多智能体搜寻者优化算法在电力系统无功优化中的应用

针对无功优化这个典型的非线性问题,提出了一种基于多Agent系统的搜寻者优化算法MASOA (Multi-agent Seeker Optimization Algorithm)来求解.该算法针对SOA算法邻域划分随意性较大,融入智能体技术,在改进SOA算法邻域划分合理性的同时,提高粒子寻优的准确度;利用SOA算法的进化机制,引入自适应思想,使新算法具有良好的非线性搜索能力,更好地适应无功优化问题.以网损最小为目标函数,在IEEE 30节点系统上进行测试,并与四种智能算法进行比较,结果表明,MASOA在算法计算精度、收敛稳定性、寻优时间等方面都具有普遍优势,能有效地应用于电力系统无功优化中.     

多智能体搜寻者优化算法在电力系统无功优化中的应用

针对无功优化这个典型的非线性问题,提出了一种基于多Agent系统的搜寻者优化算法MASOA (Multi-agent Seeker Optimization Algorithm)来求解。该算法针对SOA算法邻域划分随意性较大,融入智能体技术,在改进SOA算法邻域划分合理性的同时,提高粒子寻优的准确度;利用SOA算法的进化机制,引入自适应思想,使新算法具有良好的非线性搜索能力,更好地适应无功优化问题。以网损最小为目标函数,在IEEE 30节点系统上进行测试,并与四种智能算法进行比较,结果表明,MASOA在算法计算精度、收敛稳定性、寻优时间等方面都具有普遍优势,能有效地应用于电力系统无功优化中。     

Optimal reactive power dispatch using an adaptive genetic algorithm

This paper presents an adaptive genetic algorithm (AGA) for optimal reactive power dispatch and voltage control of power systems. In the adaptive genetic algorithm, the probabilities of crossover and mutation, p_c and p_m, are varied depending on the fitness values of the solutions and the normalized fitness distances between the solutions in the evolution process to prevent premature convergence and refine the convergence performance of genetic algorithms. The AGA applied for optimal power system reactive power dispatch is evaluated on an IEEE 30-bus power system in which the control of bus voltages, tap position of transformers and reactive power sources are involved to minimize the transmission loss of the power system.     

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。     

基于协同粒子群优化算法的电力系统无功优化

为了解决粒子算法应用在电力系统无功优化中存在的问题,提出了一种改进的协同粒子优化算法.笔者根据电力系统无功优化问题非线性、不连续、大范围以及电压等级增多、无功优化控制变量较多的特点,建立了改进的协同粒子优化算法无功优化的数学模型,并将协同粒子群算法在无功优化中进行了应用.算例结果表明,该算法有效地改善了粒子群算法的局部收敛问题,缩短了搜索时间,提高了准确性.     

Comparative seeker and bio-inspired fuzzy logic controllers for power system stabilizers

Seeker optimization algorithm (SOA) is a new heuristic population-based search algorithm. In this paper, SOA is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). In SOA, the act of human searching capability and understandings are exploited for the purpose of optimization. In SOA-based optimization, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (namely PSS2B, PSS3B and PSS4B) are optimally tuned to obtain the optimal transient performances. From simulation study it is revealed that the transient performance of the dual-input PSS is better than the single-input PSS. It is further explored that among the dual-input PSSs, PSS3B offers the best optimal transient performance. While comparing the SOA with recently reported optimization algorithms like bacteria foraging optimization (BFO) and genetic algorithm (GA), it is revealed that the SOA is more effective than either BFO or GA in finding the optimal transient performance. 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 parameters.     

Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system

An optimization algorithm is proposed in this paper to solve the problem of the economic dispatch that includes wind power generation using quantum genetic algorithm (QGA). In additional to the detail introduction for models of general economic dispatch as well as their associated constraints, the effect of wind power generation is also included in this paper. On the other hand, the use of quantum genetic algorithms to solve the process of economic dispatch is also discussed and real scenarios are used for simulation tests later on. After comparing the algorithm used in this paper with several other algorithms commonly used to solve optimization problems, the results show that the algorithm used in this paper is able to find the optimal solution most quickly and accurately (i.e. to obtain the minimum cost for power generation in the shortest time). At the end, the impact to the total cost saving for the power generation after adding (or not adding) wind power generation is also discussed. The actual operating results prove that the algorithm proposed in this paper is economical and practical as well as superior. They are quite valuable for further research.     

