基于差异进化和PC集群的并行无功优化

提出了一种在线求解电力系统无功优化问题的方法。该方法基于新的差异进化(DE)算法和并行计算技术，在PC集群上实现优化。IEEE 118节点系统的算例表明：DE算法尽管简单，但可快速收敛到近似最优解；采用并行差异进化和适当规模的PC集群，可大大缩短电力系统无功优化的计算时间，使之满足在线应用的需要。     

基于差分进化混合粒子群算法的电力系统无功优化

差分进化混合粒子群算法(DEPSO)首先利用差分进化(DE)的变异和选择算子产生新的群体,然后通过使用粒子群优化算法(pSo)进行局部搜索.该算法发挥差分进化和粒子群优化算法各自拥有的特点,并克服自身存在的问题,具有收敛速度快、搜索能力强、鲁棒性好的特点.将该算法用于电力系统无功优化,通过IEEE30节点系统的仿真计算证明了该算法的快速性和有效性.     

基于混合差异进化优化算法的电力系统无功优化

无功优化是电力系统实现电压和无功功率最优控制和调度的基础，阐述了一种基于混合差异进化算法的新无功优化方法。混合差异进化算法是一种直接随机搜索方法，由在当前种群中随机采样的个体之间的基因差异来驱动，且为缩短计算时间、避免陷入局部最优，在算法中嵌入了加速操作和种群迁移操作。将该无功优化方法在IEEE 30节点系统上进行了校验，并与基于其他算法的无功优化方法进行比较，仿真结果表明该算法具有收敛速度快、鲁棒性好、计算精度高的优点。     

基于量子差分进化算法的电力系统无功优化

以系统有功网损最小为目标,建立了一种电力系统无功优化数学模型,并提出了一种基于量子差分进化算法的电力系统无功优化方法。该算法采用量子计算中的并行、坍缩等特性,增强了对解空间的遍历能力;同时在传统选择策略的基础上加入了量子计算的概率表达特性,有效地避免了算法的早熟现象。对IEEE-30节点测试系统进行仿真分析,并将优化结果与传统差分进化算法和粒子群优化算法进行对比分析,结果表明量子差分进化算法在解决系统无功优化问题上更科学、更有效。     

基于分层分区协同进化算法的电力系统无功优化

随着电力系统的大范围互联以及电压等级的增多,无功优化问题变得越来越复杂。无功优化问题是一个多变量、非线性、非连续性问题,在不同电压等级电网中无功又有不同的特点。针对电力系统无功优化的上述特点以及大范围互联电网控制变量较多的问题,提出了按电压等级将电力系统分层,各层之间独立优化互不影响,层内分区域并采用协同进化算法优化的方法。对于电力系统分层的方法作了探讨,提出了节点分裂的方法。在此基础上针对层内无功优化详细讨论了协同进化算法的原理、步骤以及其应用。IEEE30节点的算例表明,该方法要优于DFP,BFS等经典优化算法以及普通遗传算法。     

基于DESA算法且含风电接入的配网无功优化

针对配电网无功规划优化的非线性和约束复杂的特点,提出一种DESA混合进化算法进行求解。该算法将DE和SA算法各自的优势相融合,利用DE算法的全局优化特性在解空间探寻全局最优解,利用SA算法的局部最优逃逸能力实现对DE算法不足的补偿。采用IEEE 33节点系统进行仿真分析,验证该方法的可行性和有效性。     

多目标进化算法求解无功优化问题的对比分析

对经典的多目标进化算法(multi-objective evolutionary algorithms,MOEAs)在电力系统无功优化中的应用展开比较研究。与传统设定偏好参数、将多目标问题转化为单目标问题的方法不同,直接采用计及系统网损与电压偏移的多目标模型。提出无功优化多目标进化算法统一框架,采用一致的编码策略、约束处理方法。以IEEE30节点标准系统的多目标无功优化为算例,从帕累托前沿、外部解及C指标等方面,比较各种算法的性能特点,并按照其优劣将算法分为5个性能等级。参考算法的性能等级,详细分析每种算法的优缺点。相关结论对MOEAs在无功优化及电力系统其他优化问题中的应用和改进,都具有一定的参考价值。     

电力系统无功优化综述

介绍了考虑风电场接入后、交直流混合输电系统以及电力市场下多个无功优化数学模型,探讨了现有传统优化算法、人工智能优化算法以及改进的混合优化算法,并对其中常用的优化算法进行了分析比较。针对电力系统无功优化在线运行的实用性问题,综合评价了现有无功优化控制策略及其适用情况,提出了当前电力系统无功优化研究中仍需解决的问题及未来的研究方向。     

