兼顾供电可靠性和经济性的多目标网络重构

提出了基于隐含并行寻优思想的兼顾供电可靠性提高和网损降低的配电系统多目标网络重构算法。该算法采用了大规模配电系统三相潮流计算和可靠性评估方法,实现了配电系统可靠性与经济性水平的综合最优化。该算法具有不依赖于网络开关的初始状态,也不受初始联络开关的操作次序的影响,可以高效地求解系统的有效解,实现多目标综合优化的特点。算例结果表明,该算法是灵活和高效的。     

兼顾供电可靠性和经济性的多目标网络重构

提出了基于隐合并行寻优思想的兼顾供电可靠性提高和网损降低的配电系统多目标网络重构算法.该算法采用了大规模配电系统三相潮流计算和可靠性评估方法,实现了配电系统可靠性与经济性水平的综合最优化.该算法具有不依赖于网络开关的初始状态,也不受初始联络开关的操作次序的影响,可以高效地求解系统的有效解,实现多目标综合优化的特点.算例结果表明,该算法是灵活和高效的.     

配电网络优化重构算法

配电网络重构是配电系统优化运行的重要措施之一，也是配电管理系统中的一项主要功能。针对配电网络结构运行特点，提出了一种计及安全约束和实际运行约束的支路互换算法，以实现网络优化重构。该算法简单实用，快速有效，可以应用于实时网络重构。     

基于模糊多目标协调优化的配电网络重构研究

配电网络重构通过改变开关的开合状态以提高供电的可靠性和经济性。建立以网损最小、馈线负载均衡指标最小、开关操作次数最少多个目标协调优化的配电网络重构的数学模型。引入模糊隶属度函数对各目标进行模糊化处理,根据模糊集理论的最大最小法则,将多目标优化问题转化为单目标非线性规划问题。应用禁忌算法对该配电网络重构模型进行优化求解。对某个69节点配电网络系统,比较分析了四种不同的优化方案,仿真结果验证了所提出模型和求解方法的正确有效。     

一种基于可靠性指标的复杂配电网络重构算法

以提高可靠性为目标,提出了一种简单、高效的复杂配电系统的网络重构算法.该算法将网络重构的优化过程分解成与动态变化的联络刀闸相对应的重构子问题.依次闭合每个联络开关,就可以得到具有不同网络拓扑结构的单环网,并分别对馈线内单环网和馈线间单环网提出了网络重构方法.通过一个实际的电力系统算例证实了本算法的有效性.     

一种基于可靠性指标的复杂配电网络重构算法

以提高可靠性为目标,提出了一种简单、高效的复杂配电系统的网络重构算法。该算法将网络重构的优化过程分解成与动态变化的联络刀闸相对应的重构子问题。依次闭合每个联络开关,就可以得到具有不同网络拓扑结构的单环网,并分别对馈线内单环网和馈线间单环网提出了网络重构方法。通过一个实际的电力系统算例证实了本算法的有效性。     

基于时间周期的配电网络动态重构

提出一种基于时间周期的配电网络动态重构算法,该算法以改进的均衡视在精确矩法为基础,以一段时间内的收益最大为目标,开关操作总次数约束在目标函数中得到体现。在应用配电网络重构的均衡视在精确矩法时考虑了其物理含义,合理地引入了启发式规则,在闭合联络开关模拟形成单环网后寻找需要打开的网环开关时,有效地降低了搜索空间和计算量,加快了重构的过程。33母线和69母线测试系统的计算实例表明了所提算法的正确性和有效性。     

用于降低网损的配电网络优化重构方法的研究

配电网络优化重构是降低配电网络线损的有效措施,本文综述了配电网络优化重构中常见的算法及问题。为了提高重构优化速度,提出了利用混合潮流算法快速计算网损的方法。提出了基于度的拓扑方法来判定在遗传算法优化过程中造成的不可行解,并列出了对不可行解的判断准则。本拓扑方法同样可以为前推回代法提供拓扑分析。为缩小搜索空间,对遗传算法采取了在基因操作过程中如产生不可行解采用返回重新操作的改进,使算法运算过程中不产生不可行解。同时为避免欺骗现象提出较差个体单独成群策略。实例证明,该算法应用于配电网重构是有效的。     

配电网络重构的改进最优流模式算法

本提出了一个配电网络重构的改进最优流模式法（ＩＯＦＰ）。并用一个快速有效的单环网潮流算法求解最优流模式。本了网络重构的实际运动约束，如最大刀闸操作次数，支路过载等。经测试系统的验算表明，本算法快速有效，可以用于实时环境。     

