基于动态惩罚因子的改进蚱蜢算法求解经济负荷调度和经济排放联合调度问题

电力生产装置运行中各种燃料的成本逐步增加,需要最小化成本函数以求解此类复杂经济负荷调度问题.鉴于此,提出一种基于动态惩罚因子的改进蚱蜢算法求解经济负荷调度(economic load dispatch, ELD)问题和经济排放联合调度(combined economic emission dispatch, CEED)问题.为了提高蚱蜢算法(grasshopper optimization algorithm, GOA)性能,提出一种改进的混合蚱蜢算法(hybrid grasshopper optimization algorithm, HGOA),将重力搜索算子和鸽群搜索算子-地标算子加入GOA中,增强算法的搜索能力,平衡算法的勘探和开发.同时,为了更好地解决ELD和CEED问题中的约束问题,提出6个惩罚函数,包括2个V型函数、反正切函数、反正弦函数、线性函数和二次函数,并使用动态惩罚策略代替传统的固定值惩罚策略.选取3个ELD问题案例和4个CEED问题案例验证所提出方法的有效性,实验结果表明, HGOA相较于其他元启发式算法在求解质量上表现更好,且动态惩罚策略比固定值惩罚策略效果更...     

电力系统经济负荷分配的族群优化方法

经济负荷分配（Economic Load Dispatch,ELD）是电力系统中一种重要的优化问题,它可归为一类高维、离散、非线性的多约束函数优化问题。针对这类问题,提出了一种基于线性截取策略的改进族群进化算法——EGEA/LT,并使用EGEA/LT对IEEE的3机、6机和15机3个仿真系统进行了优化实验,将实验结果与其他典型算法优化结果进行比较,说明了EGEA/LT是一种求解ELD问题的有效方法。     

基于改进布谷鸟搜索算法的电力系统多目标运行优化研究

针对RES接入后的综合电力系统运行优化问题,提出一种改进的布谷鸟搜索(MCS)算法,通过优化布谷鸟搜索算法(CS)中莱维飞行的步长,以实现快速收敛。多目标优化函数考虑了燃料和排放成本,在满足系统非线性运行约束的条件下,对改进后的IEEE 10机系统中进行了仿真分析,并与CS算法、粒子群优化算法(PSO)、混沌PSO算法进行比较,验证了MCS算法在解决复杂的电力系统优化运行问题上的优越性。     

基于多目标飞蛾算法的电力系统无功优化研究

鉴于电力需求的日益增长与传统无功优化方法的桎梏,如何更加合理有效地解决电力系统的无功优化问题逐渐成为了研究的热点。提出一种多目标飞蛾扑火算法来解决电力系统多目标无功优化的问题,算法引入固定大小的外部储存机制、自适应的网格和筛选机制来有效存储和提升无功优化问题的帕累托最优解集,算法采用CEC2009标准多目标测试函数来进行仿真实验,并与两种经典算法进行性能的对比分析。此外,在电力系统IEEE 30节点上将该算法与MOPSO,NGSGA-II算法的求解结果进行比较分析的结果表明,多目标飞蛾算法具有良好的性能,并在解决电力系统多目标无功优化问题上具有良好的潜力。     

人工智能算法在电力系统无功优化问题中的应用

介绍了电力系统无功优化问题及其模型,对人工智能算法在电力系统无功优化问题中的应用现状进行总结,指出了各种算法在解决此类问题时的优、缺点,并对其研究前景进行了展望。     

基于天牛群优化算法的多机PSS参数优化北大核心CSCD

为抑制多机电力系统的低频振荡,提出将天牛群优化(BSO)算法应用到多机电力系统稳定器(PSS)的参数协调优化中。采用传统的超前-滞后型PSS模型,将PSS参数的协调配置问题转化为一个函数优化问题。首先利用Prony算法对振荡信号进行机电模式辨识,然后通过BSO算法迭代寻优最佳PSS参数。通过MATLAB软件搭建IEEE四机两区域系统,将优化好的参数应用到多机电力系统仿真中测试其准确性。仿真结果表明,相比于粒子群优化(PSO)算法、分层多子群的均匀分布混沌粒子群优化(HUCPSO)算法,BSO算法具有更好的寻优能力,由该算法优化的PSS在改善系统阻尼和小信号稳定性方面具有良好的效果和性能。     

