改进粒子群和模拟退火混合算法及其应用

基本粒子群优化算法每个粒子代表一个可行解,通过粒子间的协作来获得最优解.考虑粒子间协同作用,引入Gaussian核函数研究基于区域影响的粒子群算法(GPSO).为了充分利用粒子群算法的快速全局收敛性和模拟退火算法能够跳出局部最优陷阱的优点,得到高精度的最优解,将GPSO算法与模拟退火算法相结合,研究了一种新的混合粒子群算法.混合算法在GPSO算法处于停滞状态时,于搜索到最优位置用模拟退火算法继续寻找最优解.数值实验结果表明,新混合算法兼顾了GPSO和模拟退火算法的优点,具有收敛速度快、搜索精度高、鲁棒性好等特点.这说明文中的混合算法不失为一种有效的进化算法.     

改进粒子群和模拟退火混合算法及其应用

基本粒子群优化算法每个粒子代表一个可行解,通过粒子间的协作来获得最优解。考虑粒子间协同作用,引入Gaussian核函数研究基于区域影响的粒子群算法(GPSO)。为了充分利用粒子群算法的快速全局收敛性和模拟退火算法能够跳出局部最优陷阱的优点,得到高精度的最优解,将GPSO算法与模拟退火算法相结合,研究了一种新的混合粒子群算法。混合算法在GPSO算法处于停滞状态时,于搜索到最优位置用模拟退火算法继续寻找最优解。数值实验结果表明,新混合算法兼顾了GPSO和模拟退火算法的优点,具有收敛速度快、搜索精度高、鲁棒性好等特点。这说明文中的混合算法不失为一种有效的进化算法。     

集成协方差矩阵自适应进化策略与差分进化的优化算法

不同智能优化算法在求解优化问题时通常表现出显著的性能差异.差分进化(DE)算法具备较好的全局搜索能力,但存在收敛慢、效率低的不足,协方差矩阵自适应进化策略(CMA–ES)局部搜索能力强,具备旋转不变性,但容易陷入局部最优,因此, DE和CMA–ES之间具有潜在的协同互补能力.针对上述问题,提出了一种集成协方差矩阵自适应进化策略与差分进化的优化算法(CMADE).在CMADE框架中, DE算法负责全局搜索, CMA–ES算法进行局部搜索.通过周期性解交换机制实现CMA–ES和DE两个算法间协同交互和反馈控制.在解交换时,从DE种群中选择优秀个体,利用CMA–ES算法在优秀个体周围进行局部搜索.同时在DE和CMA–ES的混合种群中,综合考虑解的多样性和最优性,选取一定比例的解作为DE算法的新种群进行全局搜索,实现全局搜索与局部搜索的动态平衡.将CMADE算法与CMA–ES, DE, SaDE, jDE, EPSDE, ACODE和SHADE算法在CEC2014标准测试集上进行比较实验.结果表明, CMADE整体性能显著优于其它比较算法.     

基于解空间反向跳跃和信息交互强化的新型混合蛙跳算法

种群多样性和信息交互的深度与方式对混合蛙跳算法的爬山能力、探索能力和开发能力有着深远影响.针对混合蛙跳算法易于陷入局部最优、收敛速度慢和寻优精度差等缺点,提出一种基于解空间反向跳跃和信息交互强化的新型混合蛙跳算法.首先,增加子群次优解与次劣解的信息交互,促进子群内部信息的利用,引入反向跳跃思想改进局部更新机制,降低迭代后期劣解产生概率,提升空间开发能力;然后,借鉴2-opt方法实现局部最优解变异,增加子群的多样性;最后,采用各局部最优解交叉的方式加深子群间的交互深度,同时利用反向跳跃机制防止种群同化.采用23个单峰、多峰和固定维度下的复杂多峰函数作为测试集进行仿真实验,结果表明所提出算法具有更优的搜索性能,能够有效提高种群多样性,防止算法早熟收敛,且能够适应不同类型的函数优化问题.     

