电力系统有功优化潮流的一种改进算法

本文根据电力系统运行成本主要决定于发电机有功出力这个性质，提出了一个以全网发电成 本为目标函数，节点功率平衡方程、发电机有功出力限值和支路有功限值为约束的新的优化 潮流数学模型。推导出发电总成本加上惩罚项的增广目标函数等式约束下的梯度向量公式。 并根据电力系统的特点对该公式进行了简化，得到准梯度向量公式。并用算例用基于准梯度 向量的准梯度法、准共轭梯度法和变尺度法求解有功优化潮流。结果表明，该数学模型确实 体现了影响成本的主要因素，能够得到既满足安全条件又使成本接近最小的优化潮流。对梯 度向量公式的简化是合理的。三种解决都适合本数学模型。用此法求解有功优化潮流可用于 在线最优控制，可适用于离线计算。     

稳态传热边界温度分布的正则化共轭梯度反演

针对传热学反问题的共轭梯度法存在的主要问题,通过在反演过程目标函数中引入正则化项,建立了一种求解传热学反问题的正则化共轭梯度法反演算法。在求解传热学反问题过程中,采用L曲线法确定正则化因子,并采用共轭梯度法对待反演的传热边界条件进行修正。利用数值仿真试验讨论了二维稳态传热系统边界温度分布的正则化共轭梯度反演问题,并与共轭梯度法反演算法进行了比较。结果表明,所建立的正则化共轭梯度法反演算法能够有效抑制测量误差对反演结果的影响。     

基于改进共轭梯度理论神经网络优化算法研究

文章阐述了一种改进共轭梯度理论神经网络优化算法。该方法是在传统共轭梯度算法(CG)基础上引入对输出权值进行优化的理念,故称其为输出权值优化共轭梯度算法(OWO-CG)。这种算法在进行学习时,首先根据误差函数利用共轭梯度法计算收敛因子,并修改输入层和隐含层的权值因子;接着,计算隐含层输出函数,利用相关输出权值优化理论构建并求解线性方程组得到输出层的权值因子;最后,计算误差函数,利用该算法不停地修正神经网络回路输出值与期望输出值之间的差值,直到满足精度要求为止。仿真验证结果表明,与传统共轭梯度算法相比,这种算法的学习过程更加迅速和准确。     

一种基于边界元离散的导热问题几何边界识别算法

采用基于边界元法和共轭梯度法的反演算法求解二维导热边界识别问题,即几何反问题。正问题采用边界元法求解,反问题的解则在正问题的基础上通过共轭梯度法优化目标函数得到。考虑了未知边界为偏心圆、正弦曲线、椭圆等形状时的识别情况,讨论了初值、测量误差和测量点数等因素对反演解精度的影响。结果表明,该方法能将各种不规则边界识别出来,对初值和测量误差不敏感,测量点数适当减少对反演解精度影响不大,测量点离未知边界越近反演解精度越高。     

基于轨迹灵敏度分析的电力系统稳定器参数优化设计

提出了一种基于时域仿真的电力系统稳定器(PSS)参数优化方法.该方法将PSS的参数设置归结为优化问题,首先通过轨迹灵敏度仿真得到目标函数的梯度信息,再应用共轭梯度技术优化多个PSS参数.由于在目标函数中考虑了电力系统大扰动中的非线性特征,该方法可以抑制小扰动引起的自发性振荡及有效阻尼大扰动引起的系统振荡.在两个IEEE典型系统上进行的特征值分析及时域仿真结果验证了该方法的实用性和有效性.     

最优潮流改进简化梯度法的研究及应用

研究和改进电力系统最优潮流问题的简化梯度法 ,并将改进简化梯度法应用于求解无功优化问题。在算法上结合电力系统的PQ解耦特性 ,采用简化梯度法和共轭梯度法的组合算法解算优化潮流问题 ,改进了传统意义上的简化梯度法和共轭梯度法 ,进一步提高了计算速度 ,获得了良好的收敛性 ,尤其是在接近最优点处。无功优化问题的计算实例证明了本文算法是有效的、可靠的。     

