全局安时无功优化调度的MAS方法

电力系统无功优化调度问题在数学上属于一种具有多目标、多不确定因素、多约束、多极值、非线性的组合最优化问题。通常的寻优方法遇到了许多困难，因此目前无功优化调度技术的应用还停留在计算规模较小的地区局部电网中。为实现电网全局实时的无功优化调度，提出一种基于分布式人工智能中多Agent系统(MAS)的无功优化调度模型。将全网无功调度按区域分解为若干个相互关联的调度子系统进行分布式求解，全局调度系统采用网络控制结构，各调度子系统则采用分层控制结构。在各子网局部实现无功优化的基础上，采用多Agent智能协调技术实现全网的全局无功优化。     

A tabu search approach to fault section estimation in power systems

In this paper, a new method for fault section estimation in power systems based upon the set covering theory and tabu search (TS) technique is proposed. First, a new mathematical model for the fault section estimation problem is developed based upon the simulation of protection system behavior and the well developed parsimonious set covering theory. Secondly, a new method is presented to solve this problem using a TS based method, in order to find the global optimal solution(s) efficiently. Finally, a sample power system is used to demonstrate the feasibility and efficiency of the developed method. The comparison between the developed TS based and the existing genetic algorithm (GA) based approaches to the fault section estimation problem has also been carried out, and many simulation results show that the TS based approach is more efficient than the GA based approach. The key features of this proposed method are that it can deal with very complicated fault scenarios and can find multiple global optimal solutions directly and very efficiently in a single run. Test results suggest that the developed TS based method is very efficient and of promise for on-line applications.     

基于斐波那契树辨识算法的区域电网调速系统参数辨识

当前电网中单通道输送功率占比较高,电网出现大功率扰动的概率增大,频率稳定问题重新引起人们的关注。针对目前电力系统频率特性仿真准确度不足的问题,引入具有全局和局部交替寻优特点的斐波那契树优化算法,用于区域电网调速系统参数整体辨识。通过限制树的结构深度、调整全局搜索策略等调整,提高全局寻优能力和收敛速度,使算法适用于参数辨识工作,形成斐波那契树辨识算法。基于实测频率响应曲线,运用该算法对区域电网原动机及调速系统参数进行整体辨识,并进行了多算法对比分析。仿真结果表明,斐波那契树辨识算法应用于此领域具有较好的适用性与优越性。     

基于鲸鱼优化算法的无功优化调度

针对目前电力系统中的无功优化问题尚缺乏一种能兼顾求解的高效性与全局搜索最优性的方法,本文将一种新的启发式算法--鲸鱼优化算法(WOA)运用到电网无功优化调度中,以系统有功功率损耗最低为目标函数,通过引入惩罚函数建立无功优化模型,对IEEE-14节点系统与IEEE-30节点系统进行仿真,并利用单因素方差分析法(One-way ANOVA)将所得结果与之前的粒子群优化算法(PSO)及引入加速度系数的时变粒子群优化(PSO-TVAC)进行比较,研究表明WOA算法在迭代次数、搜索能力及收敛问题上的潜力,并证明了在解决电力系统无功优化问题上的鲁棒性和有效性,同时也为解决非线性约束问题提供了新途径。     

A hybrid multi-agent based particle swarm optimization algorithm for economic power dispatch

This paper presents a new multi-agent based hybrid particle swarm optimization technique (HMAPSO) applied to the economic power dispatch. The earlier PSO suffers from tuning of variables, randomness and uniqueness of solution. The algorithm integrates the deterministic search, the Multi-agent system (MAS), the particle swarm optimization (PSO) algorithm and the bee decision-making process. Thus making use of deterministic search, multi-agent and bee PSO, the HMAPSO realizes the purpose of optimization. The economic power dispatch problem is a non-linear constrained optimization problem. Classical optimization techniques like direct search and gradient methods fails to give the global optimum solution. Other Evolutionary algorithms provide only a good enough solution. To show the capability, the proposed algorithm is applied to two cases 13 and 40 generators, respectively. The results show that this algorithm is more accurate and robust in finding the global optimum than its counterparts.     

A Simulated Annealing-Based Goal-Attainment Method for Economic Emission Load Dispatch with Nonsmooth Fuel Cost and Emission Level Functions

A novel multiobjective optimization method for economic emission load dispatch (EELD) with nonsmooth fuel cost and emission level functions in power system operation andscheduling phases is presented. In this paper, the problem treats economy, emission, and transmission line security as competing objectives. The load constraints and operating constraints are taken into account. Assuming that the decision maker (DM) has goals for each of the objective functions, the multiobjective problem is converted into a single-objective optimization by the goal-attainment method, which is then handled by the simulated annealing (SA) technique. The solution methodology can offer a global or near-global noninferior (best compromising) solution in a sense close to the DM's requirements. Results for a sample test system have been presented to demonstrate the applicability and validity of the proposed method.     

