基于RQGA和非支配排序的多目标混沌量子遗传算法

为了提高多目标优化算法的收敛性、分布性和减少算法的计算代价,借鉴实数编码遗传算法和多目标优化理论,构建一种多目标混沌量子遗传算法.在分析量子位概率的混沌特性、量子态干涉特性和量子位实数编码的基础上,采用量子位概率交叉和混沌变异的方式进化种群,以提高寻优能力和收敛速度,利用非支配排序、精英保留和分层聚类等多目标优化策略保持种群多样性的同时,保证进化向Pareto全局最优解集方向进行.通过混合算法性能对比测试验证了多算法集成的有效性,并分析关键参数对算法性能的影响.电力系统多目标无功优化的仿真结果验证了该算法的有效性和可行性.     

基于负荷趋势判断的无功优化精细化控制方法及求解策略

针对短期内母线负荷波动导致无功优化控制效果不理想的问题,提出一种无功优化精细化控制方法用于缓解因控制方案滞后而导致的控制不平滑现象,并采用多目标无功优化松弛模型来改善预测控制中可能出现的电压越限和收敛性问题。为了获得最优控制方案,给出一种基于动态搜索策略的多目标混沌差分进化算法,该算法能根据种群中可行解的比例动态调整进化过程中最优解的搜索策略,提高多目标模型Pareto最优前沿的搜索能力和求解效率。IEEE 30标准节点数据仿真结果表明,多目标优化算法在最优解集,外部解收敛性,以及解集的均匀性等方面都好于经典多目标算法;真实电网数据计算表明,精细化控制方法相比传统方法能进一步减小电压偏差和网损,并提高模型的收敛性。     

基于并行混沌搜索的非线性系统参数估计

提出了一种并行混沌搜索结合模式搜索法的混合优化算法,并应用于非线性系统参数估计.该算法采用并行混沌搜索机制,多个混沌变量同时映射一个优化变量.为了提高搜索速度,在每一次并行混沌搜索之后,由模式搜索法对并行混沌搜索结果再次寻优.该优化算法具有混沌优化的全局搜索能力,而模式搜索法则加快了局部寻优性能.该算法应用于三种不同非线性系统中,仿真结果表明其是一种有效的参数估计方法,参数估计精度优.     

基于PCPSO算法的配电网多目标动态优化*

为了适应当前配电网日趋复杂且分布式电源大量接入的情况,提出一种改进的多目标粒子群优化算法。该算法在标准粒子群算法中引入Pareto前沿和混沌搜索,通过Pareto前沿处理多目标问题,并用混沌搜索对粒子进行混沌遍历,既保留了进化过程中多目标的相对最优解,同时也兼顾了收敛性。通过算例进行仿真,结果表明该算法可以快速搜索多目标最优解,为配电网多目标动态优化提供思路和参考。     

基于诊断信息量的测点选择方法

将传统意义上测点的概念推广为电路中能够携带故障信息的任何可测量变量.用香农信息熵对在一定测点上测量前后电路故障状态的不确定性进行了定量描述,在此基础上提出了测点集合诊断信息量的概念并给出了其估计方法.将测点选择问题建模为在全体可达测点集合中选取具有最大诊断信息量的测点子集问题,用遗传算法对最优子集进行搜索.讨论了遗传算法中染色体的编码方式、适应度函数的选择以及遗传算子的设计方法.将本文提出的方法用于一个线性模拟电路的测点选择中,实验结果表明,所获得的测点子集能充分反映电路的故障状态信息.     

梯级水电站群优化调度多目标量子粒子群算法

为科学求解梯级水电站群多目标优化调度模型,提出一种基于量子行为进化机制的多目标量子粒子群算法(MOQPSO)。该方法以标准量子粒子群算法(QPSO)为基础,引入外部档案集合存储非劣粒子,利用个体支配关系实现档案集合的动态更新维护;依据个体领导能力优劣选择粒子历史最优位置与种群全局最优位置,维持搜索过程中个体进化方向的多样性;采用混沌变异算子对个体进行局部扰动,提升算法的全局收敛性能。乌江流域模拟调度结果表明,所提方法具有良好的收敛速度与寻优能力,可快速获得兼顾梯级水电系统经济性与可靠性要求的Pareto解集,能够为工程人员提供科学的决策依据。     

基于自适应粒子群的模拟电路故障诊断

为了加快模拟电路故障优化算法收敛性和优化效率,采用了一种基于自适应粒子群算法的模拟电路故障诊断方法。利用小波分解技术提取待诊断电路的测试信息作为故障特征,借助于遗传变异的思想在粒子群算法中引入"变异"理念,然后使用自适应的粒子群算法优化RBF神经网络的结构参数,实现模拟电路的故障诊断。仿真结果表明,利用自适应粒子群算法与传统的粒子群算法相比,其训练步数明显加快,扩大算法的搜索范围,从而有效提高了网络的训练速度和优化精度。     

