多目标差分进化算法的电力系统无功优化

 在传统电力系统无功优化( Reactive Power Optimization,RPO) 模型中引入电压水平 指标,建立了以网损最小,电压水平最好为目标的多目标差分进化算法( Differential Evolution Algorithm) 的模型。针对基本差分进化算法易陷入局部最优解、收敛速度慢的缺点,提出一种 具有自适应参数策略的改进差分进化算法并首次用于多目标电力系统无功优化问题。通过在 算法进化过程中调整变异因子F 和交叉因子CR,在初期增加种群的多样性、扩大全局搜索区 域; 从而可以避免算法陷入局部最优解; 同时在后期也加快了收敛速度。将该算法用于电力系 统无功优化并仿真计算了IEEE-14 节点标准测试系统,结果验证模型和算法的有效性。     

Efficient optimization technique for multiple DG allocation in distribution networks

In the last few decades, interest in the integration of Distributed Generators (DGs) into distribution networks has been increased due to their benefits such as enhance power system reliability, reduce the power losses and improve the voltage profile. These benefits can be increased by determining the optimal DGs allocation (location and size) into distribution networks. This paper proposes an efficient optimization technique to optimally allocate the multiple DG units in distribution networks. This technique is based on Sine Cosine Algorithm (SCA) and chaos map theory. As any random search-based optimization algorithm, SCA faces some issues such as low convergence rate and trapping in local solutions during the exploration and exploitation phases. This issue can be addressed by developing Chaotic SCA (CSCA). CSCA is mainly based on the iterative chaotic map which used to update the random parameters of SCA instead of using the random probability distribution. The iterative chaotic map is applied for single and multi-objective SCA. The proposed technique is validated using two stranded IEEE radial distribution feeders; 33 and 69-nodes. Comprehensive comparison among the proposed technique, the original SCA, and other competitive optimization techniques are carried out to prove the effectiveness of CSCA. Finally, a complete study is performed to address the impact of the intermittent nature of renewable energy resource on the distribution system. Hence, typical loads and generation (represented in PV power) profiles are applied. The result proves that the CSCA is more efficient to solve the optimal multiple DGs allocation with minimum power loss and high convergence rate.     

基于BP神经网络模型实现配电网络损耗优化的研究

配电网络损耗是关系到电网正常运行的关键,如何对配电网络损耗进行优化是目前亟待解决的技术问题。论文针对无功补偿设备辐射状配电网存在的网络损耗问题,提出了一种优化方案。根据辐射状配电网体系结构的特征,提出基于BP网络模型理论进行计算配电网络损耗的方法,该方法能够映射、处理任意复杂的非线性关系,数据处理精确、及时,并根据计算的结果,提出基于遗传算法优化网络损耗的方案,通过搜索最优解,有针对性地优化损耗问题。根据数据比对,论文提出的方案优化效果较好,具有一定的实用价值。     

基于Map-Reduce模型的云资源调度方法研究

为提高Map-Reduce模型资源调度问题的求解效能,分别考虑Map和Reduce阶段的调度过程,建立带服务质量(QoS)约束的多目标资源调度模型,并提出用于模型求解的混沌多目标粒子群算法。算法采用信息熵理论来维护非支配解集,以保持解的多样性和分布均匀性;在利用Sigma方法实现快速收敛的基础上,引入混沌扰动机制,以提高种群多样性和算法全局寻优能力,避免算法陷入局部最优。实验表明,算法求解所需的迭代次数少,得到的非支配解分布均匀。Map-Reduce资源调度问题的求解过程中,在收敛性和解集的多样性方面,所提算法均明显优于传统多目标粒子群算法。     

Chaotic bat swarm optimisation (CBSO)

Bat swarm optimisation (BSO) is a novel heuristic optimisation algorithm that is being used for solving different global optimisation problems. The paramount problem in BSO is that it severely suffers from premature convergence problem, that is, BSO is easily trapped in local optima. In this paper, chaotic-based strategies are incorporated into BSO to mitigate this problem. Ergodicity and non-repetitious nature of chaotic functions can diversify the bats and mitigate premature convergence problem. Eleven different chaotic map functions along with various chaotic BSO strategies are investigated experimentally and the best one is chosen as the suitable chaotic strategy for BSO. The results of applying the proposed chaotic BSO to different benchmark functions vividly show that premature convergence problem has been mitigated efficiently. Actually, chaotic-based BSO significantly outperforms conventional BSO, cuckoo search optimisation (CSO), big bang-big crunch algorithm (BBBC), gravitational search algorithm (GSA) and genetic algorithm (GA).     

Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization

This paper describes teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal power flow (MOOPF) problems while satisfying various operational constraints. To improve the convergence speed and quality of solution, quasi-oppositional based learning (QOBL) is incorporated in original TLBO algorithm. The proposed quasi-oppositional teaching learning based optimization (QOTLBO) approach is implemented on IEEE 30-bus system, Indian utility 62-bus system and IEEE 118-bus system to solve four different single objectives, namely fuel cost minimization, system power loss minimization and voltage stability index minimization and emission minimization; three bi-objectives optimization namely minimization of fuel cost and transmission loss; minimization of fuel cost and L-index and minimization of fuel cost and emission and one tri-objective optimization namely fuel cost, minimization of transmission losses and improvement of voltage stability simultaneously. In this article, the results obtained using the QOTLBO algorithm, is comparable with those of TLBO and other algorithms reported in the literature. The numerical results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal non-dominated solutions of the multi-objective OPF problem. The simulation results also show that the proposed approach produces better quality of the individual as well as compromising solutions than other algorithms.     

