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1.
在利用遗传算法(Genetic Algorthms)求解电力系统的无功优化问题,在优化编码和变异概率取值两个方面进行了研究,进一步推动了遗传算法在实际系统优化问题中的应用。在电力系统无功优化这个具有多局部极小值优方面,把遗传算法所求复的无功优化结果和传统的基于梯度寻优方向的非线性规划法所求得优化结果进行比较,指出了遗传算法在处理非连续的和非平滑的函数寻优方面优于传统的寻优方法,具备全局寻优的能力。  相似文献   

2.
遗传算法在电力系统无功优化中的应用综述   总被引:7,自引:0,他引:7  
吴疑 《广东电力》2002,15(6):6-10,14
遗传算法是一种通过在整个解空间多渠道同时搜索以找到全局最优解的寻优方法,已经在许多复杂优化问题上被证明是一种相当有效的方法。为此,就遗传算法在电力系统无功优化中的应用进行了介绍,并提出了遗传算法在大规模电力系统无功优化计算中的改进措施。  相似文献   

3.
电力市场环境下的无功优化模型及其求解方法   总被引:17,自引:6,他引:17  
该文分析了传统的电力系统无功优化模型存在的缺陷,论述了在电力市场中实行无功计价的必要性。提出了根据发电机运行的不同状况对发电机无功进行分段计价的观点,并依此建立了以有功网络损耗费用和无功费用为目标函数并包含各种运行约束的电力系统无功优化数学模型。由于电力系统无功优化问题本身的复杂性,该文将遗传算法和ALOPEX相结合的优化算法应用于上述的无功优化模型,这种方法能充分发挥遗传算法的全局寻优优势和ALOPEX算法的爬山能力突出的特点,可以克服以往优化算法的不足。给出的算例也证明了该文提出的无功优化模型可以在降低网络有功损耗的同时实现无功潮流的合理分布,起到改善系统无功环境的作用。  相似文献   

4.
利用网损对节点补偿容量的灵敏度进行无功补偿位置选址,以灵敏度平均值为标准选取补偿节点,在该选址结果的基础上提出了一种用于电力系统无功规划问题的改进逐次优化遗传算法,该方法利用逐次优化的思想,对传统遗传算法的寻优方式进行了改进,有效降低了解空间的维度,在保证算法效率的同时使得寻优的效果得到较大的改善。将算法应用于IEEE30节点系统和IEEE118节点系统,计算结果表明算法可以较好地改善寻优特性。  相似文献   

5.
在电力系统的运行控制中,电力系统的稳定运行、供电电压质量以及供电网络的损耗始终是电力系统需要解决的一个重要问题。因此无功功率控制在电力系统运行中起着极其重要的作用,利用无功优化可以提高系统的稳定性。保证电压质量并降低网络损耗。根据汉中电网实际运行情况,提出汉中电网无功优化目标函数,针对传统遗传算法的不足,采用改进后的遗传算法对汉中电网进行无功优化。计算结果表明,遗传算法用于无功优化是一种优良的电力系统无功优化方法,同时本文的研究结果也对汉中电网的实际运行有一定的指导意义。  相似文献   

6.
应用于电力系统无功优化的改进遗传算法   总被引:18,自引:4,他引:18  
周双喜 《电网技术》1997,21(12):1-3,11
遗传算法是近些年发展起来的基于自然选择规律的一种优化方法。本文在传统遗传算法的基础上,对遗传操作进行了进一步研究和改进,提出了改进遗传算法,电力系统的无功优化问题实例计算表明,改进遗传算法的优化结果可以更有效地达到或接近全局最优。  相似文献   

7.
以有功网损最小为目标函数建立了电力系统无功优化的数学模型,对于目标函数中的无功电压越界和发电机无功越界点,采用罚函数予以解决.对无功优化问题的求解采用遗传算法,针对遗传算法应用于求解无功优化等复杂非线性优化问题中容易发生"早熟"和收敛速度慢等问题,作了一些改进.通过改进,遗传算法能够跳出局部最优解,增强了全局寻优能力,...  相似文献   

