改进的PSO在补偿电容器优化配置中的应用

在建立电容器优化配置问题的非线性整数规划模型时,考虑了电压谐波畸变率约束条件,以保证优化方案下的各节点电压总谐波畸变率控制在规定限值以内.应用了一种简单有效、收敛性很好的演化计算算法-微粒群优化算法(PSO)进行问题的求解.从编码方式、操作过程、目标函数选取、参数调节以及信息共享机制等5个方面分析了该算法的优越性,并给出了用于电容器优化配置问题中算法的具体求解步骤.     

谐波影响下电力电容器的协调无功降损方法

传统采用退火—逐步优化算法对电力系统中的电容器,进行多形态高载荷负荷协调时,未综合考虑谐波影响下电容器的负荷状态,无法对总谐波畸变进行有效控制,降损效果差;因此提出新的谐波影响下电力电容器的协调无功降损方法,通过谐振影响下的电力电容器优化配置数学模型,综合考虑谐波影响下电力电容器电能消耗总金额和配置电容器所需的费用,获取谐波影响下电容器的电压总谐波畸变率;通过微粒群优化算法,确保各节点电压总谐波畸变率控制在规定限值以内,对电容器进行优化配置,实现电容器的无功降损控制。实验结果说明,采用所提方法优化配置后的电容器可有效抑制谐振发生,可降低约37%的电网损耗,具有节能高的优势。     

谐波畸变下电网的无功优化

系统中装设了大量的电容器进行无功优化以降低网损，但在谐波频率下，容易产生系统与电容器之间的谐波谐振或谐波放大，从而使系统的谐波畸变率变大，破坏了系统的安全运行，针对这一问题，提出了谐波畸变情况下对电网进行无功优化的模型，给出了用遗传算法进行求解的具体步骤：染色体编码；适用值函数的选取；遗传操作，进行系统优化，并通过灵敏度方法对遗传算法进行了改进，用VB编制了仿真程序，对Ward-Hale六节点系统进行了仿真计算，结果表明，在满足电网电压和谐波标准约束的情况下，模型能达到系统网损和谐波畸变最小，且改进的方法大大加快了遗传过程的进化速度，说明了改进方法的有效性。     

基于灵敏度分析的谐波治理设备多目标优化配置方法

在非线性负荷和分布式电源大量接入配电网的背景下,配置有源电力滤波器是解决电压波形畸变、谐波损耗激增、电能质量下降等一系列谐波污染问题的有效措施之一。为合理选择配电网中有源电力滤波器安装位置与容量,协调谐波治理成本和效果之间的关系,文中提出一种基于灵敏度分析的有源电力滤波器多目标优化配置方法,实现了有源滤波器位置和容量的快速求解。建立配电网谐波潮流计算模型,使用灵敏度分析方法处理所有节点,获得了安装节点集合;考虑节点电压的谐波畸变率和投资成本,建立滤波器容量优化模型;通过分解多目标进化算法求解优化模型;对IEEE 18节点配电系统进行仿真计算,结果表明了文章所提方法的有效性。     

抑制谐波的配电网无功优化规划

在配电网中,为进行无功优化以降低网损,装设了大量的电容器.这些电容器的装设,导致了在谐波频率下,容易产生系统与电容器之间的谐波谐振或谐波放大,从而使系统的谐波畸变率大为增加,破坏了系统的安全运行.针对这一问题,文中提出了谐波畸变情况下对配电网进行无功优化规划的模型,给出了用灵敏度指导遗传算法进行求解的具体步骤,并用VB编制了仿真程序,对实际配电网进行了仿真计算.计算结果表明,该模型能使电网在满足电压和谐波标准约束的情况下,系统网损和谐波畸变率均得到改善,且大大提高了寻优效率,验证了文中所提方法的有效性.     

电容器组串联电抗率优化选择模型和算法研究

在简要分析了电容器对谐波电流放大机理的基础上,面向整个电网研究了集中补偿电容器组串联电抗器电抗率的优化配置问题,建立了该问题的内、外双层优化数学模型。内层优化用于搜索给定电抗率配置方案下导致电网谐波畸变最严重的运行方式,外层优化用于确定电抗率的最优配置方案并利用内层优化的结果判断配置方案是否可行。针对内层优化模型中控制变量的二进制特性,提出了基于BPSO的优化算法,并给出了详细的算法流程。对IEEE14节点系统进行了算例分析,计算结果表明了所提模型及算法在抑制谐波放大方面的作用。     

电容器组串联电抗率优化选择模型和算法研究

在简要分析了电容器对谐波电流放大机理的基础上,面向整个电网研究了集中补偿电容器组串联电抗器电抗率的优化配置问题,建立了该问题的内、外双层优化数学模型.内层优化用于搜索给定电抗率配置方案下导致电网谐波畸变最严重的运行方式,外层优化用于确定电抗率的最优配置方案并利用内层优化的结果判断配置方案是否可行.针对内层优化模型中控制变量的二进制特性,提出了基于BPSO的优化算法,并给出了详细的算法流程.对IEEE14节点系统进行了算例分析,计算结果表明了所提模型及算法在抑制谐波放大方面的作用.     

