Distribution systems expansion planning by multi-stage branchexchange

The authors present a multistage branch exchange algorithm for solving expansion planning problems in distribution systems. Since it is formulated as a combinatorial optimization problem, it is difficult to solve such a large-scale problem accurately. Therefore, in order to find a solution quickly, the authors have developed a method based on the branch exchange technique which is able to find an approximate solution. To obtain a more accurate solution, the multistage branch exchange algorithm is introduced. The validity and effectiveness of the proposed algorithm are demonstrated by applying it to a 59-node, 69-branch numerical example system     

基于多种数值分析方法的电压暂降凹陷域快速算法

提出一种基于数值分析理论的电压暂降凹陷域计算方法,该方法在暂降幅值解析计算式的基础上引入多种数值分析方法求解临界故障点。将临界故障点的确定简化为二次函数根的求解问题,并在满足计算准确性的前提下灵活采用不同的数值分析方法以达到更快收敛速度。该方法对于给定网络参数的系统广泛适用,与故障点法和单一数值解法的计算结果和算法性能的对比验证了所提方法对于求解暂降凹陷域的准确性和有效性。     

基于DPSO算法以负荷恢复为目标的网络重构

研究了大停电事故后输电系统的重构优化问题,提出了一种求解最优目标网的离散粒子群优化（DPSO）算法。将网络重构问题表示为以重要负荷恢复量占已恢复负荷总量的比例最高为目标的非线性优化问题,在求解目标网时考虑了负荷重要性、网络连通性、电网所需满足的各种安全和运行约束等问题。该算法在求解输电网重构问题时,编码容易且能方便地处理网络连通性问题,求解效率高、速度快。在IEEE 57节点系统和IEEE 118节点系统中的应用结果验证了文中方法的有效性。     

基于免疫算法的配电网开关优化配置模型

给出了开关优化的数学模型及基于免疫算法的求解方法。分析了开关投资和运行维修费用、停电损失的计算方法，基于等年值法建立开关优化配置模型。该模型是一含约束、不可微、非连续的组合优化模型。基于免疫算法给出了模型的求解算法。通过模型计算确定开关的最优配置数量和位置，并得到系统的可靠性、投资费用和停电损失等。计算实例表明：提出的模型和算法有较强的工程实用性，免疫算法具有较好的全局收敛性和较快的收敛速度。     

基于蚁群最优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组合优化问题。本文提出了一种新颖的基于蚁群最优的算法来求解正常运行条件下的配电网络重构问题,以达到损失最小,蚁群最优算法法（Ant Colony Optimization,简称ACO算法）是一种新型通用内启发式算法。在求解组合最优问题上,ACO算法已被证明是非常有效的。ACO算法本质上是一个多代理系统,在这个系统中单个代理之间的交互导致了整个蚁群的复杂行为。这种方法的主要特征是正反馈,分布式计算以及富有建设性的贪婪启发式搜索的运用,为了证明本文提出的算法的可行性和有效笥,我们研究了两个算例系统,并给出了计算结果,结论表明,本文提出的算法是相当有希望的。     

基于图论方法的路径规划应用

利用图论方法来实现机器人寻优路径规划。图论是一种新的数学分支,对于求解最优化问题很有实用价值。它与通常采用的路径规划方法比较,如势能函数法、网格法、人工神经网络方法、遗传算法等,在解决两点间最短距离问题上尤具优势,从起始点出发到达目标点的寻优路径策略更具有简单实用性,具有方法简单、算法容易实现的优点。仿真实验显示了该算法可以有效地解决机器人对任意两点间的路线进行寻优问题,取得了较好的结果。     

Application of pattern search method to power system valve-point economic load dispatch

Direct search (DS) methods are evolutionary algorithms used to solve constrained optimization problems. DS methods do not require any information about the gradient of the objective function at hand, while searching for an optimum solution. One of such methods is pattern search (PS) algorithm. This study presents a new approach based on a constrained pattern search algorithm to solve well-known power system economic load dispatch problem (ELD) with valve-point effect. For illustrative purposes, the proposed PS technique has been applied to various test systems to validate its effectiveness. Furthermore, convergence characteristics and robustness of the proposed method has been assessed and investigated through comparison with results reported in literature. The outcome is very encouraging and proves that pattern search (PS) is very applicable for solving power system economic load dispatch problem.     