电力系统经济负荷分配的量子粒子群算法

本文首次将量子粒子群算法用于电力系统经济负荷分配中。该算法是以粒子群中粒子的收敛特性为基础,依据量子物理理论提出的,改变了传统粒子群算法的搜索策略,可使粒子在整个可行解空间中搜索寻求全局最优解。同时该算法的进化方程中不需要速度向量,而且进化方程的形式更简单,参数较少且容易控制。对两个算例进行仿真测试,证实该算法可有效解决经济负荷分配问题;性能对比显示,该算法求得的解优于已有的改进粒子群算法及其它优化算法所求得的解。本文为量子粒子群算法用于经济负荷分配的实用化研究奠定了必要的理论基础。     

求解经济调度问题的改进粒子群算法

提出了一种求解电力系统经济调度问题的改进粒子群算法。该算法考虑了机组的爬坡速率、工作死区等多种约束条件,并计及了网损。该算法以粒子群算法为基础,提出了新的修补策略对违反各种约束条件的粒子进行积极的修正,并与罚函数技术相结合,使粒子尽可能地在可行解区域或尽量接近可行解的区域内寻优。由于大大减少了粒子在非可行解区域内寻优的概率,因而有效地提高了算法的精度和速度。仿真算例的结果表明,该算法具有速度快、精度高和收敛性好的特点。     

计及设备动作次数约束与UPFC的无功优化算法

统一潮流控制器(UPFC)为电力系统无功优化提供了新的控制手段。针对实际工程中的新型拓扑,建立UPFC的多端注入功率模型和功率方程;基于将无功设备动作次数的约束还原成调节代价的思想,提出一种只含连续变量的基于原-对偶内点法的两阶段无功优化算法。南京西环网等值系统的算例测试结果表明,所提算法具有较高的计算精度和求解效率,可以有效减少无功设备的动作次数。     

基于人工鱼群算法的电力系统无功优化

尝试将人工鱼群算法(AFSA)用于电力系统无功优化,建立了相应的优化模型,对IEEE6、IEEE14节点系统及某地区实际电力系统进行了无功优化计算,并与遗传算法(GA)、改进Tabu搜索算法(MTSA)进行了比较,结果表明AFSA鲁棒性强,全局收敛性好,用于电力系统无功优化计算是有效、可行的。     

基于混沌优化算法的电力系统无功优化

混沌优化利用混沌变量的特定内在随机性和遍历性来跳出局部最优点 ,而线性搜索可以提高局部空间的搜索速度和精度。本文将基于线性搜索的混沌优化算法用于电力系统无功优化。应用该算法对IEEE6、1 4、30节点系统进行了无功优化计算 ,结果表明该算法是正确可行的     

电力系统无功优化的二次变异遗传算法

在自适应遗传算法的基础上引入优良个体池和二次变异操作,提出了用于电力系统无功优化和电压控制的二次变异遗传算法。该方法建立一个与群体规模等大的优良个体池,用于保存个体编码、适应度等详细数据。每计算完一代,将该代的个体与优良个体池中的个体进行生存竞争,因此优良个体池中保留了历代计算的优良个体,下一代的群体从优良个体池中选择。考虑到遗传操作后存在大量相同个体,检出重复个体进行二次变异,产生邻近的个体,避免了重复计算而且增强了算法的局部搜索能力,加快了算法的收敛速度。该方法和自适应遗传方法用IEEE30节点系统为例计算,结果表明:使用二次变异自适应遗传算法优化的网损平均值更低,寻优性能更好,优化的网损值集中在小的区间。     