基于IGSO计及谐波电压畸变的无功优化

近年来电力电子装置的广泛应用引起了谐波污染。如果直接对电力系统进行无功优化,谐波频率下容易产生系统与电容器之间的谐振或谐波放大,使系统的谐波畸变率大为增加,破坏系统的安全运行。针对这一问题,提出了计及谐波电压畸变的无功优化模型;在网损最小的基础上,将各节点基波电压和总谐波畸变率越限情况以惩罚项的形式加入目标函数中,将改进萤火虫算法（IGSO）应用到无功优化中,给出基于IGSO计及谐波电压畸变的无功优化具体步骤。通过对IEEE 30节点算例的仿真分析,验证本方法的可行性和优越性,在减小网损和总谐波畸变率的同时,提高了收敛速度和计算精度。     

一种优化配置谐波抑制装置的新方法

针对配电网谐波源的多样性、不确定性等特点,以平均谐波畸变率和经济性为目标函数,将有源滤波器容量和谐波电压畸变率作为寻优的约束条件,将滤波装置中电容器的容量与串并联谐振频率的关系作为谐振约束条件;并采用混合罚函数将非线性约束优化问题变为无约束优化问题。最后采用改进模糊粒子群算法对滤波装置优化配置,通过在算法中考虑所有个体对群体活动的导向性,以增强粒子之间相互学习的能力,有效防止粒子陷入局部最优。仿真结果表明,该优化算法在给定的电网范围内能统一优化有源滤波器和无源滤波器的安装地点及相应的参数,具有一定工程应用价值。     

基于PSO算法的有源滤波器的优化配置

在电网中安装有源滤波器来治理谐波污染,需要对有源滤波器进行优化配置,这是一个多变量的组合优化问题,粒子群优化(Particle Swarm Optimization,PSO)算法是一种有效的优化工具.本文将该算法应用于有源滤波器的优化配置,建立以电压总谐波畸变率为约束条件,以有源滤波器的造价为目标函数的数学模型.在迭代求解过程中,根据PSO算法的特点,以电网的谐波阻抗阵作为指导来修正注入某节点的谐波量,加快了收敛速度,缩短了求解时间.应用PSO算法对IEEE 18节点系统进行有源滤波器优化配置,能较快地收敛于最优解,优化结果表明了该算法的正确性.     

Capacitor placement of distribution systems using particle swarm optimization approaches

Capacitor placement plays an important role in distribution system planning and operation. In distribution systems of electrical energy, banks of capacitors are widely installed to compensate the reactive power, reduce the energy loss in system, voltage profile improvement, and feeder capacity release. The capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. Recently, many approaches have been proposed to solve the capacitor placement problem as a mixed integer programming problem. This paper presents a new capacitor placement method which employs particle swarm optimization (PSO) approaches with operators based on Gaussian and Cauchy probability distribution functions and also in chaotic sequences for a given load pattern of distribution systems. The proposed approaches are demonstrated by two examples of application. Simulation results show that the proposed method can achieve an optimal solution as the exhaustive search can but with much less computational time.     

A genetic algorithm approach in optimal capacitor selection with harmonic distortion considerations

This paper reports the research findings of an innovative genetic algorithm approach for optimizing shunt capacitor sizes and their placement in radical distribution systems with the consideration of harmonic distortion limit due to the presence of nonlinear power electronic devices. The algorithm is based on a genetic algorithm solution technique to minimize cost under the additional constraints of maximum limit in harmonic distortion factor. A harmonic distortion calculation is embedded in the genetic algorithm solution routine to enhance the optimal capacitor allocation solution. Results of simulation show that the approach is effective for such a discrete value optimization problem.     

Particle swarm optimization algorithm for capacitor allocation problem in distribution systems with wind turbine generators

This paper aims at adopting the Particle Swarm Optimization (PSO) technique to find the near-optimal solutions for the capacitor allocation problem in distribution systems for the modified IEEE 16-bus distribution system connected to wind energy generation based on a cost function. The proper allocation and the optimized number of capacitors have led to adequate power losses reduction and voltage profile enhancement. Because of the wind power generation variations due to the nature of wind speed intermittency and the lack of reactive power compensation, the problem under study have been presented involving a nonlinear fitness function. In order to solve it, the corresponding mathematical tools have to be used. The formulated fitness cost function has consisted of four terms: cost of real power loss, capacitor installation cost, voltage constraint penalty, and capacitor constraint penalty. PSO technique has been used to obtain the near-optimum solution to the proposed problem. Simulation results demonstrate the efficiency of the proposed fitness cost function when applied to the system under study. Furthermore, the application of PSO to the modified IEEE 16-bus system has shown better results in terms of power losses cost and voltage profile enhancement compared to Genetic Algorithm (GA). In order to verify the successful adaptation of PSO toward attaining adequate near-optimal capacitor allocations in distribution systems, this metaheuristic technique has been employed to the large-scale IEEE 30-bus system. The proposed PSO technique has provided adequate results while modifying the objective function and constraints to include the power factor and transmission line capacities for normal and contingency (N-1) operating conditions.     

