蚁群优化PI控制器在静止无功补偿器电压控制中的应用

静止无功补偿器(static var compensator,SVC)通常用来进行负荷补偿或系统补偿,在系统补偿时往往用于电压稳定控制,针对电压稳定控制的工况,文中提出一种采用蚁群算法优化PI控制器参数的方法,克服了常规PI控制对被控对象数学模型的依赖性,简单易于实现。蚁群优化算法中,以时间与误差绝对值乘积积分(integral of time-weighted absolute error,ITAE)准则作为寻优目标函数,对PI控制器的比例、积分参数进行调整、寻优,使SVC系统的响应过程达到最优。仿真和实验结果表明,该最优PI控制器能快速跟踪SVC系统的电压设定值,基于该PI控制器的SVC能迅速进行无功补偿,具有较强的适应性和较高的补偿精度。     

基于改进PSO算法的FESS的PI参数优化

提出了一种改进粒子群优化(IPSO)算法用于FESS的PI控制器的参数优化设计.IPSO算法通过混沌初始化、迭代中加入混沌扰动和自适应调整惯性权重系数来克服传统PSO算法效率低、易陷入局部极值和算法早熟等缺陷.基于IPSO算法,以ITAE指标最小为目标函数对FESS的PI控制器参数进行优化,并以FESS接入四机系统为例,通过非线性仿真验证了优化结果的有效性,并通过与其他优化方法比较,得出IPSO具有更好的优化性能的结论.     

基于改进PSO算法的FESS的PI参数优化

提出了一种改进粒子群优化（IPSO）算法用于FESS的PI控制器的参数优化设计。IPSO算法通过混沌初始化、迭代中加入混沌扰动和自适应调整惯性权重系数来克服传统PSO算法效率低、易陷入局部极值和算法早熟等缺陷。基于IPSO算法,以ITAE指标最小为目标函数对FESS的PI控制器参数进行优化,并以FESS接入四机系统为例,通过非线性仿真验证了优化结果的有效性,并通过与其他优化方法比较,得出IPSO具有更好的优化性能的结论。     

基于改进PSO与广义五阶CKF算法的PMSM无传感器控制

为提高永磁同步电机无传感器控制效果,针对电机转速估计精度及PI参数优化的问题,提出一种基于改进粒子群优化(PSO)的PI参数与广义五阶容积卡尔曼滤波(CKF)算法的PMSM无传感器控制方法.首先,建立了PMSM的离散数学模型,推导了其转速环传递函数,并以此为适应度函数利用基于柯西变异的改进PSO算法来对转速环PI参数进行优化;其次,利用广义五阶容积规则推导了广义五阶CKF算法,构建了基于改进PSO的PI参数和广义五阶CKF算法的PMSM无传感器控制方法;最后,实验结果表明,广义五阶CKF算法相较于三阶CKF算法,对电机转速和转子位置估计精度更高,基于改进PSO转速环PI参数的优化方法能够改善PMSM的控制效果,将改进PSO与广义五阶CKF算法相结合有利于改善PMSM的无传感器控制效果.     

基于自适应粒子群算法的直流输电PI控制器参数优化

针对传统粒子群算法(particle swarm optimization,PSO)的不足,提出了一种自适应粒子群优化(adaptive particle swarm optimization,APSO)算法,并应用于直流控制器比例–积分控制器(proportional integral,PI)的参数寻优。文中首先介绍了HVDC控制器的系统模型,然后推导出了贵广直流输电工程中的控制器传递函数,并利用APSO算法进行PI参数寻优。寻优过程采取时间乘绝对误差积分(integral of time multipled by the absolute value of error,ITAE)准则计算目标函数值,取对应ITAE目标函数最小的样本为最优PI参数,得到的PI参数可认为是全局最优解。结果表明了APSO算法在PI控制器参数优化中的全局寻优能力和有效性。     