高压电网电流互感器饱和对继电保护的影响应用分析与研究

电力系统无功优化是带有多约束的非线性组合优化问题,进化策略算法在解决这类问题时显示出独特的优势。惩罚函数法是进化算法求解约束问题最常用的方法,但其罚系数难以合理确定。文中将带随机排序策略的进化算法应用到无功优化问题中,有效地避免了罚函数法处理约束问题时罚系数难以确定的缺点,并在编码方法、进化终止判据方面做了改进,有效地提高了算法的求解效率。算例证明了改进算法的有效性。     

改进差分进化算法在电力系统无功优化中的应用

针对电力系统无功优化具有非线性、多控制变量、多约束条件、连续变量和离散变量混杂的特点,提出了一种改进的差分进化算法。该算法根据进化学习过程中积累的经验,利用优良群体引导变异的方向,同时提取优良群体各维元素的信息,以优良群体信息指导个体每一维变量的交叉操作。IEEE 30节点系统算例表明,所提算法较基本差分进化算法和粒子群算法,收敛速度快、计算精度高、稳定性好、能有效地求解电力系统无功优化问题。     

Reactive Power Optimization Using Hybrid CABC-DE Algorithm

Reactive power optimization is closely related to voltage quality and network loss, and it has great significance for the safety, reliability, and economical operation of the power system. Differential evolution (DE) algorithm has been currently applied to reactive power optimization. In order to mitigate the shortcomings of poor local search ability and premature convergence in DE, this paper presents a novel hybrid algorithm–chaotic artificial bee colony differential evolution (CABC-DE) algorithm, which improves the DE algorithm based on artificial bee colony algorithm and ideas of chaotic search. It introduces the observation bees' acceleration operation and the detective bees' chaotic search operation into CABC-DE. The validity of the proposed method is examined using IEEE-14 and IEEE-30 bus system. The experimental results show that CABC-DE algorithm is more effective than regular DE algorithm for reactive power optimization. The algorithm can save the search time greatly and get a better solution for optimization, thus making it suitable for solving reactive power optimization problems.     

基于改进差分进化算法的配电网无功优化

采用改进差分进化算法(Improved Differential Evolution Algorithm,IDEA)求解配电网无功优化问题。该算法引入基于反学习的种群初始化方法,使算法得到的初始种群具有多样性,能够充分提取搜索空间的信息;引入高斯扰动机制到交叉操作中,提高了在维尺度上的种群多样性;在进化过程中融入人工蜂群搜索思想,引入蜂群加速进化与侦查操作策略,使算法能快速跳出局部最优,避免了早熟问题。建立了配电网无功优化数学模型,并采用IDE算法对IEEE30节点系统求解该模型,并与基本DE算法进行对比,仿真结果证明了所提IDE算法具有更佳的性能,能够有效的求解配电网无功优化的问题。     

基于IBBDE算法的交直流混联系统无功优化

随着高压直流输电工程(HVDC)投产规模持续增长,交直流混联电网的格局初步形成,给传统电网无功优化带来挑战。文中提出一种改进骨干差分进化算法(Improved Bare-bones Differential Evolution,IBBDE)求解交直流混联系统无功优化问题。在骨干差分进化算法的基础上,IBBDE算法采用广义反向学习初始化种群和自适应调整交叉概率的改进措施以提升种群的全局寻优能力。以含HVDC的IEEE 30节点系统为算例进行分析,结果表明,与差分进化算法和骨干差分进化算法相比,所提IBBDE算法可获得更优的无功优化效果,且寻优稳定性更好。     

基于改进算子的免疫遗传算法的电压无功优化

针对电压无功优化问题的特点和免疫遗传算法在求解全局性优化问题中的适用性,应用免疫遗传算法对系统进行电压无功优化。在编码时采用了实整混合编码形式,求抗体相似度时进行了归一化处理,在选择操作时对适应度函数进行了变换,合理的选择变换系数的值,可以保证算法在进化前期保持种群多样性,在进化后期仍能有较快收敛速度,并在交叉变异时实数段和整数段基因采取不同的措施。取IEEE-30节点标准系统为例,利用开发的优化计算程序进行电压无功优化计算,验证了所提出的算法较其他算法在计算和收敛能力上具有优势。     

Seeker Optimization Algorithm for Optimal Reactive Power Dispatch

Optimal reactive power dispatch problem in power systems has thrown a growing influence on secure and economical operation of power systems. However, this issue is well known as a nonlinear, multimodal and mixed-variable problem. In the last decades, computation intelligence-based techniques, such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA)-based reactive power dispatch method is proposed. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, the algorithm's performance is evaluated on benchmark function optimization. Then, the SOA is applied to optimal reactive power dispatch on standard IEEE 57- and 118-bus power systems, and compared with conventional nonlinear programming method, two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms. The simulation results show that the proposed approach is superior to the other listed algorithms and can be efficiently used for optimal reactive power dispatch.      

