Application of improved genetic algorithm to service restoration problem for distribution systems

The problem of recovery from power‐system failures is the problem of handling operations that make it possible to supply power from other lines in response to power‐system failures or construction by switching between the opened and closed states of sectionalizing switches. Considerable research has already been conducted with regard to this issue. This paper addresses the issue of determining target systems for final recovery in cases when some sections remain subject to power failure (i.e., sound bank capacity < load capacity). For this purpose, intersection is conducted only for parameters within such power‐failure sections. In such research, calculations are implemented by setting a value of 2 to the sectionalizing switches of a single parameter. In addition, when the state of a sound section changes due to mutation improvements, the method of simultaneously changing the selected points and neighboring sectionalizing switches is applied. It is clear that the proposed method, consisting of conventional GA only, is superior in terms of average fitness values. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 176(1): 9–17, 2011; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21092     

遗传算法在配电网停电恢复中的应用

将遗传算法应用于配电网停电恢复中，给出了综合考虑开关操作次数最少，网损最小以及潮流分布合理等方面的停电恢复模型，采用遗虎法对停电区域进行重构，形成恢复方案，通过对实际配电网的测试分析表明，将遗传算法用于电网停电恢复中，能快速有效地提供若干个优化方案，为运行人员提供更多的参考。     

基于遗传算法的改进谐波平衡算法

针对标准谐波平衡算法存在迭代收敛慢,计算量大的问题,采用遗传算法所具有的隐含并行性、全局解空间搜索特性,以及其优化过程不受限制性条件约束的优点,提出了引入遗传算法加速其收敛过程的改进谐波平衡算法。通过将遗传算法的随机自适应优化过程与标准谐波平衡算法的迭代运算过程相结合,简化了标准谐波平衡算法迭代计算的过程,提高标准谐波平衡算法的收敛速度和计算精度。并采用该算法对Duffing-VanDerPol方程的进行了求解,得到了该方程的近似解析解,通过与Runge-Kutta法所得数值解相比较,两者的一致性较好,表明了该算法的有效性。     

A mathematical programming approach to service restoration for distribution systems

This paper aims to develop a suitable power system restoration method by using mathematical programming (MP). Generally, the problem of determining the target configuration can be formulated as a mixed integer programming (MIP) problem. Whether or not each branch is included in a target system is expressed as a 0–1 variable, while the other constraints such as the demand and supply balance conditions are formulated in terms of continuous variables. Although an MP‐based method can obtain an optimal solution under specified operational constraints, in the case of large‐scale problems the solution time often exceeds the computation time that is practically available due to combinatorial explosion. The object of this paper is to develop an efficient solution algorithm for service restoration in distribution systems. First, an MIP problem is solved to determine the sound network configuration that maximizes the available power to the out‐of‐service area, then an MIP problem is solved to determine the target configuration for the out‐of‐service network. The proposed method has been applied to distribution system restoration problems, and simulation results demonstrate its effectiveness. © 2001 Scripta Technica, Electr Eng Jpn, 135(4): 35–42, 2001     

Optimizing service restoration in distribution systems based on scheduling of crews considering priorities of loads

The service restoration to sound sections within a short period as much as possible, performed after the permanent fault occurs in a distribution system, is significant for maintaining power supply reliability. This paper proposes an efficient method to find service restoration procedures based on fault searching and the optimal work scheduling of crews, considering the power supply priorities of loads and the probability of occurrence of faults. The proposed method is composed of two-stage problems to reduce the outage sections efficiently: (i) the problem to find a series of searching procedures for unknown fault locations in the first stage and (ii) the optimization problem of work schedules of crews in the second stage. In the first stage, the order of operation of switches opened for searching for the fault direction is determined, constituting the searching tree. After that, the proposed optimization method can efficiently solve the work scheduling problem by formulating it as a mixed-integer linear programming problem in the second stage. The computational experiments using a large-scale distribution system model with many remote and manual switches show that the proposed method can provide efficient service restoration procedures within a reasonable computational time.     