轨迹灵敏度分析下的TCSC参数优化设计

提出了一种基于时域仿真的可控串联电容补偿(TCSC)及其附加阻尼控制器的参数优化方案。该方案以表征系统振荡相对动能的发电机角速度的平方最小为目标函数,通过轨迹灵敏度仿真得到目标函数的梯度信息,再应用共轭梯度法优化TCSC阻尼控制器参数。数字仿真表明,在发电机振荡模式和区域间振荡模式下,可有效提高大区域系统的动态稳定性。     

矿用防爆电动机优化设计的最优化方法

用于电机优化设计的最优化方法较多,如最速下降法、DFP法(变尺度法)、边界搜索法(几何规划方法)、Hooke-Jeeves的模矢搜索法、Powell的共轭方向法、单纯形法、随机试验法及混合搜索法等等。矿用防爆电动机优化设计的目标函数及约束函数不能构成优化变量的明显解析表达式,它们是由一百多条计算公式组成,其中还伴有几套非线性方程组,构成常反馈的多级序列系统。所以,矿用防爆电机优化问题是一个具有不等式约束,目标函数无明显解析式的高度非线性的规划问题。求解这一问题,最速下降法、DFP法等需要计算函数导数的方法以及要求目标函数为正定多项式的边界搜索法是不适用的,只能运用“迭代法”     

应用轨迹灵敏度优化设计UPFC阻尼控制器参数

提出一种优化统一潮流控制器(UPFC)的附加阻尼控制器参数的新方法.这种方法以表征系统机电振荡模式的发电机转子角速度作为优化的目标函数,使用轨迹灵敏度技术计算目标函数对阻尼控制器参数的灵敏度,并采用共轭梯度法进行参数优化.在新英格兰测试系统上进行的仿真算例说明了使用该方法所设计的UPFC阻尼控制器可有效抑制系统低频振荡.     

基于模态级数法和轨迹灵敏度的励磁调节器参数优化

基于模态级数法及轨迹灵敏度分析的励磁调节器参数优化方法通过模态级数法得到较精确的系统状态轨迹的封闭解，利用数值微商算法得到系统的轨迹灵敏度，即目标函数的梯度信息，再应用共轭梯度技术优化励磁调节器参数。模态级数法考虑了系统非线性模式的影响，所得状态轨迹解逼近时域仿真结果，所以该方法能有效地阻尼系统受到大扰动后的振荡。通过算例可验证该方法的有效性。     

最优潮流内点割平面的鲁棒算法

内点割平面算法（IPCPM）集中了割平面法和内点法的优点,非常适于求解大规模系统的离 散优化问题,但是研究发现内点法在求解松弛的线性规划问题时,如果问题具有多重解,最优解会 收敛到凸多面体的最优面的内部,此时IPCPM会由于无法得到正确的最优基信息来生成割平面 而失效。在此基础上,文中提出了一种通用的最优基判别准则,解决了原算法失效的问题,提高了 算法的鲁棒性。通过对IEEE测试系统的数值计算,表明改进后的算法能正确处理最优解的各种 情况,显著扩大了IPCPM的应用范围。     

Adaptive finite element method for shape modification in optimization of electric machines

A method for the modification of finite element meshes based on adaptive meshing for optimization of electric machines is proposed. In the optimization of the machine, the proposed method generates the mesh for the new shape from the previous mesh with minor modification. The locations of the nodes for the new shape are decided by solving a Laplacian equation whose unknowns are the displacements of the nodes. The advantages are illustrated by application to the shape optimization of an IPM motor. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 168(1): 21–28, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20773     

Chaotic ant swarm optimization to economic dispatch

This paper developed a novel algorithm named chaotic ant swarm optimization (CASO) for solving the economic dispatch (ED) problems of thermal generators in power systems. This algorithm combines with the chaotic and self-organization behavior of ants in the foraging process. It includes both effects of chaotic dynamics and swarm-based search. The algorithm was employed to solve the ED problems of thermal generators. The proposed method was applied to three examples of power systems. Simulation results demonstrated that the method can obtain feasible and effective solutions, and it is a promising alternative approach for solving the ED problems in practical power systems.     

