的应用

对向传播(CP)算法是一种有教师学习和无教师学习算法的混合体,既具有良好的模式识别性能,又能很好地解决反馈型神经网络的收敛问题。笔者提出了基于CP分类器人工神经网络的变压器故障诊断方法,建立了CP组合神经网络模型,通过比较不同训练情况下的正判率来确定CP网络中的训练次数和竞争层神经元的个数。实例证明该模型诊断结果的正判率比改良电协研法和IEC三比值法有较大的提高,具有较高的诊断准确率和应用价值。     

Power Distribution Outage Cause Identification With Imbalanced Data Using Artificial Immune Recognition System (AIRS) Algorithm

Power distribution systems have been significantly affected by many fault causing events. Effective outage cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data often degrades the outage cause identification performance. In this paper, artificial immune recognition system (AIRS), an immune-inspired algorithm for supervised classification task is applied to the Duke Energy outage data for outage cause identification using three major causes (tree, animal, and lightning) as prototypes. The performance of AIRS on these real-world imbalanced data is compared with an artificial neural network (ANN). The results show that AIRS can greatly improve the performance by as much as 163% when the data are imbalanced and achieve comparable performance with ANN for relatively balanced data     

Distribution feeder loss analysis by using an artificial neural network

This paper proposes an artificial neural network (ANN) based feeder loss analysis for distribution system analysis. The functional-link network model is examined to form the artificial neural network architecture to derive various loss calculation models for distribution feeders with different configurations. The ANN is a feedforward network that uses a standard back-propagation algorithm to adjust the weights on the connection path between any two processing elements. The typical daily load curve of the study feeder for each season is derived to field test data. A three-phase load flow program is then executed to create the ANN training sets to solve the exact feeder loss. A sensitivity analysis is performed to determine the key factors of feeder loss, which are feeder loading and power factor, primary and secondary conductor length, and transformer capacity. The above key factors form the variables of the ANN input layer. By applying the artificial neural network with pattern recognition capability, this study has developed the seasonal loss calculation models for both an overhead and an underground distribution feeder. Two practical feeders in the Taiwan Power Company (Taipower) distribution system have been selected for computer simulation to demonstrate the effectiveness and accuracy of the proposed ANN loss models. By comparing the loss models derived by the conventional regression technique, it is found that the proposed loss models can estimate feeder loss in a very effective manner and provide a better tool for distribution engineers to enhance system operation efficiency.     

模式识别在变电站遥视系统中的应用研究概述

将模式识别技术应用于变电站遥视系统中,为变电站实现无人值守提供了条件.图像处理和模式识别是变电站的两大关键技术.阐述了图像处理和图像识别的过程,介绍了常用的模式识别如统计模式识别、模糊识别和神经网络识别等方法的基本原理,并对它们的优缺点进行了分析.最后总结了对变电站内信号灯状态、指针表和数字表、环境等的识别方法的应用研究.     

电力系统短期负荷预测的模糊神经网络方法

针对电力系统短期负荷预测问题 ,考虑到气象因素对负荷的影响 ,提出了一种模糊神经网络的短期负荷预测方法 ,首先根据评价函数选取相似日学习样本 ,然后利用隶属函数对影响负荷的特征因素向量的分量进行模糊处理 ,采用反向传播算法 ,对 2 4点每点建立一个预测模型 ,提高了学习效能。适合在短期负荷预测中使用 ,具有较好的预测精度     

CORRESPONDENCE: SIMPLIFIED ESTIMATION OF END EFFECTS IN LINEAR INDUCTION MACHINES

A pattern recognition application, using artificial neural networks (ANN), may be categorized as the largest category that covers the use of a neural networks for classifications. It is proved that ANNs are excellent for the applications of complex pattern recognition such as visual images of objects, speech recognition, printed or handwritten characters, and many other types of pattern recognition. Such wide applications are developed, based on the major advantage of the neural networks of being inherently error tolerant, i.e., it can solve problems adequately with information (data) that is uncertain, incomplete, or noisy. The reliable results of the power system security and economical analysis depend on the supply of real-time data base, results of power system state estimation, programs whose major problem is the determination of the network topology configuration. The determination of the topology suffers from the uncertainty of the collected data related to the circuit breakers status, due to the possible communication errors and/or failures. This problem solution matches the potential of the neural networks that can generalize, and after training, they can handle imperfect or incomplete data, providing a degree of fault tolerance. This paper describes an efficient ANN power system topology classifier. The function of such a classifier is to determine the system topology configuration under the presence of incorrect and/or incomplete data concerning the lines and the breakers status. A double layered ANN model is developed and explained as well as the specially efficient training algorithm. Comparative training and test results are reported, and they prove the robustness of this ANN.     

基于模式的配电网规划方法研究

为了简化配电网规划的过程,对配电网规划模式进行了定义,提出了一种基于模式的配电网规划方法。针对配电网规划过程中的不确定因素,引入模糊理论的方法和思想,首先采用模糊聚类分析方法将已有的规划样本进行分类,形成几类不同的规划模式。之后采用模糊模式识别方法对待规划目标进行特征识别,找到其所属的规划模式,参考该规划模式对待规划目标进行规划。采用实际数据进行算例分析,结果表明基于模式的规划方法可以有效地对配电网规划过程进行简化。     

一种基于常规保护原理的ANN发电机差动保护研究

文中提出了一种基于常规保护原理的神经网络差动保护方法。神经网络具有非常优良的模式识别能力。文章首先从理论上分析得出，可以由一个单神经元实现差动保护中常规的比率制动特性，并实现了由一个单神经元感知器构成的比率制动特性的差动保护，在此基础上进一步提出了一个具有非线性制作特性的多层神经网络差动保护模型。该方法将传统保护中的整定值和人工神经网络结构中的权系数对应起来，利用学习算法获得保护的最佳方案，运行人员可以根据经验来选择训练结果，它彻底解决了将ANN技术应用到继电保护工程实际中去后可靠性差的问题，且容易被现场运行人员所接受。该方法无论在工程中还是在理论上都具有重要的意义。