配电网络重构的算法研究

配电网络重构是降低网损、提高电网运行经济性和供电可靠性的主要途径之一。在满足各种运行约束条件下,以网损最小或可靠性最高为目标的配电网重构问题是一个典型的非线性整数组合优化问题,配电网重构是配电系统优化运行的主要措施之一,也是配电管理系统(DMS)中的一项主要功能。针对不同配电网的实际水平,提出一种新的、实用的重构算法,该算法结合了改进支路交换算法和递归虚拟流理论的思想,同时还考虑了配电网重构的实际操作中各种约束因素,如配电网自动化水平、最大开关操作次数等。由此算法开发的配电网重构分析程序采用面向对象的编程技术编写,通过对典型的几个算例进行分析,验证了该算法的有效实用性,可以用于实际配电网重构。     

Numerical experiments with an optimal power flow algorithm based onparametric techniques

This paper presents the results of numerical experiments with a new optimal power flow (OPF) algorithm based on a parametric technique. The approach consists of relaxing the original OPF problem by incorporating parametric terms to the objective function, the equality and inequality constraints. Such relaxation assures that any arbitrary initial solution, feasible or unfeasible, be the optimal solution of the OPF problem. As the scalar parameter changes, a family of OPF problems is created, whose necessary conditions are solved by Newton's method. An efficient strategy is proposed for updating the parameter and the optimal set of active inequality constraints of each intermediate problem. Two applications of the methodology are reported: the economic dispatch problem and the minimum transmission loss problem. These problems were solved for an 810-bus and a 2256-bus equivalent network of the South/Southeast interconnected Brazilian power system. The results show that the parametric approach is robust and efficient when applied to large-scale OPF problems     

基于Tabu搜索的配电网络重构算法

配电网络重构是一个非常复杂的、大规模的组合优化问题。该文提出了一种非常有效的、鲁棒性很好的算法来求解正常运行条件下配电网络的重构问题,以减少网络中的能量损失费用。该算法基于Tabu搜索方法(TS)。作为现代启发式方法家族中的重要一员,Tabu搜索方法是近几年来发展起来的用于求解组合最优问题的一种通用算法。文中详细阐述了用于配电网络重构问题时Tabu搜索方法中各成员的设计。为了证明该文方法的可行性和有效性,还研究了3个不同规模的IEEE测试网络的重构问题。结果表明,该文提出的方法是一种非常有效的方法。     

Power Portfolio Optimization in Deregulated Electricity Markets With Risk Management

In a deregulated electric power system, multiple markets of different time scales exist with various power supply instruments. A load serving entity (LSE) has multiple choices from these instruments to meet its load obligations. In view of the large amount of power involved, the complex market structure, the risks in such volatile markets, the stringent constraints to be satisfied, and the long time horizon, a power portfolio optimization problem is of critical importance for an LSE to serve its load, maximize its profit, and manage its risks. In this paper, a midterm power portfolio optimization problem with risk management is presented. Key instruments are considered, risk terms based on semi-variances of spot market transactions are introduced, and penalties on load obligation violations are added to the objective function to improve algorithm convergence and constraint satisfaction. To overcome the inseparability of the resulting problem, a surrogate optimization framework is developed, enabling a decomposition and coordination approach. Numerical testing results show that our method effectively provides decisions for various instruments to maximize profit and manage risks, and it is computationally efficient     

Dynamic economic dispatch using artificial immune system for units with valve-point effect

Dynamic economic dispatch (DED) is an important dynamic problem in power system operation and control. The objective of the problem is to schedule power generation for the online units over a time horizon, satisfying the unit and ramp-rate constraints. In this paper, clonal selection based artificial immune system (AIS) algorithm is used to solve the dynamic economic dispatch problem for generating units with valve-point effect. The feasibility of the proposed method is validated with ten and five unit test systems for a period of 24 h. Results obtained with the proposed approach are compared with other techniques in the literature. The results obtained substantiate the robustness and proficiency of the proposed methodology over other existing techniques in terms of solution quality and computational efficiency.     

基于数据包络分析的配电网投资项目经济评价

引入数据包络分析方法用于对考虑风险的配电网投资项目进行经济评价,并通过案例进行说明。针对DEA模型对于效率值为1的各决策单元不能有效区分的问题,本文通过构造最优决策单元,计算被评价决策单元相对于最优决策单元的有效值,达到对有效决策单元进一步区分的目的。由此,为配电网投资决策人员提供了一种进行客观评价的方法和思路。     

Joint tracking coefficients and the time delay of a nonstationary linear system,preceded by a static nonlinearity

