育种粒子群算法在梯级水电站优化调度中的应用

为了提高粒子群优化(Particle Swarm Optimization,PSO)算法的计算精度和计算效率,避免"早熟",提出了育种粒子群优化算法(Breeding-based Particle Swarm Optimization,BBPSO).该算法模型将育种算法和PSO算法有机结合,构建双群体搜索机制,既利用PSO算法的快速演化能力,又利用育种算法模型中的繁殖操作增加群体多样性.将该算法模型应用于梯级水电站发电最优调度中,仿真结果表明,和标准PSO算法相比,BBPSO具有更好的全局寻优能力和较快的收敛速度,能有效应用于梯级电站发电联合优化调度中.     

基于蜜蜂进化型粒子群算法的电力系统无功优化

为了提高电能质量,降低网损,采用蜜蜂进化机制与粒子群算法相结合的蜜蜂进化型粒子群算法(Bee Evolution Modifying Particle Swarm Optimization, BEMPSO),对电力系统的无功优化问题进行求解.改进后的算法能够克服传统粒子群算法的收敛精度低,易陷入局部最优解的缺点.应用改进算法对IEEE6、30节点标准电网进行无功优化计算,并与其它优化算法相比较,结果证明BEMPSO算法具有较好的全局寻优能力,验证了该算法的正确性和有效性.     

电力系统无功优化多目标处理与算法改进

电力系统无功优化属于典型的多目标非线性复杂优化问题,求解非常困难。近年来,众多智能优化算法应用于该问题,其中粒子群优化(Particle Swarm Optimization,PSO)算法最具代表性;但PSO算法性能仍有待提高,如可能陷入局部极值。提出一种多策略融合粒子群优化(Particle Swarm Optimization with Multi-Strategy Integration,MSI-PSO)算法,对速度更新公式引入选择操作,分阶段加速因子调整和惯性权重动态调整,以平衡粒子局部搜索与全局探索能力;同时,随机选取部分性能差的粒子,将其速度更新公式中的个体认知部分修改为社会认知部分,以提高算法搜索精度和收敛速度。建立以系统网络损耗最小和系统电压稳定裕度最大为目标的无功优化仿真模型,分别考虑加权法、隶属度函数法和Pareto法实施多目标处理。针对IEEE30节点测试系统进行仿真实验,结果表明,和其他几种改进PSO算法以及基于pareto最优解集PSO算法进行对比,所提MSI-PSO算法具有更好的性能,能够有效求解电力系统多目标无功优化问题。     

基于蜜蜂进化型粒子群算法的电力系统无功优化

为了提高电能质量,降低网损,采用蜜蜂进化机制与粒子群算法相结合的蜜蜂进化型粒子群算法（Bee Evolution Modifying Particle Swarm Optimization, BEMPSO）,对电力系统的无功优化问题进行求解。改进后的算法能够克服传统粒子群算法的收敛精度低,易陷入局部最优解的缺点。应用改进算法对IEEE6、30节点标准电网进行无功优化计算,并与其它优化算法相比较,结果证明BEMPSO算法具有较好的全局寻优能力,验证了该算法的正确性和有效性。     

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。     

基于全局粒子群算法的无功优化

针对粒子群(PSO)算法的局限性,提出了全局粒子群(GPSO)算法,并将其应用于电力系统无功优化.建立基于全局粒子群算法的无功优化数学模型,给出全局粒子群算法的具体步骤.通过对IEEE30节点算例的测试,得到全局粒子群算法在无功优化问题上的收敛速度和优化效果.     

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。     

改进PSO算法在电力系统无功优化中的应用

将改进后的粒子群算法应用于电力系统无功优化问题的求解,克服了传统粒子群算法运算过程繁琐、收敛精度不高、易陷入局部最优的缺点。该优化方法对粒子群算法进行了如下改进:采用简化粒子群优化方程和添加极值扰动算子两种策略,提出了简化粒子群优化(简称SPSO)算法、带极值扰动粒子群优化(简称DPSO)算法,将二者结合起来提出了带极值扰动的简化粒子群优化(简称DSPSO)算法。DSPSO以更小的种群数和进化代数获得了非常好的优化效果,使PSO算法更加实用化。对IEEE 6节点进行无功优化计算,并与其它算法进行了比较,表明该算法具有较强的全局搜索能力和较高的收敛精度,是求解无功优化的有效方法。     