Hybrid PSO–SQP for economic dispatch with valve-point effect

This paper presents a novel and efficient method for solving the economic dispatch problem (EDP), by integrating the particle swarm optimization (PSO) technique with the sequential quadratic programming (SQP) technique. PSO is the main optimizer and the SQP is used to fine tune for every improvement in the solution of the PSO run. PSO is a derivative free optimization technique which produces results quickly and proves itself fit for solving large-scale complex EDP without considering the nature of the incremental fuel cost function it minimizes. SQP is a nonlinear programming method which starts from a single searching point and finds a solution using the gradient information. The effectiveness of the proposed method is validated by carrying out extensive tests on three different EDP with incremental fuel cost function takes into account the valve-point loadings effects. The proposed method out-performs and provides quality solutions compared to other existing techniques for EDP considering valve-point effects are shown in general.     

Neural networks with quantum architecture and quantum learning

A method is proposed for solving the two key problems facing quantum neural networks: introduction of nonlinearity in the neuron operation and efficient use of quantum superposition in the learning algorithm. The former is indirectly solved by using suitable Boolean functions. The latter is based on the use of a suitable nonlinear quantum circuit. The resulting learning procedure does not apply any optimization method. The optimal neural network is obtained by applying an exhaustive search among all the possible solutions. The exhaustive search is carried out by the proposed quantum circuit composed of both linear and nonlinear components. Copyright © 2009 John Wiley & Sons, Ltd.     

基于改进粒子群优化算法的负荷分配方法研究

针对多目标粒子群优化算法求解负荷优化分配问题时所出现的最优解分布不均,局部最优等问题,引入了精英交叉算子并基于拥挤度对非劣解集进行排序,给出了精确计及网损时的机组出力等式不等式约束处理方法。最后在有无网损两种情况下针对3机组系统进行负荷优化分配。仿真结果表明改进后的粒子群优化算法寻优能力得到提升,同样利用模糊隶属度函数筛选Pareto解集得到的结果明显优于常规粒子群优化算法,有效降低了发电成本及污染物排放,且求解结果严格满足约束条件。     

A NEW METHOD FOR SOLVING IONIZED FIELDS OF UNIPOLAR HVDC LINES INCLUDING EFFECT OF WIND. PART I: FEM FORMULATION

In this paper, a finite element method for solving ionized fields is developed based on a novel solution philosophy. Employing the Galerkin finite element method, the solution problem of an ionized field has been formulated as an optimization problem, i.e. as a problem of minimizing an objective function whose minimum gives the solution. The effect of wind is taken into account by introducing a scalar potential of its velocity. © 1997 by John Wiley & Sons, Ltd.     

Multipoint Tabu Search based on proximate optimality principle—application of parts concept

This paper proposes an algorithm–MultiPoint Tabu Search based on proximate optimality principle (POP), which has several advantages for solving combinatorial optimization problems. POP holds well for numerous engineering problems, which stipulates that ‘good solutions possess some similar structure.’ The proposed algorithm is applied to several typical combinatorial optimization problems to verify the performance of the proposed algorithm. The simulation results indicate that the proposed method has higher optimality than the conventional Tabu Search with a single search point. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Implementation of a Lagrangian relaxation based unit commitmentproblem

An effort is made to provide an understanding of the practical aspects of the Lagrangian relaxation methodology for solving the thermal unit commitment problem. Unit commitment is a complex, mixed integer, nonlinear programming problem complicated by a small set of side constraints. Until recently, unit commitment for realistic size system has been solved using heuristic approaches. The Lagrangian relaxation offers a new approach for solving such problems. Essentially, the method involves decomposition of the problem into a sequence of master problems and easy subproblems, whose solutions converge to an ϵ-optimal solution to the original problem. The authors concentrate on the implementation aspects of the Lagrangian relaxation method applied to realistic and practical unit commitment problems     

A Robust Approach to Optimal Power Flow With Discrete Variables

Optimal power flow (OPF) belongs to the nonlinear optimization problem with discrete variables. The interior point cutting plane method (IPCPM), which possesses the advantages of both the interior point method and the cutting plane method, becomes a very promising approach to the large-scale OPF. It employs a successive linearization process and iteratively solves the mixed integer linear programming problem. However, case studies have shown that: if the problem has multiple solutions, the optimal solutions will converge to the interior of the optimal face, and the cutting planes cannot be generated due to the failure to identify the optimal base. This paper presents a new general optimal base identification method for solving the problem. The new approach significantly improves the robustness and efficiency of IPCPM. Simulation results on IEEE test systems indicate that the algorithm proposed can not only properly deal with various types of optimal solutions but also greatly enlarge the application area of IPCPM.