基于自适应混沌粒子群优化算法的多目标无功优化

针对粒子群无功优化中由于随机生成代表控制变量值的粒子,使得在优化迭代过程中易陷入局部最优解,而且后期收敛速度慢等问题,将混沌优化算法融合到粒子群算法中,提出了混沌粒子群算法求解多目标无功优化问题。该算法在初始化粒子即无功优化控制变量值时,采用混沌思想,增加控制变量取值的多样性;通过粒子群无功优化算法计算各个粒子对应的适应值即无功优化目标函数值,并按照其大小择优选取控制变量值进行混沌优化以帮助无功优化控制变量跳出局部极值区域;并根据无功优化目标函数值自适应地调整其惯性权重系数以提高全局与局部搜索能力。通过算例分析表明,采用自适应混沌粒子群算法进行无功优化,能够及时跳出局部最优得到全局最优解,且收敛速度快。     

一种基于混沌神经网络的测试生成算法

基于组合电路测试生成的Hopfield神经网络模型，提出了一种利用混沌神经网络的全局搜索能力进行测试生成的有效算法。该算法综合了随机性和确定性算法的优点，通过对有关参数的调节，使网络逐渐由混沌神经网络向Hopfield神经网络过渡。实验结果验证了该测试生成算法的有效性与相关性能。     

基于自适应混沌粒子群优化算法的多目标无功优化

针对粒子群无功优化中由于随机生成代表控制变量值的粒子,使得在优化迭代过程中易陷入局部最优解,而且后期收 敛速度慢等问题,将混沌优化算法融合到粒子群算法中,提出了混沌粒子群算法求解多目标无功优化问题.该算法在初始化粒子即无功优化控制变量值时,采用混沌思想,增加控制变量取值的多样性;通过粒子群无功优化算法计算各个粒子对应的适应值即无功优化目标函数值,并按照其大小择优选取控制变量值进行混沌优化以帮助无功优化控制变量跳出局部极值区域;并根据无功优化目标函数值自适应地调整其惯性权重系数以提高全局与局部搜索能力.通过算例分析表明,采用自适应混沌粒子群算法进行无功优化,能够及时跳出局部最优得到全局最优解,且收敛速度快.     

基于特殊测试点隔离算法的测试节点选择

测试点的选择问题作为模拟电路故障诊断的基础性问题,如何找到数目最少的测试点以隔离电路的所有故障成为研究的重点,常用的测试点选择方法大多为故障字典法.研究发现,如果电路中一种特定的故障只能由一个特殊的测试点进行隔离,那么将这种特殊测试点选出可以大大简化故障字典,然后完成剩余有效测试点的选择和冗余测试点的移除,即可选出最优的测试点集合.这种方法称为特殊测试点隔离算法,通过对比实验,发现该算法很好的平衡了测试点选择中对时间和精度的要求,而且具有更高的效率.     

基于多目标粒子群算法的电力系统环境经济调度研究

提出了一种新的多目标粒子群优化(Multi-Objective Particle Swarm Optimization, MOPSO )算法,用于求解电力系统的环境/经济调度问题。通过设计特定的约束修正因子,将不可行解修正成可行解,并在此基础上用惩罚函数法构建了新的适用于多目标粒子群的适应度函数模型。根据帕累托占优条件形成历史帕累托最优解集和全局帕累托最优解集,引入稀疏度排序法选择全局最优解,基于帕累托最优前沿的斜率特性,提出用斜率法筛选非劣解,采用基于模糊数学的满意度评价模型选择POF的折衷最优解。最后,用IEEE-30节点标准测试系统对所提算法进行了仿真测试,并与其他算法进行了对比。仿真结果表明所提算法可行、有效。     

Environmental/economic power dispatch problem using multi-objective differential evolution algorithm

This paper presents a multi-objective differential evolution (MODE) algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multi-objective problem with competing and non-commensurable objectives of fuel cost, emission and system loss. The proposed MODE approach adopts an external elitist archive to retain non-dominated solutions found during the evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed approach have been carried out on the IEEE 30- and 118-bus test system. The results demonstrate the capability of the proposed MODE approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective EED problem. The comparison with reported results of other MOEAs reveals the superiority of the proposed MODE approach and confirms its potential for solving other power systems multi-objective optimization problems.     

Multi-objective evolutionary algorithm for SSSC-based controller design

In this paper, an evolutionary multi-objective optimization approach is employed to design a static synchronous series compensator (SSSC)-based controller. The design objective is to improve the transient performance of a power system subjected to a severe disturbance by damping the multi-modal oscillations namely; local mode, inter-area mode and inter-plant mode. A genetic algorithm (GA)-based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented and compared with a PI controller under various disturbances namely; three-phase fault, line outage, loss of load and unbalanced faults to show the effectiveness and robustness of the proposed approach.     