基于改进粒子群算法的风电火电联合调度方法*

针对电力系统风电并网中出现的随机波动性与不确定性问题,建立风 火联合调度数学模型,并提出一种改进粒子群算法对该模型进行求解,以提高电力系统运行的经济性和可靠性。改进粒子群算法（Improved Particle Swarm Optimization, IPSO）从添加粒子速度自适应和添加位移更新自适应两方面对基本粒子算法（PSO）进行改进。仿真结果验证了IPSO具有更高的精度和更快的收敛速度。采用IPSO对10机组算例模型进行优化计算,计算结果验证了所建模型的正确性和所提改进算法的有效性。     

改进粒子群算法在无功优化中的应用

建立了无功优化的数学模型,针对粒子群算法易陷入局部最优解、收敛精度差的缺点,将改进粒子群优化算法应用到电力系统无功优化中。对粒子群的速度公式进行了改进,并在算法中引入反正切惯性权重和阈值来增强搜索全局最优解的能力。通过对IEEE30节点的算例仿真,证明改进后的粒子群算法在电力系统无功优化问题上具有一定的可行性。与PSO的结果对比表明该算法在一定程度上提高了计算的精度。     

含维变异的量子粒子群算法在无功优化中的应用

量子粒子群优化算法(QPSO)避免了粒子群算法(PSO)不能保证收敛到全局最优解这个缺点,认为粒子具有量子的行为,并且可以在整个可行解空间进行搜索。无功优化问题是带有离散变量的非线性、不连续、多约束、多变量的复杂优化问题。本文考虑到优化过程中避免陷入局部最优,应用含维变异QPSO算法并结合动态调整罚函数的方法来解决无功优化问题。并对标准IEEE-30节点系统进行仿真计算,并与QPSO、PSO、GA算法进行了比较,表明该算法能够获得更好的全局最优解。     

基于强引导粒子群与混沌优化的电力系统无功优化

为解决粒子群优化后期搜索速度较缓慢,易陷入局部最优的问题,提出一种基于强引导粒子群与混沌寻优相结合的电力系统无功优化算法,该算法在采用强引导型粒子群的基础上引入混沌优化以进一步提高全局寻优能力,即在粒子群算法的基础上引入强引导思想,在搜索初期,对粒子位置的更新加以引导,减少算法随机性以提高搜索效率。为进一步解决寻优后期粒子可能陷入早熟收敛的问题,利用混沌优化具有"奇异吸引子"的特性,在解空间进一步搜索,两者的结合可以更有效地搜索到全局最优解。通过对某高压配电网的具体计算,最优降损率可以达到14.04%,节点最低电压从0.895 0 p.u.提高到0.995 6 p.u.,结果表明该算法应用在电力系统无功优化领域的可行性和有效性。     

粒子群优化算法及其在机组优化组合中应用

应用粒子群优化(PSO)算法对电力系统的机组优化组合问题进行研究,介绍了算法原理,分析了算法中各个参数的不同取值对算法搜索能力和收敛速度的影响,并以常用的测试函数进行验证,建立了相应的数学模型,并以IEEE3机6节点电力系统为实例进行研究。分析结果表明,PSO算法较之常用的遗传算法和混沌优化等算法,在算法结构、计算时间、搜索区间控制以及收敛速度等方面具有较好的特性,验证了该方法的有效性。     

基于量子粒子群算法的电力系统无功优化

本文提出了一种量子粒子群算法解决电力系统无功优化问题。量子粒子群算法采用实数编码,融合了量子进化算法的概率性并进行计算和粒子群算法的更新策略,在全局寻优能力和保持种群多样性方面表现出了较大优势,而且寻优速度快。另外,为了避免大量不可行初始解,本文采用倾斜分布式启发方法得到初始种群。IEEE-30系统证明了量子粒子群算法的高效性和鲁棒性。     

