基于改进粒子群算法的电力系统无功优化研究

粒子群( PSO)优化算法具有并行处理的优点,但易于陷入早熟收敛,针对这一问题,本文提出了一种改进粒子群无功优化算法,该算法使用了自适应动态惯性权重,充分利用了遗传算法中交叉变异和种群移动均匀的特性,从而有效克服了PSO算法易于陷入局部最优和早熟收敛的缺陷,具有良好的寻优速度和计算精度,实例计算取得了良好的结果,从而验...     

基于改进粒子群算法的电网无功优化方法研究

为最大限度地保证电网系统最优运行,减少不必要的损耗,提出基于改进粒子群算法(particle swarm optimization algorithm,PSO)的电网无功优化方法。该算法对传统的惯性权重进行改进,使其可以按自身需求相应的变化,并动态地变化学习因子,最后引入了变异算子来更新种群。在IEEE 30节点系统测试中,基于改进的PSO算法避免陷入局部最优,其比改进前的PSO算法更具优势,改进后的PSO算法和其他优化算法相比,收敛速度更快,优化程度更高。     

DTC系统中神经网络转速辨识器优化研究

基于直接转矩控制（DTC）系统中的非线性关系,提出了利用改进粒子群优化（PSO）算法优化BP神经网络来构造转速辨识器。利用惯性权重线性递减法和粒子调整机制相结合来改进PSO算法,能加快BP神经网络收敛速度并实现全局搜索。通过对3种改进BP神经网络的仿真和实验,验证了改进PSO—BP神经网络能够使系统具有更为良好的静态和动态性能。     

基于SA-PSO的电力系统无功优化

粒子群优化算法是一种简便易行,收敛快速的演化计算方法。但该算法也存在收敛精度不高,易陷入局部极值的缺点。针对这些缺点,对原算法加以改进,引入了自适应的惯性系数和模拟退火算法的思想,提出了一种新的模拟退火粒子群优化(simulated annealing particle swarm optimization,SA-PSO)算法,并将其应用于电力系统无功优化。对IEEE14节点系统进行了仿真计算,并与PSO算法作了比较,结果表明SA-PSO算法全局收敛性能及收敛精度均较PSO算法有了较大提高。     

基于改进PSO算法的电力系统无功优化

粒子群优化PSO(Particle Swarm Optimization)算法是一种简便易行、收敛快速的演化计算方法,但该算法也存在收敛精度不高,易陷入局部极值的缺点。针对这些缺点,对原算法加以改进,引入了自适应的惯性系数和变异算子,提出了一种新的改进粒子群优化MPSO(Modified Particle Swarm Optimization)算法,并将其应用于电力系统无功优化,建立了相应的优化模型。对IEEE-14节点系统及某地区70节点实际电力系统进行了仿真计算,并与PSO算法作了比较,结果表明MPSO优化算法能有效地应用于电力系统无功优化．其全局收敛性能及收敛精度均较PSO算法有了一定程度的提高。     

电力系统无功优化自适应粒子群算法

针对粒子群(PSO)算法存在易陷入局部最优的缺点,提出了一种新的基于种群多样性指数的自适应粒子群优化算法(ASPO)。该算法利用种群多样性信息对惯性权重进行非线性调整,并在算法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的束缚,同时又保持前期搜索速度快特性。将其应用于电力系统无功优化,对IEEE-30节点系统进行仿真计算,并与GA、PSO等算法比较,结果表明APSO算法能有效应用于电力系统无功优化,其全局收敛性能、收敛精度和收敛稳定性均较GA、PSO算法有了明显提高。     

基于改进PSO算法的含风电场电力系统网架扩展规划研究

基于改进后的PSO算法,研究了如何利用网架扩展规划,来缓解风电并网发电后部分线路出现输电阻塞的现象.在PSO算法中,惯性权重和学习因子分别是控制PSO算法全局搜索和局部搜索的关键性可调整参数.为避免陷入局部解,同时加快收敛速度,提出了同时动态优化调整惯性权重和学习因子的改进PSO算法.基于IEEE39节点的仿真算例表明：在保证获得最优解的前提下,该算法的收敛速度显著加快.     

自适应多策略粒子群算法在水库群优化调度中的应用

根据梯级水电站优化调度特点,建立粒子群算法求解多阶段最优化问题数学模型.针对基本粒子群算法(PSO)在早期存在精度较低、易发散等缺点,后期出现"趋同性"和"早熟"等现象,提出了自适应多策略粒子群算法.并将该算法与基本PSO、改进型PSO、杂交PSO和收敛因子PSO分别在雅砻江梯级水库群优化调度中应用,通过对其优化结果的比较,验证了改进算法在加快收敛速度和提高算法精度方面的有效性.     