基于模型预测控制的MMC-HVDC系统控制策略研究

为提高基于模块化多电平换流器的直流输电系统(MMC-HVDC)电流内环的动态响应速度,提出了一种MMC-HVDC系统的模型预测控制(Model predictive control, MPC)方法。该方法通过预测模型、反馈校正和滚动优化得到最优的电压控制量,克服了传统双闭环控制PI参数整定困难和动态响应慢的问题。针对外环PI控制器参数对系统参数敏感的问题,对PI控制器参数进行了自适应调整,根据控制器的输入偏差和输出的大小来调整PI参数,提高了控制器的鲁棒性。针对子模块电容的均压问题,采用了基于排序法的最近电平调制(NLM)均压调制算法,有效地实现了子模块电容电压的平衡。最后,通过Matlab/Simulink平台搭建了5电平的MMC-HVDC系统仿真模型。仿真结果表明了该控制策略的有效性和可行性。     

基于KMTOA的三相光伏逆变器双环控制研究

以离网三相光伏逆变器为研究对象,选择电压电流双闭环控制作为其控制策略,针对传统PI控制参数难整定、系统变化适应性差的问题,引入了分子动理论优化算法来辨识双闭环控制器的PI参数,实现动态优化。基于MATLAB平台的仿真实验结果表明:相比于传统的PI控制以及PSO和GA算法两种智能算法,该算法具有操作简便、超调量小、响应快、适应性强等特点;系统输出的电压、电流波形呈现良好的正弦特性,总谐波畸变率小,有效改善了逆变器的动态性能及其输出电压的质量。     

基于FOPI~λ的复合控制在CVCF逆变电源中的应用

提出一种基于分数阶比例积分(FOPI~λ)的复合控制策略。介绍了三相逆变电源的系统结构,分析了d,q坐标系下的传递函数,研究了FOPI~λ控制和重复控制的基本理论和实现方法,并设计FOPI~λ控制器和重复控制器,对两种控制策略使用可变加权因子有机结合形成复合控制器。针对该分数阶控制器待整定参数较多的问题,提出基于粒子群优化(PSO)算法对参数进行整定优化的方法。与传统复合控制相比,仿真验证所提方案可保证系统有较高稳压精度和较快动态响应,输出电压总谐波畸变率(THD)更小;实验结果表明该方案有效、可行。     

基于蚁狮优化算法分数阶PI的PMSM矢量控制

针对永磁同步电机(PMSM)双闭环矢量控制系统中传统工程经验比例积分(PI)参数整定易出现超调、振荡、鲁棒性不强等问题,提出采用分数阶PI(FOPI)控制器应用于系统的转速外环,并利用蚁狮优化(ALO)算法对FOPI控制器进行参数优化.该算法利用自然界中蚁狮狩猎蚂蚁的过程,将待优化的参数看作蚁狮个体所处空间位置,并利用误差性能指标ITAE作为目标函数,搜索待求参数的全局最优解.为满足电机驱动控制的实时性要求,采用ALO算法离线整定待求参数.实验结果表明,ALO算法优化的转速外环FOPI控制器具有减小调节时间、提高响应速度和增强系统抗干扰能力的优势,证明了优化策略的可行性和有效性.     

MMC均压控制参数优化研究

模块化多电平换流器(MMC)因特殊的级联拓扑导致了子模块电容电压均衡和以二倍频负序为主的环流问题,使桥臂正弦特性电流波形发生畸变,危害换流器安全运行。在现有的MMC电容电压均衡控制系统的基础上,研究了基于蚁群算法的分数阶PI控制在MMC电容电压均衡中的应用,以绝对误差积分准则作为目标函数,通过分数阶PI的引入增强了控制器对输入指令的追踪能力,利用蚁群算法的最短路径寻优能力对控制器参数进行优化整定,增强了电容电压均衡能力与环流抑制能力。通过MATLAB/Simulink仿真验证了所提策略的有效性,波形THD从44.25%下降至12.28%。     

Optimal DG Allocation in Radial Distribution Systems with High Penetration of Non-linear Loads