An efficient technique for real-time control of system voltages and reactive power

This paper presents an efficient technique for the real-time control of reactive power dispatch. The proposed technique is based on a new voltage-reactive power model. This new model linearizes the load flow equations in Cartesian form with proper compensation to the second-order terms. The optimization process is non-iterative and thus is suitable for on-line applications. Different objective functions are incorporated in a global optimization technique and a multi-objective linear programming is formulated to solve this multiple decision-making problem. The non-dominance of the best compromise solution for the multiple objectives is assured by utilizing the operator-computer interactive capabilities of the multi-objective linear programming to reflect the urgency of the operating conditions and/or the predicted situations of the power system operation.     

An improved PSO technique for short-term optimal hydrothermal scheduling

This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using improved particle swarm optimization (IPSO) technique. The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network, water transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved PSO technique is suggested that deals with an inequality constraint treatment mechanism called as dynamic search-space squeezing strategy to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed IPSO is applied on a multi-reservoir cascaded hydro-electric system having prohibited operating zones and a thermal unit with valve point loading. Numerical results are compared with those obtained by dynamic programming (DP), nonlinear programming (NLP), evolutionary programming (EP) and differential evolution (DE) approaches. The simulation results reveal that the proposed IPSO appears to be the best in terms of convergence speed, solution time and minimum cost when compared with established methods like EP and DE.     

基于改进遗传算法与原对偶内点法的无功优化混合算法

基于改进遗传算法和原对偶内点法提出一种求解无功优化问题的混合算法。首先通过改进遗传算法求解无功优化问题中的离散变量,然后采用原对偶内点法求解与已获得离散变量最匹配的连续变量。在改进遗传算法中采用交叉、变异算子并基于可行域规则处理离散约束,有效提高了混合优化算法的整体寻优效率。在IEEE 118节点系统中的仿真计算结果验证了本文方法的有效性。该方法已应用于福建电网自动电压控制系统中。     

局部搜索量子遗传算法及其无功优化应用

针对量子遗传算法局部寻优能力差的不足,提出一种局部搜索量子遗传算法,用于电力系统无功优化.该方法将局部搜索引入到量子遗传算法中,先进行全局寻优,当全局寻优搜索到的最优解经过多次迭代没有变化时,在此解附近产生小的寻优区间,进行局部寻优,以使算法同时具有较强的全局和局部搜索能力.复杂测试函数和IEEE30节点测试系统的仿真实验表明,该方法在寻优能力、收敛速度和稳定性方面优于文献中的新量子遗传算法、进化规划等多种方法.     

DSP+CPLD实现CPS-SPWM下的单相多电平脉冲

载波相移调制SPWM(CPS-SPWM)技术是多重化和正弦波脉宽调制(SPWM)技术的有机结合。针对在CPS-SPWM调制下,单片DSP计数器和PWM输出口的局限,设计了一种DSP+CPLD的系统,实现了单相7电平和9电平脉冲输出。实验表明:用DSP+CPLD构成的系统突破了目前单片MCU的限制,使得CPS-SPWM技术在大功率装置上应用更加灵活、实用。     

发输配全局潮流计算第一部分:数学模型和基本算法

提出发输配全局潮流计算这一新课题,它以全局电力系统为研究对象,能计算出一体化的全局电力系统状态,弥补了传统潮流计算中发输电系统和配电系统完全独立进行的局限性,可满足全局控制决策的需要。建立了数学模型,构造了全局潮流主从分裂法,自然地将规模庞大的全局潮流问题分解成发输电潮流和一系列小规模的配电馈线潮流子问题,发输电和配电可用不同的潮流算法,满足了全局潮流在分布式计算的要求。     

基于进化策略的多机系统PSS参数优化

该文提出了一种基于进化策略的多机系统PSS参数优化的新方法。在这种方法中，目标函数设定为度量所有机电振荡模态性能的函数，将PSS的参数优化表示为带不等式约束的非光滑优化问题。进化策略用于该优化问题的求解，从而找出PSS的优化参数。进化策略属现代全局优化方法的一种，它对优化问题本身几乎无任何限制，冈其具有全局寻优能力，故可得到比常规优化方法更好的结果。且进化策略直接采用实型编码，因而可提高优化计算的效率。Anderson 3机系统和New England 10机系统的仿真结果表明，该方法是一种有效的参数优化方法，得到的优化参数对系统运行方式的变化具有良好的鲁棒性。     