改进的BT—SVM应用于电力系统SSA

随着电力系统的广泛发展,电力系统静态安全评估已变得越来越重要。文中比较了现在几种常用的人工智能方法,选择了支持向量机算法解决这一问题。由于解决大样本问题时,支持向量机所需训练时间显著增加,文中提出了约简样本的方法,并结合适合于电力系统的二叉树结构,提出了一种改进的简化二叉树支持向量机算法。将这种新的支持向量机算法应用于IEEE57节点电力系统,结果表明,文中提出的算法取得了比较好的结果,有效可行。     

混沌粒子群算法及其在发电资源调度中的应用

为了有效地解决水火电力系统资源短期优化调度问题,提出了一种基于混沌粒子群算法的调度方案。设计了水火电力系统资源调度问题的数学模型,给出了混沌粒子群调度算法的框架,通过引入最优粒子的混沌搜索机制、优势粒子和劣势粒子的权重自适应调节机制,从而使算法具有动态自适应性,能够较容易地跳出局部最优。实验结果表明,本算法方案能有效解决水火发电资源调度问题,具有较好的应用价值。     

基于改进BBO算法的水轮发电机组PSS2B参数优化

为了使电力系统稳定器(PSS)参数能够适应电力系统的多种运行方式,本文提出了一种可用于PSS参数优化的算法。该算法结合差分进化算法(DE)变异操作和自适应迁移操作对生物地理学算法(BBO)进行改进,提高了寻优能力和速度。最后,借助电力系统数字仿真软件对该算法在PSS参数优化方面的有效性进行验证,仿真结果表明:经本文所提优化算法计算的PSS参数能够适应电力系统的不同运行工况。     

小生境网格进化算法在电力系统控制中的应用

研究电力系统自动控制的准确性.电力系统的供电输出需要满足多条线路目标的最优状态输出,即在电力系统的自动控制中就存在多目标优化问题,并且实际的电力系统输出线路需电量不均衡,使得多目标优化的解集存在非均匀分布的情况,造成电力系统稳定性和可靠性受到影响.传统的多目标优化算法不能有效处理解集非均匀分布的问题.为了提高优化解集接近真实解的程度,提出一种小生境网格进化算法来解决电力系统中的多目标优化解集非均匀问题.采用小生境技术在算法中设置排挤因子,利用个体间的进化排挤作用形成小的个体环境,可维持个体解集的分布多样性.实验表明,改进方法能够有效解决电力系统多目标优化的解集非均匀问题,保证了电力系统自动控制的准确性.     

A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems

This paper presents the design and application of an efficient hybrid heuristic search method to solve the practical economic dispatch problem considering many nonlinear characteristics of power generators, and their operational constraints, such as transmission losses, valve-point effects, multi-fuel options, prohibited operating zones, ramp rate limits and spinning reserve. These practical operation constraints which can usually be found at the same time in realistic power system operations make the economic load dispatch problem a nonsmooth optimization problem having complex and nonconvex features with heavy equality and inequality constraints.The proposed approach combines in the most effective way the properties of two of the most popular evolutionary optimization techniques now in use for power system optimization, the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. To improve the global optimization property of DE, the PSO procedure is integrated as additional mutation operator.The effectiveness of the proposed algorithm (termed DEPSO) is demonstrated by solving four kinds of ELD problems with nonsmooth and nonconvex solution spaces. The comparative results with some of the most recently published methods confirm the effectiveness of the proposed strategy to find accurate and feasible optimal solutions for practical ELD problems.     

Estimation of distribution and differential evolution cooperation for large scale economic load dispatch optimization of power systems

Economic Load Dispatch (ELD) is an important and difficult optimization problem in power system planning. This article aims at addressing two practically important issues related to ELD optimization: (1) analyzing the ELD problem from the perspective of evolutionary optimization; (2) developing effective algorithms for ELD problems of large scale. The first issue is addressed by investigating the fitness landscape of ELD problems with the purpose of estimating the expected performance of different approaches. To address the second issue, a new algorithm named “Estimation of Distribution and Differential Evolution Cooperation” (ED-DE) is proposed, which is a serial hybrid of two effective evolutionary computation (EC) techniques: estimation of distribution and differential evolution. The advantages of ED-DE over the previous ELD optimization algorithms are experimentally testified on ELD problems with the number of generators scaling from 10 to 160. The best solution records of classical 13 and 40-generator ELD problems with valve points, and the best solution records of 10, 20, 40, 80 and 160-generator ELD problems with both valve points and multiple fuels are updated in this work. To further evaluate the efficiency and effectiveness of ED-DE, we also compare it with other state-of-the-art evolutionary algorithms (EAs) on typical function optimization tasks.     