Krill herd algorithm for optimal location of distributed generator in radial distribution system

Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.     

改进的自适应混沌差分进化算法

为了提高差分进化算法的寻优速度、克服启发式算法常见的早熟收敛问题, 提出一种基于帐篷映射(Tent)的自适应混沌嵌入式差分进化算法(CLSDE). 算法采用 Tent 映射生成的混沌序列来取代基本DE算法选择操作中的随机数, 充分利用了混沌运动的随机性、遍历性和规律性. 通过与混沌PSO算法与普通的DE算法比较, 测试函数仿真结果表明, 该算法具有良好的全局搜索能力, 寻优精度较高, 收敛速度快, 鲁棒性好.     

Vehicle power train optimization using multi-objective bird swarm algorithm

In this paper, a multi-objective bird swarm algorithm (MOBSA) is proposed to cope with multi-objective optimization problems. The algorithm is explored based on BSA which is an evolutionary algorithm suitable for single objective optimization. In this paper, non-dominated sorting approach is used to distinguish optimal solutions and parallel coordinates is applied to evaluate the distribution density of non-dominated solution and further update the external archive when it is full to overflowing, which ensure faster convergence and more widespread of Pareto front. Then, the MOBSA is adopted to optimize benchmark problems. The results demonstrate that MOBSA gets better performance compared with NSGA-II and MOPSO. Since a vehicle power train problem could be treated as a typical multi-objective optimization problem with constraints, with integration of constrained non-dominated solution, MOBSA is adopted to acquire optimal gear ratios and optimize vehicle power train. The results compared with other popular algorithm prove the proposed algorithm is more suitable for constrained multi-objective optimization problem in engineering field.

     

机组短期负荷环境／经济调度多目标混合优化

环境/经济短期负荷调度主要由调度周期内的最优机组组合和负荷环境/经济分配组成,本文将变权重多目标进化算法与混沌局部优化相结合形成混合优化算法应用到电站机组环境/经济运行多目标优化问题中,在混合多目标优化算法中采用组合结构基因,其中机组基因用于机组组合全局粗寻优,参数基因用于负荷分配局部优化,基因修正与罚函数结合解决约束问题．通过对优秀个体进行基于线性搜索的混沌局部优化,可加快收敛速度和降低计算时间．实例仿真结果说明所提出的算法能获得较好分布的Pareto优化解．     

多可再生能源冷热电联供微网系统环境经济优化调度

针对含多种可再生能源的冷热电联供微网系统调度优化问题,提出微网系统最小运行费用和二氧化碳排放的多目标调度优化模型,并结合启发式调度规则,采用改进多目标交叉熵算法获取Pareto最优解集.为了提高算法的收敛速度和求解精度,依据重要抽样理论将多目标优化定义为小概率事件,并引入样本分段生成策略和参数更新机制.算例仿真表明,所提出的多目标模型及其优化算法能够使微网系统获取较好的经济和环境效益,满足用户多样性的优化需求.     

Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm

This paper deals with the optimal placement of distributed generation （DG） units in distribution systems via an enhanced multi-objective particle swarm optimization （EMOPSO） algorithm. To pursue a better simulation of the reality and provide the designer with diverse alternative options, a multi-objective optimization model with technical and operational con- straints is constructed to minimize the total power loss and the voltage fluctuation of the power system simultaneously. To enhance the convergence of MOPSO, special techniques including a dynamic inertia weight and acceleration coefficients have been inte- grated as well as a mutation operator. Besides, to promote the diversity of Pareto-optimal solutions, an improved non-dominated crowding distance sorting technique has been introduced and applied to the selection of particles for the next iteration. After verifying its effectiveness and competitiveness with a set of well-known benchmark functions, the EMOPSO algorithm is em- ployed to achieve the optimal placement of DG units in the IEEE 33-bus system. Simulation results indicate that the EMOPSO algorithm enables the identification of a set of Pareto-optimal solutions with good tradeoff between power loss and voltage sta- bility. Compared with other representative methods, the present results reveal the advantages of optimizing capacities and loca- tions of DG units simultaneously, and exemplify the validity of the EMOPSO algorithm applied for optimally placing DG units.     