改进自适应遗传算法在网络重构中的应用

研究了大停电事故后输电系统的重构优化问题,提出一种求解最优目标网的改进自适应遗传算法。将网络重构问题表示为以恢复负荷总量最高为目的的非线性优化问题,在求解目标网时考虑了负荷的重要性、网络连通性、电网所需满足的各种安全和运行约束等问题。采用改进的自适应遗传算法对问题进行求解,通过适应值函数的计算得到了系统允许条件下的最大允许恢复负荷量。该算法在求解电网重构问题时,编码容易且能方便地处理网络连通问题,求解效率高,用IEEE30节点算例验证了本文方法的有效性。     

基于改进二进制粒子群与动态微增率逐次逼近法混合优化算法的水电站机组组合优化

针对水电站机组组合问题具有高维、非凸、离散、非线性等特点,提出了一种适用于求解大容量、多机组巨型水电站机组组合问题的改进二进制粒子群优化算法,改进了粒子概率变换和位置更新方程,使其具有更强的全局寻优能力和更快的收敛速度。通过将改进二进制粒子群算法与动态微增率逐次逼近法混合嵌套,分别对水电站外层机组组合和内层机组间负荷分配进行交替迭代优化来求解水电站机组组合问题。同时引入启发式机组最短开停机时间修补策略和基于机组启停优先顺序表的系统备用容量修补技术,有效处理了多重约束条件,提高了算法的收敛速度和寻优能力。以三峡水电站为工程应用背景进行了实例研究,并与DP和BPSO算法以及实际耗水量进行了比较分析,结果表明所提算法简单快速,优化效果较好,具有较强的工程实用价值。     

基于最优中心参数的多中心校正内点最优潮流算法

为加快最优潮流(optimal power flow,OPF)问题的求解,基于最优中心参数(optimal centering parameter,OCP)及改进多中心校正(improved multiple centrality corrections,IMCC)技术,提出一种求解最优潮流(optimal power flow,OPF)问题的新型快速内点算法(OCP-IMCC interior point method,OCP-IMCCIPM)。结合均衡距离–评价函数(equilibrium distance-quality function,ED-QF),给出最优中心参数评价模型,采用线性化技术对模型近似,以降低模型计算量。利用线搜索技术实现近似模型求解以确定最优中心参数,该参数使得所提算法具有更多的优势步和更少的迭代次数。IMCC技术可进一步拉大迭代步(尤其是非优势步)步长,实现算法更快收敛。14—1047节点系统的仿真结果表明,与其他多种内点算法相比,所提OCP-IMCCIPM算法具有更大的迭代步长和更快的收敛速度以及更好的计算效果。     

基于解集动态分析含风电接入的多目标机组组合研究

风电的大规模接入给多目标节能减排发电调度带来了新的机遇和挑战。由于风电场出力具有随机性,采用置信区间简化风电场景模拟数量,考虑到多目标模型的复杂性,利用Benders分解技术对模型进行降维,设计一种基于解集动态分析的多目标自适应优化算法对降维后的多目标主问题进行求解,并提出一种提高模型整体求解效率的预处理机制加速收敛。仿真结果表明所提方法能够有效求解含风电的多目标机组组合问题,并验证了所提多目标算法和预处理机制在求解模型中的优势。     

基于人工鱼群算法的最优潮流计算

提出了基于人工鱼群优化算法(AFSA)的最优潮流(OPF)计算方法;算法结合动态调整罚函数的方式,将最优潮流问题转化为一个无约束求极值问题,有效提高了算法的全局收敛能力和计算精度.应用此算法对标准IEEE30节点的电力系统进行最优潮流计算,并与粒子群算法和遗传算法进行了比较,仿真结果表明,该算法能够更好地获得全局最优解,具有实用意义.     

考虑泵类负荷特性的低压配电网潮流计算

提出了一种考虑泵类负荷特性的低压配电网潮流计算方法。通过将泵类负荷的电动机转矩平衡方程和配电网各节点功率平衡方程组合起来,构成了考虑泵类负荷特性的低压配电网潮流计算模型。在求解配电网潮流计算的牛拉法的基础上,提出通过将泵类负荷电动机转矩平衡方程与各节点功率平衡方程进行统一迭代和交替迭代两种方法进行求解。通过对实际低压配电网台区的计算分析,验证了该方法能获得更加准确的配网负荷节点电压,而且能够获得泵类负荷正常运行时和启动时的节点电压。     

A tutorial description of an interior point method and itsapplications to security-constrained economic dispatch

