Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band

An attempt has been made to the effective application of a recently introduced, powerful optimization technique called differential search algorithm (DSA), for the first time to solve load frequency control (LFC) problem in power system. In this paper, initially, DSA optimized classical PI/PIDF controller is implemented to an identical two-area thermal-thermal power system and then the study is extended to two more realistic power systems which are widely used in the literature. To assess the usefulness of DSA, three enhanced competitive algorithms namely comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE), and success history based DE (SHADE) are studied in this paper. Moreover, the superiority of proposed DSA optimized PI/PID/PIDF controller is validated by an extensive comparative analysis with some recently published meta-heuristic algorithms such as firefly algorithm (FA), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), craziness based particle swarm optimization (CRPSO), differential evolution (DE), teaching-learning based optimization (TLBO), particle swarm optimization (PSO), and quasi-oppositional harmony search algorithm (QOHSA). A case of robustness and sensitivity analysis has been performed for the concerned test system under parametric uncertainty and random load perturbation. Furthermore, to demonstrate the efficacy of proposed DSA, the system nonlinearities like reheater of the steam turbine and governor dead band are included in the system modeling. The extensive results presented in this article demonstrate that proposed DSA can effectively improve system dynamics and may be applied to real-time LFC problem.     

Smart dispatching and metaheuristic swarm flow algorithm

The resource saving dispatching aims at finding the optimal combination of powers produced by power generating units that minimizes the total costs subject to given constraints. A metaheuristic swarm flow algorithm is proposed. Results of the comparative analysis of the efficiency of this algorithm on benchmark problems are presented. The comparison was performed with the particle swarm optimization, genetic, and biogeography-based optimization algorithms using systems consisting of 6 and 20 power generating units as examples. The flow algorithm converges to the optimal solution using less computational resources.     

Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis

This paper presents an evolving ant direction particle swarm optimization algorithm for solving the optimal power flow problem with non-smooth and non-convex generator cost characteristics. In this method, ant colony search is used to find a suitable velocity updating operator for particle swarm optimization and the ant colony parameters are evolved using genetic algorithm approach. To update the velocities for particle swarm optimization, five velocity updating operators are used in this method. The power flow problem is solved by the Newton–Raphson method. The feasibility of the proposed method was tested on IEEE 30-bus, IEEE 39-bus and IEEE-57 bus systems with three different objective functions. Several cases were investigated to test and validate the effectiveness of the proposed method in finding the optimal solution. Simulation results prove that the proposed method provides better results compared to classical particle swarm optimization and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices.     

Particle swarm optimization with crazy particles for nonconvex economic dispatch

The paper presents an effective evolutionary method for economic power dispatch. The idea is to allocate power demand to the on-line power generators in such a manner that the cost of operation is minimized. Conventional methods assume quadratic or piecewise quadratic cost curves of power generators but modern generating units have non-linearities which make this assumption inaccurate. Evolutionary optimization methods such as genetic algorithms (GA) and particle swarm optimization (PSO) are free from convexity assumptions and succeed in achieving near global solutions due to their excellent parallel search capability. But these methods usually tend to converge prematurely to a local minimum solution, particularly when the search space is irregular. To tackle this problem “crazy particles” are introduced and their velocities are randomized to maintain momentum in the search and avoid saturation. The performance of the PSO with crazy particles has been tested on two model test systems, compared with GA and classical PSO and found to be superior.     

基于PSODE的网格任务调度算法

针对网格计算中任务在各个资源之间的调度问题,提出了一种网格环境下PSODE的任务调度算法.该算法实现了计算资源、存储资源、带宽资源、数据资源的利用率最高化和代价最低化.对基本粒子群算法和差分进化算法进行了分析,通过构造算法函数、适应值函数和权重公式,建立了粒子群差分混合算法并对其进行优化,介绍了算法的实现过程.实验结果表明,该算法与其它调度算法比较,具有良好的性能.     