基于多目标遗传优化的容差电路故障屏蔽诊断

将多目标遗传优化算法与故障屏蔽原理相结合,提出了一种新的容差模拟电路故障屏蔽诊断方法.通过分析计算故障与容差效应对端口特征量的影响,建立了模拟电路具容差情况下的故障屏蔽模型,创建了目标函数与相应的适应值函数,用以搜索能极大限度屏蔽故障支路的最优激励,根据最优激励的Pareto解集及所提隶属度函数来实现故障定位.模拟试验结果表明:所提方法避免了故障效应与容差效应的相互交叠,故障定位准确率高.     

Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection

Wind power has emerged as the most promising option for providing sustainable eco-friendly power supply to the modern world. Due to its unpredictable nature, integration of wind power into the conventional power grid is a very challenging task having dynamic characteristics. Due to the inherent uncertainty associated with wind availability, additional spinning reserve needs to be scheduled in order to maintain security and supply reliability. Multi-period multi-objective optimal dispatch (MPMOOD) is presented for wind integrated power system with reserve constraints. The complex relationship between wind power availability, spinning reserve allocation and their impact on economic/environmental cost are analysed using an elaborate model.A new multi-objective Series PSO-DE (SPSO-DE) hybrid algorithm is proposed where the two paradigms, differential evolution (DE) and particle swarm optimization (PSO) share domain information and maintain a synergistic cooperation to overcome their individual weaknesses. For multi-objective (MO) problems, the selection operation in SPSO-DE is replaced by a 5-class time-varying fuzzy selection mechanism (TVFSM) to avoid saturation and to increase Pareto diversity. To promote convergence towards the central part of the Pareto front and to quickly isolate the boundary solutions, Guassian membership function is employed. Elitism is applied to preserve good solutions and momentum operation is used to stop premature convergence. The proposed method expedites the search for the best solution, i.e. the solution which satisfies all the objectives of the MO problems. To test the performance and computational efficiency, the proposed method is applied on two standard test power systems.     

多目标电能质量监测器的优化配置

针对电能质量监测器的优化配置问题,建立了以监测程度和监测器个数为指标的多目标优化配置模型。采用带精英策略的快速非支配排序遗传算法(non-domtnated soring genetic algorithm,NSGA-Ⅱ),获得此多目标优化问题的Pareto最优解集。该方法能保证种群的多样性,避免传统加权求解时权值的选择和解的偏好性。最后,对Pareto最优解集的各个目标函数进行归一化处理,将最大值对应的方案作为合适的最优解。通过对2个算例进行仿真,得到了合理的电能质量监测器的配置方案,验证了该方法的可行性和有效性。     

多目标无功优化的向量评价自适应粒子群算法

为了克服粒子群算法在高维复杂问题寻优时有相当可能陷入局部极优的现象,提出了一种自适应粒子群算法。该算法利用种群多样性信息对惯性权重进行非线性的调整,并在算法的后期引入速度变异算子和位置交叉算子,使算法摆脱后期易于陷入局部最优点的束缚。对基于向量评价的粒子群算法进行了扩展,提出了基于向量评价的自适应粒子群算法(vector evaluated particle adaptive swarm optimization,VEAPSO)来解决多目标无功优化问题,求解出问题的Pareto最优解集。为帮助决策者从Pareto最优解集中选取合适的最优解,该文提出一种基于决策者偏好及投影寻踪模型的多属性决策法,使决策结果更加真实可靠。将该算法应用于多目标无功优化问题中,IEEE 30和IEEE 118节点系统算例仿真表明该方法用于解决多目标无功优化问题是有效可行的。     

Multi-objective Phasor Measurement Unit Placement in Electric Power Networks: Integer Linear Programming Formulation

A multi-objective optimal phasor measurement unit placement model using integer linear programming is presented in this article. The proposed model simultaneously optimizes two objectives, i.e., minimization of phasor measurement unit numbers and maximization of measurement redundancy. To calculate the redundancy criteria, the single-line outage and the phasor measurement unit loss are considered simultaneously. A linear formulation is presented for both objective functions. Also herein, to address conflicting attributes and identify Pareto optimal solutions of the multi-objective optimal phasor measurement unit placement problem, a new multi-objective mathematical programming method is proposed. Finally, a new index, i.e., minimum distance to utopia point, is implemented to select the most preferred solution among the available Pareto front based options on the goal to achieve judicious decision makers. Two test systems, i.e., a modified 9-bus and an IEEE 118-bus test systems, are used to illustrate the effectiveness of the proposed framework.