The authors deal with the use of successive linear programming (SLP) for the solution of the security-constrained economic dispatch (SCED) problem. They tutorially describe an interior point method (IPM) for the solution of linear programming (LP) problems, discussing important implementation issues that really make this method far superior to the simplex method. A study of the convergence of the SLP technique and a practical criterion to avoid oscillatory behavior in the iteration process are also proposed. A comparison of the proposed method with an efficient simplex code (MINOS) is carried out by solving SCED problems on two standard IEEE systems. The results show that the interior point technique is reliable, accurate, and more than two times as fast as the simplex algorithm     

几种改进BP神经网络算法预测飞灰含碳质量分数效果的比较

分别介绍了动量梯度下降法、L-M数值优化算法和贝叶斯算法,建立了预测模型,对某电厂飞灰含碳质量分数进行预测。通过预测结果的分析,对比了不同算法对预测精度的影响。结果表明:在该模型下,当训练样本量为30甚至更多时,动量梯度下降法不能收敛,L-M算法和贝叶斯算法的收敛速度比动量梯度下降算法要快很多,而贝叶斯算法的预测精度最高。     

Least‐squares‐based adaptive target localization by mobile distance measurement sensors

A least‐squares‐based adaptive algorithm with forgetting factor is proposed for localization of a target by a mobile distance measurement sensor. This problem, in its most general form, was tackled in a recent paper using a gradient adaptive algorithm, assuming distance measurements are directly available. We establish that the proposed algorithm bears the same stability and convergence properties as the gradient algorithm previously studied. It is demonstrated via simulations that the proposed algorithm converges significantly faster to the location estimates than the gradient algorithm for high forgetting factor values and significantly reduces the noise effects for small values of the forgetting factor. Furthermore, a more challenging form of the original problem is considered, where distance information is required to be deduced from time of flight measurements, considering a time of flight‐based active distance measurement sensor and an environment with unknown signal permittivity/speed; the proposed algorithm is redesigned to solve this problem. Copyright © 2014 John Wiley & Sons, Ltd.     

基于均衡KNN算法的电力负荷短期并行预测

为提高电力负荷预测精度,应对海量、高维数据带来的单机计算资源不足的问题,提出一种基于均衡KNN算法的短期电力负荷并行预测方法。针对电力负荷数据特征,采用K均值聚类算法进行电力负荷场景划分;为提高场景划分精度,采用反熵权法量化负荷特征的权重系数;针对不均衡的负荷场景,提出均衡KNN算法对待预测负荷进行精确的场景归类;采用BP神经网络算法对海量历史数据进行负荷预测模型的分场景训练与预测;采用ApacheSpark架构对提出的模型进行并行化编程,提高其处理海量、高维数据的能力。选取某小区居民用电数据进行算例分析,在30节点云计算集群上进行测试验证,结果表明基于该模型的负荷预测精度与执行时间均优于传统预测算法,且提出的算法具有优异的并行性能。     

Optimal reactive dispatch through interior point methods

An implementation of an interior point method to the optimal reactive dispatch problem is described. The interior point method used is based on the primal-dual algorithm and the numerical results in large scale networks (1832 and 3467 bus systems) have shown that this technique can be very effective to some optimal power flow applications     

Fast computation of Q‐value‐based dynamic programming on road networks

One of the essential components of vehicle navigation systems is route planning. The single shortest path problem and multiple shortest path problem have been widely studied for route planning. This paper introduces a Q‐value‐based dynamic programming using the division concept for solving both single and multiple shortest path problems on road networks. The proposed algorithm divides the whole network into different divisions, and the updating of Q values in each division is one stage for searching the optimal routes on road networks. The proposed algorithm can greatly save the computational time without any preprocessing on the road networks. The proposed algorithm is also systematically studied in various sizes of road networks. The simulation results show the efficiency and effectiveness of the proposed algorithm on large‐scale road networks. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于Spark的并行模拟退火算法求解TSP

模拟退火算法是求解无约束优化问题的有效方法,但求解旅行商问题时存在精度较差、容易陷入局部最优且收敛速度慢等缺点。为了改进上述问题,本文提出了一种基于Spark平台的并行模拟退火算法。修改模拟退火算法的降温函数,构造旅行商问题的解空间,采用大邻域搜索技术和2-opt算子增强局部搜索能力,引入OX交叉思想增强全局搜索能力,提出交叉协同试验并行策略与Spark平台并行实现。选取若干TSPLIB数据集进行仿真实验,对求解质量和运行时间两个方面进行测试,与其它Spark框架的并行算法进行对比实验。仿真结果表明,该算法求解精度有较大的提高,求解速度上对比其他算法提升3-10倍,能够有效求解旅行商问题。