基于改进粒子群算法的主汽温控制系统PID参数优化

介绍了一种基于改进的粒子群优化(PSO)算法的PID控制器参数优化方法。通过将PSO基本算法中的惯性权重进行线性递减,很好地协调了PSO的全局与局部寻优能力。将改进的粒子群PID控制器参数优化方法应用于多扰动、大惯性的电厂主汽温控制系统,仿真结果表明,该方法在保证控制系统稳定性的基础上极大地提高控制系统的精度和快速性。     

基于动态多种群粒子群算法的无功优化

提出了一种基于动态多种群策略的改进粒子群算法。该算法将传统粒子群优化算法(particle swarm optimization，PSO)中的种群划分成多个子群，每个子群相对独立地朝同一目标进化，仅通过一种轮形结构的弱联系进行交流。在进化过程中各种群不断分裂和聚类重组，动态调整种群规模以更好地适应进化。该算法可以较好地避免PSO算法过快收敛于局部最优解，并且有较快的收敛速度。文中将该算法应用于求解电力系统无功优化问题，并与标准PSO算法的性能进行了对比，仿真计算证明该算法是有效、可行的。     

An improved PSO technique for short-term optimal hydrothermal scheduling

This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using improved particle swarm optimization (IPSO) technique. The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network, water transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved PSO technique is suggested that deals with an inequality constraint treatment mechanism called as dynamic search-space squeezing strategy to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed IPSO is applied on a multi-reservoir cascaded hydro-electric system having prohibited operating zones and a thermal unit with valve point loading. Numerical results are compared with those obtained by dynamic programming (DP), nonlinear programming (NLP), evolutionary programming (EP) and differential evolution (DE) approaches. The simulation results reveal that the proposed IPSO appears to be the best in terms of convergence speed, solution time and minimum cost when compared with established methods like EP and DE.     

随机差分变异粒子群混合优化算法

针对传统粒子群优化算法与差分进化算法都易出现早熟等问题,提出了一种随机差分变异粒子群混合优化算法。算法结合粒子群与差分算法的各自特点,首先采用差分变异方法产生试探性候选个体,再将其代入到粒子群速度更新公式,引导粒子飞行方向,从而扩大搜索空间,增强算法的全局勘探能力。为避免粒子陷入局部最优解,采用随机差分变异方式对当前最优粒子进行扰动,使算法在有效提高局部开采能力的同时,有效避免停滞现象的发生。算法分别在单峰及多峰等8个测试函数上与3个相关算法进行对比实验,实验结果表明,新的混合算法优于其他对比算法,有效提高了算法的性能。     

Adaptive Particle Swarm Optimization for Simultaneous Design of UPFC Damping Controllers

An adaptive particle swarm optimization based on nonlinear time-varying acceleration coefficients (NTVAC-PSO) is proposed for solving global optimization problems and damping of power system oscillations. The new method aims to control the global exploration ability of the original PSO algorithm and to increase its convergence rate with an acceptable solution in less iteration. A set of 10 well-known benchmark optimization problems is utilized to validate the performance of the NTVAC-PSO as a global optimization algorithm and to compare with similar methods. The numerical experiments show that the proposed algorithm leads to a significantly more accurate final solution for a variety of benchmark test functions faster. In addition, the simultaneous coordinated design of unified power flow controller-based damping controllers is presented to illustrate the feasibility and effectiveness of the new method. The performance of the proposed algorithm is compared with other methods through eigenvalue analysis and nonlinear time-domain simulation. The simulation studies show that the controllers designed using NTVAC-PSO performed better than controllers designed by other methods. Moreover, experimental results confirm superior performance of the new method compared with other methods.     

Making rotational invariance of particle swarm optimization based on correlativity

In this letter, we point out that particle swarm optimization (PSO) lacks rotational invariance through an analysis using numerical experiments. After the analysis based on rotational invariance, we develop PSO with rotational invariance based on correlativity. The performance of the proposed PSO with rotational invariance is verified through numerical experiments for typical separable benchmark functions without and with rotation.     