基于混合蛙跳思想的蚁群算法及路径优化研究

针对基本蚁群算法存在容易陷入局部最优解出现早熟停滞状态的缺点,提出了基于混合蛙跳思想的蚁群算法,并应用于城市交通路径寻优研究。通过引入混合蛙跳算法的全局信息共享和局部深度搜索机制,提高了蚁群算法跳出局部最优解的能力与全局收敛性。以重庆市渝中半岛的路网为实例计算以行程时间为目标的最优路径,实验结果表明该算法有效改善了基本蚁群算法的全局搜索能力,同时为解决城市交通路径寻优问题获得了较好的效果。     

基于混沌自适应萤火虫算法的UAVs分配策略

针对多无人机多类型作战任务分配问题,提出一种混沌自适应萤火虫优化算法.将全局历史最优值和自适应惯性权重引入位置公式,并采用自适应步长以加快收敛速度、提高精度.运用变尺度混沌方法改进光吸收强度系数防止其陷入局部最优解.将改进算法的应用效果与粒子群优化算法(PSO)和萤火虫算法(FA)对比,结果表明,该算法能够提升多无人机...     

趋优变异反向学习的樽海鞘群与蝴蝶混合优化算法

针对蝴蝶优化算法(butterfly optimization algorithm, BOA)易陷入局部最优,且收敛速度慢和寻优精度低等问题,提出了一种趋优变异反向学习的樽海鞘群与蝴蝶混合优化算法(hybrid optimization algorithm for salp swarm and butterfly with reverse mutation towards optimization learning, OMSSBOA)。引入柯西变异对最优蝴蝶个体进行扰动,避免算法陷入局部最优;将改进的樽海鞘群优化算法(salp swarm algorithm, SSA)嵌入到BOA,平衡算法全局勘探和局部开采的比重,进而提高算法收敛速度;利用趋优变异反向学习策略扩大算法搜索范围并提升解的质量,进而提高算法的寻优精度。将改进算法在10种基准测试函数上进行仿真实验,结果表明,改进算法具有较好的寻优性能和鲁棒性。     

基于MapReduce的混合蚁群算法研究

传统的蚁群算法在收敛速度上较慢且容易导致局部最优解,本文提出一种基于双模式的混合蚁群算法,即在算法的每次迭代中有比例地选择其中一种模式来获得蚂蚁的最优路径,可以实现在相对较少的时间内寻找出最优路径,且避免陷入局部最优解。由于蚁群算法天然具有并行化的特性,本文将混合蚁群算法与MapReduce结合,大大缩短了算法的执行时间。实验结果表明,基于MapReduce的混合蚁群算法可以实现在相对较少的时间内寻找出较优的路径。     

基于混沌优化策略的SFLA算法

针对基本混合蛙跳算法的缺陷, 提出了一种基于混沌优化策略的改进混合蛙跳算法(SFLA)。在青蛙更新策略中引入自适应扰动机制, 平衡了算法搜索深度, 并利用高斯变异算子代替随机更新操作, 提高了算法搜索速度; 在全局迭代中借鉴混沌优化策略思想, 以概率形式对最优个体进行优化, 避免了族群陷入局部最优, 并证明了改进算法以概率1收敛于全局最优解。最后用MATLAB对测试函数进行了仿真, 仿真结果表明改进的混合蛙跳算法在收敛速度、优化精度上有较大改善。     

一种求解动态优化问题的改进自适应差分进化算法

针对原始动态自适应差分进化(SADE)算法局部搜索能力弱和寻优精度低的问题,提出一种求解动态优化问题的邻域搜索差分进化(NSDE)算法.通过引入邻域搜索机制,在划分种群最优个体的邻域空间范围内产生候选解,选取候选解集合中的最优解并对种群最优个体进行迭代,增强算法局部搜索能力.在传统基于距离的排斥方案中,引入hill-v...     