遗传粒子群混合算法在电厂机组负荷组合优化中的应用

粒子群优化算法应用于火电厂机组组合问题中存在早熟收敛等现象,提出3方面改进的遗传粒子群混合算法:改进粒子群初始化方法,提出粒子初始化机组运行状态组合合理性判据,并初始化一定比例的粒子使其机组负荷随机在对应机组负荷上限附近赋值;采用部分解除约束结合惩罚函数的约束处理方法,对粒子进行机组负荷平衡操作,使大部分粒子满足约束条件;通过引入遗传算法中的交叉和变异操作增加了粒子的多样性,减小了算法陷入局部极值的可能性。采用改进的遗传粒子群混合算法对3机及5机火电厂机组负荷组合进行优化,仿真结果表明,优化成功率能达到100%。     

Optimal sizing and design of CMOS analogue amplifier circuits using craziness‐based particle swarm optimization

This paper presents the optimal designs of two analogue complementary metal–oxide–semiconductor (CMOS) amplifier circuits, namely differential amplifier with current mirror load and two‐stage operational amplifier. A modified Particle Swarm Optimization (PSO), called Craziness‐based Particle Swarm Optimization (CRPSO) technique is applied to minimize the total MOS area of the designed circuits. CRPSO is a highly modified version of conventional PSO, which adopts a number of random variables and has a better and faster exploration and exploitation capability in the multidimensional search space. Integration of craziness factor in the fundamental velocity term of PSO not only brings diversity in particles but also pledges convergence close to global best solution. The proposed CRPSO‐based circuit optimization technique is reassured to be free from the intrinsic disadvantages of premature convergence and stagnation, unlike Differential Evolution (DE), Harmony Search (HS), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). The simulation results achieved for the two analogue CMOS amplifier circuits establish the efficacy of the proposed CRPSO‐based approach over those of DE, HS, ABC and PSO in terms of convergence haste, design conditions and design goals. The optimally designed analogue CMOS amplifier circuits occupy the least MOS area and show the best performance parameters like gain and power dissipation, in compared with the other reported literature. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

基于自适应小生境粒子群优化算法的电力系统无功优化

针对电力系统无功优化问题,提出用自适应小生境粒子群优化ANPSO(Adaptive Niche Panicle Swarm Optimization)算法来克服粒子群优化(PSO)算法容易早熟而陷入局部最优解的缺点.以粒子的位置状况及其2个向量点积的符号动态生成小生境半径,根据各粒子之间的距离组成小生境种群.在小生境群体中运用粒子群优化算法进行寻优,对于更新后的群体根据粒子间的距离,利用共享机制改变粒子的适应度,用以提高整个群体的全局寻优能力.通过对IEEE 6、14、30和118节点测试系统的无功优化问题计算及结果分析,并且与其他算法进行比较,结果表明该算法收敛成功率高,能获得较好的解.     

PSO based bio inspired algorithms for reactive power planning

Reactive power source planning problem has significant role for secure and economic operation of power system. Reactive power planning problem is nothing but the proper coordination of existing Var sources which lead to loss minimization and cost economic operation of the system. In the proposed approach several bio-inspired algorithms like Particle Swarm Optimization (PSO), Evolutionary Particle Swarm Optimization (EPSO), Adaptive Particle Swarm Optimization (APSO), Hybrid Particle Swarm Optimization (HPSO) and Bacterial Foraging Algorithm (BFA) are used for the reactive power planning problem. Finally a comparison of all these techniques are made on the basis of the results obtained when applied to different standard test system.     

Modulated particle swarm optimization for economic emission dispatch

The economic emission dispatch (EED) problem of thermal generating units is a highly complex combinatorial multi-constraint, non-convex optimization problem with conflicting objectives. This paper presents a Modulated Particle Swarm Optimization (MPSO) method to solve the EED problem of thermal units. The conventional PSO is modified by modulating velocity of particles for better exploration and exploitation of the search space. The modulation of particles’ velocity is controlled by introducing a truncated sinusoidal constriction function in the control equation of PSO. The conflicting objectives of the EED problem are combined in fuzzy framework by suggesting adjusted fuzzy membership functions which is then optimized using proposed PSO. The effectiveness of the proposed PSO is tested on three standard test generating systems considering several operational constraints like valve point effect, and prohibited operating zones (POZs). The application results and their comparison with other existing methods show that the proposed MPSO is promising for EED problem of thermal generating units.