An application of reactive tabu search for service restoration in distribution systems and its comparison with the genetic algorithm and parallel simulated annealing

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine power sources for each load considering various operational constraints in distribution systems. Up to now, the problem has been dealt with using conventional methods such as the branch and bound method, expert systems, neural networks, and fuzzy reasoning. Recently, modern heuristic methods such as genetic algorithms (GA), simulated annealing (SA), and tabu search (TS) have been attracting notice as efficient methods for solving large combinatorial optimization problems. Moreover, reactive tabu search (RTS) can solve the parameter tuning problem, which is recognized as the essential problem of the TS. Therefore, RTS, GA, and SA can be efficient search methods for service restoration in distribution systems. This paper develops an RTS for service restoration and compares RTS, GA, and PSA (parallel SA) for the problem. The feasibility of the proposed methods is shown and compared on a typical distribution system model with promising results. © 2000 Scripta Technica, Electr Eng Jpn, 133(3): 71–82, 2000     

Cogeneration systems planning using structured genetic algorithms

This paper proposes a new approach to plan cogeneration systems, that of distributed energy systems. The proposed approach uses structured genetic algorithms. Cogeneration systems planning provides optimal allocation of cogeneration systems, a layout of the pipeline network structure for distributing heat energy between cogeneration systems and demand areas, and optimal heat and electric energy supply to meet the energy demands. The planning is formalized as a combinatorial optimization problem with minimizing cost of energy supply as its objectives. The traditional solution method is based on mathematical programming methods. But it is difficult to get an optimal solution as the number of areas increases because of combinatorial explosion and nonlinearity. This paper describes a new method to solve the cogeneration systems planning based on genetic algorithms. The solution of the cogeneration systems planning problem has a network structure. The proposed method applies structured genetic algorithms whose genotype has a tree structure to represent a network structure. The characteristics of the proposed method are analyzed by applying the new method to empirical data of the area around station K. © 1997 Scripta Technica, Inc. Electr Eng Jpn 119(2): 26–35, 1997     

考虑时间尺度的含DG配电网故障动态恢复策略

提出了考虑分布式电源(Distributed generation, DG)随时间变化的动态模型来解决含分布式电源的配电网故障恢复问题。实际情况中不同类型DG的实时输出功率随时间变化,考虑将时间尺度拉长,进行多时段的动态故障恢复。选取故障恢复时段内损失电量之和最小为主要目标函数,开关操作次数最少作为次要目标函数。该算法首先利用改进二进制粒子群算法(Binary Particle Swarm Optimization, BPSO)求得辐射状网络结构,排除大量不可行解,然后利用所提出的基于最优故障恢复路径策略进行切负荷操作。最后通过枚举组合对最小开关次数进行求解,得出最优的开关操作方案。算例结果证明,进行配电网故障恢复时考虑主网和DG的动态配合,能够最大限度恢复供电。     

Application of parallel tabu search‐based hierarchical optimization to distribution system service restoration

This paper proposes a hybrid method of hierarchical optimization and parallel tabu search (PTS) for distribution system service restoration with distributed generators. The objective is to evaluate the optimal route to recover the service. The improvement of power quality makes the service restoration more important. Distribution system service restoration is one of the complicated combinational optimization problems that are expressed as nonlinear mixed integer programming. In this paper, an efficient method is proposed to restore the service in a hierarchical optimization with PTS. The proposed method is tested in a sample system. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 164(4): 15–23, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/ eej.20463     

采用云理论自适应遗传算法的舰船电力系统故障智能恢复策略

提出了基于云理论自适应遗传算法并面向负荷投切的舰船电力系统故障智能恢复策略,该方法在考虑负荷和开关优先级及线路负荷分配均衡的基础上,借助于正态云固有的稳定倾向性和随机性,由云发生器自适应调整交叉率和变异率,增强了算法鲁棒性,改善了算法收敛速度.算例分析验证了基于云理论自适应遗传算法多目标故障恢复策略的可行性和有效性,且优于标准遗传算法和传统自适应遗传算法,具有一定的实用价值.     

基于混沌遗传算法的舰船电力系统故障恢复

为了解决舰船电力系统故障恢复的问题,根据故障恢复快速性的要求提出了一种新的混沌遗传算法,尝试改进遗传算法,采用遗传算法代替混沌优化算法中的"细搜索";同时用混沌优化算法中的"粗搜索"来初始化遗传算法的种群,以保证初始种群含有较丰富的模式,从而增加搜索快速收敛于全局最优解的可能。对典型的模型仿真结果表明,该算法具有更好的收敛性能,有效提高了故障恢复的速度和精度,避免了陷入局部最优的可能。     

遗传算法在等效电路模型初值估计中的应用

现有的阻抗谱拟合软件如EQUIVCRT都没有很好地解决参数初值选取的问题,这直接影响了阻抗曲线拟合的效果.应用遗传算法设计了一种估计等效电路模型初值的方法,结合高斯牛顿算法进行迭代,较好地解决了初值选取的问题.     