This paper addresses the optimal distributed generation sizing and siting for voltage profile improvement, power losses, and total harmonic distortion (THD) reduction in a distribution network with high penetration of non-linear loads. The proposed planning methodology takes into consideration the load profile, the frequency spectrum of non-linear loads, and the technical constraints such as voltage limits at different buses (slack and load buses) of the system, feeder capacity, THD limits, and maximum penetration limit of DG units. The optimization process is based on the Genetic Algorithm (GA) method with three scenarios of objective function: system power losses, THD, and multi-objective function-based power losses and THD. This method is executed on the IEEE 31-bus system under sinusoidal and non-sinusoidal (harmonics) operating conditions including load variations within the 24-hr period. The simulation results using Matlab environment show the robustness of this method in optimal sizing and siting of DG, efficiency for improvement of voltage profile, reduction of power losses, and THD. A comparison with particle swarm optimization (PSO) method shows that the proposed method is better than PSO in reducing the power losses and THD in all suggested scenarios.     

基于神经元自适应控制的动态电压恢复器研究

针对微电网并网系统中存在的电压跌落电能质量问题,研究了一种基于神经元自适应控制的蓄电池储能动态电压恢复器(DVR)。主要由蓄电池、逆变器、LC滤波器、变压器4个部分组成。其中,为了提升该DVR的控制性能,引入神经元自适应控制算法改进了外环电压PI调节器,实现了外环控制参数的自适应调节,从而得到新的双闭环控制策略。然后,结合空间电压矢量调制法最终实现控制。最后,在MATLAB/Simulink仿真软件中搭建了基于神经元自适应控制的蓄电池储能DVR的并网系统模型。试验结果表明,采用神经元自适应控制的蓄电池储能DVR能够补偿微电网的电压跌落,具有较强的动态响应能力和抗扰能力。     

Compensation of voltage disturbances using PEMFC supported Dynamic Voltage Restorer

The continuity of supply and quality of power are the two main significant aspects of today’s power delivery system. The Dynamic Voltage Restorer (DVR) is a series connected custom power device which improves the quality of power delivered to the consumers. This paper deals with the effectual exploitation of DVR for interconnecting the proton exchange membrane fuel cell (PEMFC) stack to the grid based on optimized proportional integral (PI) and fuzzy logic (FL) Controller. The real coded Genetic algorithm (GA) is used to optimize the PI controller parameters. The PEMFC operated boost converter is used to boost up the fuel cell output voltage to balance the DC side necessities of the voltage source converter (VSC). The proposed DVR provides balanced and unbalanced voltage sag/swell compensation, harmonic reduction as well as an active power injection to the grid. The designed method also protects the sensitive loads from source side power quality disturbances including short term interruption. In addition, the harmonic compensation performance of the proposed work is validated by comparing with the results of the H^∞ controller based DVR under medium level voltage condition. The simulation results from MATLAB/SIMULINK are discussed to prove the effectiveness of the planned method.     

基于协同进化的电力系统多目标优化*

随着社会的发展与科技的进步,电力系统在不断的发展,人们所追求的也不仅仅是经济效益,电力系统所造成的能源浪费及其安全性等问题也受到人们的广泛关注。文中选取电力系统燃料成本、有功网损、电压质量三个目标作为优化目标,基于粒子群、遗传与差分进化算法提出一种基于协同进化的多目标优化算法。分别在标准测试函数与IEEE-30节点进行实验,结果显示文中提出的算法有更好的收敛性与稳定性,更易获得完整的Pareto前沿。     

Optimal controllers designs for automatic reactive power control in an isolated wind-diesel hybrid power system