Joint operations algorithm for large-scale global optimization

Large-scale global optimization (LSGO) is a very important but thorny task in optimization domain, which widely exists in management and engineering problems. In order to strengthen the effectiveness of meta-heuristic algorithms when handling LSGO problems, we propose a novel meta-heuristic algorithm, which is inspired by the joint operations strategy of multiple military units and called joint operations algorithm (JOA). The overall framework of the proposed algorithm involves three main operations: offensive, defensive and regroup operations. In JOA, offensive operations and defensive operations are used to balance the exploration ability and exploitation ability, and regroup operations is applied to alleviate the problem of premature convergence. To evaluate the performance of the proposed algorithm, we compare JOA with six excellent meta-heuristic algorithms on twenty LSGO benchmark functions of IEEE CEC 2010 special session and four real-life problems. The experimental results show that JOA performs steadily, and it has the best overall performance among the seven compared algorithms.     

新型爬山算法在大惯性风电系统中的应用

针对风力发电风速的随机性和不确定性,以及大惯性永磁风力发电系统输出功率受风机机械功率和所储的机械势能的变化率的交叉影响特点,提出一种基于爬山搜索法的新型最大风能捕捉算法。为了得到较宽的变速范围,在三相二极管整流器和PWM逆变器之间接有升压斩波器,新型爬山算法采用最佳直流电流控制,调节输入直流电流以跟从最优的参考电流,产生不同转矩控制电机的转速实现最大功率跟踪,并在爬山算法基础上添加数据存储和输出功能,避免了盲目的爬山搜索。最后通过Matlab对一个50kW永磁直驱风力发电系统进行了仿真分析,对比了传统爬山算法与新型爬山算法在最佳功率点跟踪上的差别。仿真结果验证了该算法的可靠性,表明新型爬山算法在大惯性变速恒频风力发电系统中具有更好的实用性。     

A second-order sliding mode and fuzzy logic control to optimal energy management in wind turbine with battery storage

The optimal control of large-scale wind turbine has become a critical issue for the development of renewable energy systems and their integration into the power grid to provide reliable, secure and efficient electricity, despite any possible constraints such as sudden changes in wind speed. This paper deals with the modeling and control of a hybrid system integrating a permanent magnet synchronous generator (PMSG) in variable speed wind turbine (VSWT) and batteries as energy storage system (BESS). Moreover a new supervisory control system for the optimal management and robust operation of a VSWT and a BESS is described and evaluated by simulation under wind speed variation and grid demand changes. In this way, the proposed coordinated controller has three subsystems (generator side, BESS side and grid side converters). The main function of the first one is to extract the maximum wind power through controlling the rotational speed of the PMSG, for this a maximum power point tracking algorithm based on fuzzy logic control and a second-order sliding mode control (SOSMC) theory is designed. The task of the second one is to maintain the required direct current (DC) link voltage level of the PMSG through a bidirectional DC/DC converter, whereas in the last, a (SOSMC) is investigated to achieve smooth regulation of grid active and reactive powers quantities, which provides better results in terms of attenuation of the harmonics present in the grid courant compared with the conventional first-order sliding controller. Extensive simulation studies under different conditions are carried out in MATLAB/Simulink, and the results confirm the effectiveness of the new supervisory control system.

     

Robust nonlinear control strategy to maximize energy capture in a variable speed wind turbine with an internal induction generator

This paper proposes a control strategy to maximize the wind energy captured in a variable speed wind turbine,with an internal induction generator,at low to medium wind speeds.The proposed strategy cont...     

Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach

A novel approach of combination of radial basis function neural network (RBFNN) and particle swarm optimization (PSO) is proposed to achieve the maximum power point tracking (MPPT) in this study. The measured data of the small wind generator (250 W), including wind speed, generator speed and output power of wind power generator, are applied to estimate the wind speed and output power by the proposed wind speed ANN_wind and power estimation ANN_Pe-PSO modules, respectively. Using the predicted results by the two modules of Matlab/Simulink, the MPPT point can be obtained by manipulating the generator speeds. The experimental results show that the proposed RBFNN-based approach can increase the maximum output power of the wind power generator even if the wind speed and load varies.     