遗传算法在汽车永磁起动机磁极优化中的应用

汽车起动机的励磁方式分为电励磁式和永磁式。永磁启动与电励磁起动机相比，具有体积小、重量轻、效率高、比功大的特点，所以永磁起动机已成为汽车起动机的发展方向。针对目前国内永磁材料价格偏高的实际情况，为了降低永磁起动机的成本，进一步缩小外形尺寸，本文应用遗传算法对两种永磁起动机进行了磁极优化。     

Application layer multicast routing solution based on genetic algorithms

Application layer multicast routing is a multiobjective optimization problem. Three routing constraints, tree’s cost, tree’s balance and network layer load distribution are analyzed in this paper. The three fitness functions are used to evaluate a multicast tree on the three indexes respectively and one general fitness function is generated. A novel approach based on genetic algorithms is proposed. Numerical simulations show that, compared with geometrical routing rules, the proposed algorithm improve all three indexes, especially on cost and network layer load distribution indexes. __________ Translated from Control and Decision, 2006, 21(4): 381–384 [译自:控制与决策]     

黑启动中考虑动态过程的负荷最优恢复

针对黑启动过程中的薄弱电网下如何最大限度地恢复负荷进行了研究.考虑到投入大量负荷后的系统过渡过程中电压、频率变化巨大,稳态潮流合格并不代表在暂态过程中的频率和电压一定合格,针对此动态阶段存在的潜在不确定性,将负荷恢复归结为包含大扰动动态过程的频率、电压、功角等约束的最优控制问题,利用自适应遗传算法对问题进行求解,在保持系统动态过程稳定的要求下,使总的负荷恢复量最大.算例求解结果表明了该算法在负荷恢复问题中的有效性.     

考虑负荷变化和设备过载能力的多目标配网供电恢复

介绍了一种配电网供电恢复模型,这种模型考虑了故障修复期间负荷的变化和承担负荷转移的设备的过载能力,以恢复最多负荷、最小开关数、最小过负荷三级目标作为评价恢复方案指标,使用启发式搜索算法,并使用回溯算法作为进一步优化的方法。最后的算例证明了本算法的可行性。     

一种快速启发式配电网故障恢复算法

提出一种基于网络拓扑分析快速有效的配电网故障恢复算法。该算法在满足系统电流、电压的约束条件下,充分考虑负荷的优先级别,使得在开关操作次数尽可能少的情况下,尽可能多的恢复对重要负荷的供电以及其他负荷的供电,尽可能减小全系统的停电损失。算例计算表明该算法是高效和可行的。     

具有改进变异的遗传算法在无功优化中的应用

遗传算法是一种基于自然规律选择的优化方法,在电力系统的无功优化中得到了广泛的研究。该文在对遗传算法随机变异研究基础上,结合电力系统无功优化的特征,提出了优化变异,即以目标函数的灵敏度系数向量来修正变异方向,加快搜索速度。并对IEEE14节点系统进行了计算,其结果验证了算法的有效性。     

Application of parallel genetic algorithms to generation expansion planning using parallel processors

This paper presents an application of parallel genetic algorithms (PGA) to the optimal long-range generation expansion planning. The problem can be formulated as a combinatorial optimization problem that determines the order of introduced generation units at each interval of the year. The proposed method considers introduced power limits of each technology, maximum loads at each interval, and load duration curves at each interval. Appropriate string representation for the problem is presented. Binary and decimal coding and three selection methods are compared. The method is developed on a transputer that is one of the parallel processors. The feasibility of the proposed method is demonstrated using a typical expansion problem with four technologies and five intervals. The method is then compared with conventional dynamic programming and a simple genetic algorithm with promising results.     

基于遗传算法的风-光互补发电系统的动态优化

风-光互补发电是一种新型的清洁能源,将为能源结构的调整和环境保护做出巨大贡献。但是,风-光互补发电系统具有强非线性和多目标性,很难建立精确的数学模型,用传统的方法很难对其进行动态管理。采用一种新的遗传算法,以风-光互补发电系统的发电成本最低为目标,并根据控制规则,可对风-光互补电场进行动态优化管理。结果表明,该算法能很好地跟踪负荷、风速及光照强度的变化,能有效地动态管理风-光互补发电系统,说明遗传算法在风-光互补发电系统的动态管理中是有效的。