基于蛙跳退火粒子群算法的民航发动机单元体修理级别决策及成本优化

针对民航发动机单元体送修工作范围决策及全寿命维修成本优化问题，提出了以返厂时间间隔为变量的基于蛙跳退火粒子群优化算法的发动机单元体修理级别决策及成本优化模型。首先，考虑维修指导手册中的各单元体送修逻辑图及限寿件到寿更换情况，构建了发动机送修成本函数。其次，借助蛙跳退火粒子群优化算法确定了全寿命期间内不同返厂次数的送修成本及各单元体维修等级。最后，通过算例将所提算法与基本粒子群优化算法、退火粒子群优化算法、混合蛙跳优化算法进行对比，分析了不同返厂次数对送修成本及可靠性的影响。实验结果表明，当发动机在全寿命期内进行5次返厂送修时，蛙跳退火粒子群优化算法的成本平均值为322.479 1美元/飞行小时，与其他三种优化算法相比成本最优，可为航空公司和大修企业提供送修决策支持。     

基于蛙跳退火粒子群算法的民航发动机单元体修理级别决策及成本优化

针对民航发动机单元体送修工作范围决策及全寿命维修成本优化问题,提出了以返厂时间间隔为变量的基于蛙跳退火粒子群优化算法的发动机单元体修理级别决策及成本优化模型。首先,考虑维修指导手册中的各单元体送修逻辑图及限寿件到寿更换情况,构建了发动机送修成本函数。其次,借助蛙跳退火粒子群优化算法确定了全寿命期间内不同返厂次数的送修成本及各单元体维修等级。最后,通过算例将所提算法与基本粒子群优化算法、退火粒子群优化算法、混合蛙跳优化算法进行对比,分析了不同返厂次数对送修成本及可靠性的影响。实验结果表明,当发动机在全寿命期内进行5次返厂送修时,蛙跳退火粒子群优化算法的成本平均值为322.479 1美元/飞行小时,与其他三种优化算法相比成本最优,可为航空公司和大修企业提供送修决策支持。     

混合自适应粒子群算法在电力经济调度中的应用

以电力系统中发电成本最低为目标,结合实际发电运行中系统平衡约束和机组操作约束条件,建立电力经济调度(ED)模型。由于标准粒子群算法存在易陷入局部最优的问题,用这种方法求解ED模型得到的最终结果会不太理想。为此,本文提出一种非线性自适应权重调整策略来增强算法全局搜索和局部搜索能力,首先引入小生境优化种群策略使算法跳出局部最优,然后将这种改进后的混合自适应粒子群算法（HAPSO）应用于求解ED模型。最后,算例分析结果表明本文所改进算法的有效性,提高了求解精度。     

Wind Farm Layout Design Using Cuckoo Search Algorithms

ABSTRACT

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization algorithms which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and particle swarm optimization algorithms for the given test scenarios in terms of yearly power output and efficiency.     

基于SA-PSO的小波神经网络汽轮机故障诊断

针对传统小波神经网络的问题,提出了一种基于模拟退火粒子群算法优化小波神经网络并用于汽轮机故障诊断.先使用模拟退火粒子群算法对小波神经网络的参数进行初步优化,再用小波神经网络进行二次优化训练.实验结果表明,所提出的SA-PSO-WNN算法与WNN、PSO-WNN算法相比,网络的训练速度更快,全局搜索能力更强,网络的泛化能力更好,具有很好的实用价值.     

Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is mea sured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control, automatic generation control (AGC) plays a crucial role. In this paper, multi-area (Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative (PID) controller as a supplemen tary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm (FFA). The experimental results demonstrated the comparison of the proposed system performance (FFA-PID) with optimized PID controller based genetic algorithm (GA PID) and particle swarm optimization (PSO) technique (PSO PID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error (ITAE) cost function with one percent step load perturbation (1% SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.      

一种自适应扩展粒子群优化算法

在粒子群优化算法的基础上,首先把粒子群优化算法的速度更新式中的个体最优位置用粒子群中所有个体最优位置的平均值代替,得到扩展粒子群优化算法;然后,建立了加速系数和粒子群中所有粒子的平均适应度与整体最优位置适应度之差的一种非线性函数关系,得到自适应加速系数扩展粒子群优化算法。由于新的算法利用了所有个体最优粒子的信息,并在进化过程中通过建立的非线性时变加速系数自适应地调整“认知”部分和“社会”部分对粒子的影响,从而提高了算法的收敛速度和精度。4个基准测试函数的对比实验结果说明自适应扩展粒子群优化算法的有效性和优良性能。     

免疫粒子群优化算法

受生物体免疫系统免疫机制的启发,论文把免疫系统的免疫信息处理机制引入到粒子群优化算法中,给出了免疫粒子群优化算法。这种免疫粒子群优化算法结合了粒子群优化算法具有的全局寻优能力和免疫系统的免疫信息处理机制,并且实现简单,改善了粒子群优化算法摆脱局部极值点的能力,提高了算法进化过程中的收敛速度和精度。一个求多维函数最优值的计算机仿真对比结果表明,免疫粒子群优化算法的收敛性能优于粒子群优化算法。     