基于改进粒子群优化算法的PID参数整定

粒子群优化算法PSO(ParticleSwarmOptimization)是近年来出现的一种新型演化计算方法,其算法简单易懂,优化性能良好。该文提出改进的PSO算法结合Matlab强大的矩阵计算和系统仿真功能,对文中实例的PID参数进行了优化整定。仿真显示优化结果比遗传算法好,收敛性能比遗传算法高。     

An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems

This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using an improved particle swarm optimization (IPSO). Although the particle swarm optimization (PSO) approaches have several advantages suitable to heavily constrained nonconvex optimization problems, they still can have the drawbacks such as local optimal trapping due to premature convergence (i.e., exploration problem), insufficient capability to find nearby extreme points (i.e., exploitation problem), and lack of efficient mechanism to treat the constraints (i.e., constraint handling problem). This paper proposes an improved PSO framework employing chaotic sequences combined with the conventional linearly decreasing inertia weights and adopting a crossover operation scheme to increase both exploration and exploitation capability of the PSO. In addition, an effective constraint handling framework is employed for considering equality and inequality constraints. The proposed IPSO is applied to three different nonconvex ED problems with valve-point effects, prohibited operating zones with ramp rate limits as well as transmission network losses, and multi-fuels with valve-point effects. Additionally, it is applied to the large-scale power system of Korea. Also, the results are compared with those of the state-of-the-art methods.      

Pattern synthesis of antennas based on a modified particle swarm optimization algorithm

In order to overcome the drawbacks of standard particle swarm optimization (PSO) algorithm, such as prematurity and easily trapping in local optimum, a modified PSO algorithm is proposed, in which special techniques, as global best perturbation and inertia weight jump threshold are adopted. The convergence speed and accuracy of the algorithm are improved. The test by some benchmark problems shows that the proposed algorithm achieves relatively higher performance. Thereafter, the applications of the modified PSO in the radiation pattern synthesis of antenna arrays are presented. __________ Translated from Chinese Journal of Radio Science, 2006, 21(6): 873–878 [译自: 电波科学学报]     

基于免疫粒子群算法的电力系统无功优化

为提高粒子群优化(particle swarm optimization,PSO)算法的收敛性能,将免疫算法(immunity algorithms,IA)的免疫信息处理机制引入到标准粒子群算法,形成一种新的优化算法,即免疫粒子群算法。该算法将免疫算法的免疫记忆和自我调节机制引入PSO,并采用基于粒子浓度机制的多样性保持策略;同时,用免疫算法的"接种疫苗"和"免疫选择"来指导搜索过程。改进后的算法可以很好的保持优化过程中粒子群的多样性,抑制优化过程中出现的退化现象,保证算法的收敛精度和收敛速度。IEEE 30节点系统算例仿真表明,IA-PSO算法与标准PSO算法相比,能够及时跳出局部最优得到全局最优解,且收敛速度快、精度高。     

Optimal location of FACTS devices using Improved Particle Swarm Optimization

In this paper a new, an Improved Particle Swarm Optimization (IPSO) is proposed for optimizing the power system performance. Recently, the Particle Swarm Optimization (PSO) technique has been applied to solve power engineering optimization problems giving better results than classical methods. Due to slow convergence and local minima, particle swarm optimization fails to give global results. To overcome these drawbacks, in this paper presents the application of improved particle swarm optimization for optimal sizing and allocation of a Static Compensator (STATCOM) and minimize the voltage deviations at all the buses in a power system. This algorithm finds an optimal settings for present infrastructure as well as optimal locations, sizes and control settings for Static Compensator (STATCOM) units. A 30 bus system is used as an example to illustrate the technique. Results show that the Improved Particle Swarm Optimization (IPSO) is able to find the best solution with statistical significance and a high degree of convergence. The simulation results are presented to show a significant improvement of the power system reliability and feasibility and potential of this new approach.     

Maximum loadability limit of power system using hybrid differential evolution with particle swarm optimization

Differential evolution (DE), a simple evolutionary algorithm which shows superior performance in global optimization. Since it utilizes the differential information to get the new candidate solution, sometimes it results in instability of performance. Particle swarm optimization (PSO) is widely used to solve the optimization problems as it can converge quickly. But PSO easily gets stuck in local optima. Hybridization of DE and PSO (DEPSO) eliminates the disadvantages of both. This paper presents the application of DEPSO algorithm to determine the maximum loadability limit of power system. It is tested on Matpower 30 bus and IEEE 118 bus systems. To compare the performance of this DEPSO algorithm with other evolutionary algorithms like DE and Multi Agent Hybrid PSO, statistical measures like best, mean, standard deviation of results and average computation time over 20 independent trials are considered here. The results show the better performance of DEPSO algorithm to solve the maximum loadability problem. DEPSO algorithm provides high maximum loading point in reduced time.