A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems

Distributed generation (DG) sources are becoming more prominent in distribution systems due to the incremental demands for electrical energy. Locations and capacities of DG sources have profoundly impacted on the system losses in a distribution network. In this paper, a novel combined genetic algorithm (GA)/particle swarm optimization (PSO) is presented for optimal location and sizing of DG on distribution systems. The objective is to minimize network power losses, better voltage regulation and improve the voltage stability within the frame-work of system operation and security constraints in radial distribution systems. A detailed performance analysis is carried out on 33 and 69 bus systems to demonstrate the effectiveness of the proposed methodology.     

Particle swarm optimization for determining shortest distance to voltage collapse

This paper describes an algorithm for computing shortest distance to voltage collapse or determination of CSNBP using PSO technique. A direction along CSNBP gives conservative results from voltage security view point. This information is useful to the operator to steer the system away from this point by taking corrective actions. The distance to a closest bifurcation is a minimum of the loadability given a slack bus or participation factors for increasing generation as the load increases. CSNBP determination has been formulated as an optimization problem to be used in PSO technique. PSO is a new evolutionary algorithm (EA) which is population based inspired by the social behavior of animals such as fish schooling and birds flocking. It can handle optimization problems with any complexity since mechanization is simple with few parameters to be tuned. The developed algorithm has been implemented on two standard test systems.     

Electricity generation scheduling with large-scale wind farms using particle swarm optimization

Large-scale integration of wind power in the electricity system presents some planning and operational difficulties, which are mainly due to the intermittent and difficult nature of wind prediction process. Therefore it is considered as an unreliable energy source.     

Capacitor placement of distribution systems using particle swarm optimization approaches

Capacitor placement plays an important role in distribution system planning and operation. In distribution systems of electrical energy, banks of capacitors are widely installed to compensate the reactive power, reduce the energy loss in system, voltage profile improvement, and feeder capacity release. The capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. Recently, many approaches have been proposed to solve the capacitor placement problem as a mixed integer programming problem. This paper presents a new capacitor placement method which employs particle swarm optimization (PSO) approaches with operators based on Gaussian and Cauchy probability distribution functions and also in chaotic sequences for a given load pattern of distribution systems. The proposed approaches are demonstrated by two examples of application. Simulation results show that the proposed method can achieve an optimal solution as the exhaustive search can but with much less computational time.     

An efficient optimal capacitor allocation in DG embedded distribution networks with islanding operation capability of micro-grid using a new genetic based algorithm

Capacitor banks are commonly used in electric distribution networks as a kind of reactive power sources. These sources are located in distribution networks for power factor correction, loss reduction, and voltage profile improvement. For these purposes optimal capacitor placement is needed to determine capacitors types, sizes and locations. Distribution system with Distributed Generation (DG) can have micro-grid that it will operate in both grid-connected and islanded modes of operation. The aim of this paper is to provide a method for optimal capacitor (fixed and switchable) placement in such a distribution network. The effect of different operation modes of DGs on the network is also investigated. The proposed method can guarantee the benefits of capacitor installation at different load levels. It is based on genetic algorithm (GA) with new coding and operators. Switching table of the allocated capacitors can be found through the proposed structure of the chromosome. Some case studies developed to illustrate the efficiency of the proposed method.     

粒子群优化算法及其在机组优化组合中应用

应用粒子群优化(PSO)算法对电力系统的机组优化组合问题进行研究,介绍了算法原理,分析了算法中各个参数的不同取值对算法搜索能力和收敛速度的影响,并以常用的测试函数进行验证,建立了相应的数学模型,并以IEEE3机6节点电力系统为实例进行研究。分析结果表明,PSO算法较之常用的遗传算法和混沌优化等算法,在算法结构、计算时间、搜索区间控制以及收敛速度等方面具有较好的特性,验证了该方法的有效性。     

一种适合于解决辐射状配电网电容器最优配置问题的遗传算法

提出了一种可解决辐射状配电网电容器最优配置问题的遗传算法。将电容器看作离散变量 ,对给定的目标函数求最佳的电容器安装位置和容量 ;设计了针对该问题有效的遗传因子 ,并对一个实际的配电网进行了计算 ,得到了较好的结果。对遗传算法用于辐射状配电网的电容器最佳配置问题进行了探讨     