Parallel simulated annealing for power system decomposition

This paper proposes a parallel optimization approach to power system decomposition for voltage control. In this paper, a parallel simulated annealing (PSA) technique is developed to decompose power systems so that subsystems are equally separated in terms of the number of nodes and control variables. System decomposition is one of difficult discrete number combinatorial problems. The PSA technique provides better solutions than the conventional SA because of searching a solution near a global minimum over a wide range. The proposed method is tested in IEEE 30, 57, and 118 node systems     

考虑负荷恢复过程的配电网用电可靠性评估指标与方法

针对传统供电可靠性定义中未对停电后的负荷恢复过程(LRP)进行有效计及的问题,提出考虑LRP的配电网用电可靠性评估指标与方法.对配电网用电可靠性进行定义,对LRP责任区分进行阐明,并在此基础上从供电持续性、LRP影响、全局性和对比性4个维度建立配电网用电可靠性评估指标体系,其中重点从结果、过程及影响范围特征上对LRP影响进行描述,并提出全局性指标和对比性指标以满足实际工程中的单指标评估.对于供电持续性及LRP影响,考虑指标间相关性提出网络层次分析法、改进相关性权重法和改进逼近理想点排序法相结合的综合评估模型,以对用电可靠性进行综合评估.实例分析表明,所提指标与方法能对用户受到的停电影响进行精细化描述.     

基于树木生长算法的含UPFC的最优潮流计算

最优潮流(OPF)计算是一个非凸优化问题,统一潮流控制器(UPFC)的引入增加了OPF问题的非凸程度,使得基于内点法的传统优化算法难以获取全局最优解。文中提出基于树木生长算法(TGA)的计及UPFC的最优潮流计算方法,将发电成本与有功网损、电压偏移加权作为目标函数,并考虑网络与UPFC设备的安全运行约束,优化了OPF模型。最后基于IEEE 30节点系统以及南京西环网116节点实际系统进行算例测试,对比TGA、粒子群与内点法的结果,并使用蒙特卡洛方法对不同的启发式算法分别进行50次计算,验证了TGA具有更好的求解精度与鲁棒性。     

A semidefinite programming method with graph partitioning technique for optimal power flow problems

A new semidefinite programming (SDP) method with graph partitioning technique to solve optimal power flow (OPF) problems is presented in this paper. The non-convex OPF problem is converted into its convex SDP model at first, and then according to the characters of power system network, the matrix variable of SDP is re-arranged using the chordal extension of its aggregate sparsity pattern by the graph partitioning technique. A new SDP-OPF model is reformulated with the re-arranged matrix variable, and can be solved by the interior point method (IPM) for SDP. This method can reduce the consumption of computer memory and improve the computing performance significantly. Extensive numerical simulations on seven test systems with sizes up to 542 buses have shown that this new method of SDP-OPF can guarantee the global optimal solutions within the polynomial time same as the original SDP-OPF, but less CPU times and memory.     

Optimal multi-objective VAr planning using an interactivesatisfying method

In this paper, an interactive satisfying method, a two-level structure, is presented for solving a multiobjective power system VAr planning problem. In this structure, the analysis level is concerned with the calculation of a possible or it set of possible solutions to the multiobjective problem, and the decision level is concentrated on the task of inducing the analysis level to generate noninferior solutions that meet the decisionmakers' preferences. On the analysis level, the ϵ-constraint method based on the simulated annealing approach is developed to find a global noninferior solution. Through interaction with the decisionmaker, the proposed method guarantees the solution to be a desirable, global noninferior solution for a general multiobjective VAr planning problem. The presented method was applied to the AEP-14 bus system as well as to a large, actual-size system with satisfactory results     

基于同步相量测量技术的功角监测系统

同步发电机并网运行后，其功角是用来观察和判断该机组和系统并列运行稳定性的一个很重要的状态变量，而实现全网功角实时监测具有更为重要的意义。本文提出了一种新的PMU构成方案，并利用它构建功角实时监测系统。利用同步相量测量技术，获得以GPS时间为基准的功角信息，由各监测点的相对功角变化来预测系统稳定性。并给出了一种新的发电机功角计算方法。本方案实时性强，同步性好，可以实现全局实时监控。     

电力系统机组组合中Kuhn-Tucker最优性条件的扩展应用

机组组合(ED)是电力系统规划中常见的混合整数非线性组合优化问题.结合电力系统机组组合问题的特点,将经典数学规划理论中的Kuhn-Tucker最优性条件与现代优化计算方法中的启发式算法相结合,实现机组组合问题中的整数变量优化部分与连续变量优化部分的信息融合,使主问题与子问题的求解信息同时影响全局优化的搜索过程.提出了一种随机的启发式越限处理方法,该方法突破了K-T最优性条件在电力系统中的传统应用规则.最后,通过仿真计算,说明提出的方法的优越性.