8.
为充分发挥遗传算法和内点法在求解无功优化问题中的优势,提出了一种混合算法用于电力系统的无功优化,将无功优化问题分解为离散优化和连续优化2个子问题,采用遗传算法和内点法分别求解。首先在遗传算法初始群体的确定中嵌入内点法计算,在改善遗传算法初始种群质量的同时求解连续优化子问题,并在初始种群中添加一组纯内点法的优化结果,使系统在一种次优状态再转入离散优化子问题,缩小搜索空间,大大加快了遗传算法的收敛速度。并对两种算法进行了实用性的改进,提高了算法的寻优效率。IEEE30节点系统仿真计算结果表明,与其他混合算法相比,该算法在计算速度和收敛能力方面都具有优势,且优化效果也可满足实际的需要。  相似文献   

9.
基于遗传算法的无功规划优化   总被引:81,自引:8,他引:73  
提出了一种应用于电力系统无功规划优化问题的改进遗传算法。该算法对传统遗传算法的编码方式、群体规模以及遗传3算法等方面进行了  相似文献   

10.
遗传算法在电力系统无功优化中的应用   总被引:6,自引:1,他引:6  
遗传算法根据自然界适者生存的原则进行搜索和优化。将遗传算法应用于电力系统无功优化,不仅能避免一般优化算法的局部最优问题,并能解决无功优化中变量的离散问题,避免维数灾难,提供最优及次优方案,使无功优化更切实际。遗传算法的引入,为电力系统无功优化提供了一种新的计算方法,使无功优化方法更加完善和实用。  相似文献   

11.
Optimal reactive power dispatch problem in power systems has thrown a growing influence on secure and economical operation of power systems. However, this issue is well known as a nonlinear, multimodal and mixed-variable problem. In the last decades, computation intelligence-based techniques, such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA)-based reactive power dispatch method is proposed. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the 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. In this study, the algorithm's performance is evaluated on benchmark function optimization. Then, the SOA is applied to optimal reactive power dispatch on standard IEEE 57- and 118-bus power systems, and compared with conventional nonlinear programming method, two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms. The simulation results show that the proposed approach is superior to the other listed algorithms and can be efficiently used for optimal reactive power dispatch.   相似文献   

12.
Optimal reactive power dispatch (ORPD) has a growing impact on secure and economical operation of power systems. This issue is well known as a non-linear, multi-modal and multi-objective optimization problem where global optimization techniques are required in order to avoid local minima. In the last decades, computation intelligence-based techniques such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA) based method is proposed for ORPD considering static voltage stability and voltage deviation. The SOA is based on the concept of simulating the act of human searching where search direction is based on the empirical gradient by evaluating the response to the position changes and step length is based on uncertainty reasoning by using a simple Fuzzy rule. The algorithm's performance is studied with comparisons of two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms on the IEEE 57 and 118-bus power systems. The simulation results show that the proposed approach performed better than the other listed algorithms and can be efficiently used for the ORPD problem.  相似文献   

13.
潮流计算中的PIf节点   总被引:3,自引:4,他引:3  
本文作者将潮流计算中那些励磁电流越限的发电机节点定义为PIf节点,分别建立了在极坐标和直角坐标下PIf节点的模型。PIf节点可以较真实地模拟发电机在低电压运行条件下的无功出力行为,它可以应用于电力系统安全分析、电力系统规划和电压稳定计算等方面。用包含PIf节点功能的潮流程序对浙江电网进行了计算,其结果与传统的方法进行了比较。  相似文献   

14.
遗传算法(GA)是近10年来发展的基于自然选择规律的一种优化方法,算法能成功的解决无功变量中的离散问题,避免常规数学优化方法的局部最优现象。根据众多参考文献,阐述了简单遗传算法(SGA)以及GA与其他算法相结合的算法在电力系统无功优化中的应用和今后的发展方向。  相似文献   