Network loss minimization with voltage security using differential evolution

This paper presents a differential evolution (DE) based optimal power flow (OPF) for reactive power dispatch in power system planning studies. DE is a simple population-based search algorithm for global optimization and has a minimum number of control parameters. The problem is formulated as a mixed integer non-linear optimization problem taking into account both continuous and discrete control variables. The proposed method determines control variable settings such as generator voltages (continuous), tap positions (discrete) and the number of shunt reactive compensation devices to be switched (discrete) for real power loss minimization in the transmission system using DE algorithm. Most of the evolutionary algorithm applications to optimization problems apply penalty function approach to handle the inequality constraints, involving penalty coefficients. The correct combination of these coefficients can be determined only by a trial and error basis. In the proposed approach, the inequality constraints are handled by penalty parameterless scheme. Voltage security margin was evaluated using continuation power flow (CPF), to ensure the feasibility of the optimal control variable setting. The suitability of the method was tested on IEEE 14 and IEEE RTS 24-bus systems and results compared with sequential quadratic programming (SQP) method. The DE provides near global solutions comparable to that obtained using SQP.     

基于混沌遗传算法的电力系统无功优化

针对遗传算法在求解大规模电力系统无功优化问题中存在的收敛速度慢、易早熟的缺点,提出了一种新的无功优化算法——混沌遗传算法CGA。该方法结合混沌优化所具有的遍历性、随机性和规律性的特点,在遗传进化过程中引入混沌移民算子,通过混沌移民操作维持群体中染色体的多样性,以克服传统遗传算法中由于近亲繁殖所导致的早熟问题,确保算法的全局收敛性,加快计算速度。通过对某地区42节点系统进行仿真计算,该方法相比于简单遗传算法,计算速度提高了45%,收敛到全局最优的概率提高了1.25倍。     

基于差异进化算法的并行容错无功优化

容错是应用分布式并行计算系统时必须解决的一个关键难点.在基于广域网络的新型计算环境下实现基于进化算法的电力系统优化应用时,需要对大量个体进行频繁的迭代评估,现有的各类容错技术难以实现对此类应用的高效容错.文中结合进化算法概率性搜索、个别个体失效不会影响系统整体性能的特点,提出以父代个体取代未按时返回的子代个体的方式实现容错,并结合基于差异进化算法的无功优化问题对所提出的方法进行了仿真分析.IEEE 118节点系统测试表明该方法能以优化性能的降低为代价实现高效容错.在该容错手段支持下,可通过采用更大范围网络计算资源基础上更大的群体规模,取得一致性更好、更接近全局最优的解.     

Optimal power system operation using parallel processing system and PSO algorithm

In recent studies, PSO algorithm is applied to solve OPF problem. However, population based optimization method requires higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallelization of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC-cluster system with 8 Intel Pentium IV 2 GHz processors. The proposed approach has been tested on the test systems. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.     

Quasi-oppositional differential evolution for optimal reactive power dispatch

This paper presents quasi-oppositional differential evolution to solve reactive power dispatch problem of a power system. Differential evolution (DE) is a population-based stochastic parallel search evolutionary algorithm. Quasi-oppositional differential evolution has been used here to improve the effectiveness and quality of the solution. The proposed quasi-oppositional differential evolution (QODE) employs quasi-oppositional based learning (QOBL) for population initialization and also for generation jumping. Reactive power dispatch is an optimization problem that reduces grid congestion with more than one objective. The proposed method is used to find the settings of control variables such as generator terminal voltages, transformer tap settings and reactive power output of shunt VAR compensators in order to achieve minimum active power loss, improved voltage profile and enhanced voltage stability. In this study, QODE has been tested on IEEE 30-bus, 57-bus and 118-bus test systems. Test results of the proposed QODE approach have been compared with those obtained by other evolutionary methods reported in the literature. It is found that the proposed QODE based approach is able to provide better solution.