The paper presents the bacterial foraging optimization algorithm (BFOA) and particle swarm optimization (PSO) algorithm based robust controllers for voltage deviations due to the variation of reactive power in an isolated wind-diesel hybrid power system. The isolated wind-diesel system consists of wind energy conversion system (WECS) utilizing a permanent magnet induction generator (PMIG). Further, a synchronous generator (SG) is used with the diesel engine set for power generation. The mismatch between generated and consumed reactive power in the system causes voltage fluctuations, which will occur at generator terminals. These oscillations further causes reduction in the stability and quality of the power supply. The static synchronous compensator (STATCOM) and an automatic voltage regulator (AVR) are used to suppress voltage fluctuations in an isolated wind-diesel hybrid power system. The STATCOM is used as a reactive power compensator and the AVR is used to keep the terminal voltage constant for the synchronous generator. Both STATCOM and AVR are having proportional and integral (PI) controllers with single input. In modeling for the system, a normalized co-prime factorization is applied to show the possible unstructured uncertainties in the power system such as variation of system parameters and generating and loading conditions. The performance and robust stability conditions of the control system are formulated as the optimization problem, which is based on the Hα loop shaping. BFOA and PSO algorithms are implemented to solve this optimization problem and to achieve PI control parameters of STATCOM and AVR simultaneously. In order to show the efficiency of the proposed controllers, the performance of the proposed controllers is compared with the performance of the conventional controller and genetic algorithm (GA) based PI controllers for the same wind-diesel system. The dynamic responses of the system for four different small-disturbance case studies has been carried out in MATLAB environment.     

含抽水蓄能电站的互联电网负荷频率自抗扰优化控制研究

针对发电能源结构的多元化发展给互联电网负荷频率的稳定性控制带来较大的挑战,建立含抽水蓄能电站的两区域互联电网多元混合发电的负荷频率控制模型,提出一种基于粒子群优化算法的负荷频率线性自抗扰控制器参数整定优化策略,通过粒子群算法的迭代寻优计算获得最优的线性自抗扰控制器参数。考虑互联电网各区域发生不同的负荷扰动,在抽水蓄能电站的抽水和发电2种工况下,对所提出的控制方法进行系统仿真。仿真结果表明,通过粒子群算法优化的负荷频率线性自抗扰控制器,与传统PI控制器对比,前者具有更强的抗扰动能力和适应性,系统动态稳定性更好。     

遗传粒子群混合算法在配电网络重构多目标优化中的应用

针对配电网络重构多为单一性能最优重构的问题,文章提出了使配电网线损、负荷均衡、供电电压质量最佳的多目标配网优化模型。结合GA中的进化思想和粒子群算法(PSO)中的群体智能技术,采用遗传粒子群混合算法寻优,通过随机权重方法来获得目标是Pareto前沿面的可搜索方向,体现出较GA和PSO更好的寻优性能。寻优过程中,部分个体以PSO方法迭代,其它个体进行GA中的选择、交叉和变异操作,整个群体信息共享,同时采用自适应参数机制和优胜劣汰的思想进化。在此基础上制定的配网优化方案能够在保证配网呈辐射状、满足馈线热容、电压降落要求和变压器容量等的前提下,最大限度地提高配电系统安全性和经济性。算例表明该算法在求解性能和效率两方面都有比较显著的优势。     

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。     

Two degrees of freedom dc voltage controller of grid interfaced PV system with optimized gains

This paper proposes the application of fictitious reference iterative tuning (FRIT) method to optimize the gains of dc voltage controller of grid connected photo-voltaic (PV) system. It may be difficult to achieve good control of dc voltage using conventional PI controller having only one-degree-of-freedom (1-DOF) due to the trade-off between overshoot (in step response) and disturbance response. In this paper, the optimal control of dc voltage is proposed with improved disturbance response by implementing 2-DOF PI controller structure. FRIT method has been programmed in MATLAB based upon the particle swarm optimization (PSO) algorithm. The fundamental idea related to FRIT method is the extraction of input and output data, reference model setting and range of controller gains. The performance of dc voltage control for the optimized 2-DOF PI controller is also compared with the fuzzy logic controller (FLC) response.     

具有谐波抑制功能的电网电能质量最优控制方法

电网运行过程中容易受谐波干扰,造成电压畸变、网损过度.为此,提出具有谐波抑制功能的电网电能质量最优控制方法,优化电网电能质量的同时有效抑制谐波干扰.采用分层控制策略,初级控制采用多目标电压无功谐波优化算法获取电压差异数据、电压不稳定性数据、网络损坏率数据、电网谐波数据优化电网电压输出,同时采用模糊PI控制器完成复合电流...