优化爬山算法在直驱永磁风力发电系统中的应用

为了缩短爬山算法的计算周期,提高直驱永磁风力发电系统最大风能捕捉的响应速度,提出一种根据功率扰动寻求最大功率的优化爬山算法.该算法在变步长爬山算法盲目搜索的基础上添加数据存储输出功能,构造了与电机转速对应的最大输出功率表,避免重复的计算,同时结合功率反馈控制来实现系统最大功率追踪.运用MATLAB对该算法进行仿真验证,结果表明本文所述算法的响应速度和稳定性明显超过传统变步长爬山算法.     

分段变步长MPPT算法在风力发电系统中的应用

针对传统定步长爬山搜索(HCS)法在风力发电系统最大功率跟踪(MPPT)控制过程中的快速性和准确性矛盾,提出了一种基于爬山搜索法和模糊控制的分段变步长MPPT算法.该算法根据发电机P-ω特性曲线对最大功率点(MPP)跟踪过程进行分段,使系统能够根据工作点所在的区域选择合适的跟踪算法和步长完成最大功率跟踪.在Matlab/Simulink中分别对提出的模糊分段变步长算法和传统爬山搜索法进行了仿真.仿真结果表明:所提算法明显地改善了系统跟踪MPP的速度和稳态精度,在MPPT方面明显优于传统的爬山搜索法.     

自适应变步长最大风能捕获算法

风能具有随机性、不稳定性的特点,为了提高风力发电系统中风能的利用效率,在比较各种最大风能捕获算法的基础上,分析了爬山搜索法和叶尖速比法的不足,提出了自适应变步长搜索算法来捕获最大风能.通过改进爬山搜索法的变步长策略,明显加快了搜索速度,通过引入初始估计叶尖速比值,大大缩小了搜索范围.该算法不需要实时检测准确风速,不依赖风力机最佳功率曲线,有效地降低了成本,提高风力发电的效率.文中重点分析了算法的自适应性和变步长策略,仿真结果表明,该算法能够使风力机更快速到达最大功率点,动态响应快,收敛性好.     

An optimal fuzzy PI controller to capture the maximum power for variable-speed wind turbines

Wind energy conversion systems can work by fixed and variable speed using the power electronic converters. The variable-speed type is more desirable because of its ability to achieve maximum efficiency at all wind speeds. The main operational region for wind turbines according to wind speed is divided into partial load and full load. In the partial-load region, the main goal is to maximize the power captured from the wind. This goal can be achieved by controlling the generator torque such that the optimal tip speed ratio is tracked. Since the wind turbine systems are nonlinear in nature and due to modeling uncertainties, this goal is difficult to be achieved in practice. The proportional-integral (PI) controller, due to its robustness and simplicity, is very often used in practical applications, but finding its optimal gains is a challenging task. In this paper, to cope with nonlinearities and at the same time modeling uncertainties of wind turbines, a PI torque controller is proposed such that its optimal gains are derived via a novel scheme based on particle swarm optimization algorithm and fuzzy logic theory. The proposed method is applied to a 5-MW wind turbine model. The simulation results show the effectiveness of the proposed method in capturing maximum power in the partial-load region while coping well with nonlinearities and uncertainties.     

考虑风场高维相依性的电网动态经济调度优化算法

大规模风电并网给电力系统的调度运行带来了巨大的挑战.本文提出改进的二阶段带补偿随机优化算法,用于考虑风场出力高维相依性的电网动态经济调度问题求解.首先,利用Copula函数描述多风场出力的高维相依性,获得多风场出力的联合分布;随后,引入二阶段带补偿随机优化算法解耦求解动态经济调度模型中的常规变量与随机变量;求解过程中,针对补偿费用期望值的计算受限于相依性风场维数,且对迭代方向指导不明确,导致算法收敛耗时长的问题,引入基于整体最小二乘的递推动态多元线性回归法对二阶段带补偿随机优化算法进行改进,通过补偿费用期望值的动态更新,促使两阶段模型的迭代求解快速收敛,克服了传统随机优化方法的"维数灾"弊端,使该算法能够用于考虑风场高维相依性的电网动态经济调度模型求解.最后利用IEEE 118节点系统和某省级实际电网系统验证了所提算法的有效性和实用性.