基于改进粒子群算法的电力系统经济负荷分配

提出了一种应用随机优化理论求解电力系统经济负荷分配的新方法,该方法以电力市场全天购电费用最小为目标函数,将高斯算子和交叉算子引入基本粒子群算法中。针对基本粒子群算法（PSO）的局限性,通过引入新的算子,克服了PSO算法前期精度低、后期收敛速度慢、易于陷入局部最优等缺点,在速度和精度上满足了计算要求。算例结果表明,所提出的方法能有效解决电力市场电力系统经济负荷分配问题。     

基于粒子群-差异进化混合算法的电力系统无功优化

针对传统粒子群算法中收敛速度快但易于陷入局部最优等特点,将差异进化算法与粒子群算法相结合,提出了一种粒子群-差异进化混合算法。该算法在粒子寻优过程中除跟踪个体极值和全局极值外,还跟踪粒子差异进化产生的第三个值;同时,当粒子在某一维上的速度小于给定值时,将重新初始化该维度粒子速度。建立了无功优化数学模型,并将合算法应用到无功优化中。通过MATLAB编程对IEEE-30节点系统进行优化计算,并与遗传算法和粒子群算法比较,结果表明本文提出的算法应用于无功优化拥有较快的收敛速度和全局寻优能力,具有广阔的发展前景。     

An investigation about the impact of the optimal reactive power dispatch solved by DE

With the advent of new technology based on power electronics, power systems may attain better voltage profile. This implies the proposition of careful strategies to dispatch reactive power in order to take advantage of all reactive sources, depending on size, location, and availability. This paper proposes an optimal reactive power dispatch strategy taking care of the steady state voltage stability implications. Two power systems of the open publications are studied. Power flow analysis has been carried out, which are the initial conditions for Transient Stability (TS), Small Disturbance (SD), and Continuation Power Flow (CPF) studies. Steady state voltage stability analysis is used to verify the impact of the optimization strategy. To demonstrate the proposal, PV curves, eigenvalue analyses, and time domain simulations, are utilized.     

基于纵横交叉算法的热电联产经济调度

为解决热电联产经济调度优化问题,提出了一种基于纵横交叉算法(Crisscross optimization algorithm,CSO)的新求解方法。CSO采用一种双交叉搜索机制,其中横向交叉引入扩展因子增强全局搜索能力,纵向交叉引入维交叉概念,从而避免维局部最优问题。CSO的全局并行搜索,避免了陷入局部最优,有效提高了收敛速度。以一个包含纯发电机组、热电联产机组、纯发热机组的48机组系统为例,建立了热电联产经济调度问题的模型。仿真结果表明,CSO解决热电联产经济调度问题具有可行性和有效性,为实际调度系统提供了一个较好的方法。     

基于改进粒子群算法的电力系统无功优化

电力系统无功优化问题是一个多变量、多约束的混合非线性规划问题。提出了一种改进粒子群算法用以解决这一复杂优化问题。在改进的算法中,首先结合混沌优化思想对粒子群进行初始化,减轻了粒子初始位置的选择对算法优化性能的影响;在进化过程中引入了自探索行为,使得粒子的搜索过程更加符合实际;引入了变异机制及3种判断陷入局部最优的标准,当发现粒子群陷入局部最优时,通过变异,帮助粒子跳出局部陷阱,增加发现最优解的机会。给出了问题的求解方法,并对IEEE 6、14节点系统进行了仿真计算,实验数值对比表明了算法的可行性和有效性。     

Low-carbon emission/economic power dispatch using the multi-objective bacterial colony chemotaxis optimization algorithm considering carbon capture power plant

To study the constrained emission/economic dispatch problem involving competing objectives in electric power systems with carbon capture system (CCS) technology, this paper proposes a multi-objective optimization approach based on bacterial colony chemotaxis (MOBCC) algorithm. In this algorithm, a Lamarckian constraint handling method based approach is improved to update the bacterial colony and the external archive. Finally, the optimization tests of the proposed algorithm are carried out in the IEEE 30-bus test system. Results demonstrate this approach has the advantage of dealing with highly non-linear and multi-objective functions of carbon capture thermal generator’s emission/economic dispatch problem.     

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.