用于配电网多目标无功优化的改进粒子群优化算法

针对配电网多目标无功优化的应用需求以及优化算法存在的收敛性和多样性问题,基于Pareto熵的多目标粒子群优化算法,提出一种应用于多目标无功优化的改进粒子群优化算法。该算法在全局外部档案更新过程中引入冗余集策略,避免迭代过程中陷入局部最优解。将算法应用于配电网无功优化中时,采用离散变量取整方法,加快算法的收敛速度。建立网损、电压偏差及无功补偿装置投资最小的配电网多目标无功优化模型,并以IEEE 33节点配电网络为算例进行仿真,结果表明改进后的算法兼顾了优化的收敛性和多样性,能够在不同的优化要求下得到有效的无功优化方案。     

Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer

In this paper, chaotic ant swarm optimization (CASO) is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). This algorithm explores the chaotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. While comparing CASO with either particle swarm optimization or genetic algorithm, it is revealed that CASO is more effective than the others in finding the optimal transient performance of a PSS and automatic voltage regulator equipped single-machine-infinite-bus system. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. Takagi Sugeno fuzzy logic (SFL) based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer variables.     

Optimal capacitor placement for enhancing voltage stability in distribution systems using analytical algorithm and Fuzzy-Real Coded GA

Two algorithms for optimal capacitor placement, with a view to enhance voltage stability are introduced. In the analytical algorithm the nodes, whose voltage stability index values are lower than a threshold value, are ranked in ascending order as the candidate nodes for compensation. The additional reactive power compensation to be provided at a node is obtained by solving linearized VSI formula. The maximum compensation at each node is limited to the initial reactive power delivered by the respective node prior to compensation for avoiding over-dimensioning of the capacitor banks. However, capacitor placement based on voltage stability index has proven less than satisfactory and not always indicated the appropriate placement.As an alternative a fuzzy expert system is used for extracting suitability of capacitor location from power loss reduction index and improving the voltage profile within voltage constraints. A combination of fuzzy expert system for capacitor placement and real coded GA for capacitor sizing, with a view to enhance voltage stability is proposed for optimal capacitor placement. The result is enhancement of the overall system stability index and potential achievement of maximum net money savings due to power and energy loss reduction vs. expenditure in capacitors.The overall accuracy and reliability of the proposed Fuzzy-Real Coded GA algorithm has been validated and tested on 33-node radial distribution system. Comparison of obtained results with those in recent publications showed that the Fuzzy-Real Coded GA algorithm is capable of producing high-quality solutions with good performance of convergence, and demonstrated viability.     

Reactive power control of wind farm made up with doubly fed induction generators in distribution system

In recent years, the number of small size wind farm made up with doubly fed induction generators (DFIG) located within the distribution system is rapidly increasing. DFIG can be utilized as the continuous reactive power source to support system voltage control by taking advantage of their reactive power control capability. In this paper, considering both reactive power control and distribution network reconfiguration can be used to reduce power losses and improve voltage profile, a joint optimization algorithm of combining reactive power control of wind farm and network reconfiguration is proposed to obtain the optimal reactive power output of wind farm and network structure simultaneously. The proposed algorithm has been successfully implemented on the 16 bus distribution network and the results obtained demonstrate the efficiency of the algorithm.     

基于混合智能粒子群算法的广义电源主动配电网优化配置

研究广义电源接入主动配电网的优化配置问题。提出一种电压偏差指标。建立综合考虑投资经济效益、电压偏差及污染气体排放指标的多目标优化配置模型。提出一种混合智能粒子群算法,在优化过程中引入快速非支配排序策略、精英保留策略和拥挤距离计算策略以改善其全局搜索能力。对IEEE-33节点、PG&E-69节点配电系统进行计算,分析在不同负荷水平下各指标的变化情况,研究负荷变化时广义电源的最佳配置。研究表明,广义电源的接入与合理配置能够有效提高投资运行效益和系统电压稳定性,同时说明该方法能够保证配置方案的多样性和多目标优化过程的寻优性。     

Genetic algorithm and particle swarm optimization algorithm for speed error reduction in railway signaling systems

The paper deals with common concepts of modern methods of train speed determination with minimal errors. Balise locations depend on a variety of parameters. With genetic algorithm and particle swarm optimization, as two new intelligent algorithms, and Kalman filtering concept used, the best locations are determined to reduce tachometer errors. Copyright © 2012 John Wiley & Sons, Ltd.     

Balancing risk and cost in fuzzy economic dispatch including wind power penetration based on particle swarm optimization

Utilization of renewable energy resources such as wind energy for electric power generation has assumed great significance in recent years. Wind power is a source of clean energy and is able to spur the reductions of both consumption of depleting fuel reserves and emissions of pollutants. However, since the availability of wind power is highly dependent on the weather conditions, the penetration of wind power into traditional utility grids may incur certain security implications. Therefore, in economic power dispatch including wind power penetration, a reasonable tradeoff between system risk and operational cost is desired. In this paper, a bi-objective economic dispatch problem considering wind penetration is formulated, which treats operational costs and security impacts as conflicting objectives. Different fuzzy membership functions are used to reflect the dispatcher’s attitude toward the wind power penetration. A modified multi-objective particle swarm optimization (MOPSO) algorithm is adopted to develop a power dispatch scheme which is able to achieve compromise between economic and security requirements. Numerical simulations including sensitivity analysis are reported based on a typical IEEE test power system to show the validity and applicability of the proposed approach.     