一种高速收敛粒子群优化算法

针对粒子群优化算法早熟问题,提出一种克服早熟的高速收敛粒子群算法．该算法首先采用混沌序列初始化粒子位置,以增强搜索多样性;其次,在算法中嵌入有效判断早熟停滞的方法,一旦检索到早熟迹象,便随机地选择最优解任意一维的分量值,用一个随机值取代它,以扰乱粒子的当前搜索轨迹,使其跳出局部最优．大量仿真实验表明,大多数连续函数的寻优过程只需用几个粒子、迭代几十次便能完成,可实现全局寻优过程的高速收敛．     

求解多目标最小生成树的改进多目标蚁群算法

多目标最小生成树问题是典型的NP问题。针对此问题,提出一种改进的多目标蚁群算法。为获得更好的非劣前端,通过合理选取多个信息素扩散源与扩散策略来避免其早熟收敛,并引入非支配排序算子,提高种群多样性并避免算法过早陷入局部最优解。对比实验结果表明：对于多目标最小生成树问题,该算法是有效的,不但在求解效率和解的质量方面优于相关算法,而且随着问题规模的扩大,算法仍保持较好的性能。     

分阶段二次变异的多目标混沌差分进化算法

提出一种结合分阶段二次变异和混沌理论的改进差分进化(DE)算法,以解决多目标约束优化问题.其核心思想是,在DE进化前期采用基于非支配解的随机二次变异来提高算法的全局寻优能力,进化后期采用基于非支配解的混沌二次变异来提高DE的局部寻优能力.通过对典型测试问题的仿真实验验证了所提出的算法能在全局搜索性能与局部搜索性能之间维持较好平衡,而且保持了DE算法的简洁性能,其收敛性、分布度和均衡性均优于标准DE.     

基于博弈机制的多目标粒子群优化算法

为改善多目标粒子群算法存在优化解的多样性不足和算法的收敛性问题,提出一种基于博弈机制的多目标粒子群优化算法。使用博弈机制,无需外部储备集,通过非占优排序和拥挤距离选出一部分优秀的粒子,从这些优秀的粒子中随机选择一个作为全局最优粒子,有效提升算法的收敛性和种群的多样性。算法初期使用多尺度混沌变异策略,避免算法陷入局部最优。通过与6个多目标算法在3个系列标准测试函数上进行比较,验证了该算法所得解分布性较好,能快速收敛到真实Pareto前端。     

一种改进的多目标合作型协同进化遗传算法

针对传统多目标算法早熟收敛及多样性不足的问题,提出了一种改进的非支配排序合作型协同进化遗传算法（Improved Non-dominated Sorting Cooperative Coevolutionary Genetic Algorithm,INSCCGA）。该算法利用外部档案存储每一代进化过程中产生的精英个体,并对其不断进行更新,以加快算法的收敛速度。同时提出了一种新型子种群之间协同进化的方式,增强候选解的多样性。利用ZDT系列标准测试函数,与经典的多目标进化算法NSGA-II以及多目标协同进化算法NSCCGA进行了对比,结果表明改进算法具有更好的收敛性以及均匀的解分布。     

求解PMU多目标优化配置问题的非劣排序微分进化算法

为实现电网完全可观测,同时保证PMU(同步相量测量单元)的安装数日尽量少,且系统的N-1量测可靠性尽量高,笔者提出了一种混合算法,对电网中PMU进行多目标优化配置.在此算法中,通过将Pareto非劣排序操作与微分进化算法有机融合,并对个体的排挤机制和变异策略进行改进以克服进化早熟和搜索不均匀的问题,设计出了一种新的非劣排序微分进化算法对模型进行求解,并采用模糊集理论提取出最优折中解.最后以IEEE39母线系统为例进行了PMU多目标优化配置,结果表明该方法可简单快速地实现全局多目标寻优,找到更多更合理的PMU优化配置方案,能得到准确而完整的Pareto最优前沿.     

基于分组混沌PSO算法的模糊神经网络建模研究

为改善记忆功放建模的精度,且针对粒子群算法早期收敛速度较快,但在后期易陷入早熟收敛,局部最优等特点,提出了一种分组并行混沌粒子群优化算法（Grouping Parallel-Chaotic Particle Swarm Optimization,GP-CPSO）,将分组粒子群优化算法与混沌思想相结合,并用该算法优化动态模糊神经网络（Dynamic Fuzzy Neural Network,DFNN）参数,建立DFNN功放模型。引入分组的CPSO群算法,将种群划分为若干个组,每组单独计算,大大提高了收敛速度,同时将混沌思想运用到每个粒子当中去,避免早熟和局部最优,缩短了迭代时间。通过仿真结果可以看到,GP-CPSO优化后的动态模糊神经网络建模的训练误差减小到0.1以内,收敛速度提高32.5%,从而验证了这种建模方法有效且可靠。     

支持强化学习RNSGA-II算法在航迹规划中应用

针对传统第二代非支配排序遗传算法（NSGA-II）求解无人机多目标三维航迹规划早熟收敛及多样性不足的局限性,提出了支持强化学习RNSGA-II算法。设置两个独立种群分别用NSGA-II算法独立演化,隔代在两种族之间迁徙,接着各种群进行寻优进化,根据种群多样性的变化运用强化学习算法动态地优化各种群间“迁徙”的比例参数,从而使进化过程保持种群多样性,一定程度上解决了收敛速度和全局收敛性之间的矛盾。仿真结果表明,RNSGA-II算法较单一NSGA-II收敛精度更高,解集具有更好的分布性和多样性。