Colonial competitive differential evolution: An experimental study for optimal economic load dispatch

Differential evolution (DE) algorithm is a population-based algorithm designed for global optimization of the optimization problems. This paper proposes a different DE algorithm based on mathematical modeling of socio-political evolution which is called Colonial Competitive Differential Evolution (CCDE). The two typical CCDE algorithms are benchmarked on three well-known test functions, and the results are verified by a comparative study with two original DE algorithms which include DE/best/1 and DE/rand/2. Also, the effectiveness of CCDE algorithms is tested on Economic Load Dispatch (ELD) problem including 10, 15, 40, and 140-unit test systems. In this study, the constraints and operational limitations, such as valve-point loading, transmission losses, ramp rate limits, and prohibited operating zones are considered. The comparative results show that the CCDE algorithms have good performance and are reliable tools in solving ELD problem.     

Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization

Economic load dispatch (ELD) problems have been an important issue in optimal operation and planning of power system. Characterized by non-convex/non-smooth properties and various practical constraints, the ELD problems are difficult to solve using conventional optimization techniques. In this paper, an improved orthogonal design particle swarm optimization (IODPSO) algorithm is presented for solving the single-area and multi-area ELD problems with nonlinear characteristics of the generators, such as valve-point effects, prohibited operating zones, ramp rate limits and multiple fuels. In the IODPSO algorithm, an orthogonal designed method is used to construct a promising exemplar. Multiple auxiliary vector generating strategies are proposed to enhance the efficiency and effectiveness of orthogonal design operations. A tent chaotic map is employed for the adaptation of the acceleration coefficients, thus improving the proposed algorithm's robustness and global search capabilities. In addition, we designed a repair method to handle the practical constraints. Six cases of ELD problems with different characteristics are utilized to benchmark the proposed algorithm. Experimental results demonstrate that IODPSO algorithm is a promising approach for solving the non-convex/non-smooth ELD problems.     

Solution of economic load dispatch using hybrid chemical reaction optimization approach

In this paper, a new and efficient optimization technique based on hybridization of chemical reaction optimization (CRO) with differential evolution (DE) is developed and demonstrated to solve the ELD problem with thermal cost function having valve point loading effect together with and without multiple fuel options and with and without considering prohibited operating zone and ramp rate constraint. When valve-point effects, multi-fuel operations and the constraints of prohibited operating zone and ramp rate are taken into account, ELD problem become more complex than conventional ELD problem. To show the priority of the proposed algorithm, it is implemented on six different test systems for solving ELD problems. Comparative studies are carried out to examine the effectiveness of the proposed HCRO-DE approach with conventional DE, CRO and the other algorithms reported in the literature. The simulation results show that the proposed HCRO-DE method is capable of obtaining better quality solutions than DE, CRO and the other well popular optimization techniques.     

基于QoS需求的认知无线电资源分配算法

在误比特率、总发射功率以及主用户可容忍干扰门限的约束下,提出一种基于正交频分复用的认知无线电系统多目标优化贪婪分配算法。以最小化发射功率、最小化跨频干扰及满足次用户QoS需求为优化目标,线性组合这3个目标函数,把多目标优化问题转化为单目标优化问题,采用贪婪算法实现多目标优化。仿真结果表明,该算法的系统总容量趋近于最优,在降低对主用户跨频干扰的同时,能够有效地满足次用户的QoS需求。     

电力系统经济负荷分配的混合粒子群优化算法

为解决电力系统中的经济负荷分配问题,提出一种将约束优化与粒子群优化算法相结合的混合算法,同时引入直接搜索方法。使得混合后的粒子群优化算法不但具有高效的全局搜索能力,而且具有较强的局部搜索能力,避免陷入局部最优,提高求解精度。对两个实例进行测试,与其他智能算法的结果比较,证明提出的算法可以有效找到可行解,避免陷入局部最优,实现问题的快速求解。     

IEEE 802.11n系统中自适应混合优化功率分配算法*

为解决IEEE 802.11n系统中的功率浪费现象,提出了一种先注水后调和平均值（first-water filling-last-harmonic, FWLH）的自适应混合优化功率分配算法。首先利用注水算法（water-filling algorithm）计算判别信道质量的阈值,对阈值以下的信道关闭不分配功率,对阈值以上的信道采用调和平均值算法(harmonic algorithm, HARM)进行功率分配。仿真表明,在误码率方面,FWLH算法比注水算法和HARM算法分别降低了大约100倍和20倍,使