含分布式风光电源的配电系统故障恢复策略

具有随机性的风力发电和光伏发电接入配电系统对配电系统的故障恢复有很大的影响。建立了风电场异步电机的稳态模型,将异步风力发电机中的滑差修正量引入到雅克比矩阵中,计算得到含风电场的潮流分布,引入随机潮流分析风电和光伏发电对配电系统故障恢复的影响,建立以系统损耗的期望值最小和开关次数最少为指标的多目标配电系统故障恢复模型,利用基于带精英策略的非支配排序单亲遗传算法求解多目标问题,并通过IEEE 33节点配电系统进行仿真,研究结果说明模型的合理性以及方法的有效性。     

锦—苏特高压直流对江苏电网变压器直流偏磁的影响

±800 kV锦-苏特高压直流工程大功率双极不平衡调试期间,江苏电网建设了直流偏磁在线监测系统,开展了直流偏磁带电检测工作。对距苏州换流站接地极100 km内主要500 kV和220 kV主变开展直流偏磁测试,包括中性点直流电流、振动、噪声等,并研究其对江苏电网的影响。研究发现,该工程直流偏磁会造成附近主变振动加剧、噪声增加,但未见造成局部过热现象,对主变的安全运行影响较小。提出了对新投运主变出厂试验增加直流偏磁试验和加强直流偏磁在线监测跟踪的建议。     

An Efficient Codification to Solve Distribution Network Reconfiguration for Loss Reduction Problem

Evolutionary algorithms have been used to try to solve distribution network reconfiguration for loss reduction problem with a certain degree of success. But some problems, specially related to a codification that is able to represent and work with a complex multiconstraint and combinatorial problem such as this one, have prevented the use of the full potential of these algorithms to find quality solutions for large systems with minor computational effort. This paper proposes a solution to this problem, with a new codification and using an efficient way for implementing the operator of recombination to guaranty, at all times, the production of new radial topologies. The algorithm is presented and tested in a real distribution system, showing excellent results and computational efficiency.      

Online optimal and adaptive integral tracking control for varying discrete-time systems using reinforcement learning

Conventional closed-form solution to the optimal control problem using optimal control theory is only available under the assumption that there are known system dynamics/models described as differential equations. Without such models, reinforcement learning (RL) as a candidate technique has been successfully applied to iteratively solve the optimal control problem for unknown or varying systems. For the optimal tracking control problem, existing RL techniques in the literature assume either the use of a predetermined feedforward input for the tracking control, restrictive assumptions on the reference model dynamics, or discounted tracking costs. Furthermore, by using discounted tracking costs, zero steady-state error cannot be guaranteed by the existing RL methods. This article therefore presents an optimal online RL tracking control framework for discrete-time (DT) systems, which does not impose any restrictive assumptions of the existing methods and equally guarantees zero steady-state tracking error. This is achieved by augmenting the original system dynamics with the integral of the error between the reference inputs and the tracked outputs for use in the online RL framework. It is further shown that the resulting value function for the DT linear quadratic tracker using the augmented formulation with integral control is also quadratic. This enables the development of Bellman equations, which use only the system measurements to solve the corresponding DT algebraic Riccati equation and obtain the optimal tracking control inputs online. Two RL strategies are thereafter proposed based on both the value function approximation and the Q-learning along with bounds on excitation for the convergence of the parameter estimates. Simulation case studies show the effectiveness of the proposed approach.     

基于改进遗传退火算法的输配电网协调规划方法

针对目前对输配电网协调性考虑不足的问题,考虑机组间负荷优化分配因素,引入耗量成本,构建了包含输配电网协调性指标的电网评价指标体系。结合电网经济性指标和可靠性指标,通过优化算法选出综合性能最优的输配电网规划方案。在解决遗传算法"早熟"、易陷于局部最优等问题的基础上,提出了一种优化算法并将其应用于考虑输配网协调性的电网规划问题中。仿真计算结果表明,所提出的方法是可行、高效的。     

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     

基于Shapley值抽样估计法的风-光-水互补发电增益分配方法

在多利益主体市场环境下,建立公平、合理、高效的互补发电增益分配方法是保障风-光-水互补发电优化调度能够实施的关键.针对风-光-水互补发电的特点,在建立考虑随机性、波动性和互补效益的风-光-水互补发电优化调度模型的基础上,提出风-光-水互补发电增益量化方法;在经典Shapley值(SV)法的基础上,提出基于Shapley值抽样估计(SSVE)法的风-光-水互补发电增益分配方法,并提出基于强化学习(RL)的样本量分配法,以提高SSVE法的精确性和计算效率.算例结果验证了采用RL样本量分配法的SSVE法的有效性,且SSVE法能够有效减少计算量以及解决经典SV法的组合爆炸问题.     

强化学习理论在电力系统中的应用及展望

强化学习理论是人工智能领域中机器学习方法的一个重要分支,也是马尔可夫决策过程的一类重要方法.所谓强化学习就是智能系统从环境到行为映射的学习,以使奖励信号(强化信号)函数值最大.强化学习理论及其应用研究近年来日益受到国际机器学习和智能控制学术界的重视.系统地介绍了强化学习的基本思想和算法,综述了目前强化学习在安全稳定控制、自动发电控制、电压无功控制及电力市场等方面应用研究的主要成果与方法,并探讨了该课题在电力系统运行控制中的巨大潜力,以及与经典控制、神经网络、模糊理论和多Agent系统等智能控制技术的相互结合问题,最后对强化学习在电力科学领域的应用前景作出了展望.     