几种改进PSO算法在带时间窗车辆路径问题中的比较与分析

车辆路径问题属于完全NP问题,也是运筹学中的热点问题。虽然目前有很多人进行研究,但搜索效率和迭优率较低,而且计算所得平均费用偏高。鉴于此,本文分别用二阶振荡PSO、随机惯性权重PSO、带自变异算子PSO、模拟退火PSO求解带时间窗车辆路径问题。通过仿真实验给出了这四种改进PSO算法在求解该问题时的不同;同时,与文献[1]中中的遗传算法、标准PSO算法求解该问题进行了比较并得出结论：本文中用到的四种改进PSO算法都能更有效地降低成本,缩短运行时间,提高达优率,而且随机惯性权重PSO表现尤为突出。     

求解最小属性约简的病毒协同进化微粒群算法

提出一种基于病毒协同进化微粒群的最小属性约简算法.在算法中,进化在宿主与病毒种群之间协同进行,通过满足约简分辨力不变条件的最优病毒种子复制操作产生病毒库,病毒通过感染操作在宿主种群完成横向局部搜索,以提高算法局部精确解搜索能力;同时通过删减操作完成自我更新,实现增加局部搜索范围的目的.最后对UCI数据集进行属性约简实验,结果表明该算法在搜索最小属性约简解方面优于其他进化算法,同时收敛速度及寻优效率也有较大提高.     

核电站蒸汽发生器水位控制系统的仿真研究

研究核电站蒸发器水位控制优化问题,由于蒸汽发生器是核电站中最重要设备之一,水位控制对核电站的安全运行起着决定性的作用,并要求系统稳定运行,快速响应。针对蒸汽发生器是一个高度复杂、非线性、时变的系统,传统的串级PID控制等控制方法难以取得满意的控制效果,把自抗扰控制方法引入蒸汽发生器水位的串级控制系统中,解决传统PID快速性和超调的矛盾,且能够动态补偿对象模型的内扰和外扰。另外,自抗扰控制器的参数较多且参数难以整定,采用混沌搜索的粒子群混合优化算法来对优化选择参数进行仿真。仿真结果表明,改进方法的鲁棒性和控制品质优于传统的串级PID控制方法,方法的可行性和有效性。     

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。     

分布式可再生能源发电并网的无功优化研究

提出了基于杂交粒子群优化算法的分布式可再生能源并网的无功优化算法,从网损和静态电压稳定裕度两个角度出发,构建了含分布式发电系统的配电网无功优化的数学模型.在美国PG&E 69节点配电系统上进行效验.结果表明,该算法收敛性好、精度高;分布式电源并网后能有效降低系统的有功网损,提高电压稳定性,对分布式电源并网运行具有一定的...     

基于改进多目标骨干粒子群算法的电力系统环境经济调度

针对粒子群优化算法种群多样性不足、易陷入局部寻优的问题,提出一种基于改进多目标骨干粒子群优化算法(improved bare-bones multi-objective particle swarm optimization,IBBMOPSO)的电力系统环境经济调度的求解方法.IBBMOPSO采用一种搜索权重非线性递减...     

Optimal maintenance scheduling of generators using multiple swarms-MDPSO framework

In this paper, a challenging power system problem of effectively scheduling generating units for maintenance is presented and solved. The problem of generator maintenance scheduling (GMS) is solved in order to generate optimal preventive maintenance schedules of generators that guarantee improved economic benefits and reliable operation of a power system, subject to satisfying system load demand, allowable maintenance window, and crew and resource constraints. A multiple swarm concept is introduced for the modified discrete particle swarm optimization (MDPSO) algorithm to form a robust algorithm for solving the GMS problem. This algorithm is referred to by the authors as multiple swarms-modified particle swarm optimization (MS-MDPSO). The performance and effectiveness of the MS-MDPSO algorithm in solving the GMS problem is illustrated and compared with the MDPSO algorithm on two power systems, the 21-unit test system and 49-unit Nigerian hydrothermal power system. The GMS of the two power systems are considered and the results presented shows great potential for utility application in their area control centers for effective energy management, short and long term generation scheduling, system planning and operation.