Design and allocation of power system stabilizers using the particle swarm optimization technique for an interconnected power system

In this paper, three particle swarm optimization (PSO) based power system stabilizers (PSSs) are developed for three power systems. The system under study here is a power pool consisting of 3 power systems. System I represents the Egyptian power system, system II represents the Jordan and Syrian power systems, and system III for the Libyan power system, which are originally self standing and completely independent systems. As a matter of fact each of them should equipped with its own PSS. For this reason this work is started by designing an optimum power stabilizer for each of them standing alone. After which, the developed PSSs are firstly installed one at a time. Then the three PSSs are installed together in the interconnected power system and their effect on its dynamic performance is studied.As a test for stabilization efficiency, the detailed power system model is subjected to a forced outage of a 600-MW generator, which is the biggest unit in the pool, when it is fully loaded. This outage results in loosing of about 3% of the spinning capacity of system I and about 2% of the spinning capacity of the whole interconnected system. The obtained results show an improvement in the power pool performance accompanied with an improvement in the inter-area oscillation.     

电力系统无功优化的多智能体粒子群算法研究

为了更好地实现无功功率最优控制和提高电压质量,在现有基础上,提出了引用多智能体粒子群优化算法（MAPSO）.该算法结合了JADE系统和粒子群优化技术,粒子间构建了三维球形环境.PSO种群中,每一个Agent相当于算法中的一个粒子,他们通过Agent间进行竞争与合作操作和自学习操作,吸收了PSO算法的进化机理,能够更快地,更精确地收敛到全局最优解.经IEEE 14节点系统校验,并且与基于Matlab的PSO算法进行比较,结果表明,该算法具有收敛速度快,计算精度高的优点。     

基于改进粒子群算法的配电网分布式电源规划

合理地对分布式电源进行选址和定容对于实现配电网网损最小是至关重要的.应用改进粒子群优化算法进行配电网分布式电源(DG)规划,并结合罚函数法将DG规划问题转化成无约束求极值问题,从而有效地提高了改进粒子群优化算法的全局收敛能力和计算精度.对69节点和33节点配电测试系统进行仿真计算,结果表明了论文采用的DG规划模型和改进粒子群优化算法的正确性和适用性.     

基于人工鱼群算法的配电网开关优化配置研究

配电网开关优化配置是配电网自动化和配电系统优化的重要方面。开关优化就是确定馈线上开关设备的最佳位置和数量,以提高系统可靠性,减少停电损失,提高资金的使用效率。该文对配电网开关优化配置进行了分析并建立了数学模型,应用人工鱼群算法来确定配电网馈线相应位置的开关种类和数量。仿真实验结果表明,人工鱼群算法收敛速度快,具有很好的全局搜索能力,将该算法应用到配电网开关优化配置中是可行且有效的。     

PSO加速寻优的免疫算法在配电网重构中的应用

提出了一种新的使用PSO加速寻优的免疫克隆算法用于配电网重构,以减少网损。高频变异和免疫补充算子的采用,能有效维持种群的多样性,避免算法早熟收敛。同时提出利用PSO更新个体的速度和位置,提高收敛速度。通过对PG&E69节点配电网络算例的仿真分析,进一步表明该算法具有较高的计算效率。     

基于变异粒子群算法的主动配电网故障恢复策略

为提高主动配电网故障恢复的快速性和可靠性,提出一种基于变异粒子群算法的恢复策略.光储系统与负荷特性模型的构建是研究策略的基础,利用光储模型保证负荷可靠恢复,在构建负荷特性模型时考虑负荷时变性、需求时变性及负荷可控性的特点.在建立的光储系统与负荷特性模型基础上研究故障恢复策略,首先对配电网进行动态孤岛划分,利用光储系统对孤岛内负荷进行可靠恢复,保证用户侧需求度高的负荷优先恢复,然后以总失电负荷最少、网损最小及开关动作次数最少为综合目标函数,运用变异粒子群算法得到孤岛与主网配合的配电网综合恢复策略,提高了主动配电网可靠性.最后,采用IEEE 33节点系统进行算例分析,结果验证了模型与恢复策略的优越性.