The paper considers a block-oriented nonlinear Hammerstein system that consists of a linear block with real-valued time-varying unknown coefficients and a pure time delay, preceded by the static nonlinearity. The joint tracking of linear block coefficients and the time delay while processing observation samples is under consideration. The difficulty of the tracking process consists in the initial nonconvexity of the objective function to be minimized, in respect of the time delay. To obtain current estimates of the true coefficients and the time delay it is necessary to overcome the multiextremality of the objective function. A global optimization problem could be solved by changing a form of the nonconvex objective function into a convex one. The optimization problem has been subdivided into two problems that are analyzed in this article, as well. The first one consists of removal of the objective function multiextremality in respect of the time delay, while hunting for the global extremum. Therefore, the well-known approach based on the method of corrective operators, which is used to transform the multiextremal criterion into a unimodal function for the nonstationary Wiener system, is developed here for the Hammerstein one. The other problem consists of the applicability of unified adaptive algorithms, used for Wiener systems in the case of Hammerstein systems. The recursive parametric identification technique, based on the adaptive algorithm procedures, is developed here for the nonstationary block-oriented Hammerstein systems, too. The applicability of algorithms is supported by various simulation tests solved by computer for distinct inputs and for two static nonlinearities.     

A new Optimal reactive power planning based on Differential Search Algorithm

This paper proposes a new multi-level methodology based on the optimal reactive power planning. The developed methodology is designed to solve the problem of the non-feasibility solution of the fuel cost minimization problem (for a given operating point) where the classical method such as interior point method (IPM) is applied. The proposed solution to solve this problem is based on the application of the optimal reactive power planning problem considering voltage stability as the initial solution of the fuel cost minimization problem. To improve the latter the load voltage deviation problem is applied to improve the system voltage profile. For à good result improvement, the reactive power planning problem and the load voltage deviation minimization problems are solved using a new optimization method namely the Differential Search Algorithm (DSA). Moreover, the fuel cost minimization problem is solved using IPM. To identify the candidate placements of compensation devices for the optimal reactive power planning problem, a new voltage stability index namely: The Fast Voltage Stability Index (FVSI) is used. The methodology has been tested with the equivalent Algerian power system network, and the simulation results show the effectiveness of the proposed approach to improve the reactive power planning problem and to minimize the system voltage deviation.     

计及输电容量约束的发电公司最优报价策略

在采用暗标拍卖的电力市场环境中,发电公司可以通过估计竞争对手的报价行为来构造最优的报价策略。在不考虑输电容量约束的情况下,已提出了解决这一问题的计算效率较高的直接优化方法。计及输电容量约束后,该问题要复杂得多,文中对此问题进行了研究,提出了互补直接优化方法,该方法在本质上是用求解线性方程组来替代费时的蒙特卡罗仿真,其计算速度较后者快得多,较好地解决了这一困难的问题。最后,用IEEE30节点测试系统对所提出的方法进行了计算分析,说明了其可行性和有效性。     

基于遗传算法的电力系统故障诊断的解析模型与方法──第一部分 : 模型与方法

本文共分三部分。第一部分发展了电力系统故障诊断的解析模型与基于遗传算法的方法;第二部分首先提出了用计算机自动形成故障诊断的目标函数的方法,这是实现在线故障诊断所必需的。之后介绍了为浙江省电力局开发的在线故障诊断实用软件的组成与功能;第三部分介绍了将该软件应用于浙江省２２０ｋＶ以上电力系统时其ＥＭＳ信息的获取方法及模拟在线测试结果。这里是第一部分,着重介绍了所发展的同时利用保护和断路器信息的故障诊断的解析模型。其在数学上是一个无约束０－１整数规划模型,借助于充分利用断路器的跳阐信息。这种模型在一定程度上解决了保护信息不完整时的电力系统的故障诊断问题。之后,概述了利用实时网络拓扑分析方法识别故障平息后的停电（故障）区域的方法,从而可将故障诊断局限于这些小的停电区域之中,以大大节省计算时间。此外,还简要介绍了用遗传算法求解故障诊断问题的基本步骤。     

Cogging Torque Reduction and Optimization in Surface-mounted Permanent Magnet Motor Using Magnet Segmentation Method

One of the main challenges in permanent magnet electrical machine design is cogging torque reduction. In this article, the magnet segmentation method is used for cogging torque reduction. For this end, each surface permanent magnet is divided into eight parts, and a symmetrical structure with equal angular widths and considering the angular gaps between them is used for minimizing a number of optimization parameters. In this article, three optimization algorithms—response surface methodology, genetic algorithm, and particle swarm optimization—are used to determine the optimal values of optimization parameters. Finally, the result is obtained that the optimum values of response surface methodology are more efficient than of those of the genetic algorithm and particle swarm optimization in cogging torque reduction, because the objective function of the response surface methodology is cogging torque that is calculated using the finite-element method, whereas the objective function in the genetic algorithm and particle swarm optimization is based on the analytical methods. However, the main objection of the magnet segmentation method is the simultaneous reduction of average torque with cogging torque.