15.
Genetic algorithms (GAs) are search procedures for combinatorial optimization problems. Because GAs are based on multipoint search and use the crossover operator, they have an excellent global search ability. However, GAs are not effective for searching the solution space locally due to crossover-based search, and the diversity of the population sometimes decreases rapidly. In order to overcome these drawbacks, we propose a new algorithm called immunity-based GA (IGA), combining features of the immune system with GAs. IGA is expected to improve the local search ability of GAs and to maintain the diversity of the population. We apply IGA to the VLSI floor-plan design problem. Experimental results show that IGA performs better than GAs.  相似文献   

16.
基于协同进化法的电力系统无功优化   总被引:28,自引:6,他引:28  
针对无功优化问题非线性、非连续性等特点以及大范围内无功优化控制变量较多的问题,提出基于协同进化的无功优化算法以及相应的求解步骤。协同进化算法借鉴分解协调的思想,将无功优化问题分解为一系列相互联系的子优化问题,每个子优化问题对应于进化算法的一个种群,各种群通过共同的系统模型相互作用,共同进化,从而使整个系统不断演进,最终达到问题求解的目的。与常规的遗传算法相比,协同进化算法不但能得到更好的优化结果,收敛性好,而且克服了普通遗传算法计算时间过长的缺点,算例结果表明,该算法更适合于求解大系统的无功优化问题。  相似文献   

17.
Reactive power control, which is one of the important issues of power system studies, has encountered some intrinsic changes because of the presence of the hybrid AC/DC systems. The uncertainty in determination of some ill-defined variables and constraints underlines the application of fuzzy set as an uncertainty analysis tool. Herein a fuzzy objective function and some fuzzy constraints have been modeled for the purpose of reactive power optimization then this fuzzy model is dealt with as a linear programming problem to be solved. Contrary to the separate modeling of the conventional AC/DC optimization methods, this study attempts to attain the most optimal solution by the simultaneous employment of the total contributing factors of both AC and DC parts. In this way, the conventional issue of the coordinated control of firing angle and the transformer tap of the DC terminals is resolved, yet the method provides more flexibility to gain the most optimal condition since it uses more control factor for solving the optimization problem. The proposed method is performed on the modified IEEE 14 and 30-bus systems; and it is shown to have less computational burden and further minimized objective function than the conventional method.  相似文献   

18.
Genetic algorithms (GAs) are widely used for optimal allocation of capacitors in distribution systems. When dealing with large-scale systems, such as in case of unbalanced multi-converter distribution systems, these algorithms can require significant computational efforts, which reduce their effectiveness. In order to reduce processing time for GAs and simultaneously maintain adequate levels of accuracy, methods based on the reduction of the search space of GAs or based on micro-genetic algorithms have been proposed. These methods generally guarantee good solutions with acceptable levels of computational effort. In this paper, some fast, GA-based methods are compared and applied for solving the problem of optimal sizing and siting of capacitors in unbalanced multi-converter distribution systems. The algorithms have been implemented and tested on the unbalanced IEEE 34-bus test distribution system, and their performances have been compared with the performance of the simple genetic algorithm technique.  相似文献   

19.
多目标无功优化的向量评价自适应粒子群算法   总被引:12,自引:2,他引:10  
为了克服粒子群算法在高维复杂问题寻优时有相当可能陷入局部极优的现象,提出了一种自适应粒子群算法。该算法利用种群多样性信息对惯性权重进行非线性的调整,并在算法的后期引入速度变异算子和位置交叉算子,使算法摆脱后期易于陷入局部最优点的束缚。对基于向量评价的粒子群算法进行了扩展,提出了基于向量评价的自适应粒子群算法(vector evaluated particle adaptive swarm optimization,VEAPSO)来解决多目标无功优化问题,求解出问题的Pareto最优解集。为帮助决策者从Pareto最优解集中选取合适的最优解,该文提出一种基于决策者偏好及投影寻踪模型的多属性决策法,使决策结果更加真实可靠。将该算法应用于多目标无功优化问题中,IEEE 30和IEEE 118节点系统算例仿真表明该方法用于解决多目标无功优化问题是有效可行的。  相似文献   

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