Optimal capacitor placements in distribution systems. II. Solutionalgorithms and numerical results

For pt.I see ibid., vol.5, no.2, p.634-42 (1990). A general solution algorithm based on simulated annealing for optimal capacitor placements in distribution systems is proposed and analyzed. The solution algorithm can provide the global optimal solution for the capacitor placement problem. The solution algorithm has been implemented into a software package and tested on a 69 bus system with very promising results     

Optimal allocation of capacitor banks in radial distribution systems for minimization of real power loss and maximization of network savings using bio-inspired optimization algorithms

In this paper, two new algorithms are implemented to solve optimal placement of capacitors in radial distribution systems in two ways that is, optimal placement of fixed size of capacitor banks (Variable Locations Fixed Capacitor banks-VLFQ) and optimal sizing and placement of capacitors (Variable Locations Variable sizing of Capacitors-VLVQ) for real power loss minimization and network savings maximization. The two bio-inspired algorithms Bat Algorithm (BA) and Cuckoo Search (CS): search for all possible locations in the system along with the different sizes of capacitors, in which the optimal sizes of capacitor are chosen to be standard sizes that are available in the market. To check the feasibility, the proposed algorithms are applied on standard 34 and 85 bus radial distribution systems. And the results are compared with results of other methods like Particle Swarm Optimization (PSO), Harmonic Search (HS), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Teaching Learning Based Optimization (TLBO) and Plant Growth Simulation Algorithm (PGSA), as available in the literature. The proposed approaches are capable of producing high-quality solutions with good performance of convergence. The entire simulation has been developed in MATLAB R2010a software.     

Discussion of “Optimal reconfiguration and capacitor allocation in radial distribution systems for energy losses minimization” by L.W. de Oliveira et al., International Journal of Electrical Power and Energy Systems 32 (2010) 840–848

This article indicates the accurate method for load level calculation to solve the power flow problem when capacitor reactive power is involved. Using the correct method causes some changes in the results and conclusion stated in the discussed paper on optimal reconfiguration and capacitor placement in radial distribution systems.     

基于粒子群优化算法的动态无功优化

提出了用于电力系统动态无功优化的粒子群优化算法(PSO),应用一个时间优先级序列来选择有载调压变压器分接头和可投切并联电容器组的动作时刻,将动态无功优化转化为一系列的静态无功优化问题.针对每一个时刻的静态优化,用罚函数将有约束问题转化为无约束问题,最后用粒子群优化算法加以解决.算例充分验证了本算法的正确性和有效性,以及在限制控制设备动作次数方面取得的成功,适用于解决动态无功优化问题.     

粒子群优化算法在电力系统中的应用综述

粒子群优化(PSO)算法是一种新兴的群体智能优化技术,其思想来源于人工生命和演化计算理论,PSO通过粒子追随自己找到的最优解和整个群的最优解来完成优化。该算法简单易实现,可调参数少,已得到广泛研究和应用。在大量参阅国内外相关文献的基础上,简要介绍了PSO算法的工作原理,较为全面地详述了粒子群优化方法在电力系统中的应用,如电网规划、检修计划、短期发电计划、机组组合、负荷频率控制、最优潮流、无功优化、谐波分析与电容器配置、参数辨识、状态估计、优化设计等方面,并对今后可能的应用指出了研究方向。     

避免谐波对电容器组危害的运行投切原则

电容器是变电站的重要无功补偿装置,而电抗率的选择不当有可能会放大通过电容器的谐波电流,导致电容器上的谐波电流和谐波电压过大,危及电容器的寿命甚至导致电容器爆裂。据此进行了分析并提出相应的改进措施。     

New optimal placement of capacitors and dispersed generators using bacterial foraging oriented by particle swarm optimization algorithm in distribution systems

The shunt capacitor devices are utilized in distribution systems to possibly reduce reactive component of power losses. Besides, the dispersed generator (DG) units can be used to supply active power of loads and reduce active component of power losses. In this paper, by applying the multi-objective problem, optimal placements of these devices are determined based on bacterial foraging (BF) oriented by particle swarm optimization (PSO) algorithm (BF-PSO). The considered objective function includes the cost reduction of power losses and installation costs of shunt capacitor devices and DG units. Also, the problem solution at different load levels and the utilization of capacitor discrete values are performed for optimization. Finally, the proposed method is compared with genetic algorithm (GA), differential evolution (DE), and PSO methods. They are investigated on the IEEE 69-bus distribution system. The simulation results indicate the advantages of the proposed method for the optimization problem.