基于阻抗补偿的三相四线制配电网前推回代潮流算法

在三相四线制配电网中,中性线重复接地对前推回代潮流算法存在收敛性问题。分析了中性线重复接地导致前推回代潮流算法难收敛的原因,提出一种基于阻抗补偿的三相四线制配电网前推回代潮流算法,改善了潮流计算的收敛性,并保证补偿前后的潮流结果不变。该方法通过在接地电阻和重复接地点之间补偿一对大小合适且取值相反的阻抗,减小了不动点迭代雅可比矩阵的谱半径,使之满足压缩映射的条件,从而保证潮流计算收敛。算例验证了所提方法的正确性。     

配电网综合规划模型与算法的研究

该文建立了综合考虑变电站规划和配电网线路规划的数学模型，该模型以总体负荷矩最小为目标函数，以各种可能的电力约束为约束条件。针对模型，提出了两层改进的遗传算法与一层最短路算法相互嵌套的新算法。该算法可以灵活地用于配电网综合规划问题的求解，其子算法也可用于馈线路径规划问题及配电网网络重构问题的求解。经过实例应用，证明该算法能够有效地兼顾求解可接受的最优解与求解时间之间的要求。该文提出的规划模型及相应的求解方法，对实际的配电网规划项目以及配电网规划软件的编制都有一定的指导意义。     

配电网络重构的研究

总结了求解配电网重构的各种方法。指出由于组合数学的特性,数学优化理论不适用于配电网重构;基于模拟退火方法的配电网重构算法可以获得全局最优解,但存在算法依赖参数和计算量大的缺点;基于人工神经网络的算法,其精度取决于样本,获得完整样本困难,而且训练样本的时间较长;遗传算法的很多特点适于求解配电网重构的问题,如果能结合配电网重构的特点对算法收敛性进一步研究,提高其速度,则将在配电网重构中得到更好的应用;模糊数学和专家系统必须依赖于其他技术的发展;最优流模式和基于开关交换的算法不能保证得到全局最优解,但与启发式规则结合后,可以较快地得到满意的结果,是目前解决配电网重构的有效算法。     

考虑负荷概率分布的随机最优潮流方法

针对考虑负荷概率分布的随机最优潮流问题，建立了相应的机会约束规划模型。基于经典最优潮流问题的内点法和随机潮流方法，设计了求解该模型的一种启发式方法。该方法以确定性负荷最优潮流计算结果为基础，通过求取受机会约束的变量的概率分布判断概率约束是否满足。若不满足，则根据变量分布和等效的机会约束，形成新的上下限约束，继续计算负荷为期望值时的最优潮流，直至所有概率约束满足。对5节点和IEEE 118节点系统的测试表明该算法的有效性。     

Reinforcement Learning approaches to Economic Dispatch problem

This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then two variants of RL algorithms are presented. A third algorithm which takes into consideration the transmission losses is also explained. Efficiency and flexibility of the proposed algorithms are demonstrated through different representative systems: a three generator system with given generation cost table, IEEE 30 bus system with quadratic cost functions, 10 generator system having piecewise quadratic cost functions and a 20 generator system considering transmission losses. A comparison of the computation times of different algorithms is also carried out.     

A reinforcement learning approach to automatic generation control

This paper formulates the automatic generation control (AGC) problem as a stochastic multistage decision problem. A strategy for solving this new AGC problem formulation is presented by using a reinforcement learning (RL) approach. This method of obtaining an AGC controller does not depend on any knowledge of the system model and more importantly it admits considerable flexibility in defining the control objective. Two specific RL based AGC algorithms are presented. The first algorithm uses the traditional control objective of limiting area control error (ACE) excursions, where as, in the second algorithm, the controller can restore the load-generation balance by only monitoring deviation in tie line flows and system frequency and it does not need to know or estimate the composite ACE signal as is done by all current approaches. The effectiveness and versatility of the approaches has been demonstrated using a two area AGC model.     

配电网降压节能装置中电容器组的选址定容问题研究

近年来国内电网在用电峰荷时期时常出现供电容量不足的情况,降压节能技术能够有效解决该问题。对此提出一种针对配电网降压节能装置中并联电容器组的多目标选址定容方法。该方法以系统电压偏差以及有功网络损耗最小为目标,考虑电容器组的安装成本,通过带精英策略的快速非支配排序遗传算法(NEGA-II)与基于满意度的模糊聚类方法的有机结合,寻求并联电容器组配置的最优解。仿真算例证明所提出的方法能够有效实现目标的优化,为降压节能技术的实现提供良好的电压无功条件。     

用改进遗传算法求解水火电力系统的有功负荷分配

水火电力系统的短期有功负荷分配在电力系统的经济运行中发挥着重要的作用，从本质上讲它是一个具有复杂约束条件的非线性大型动态优化问题，处理起来十分复杂，采用传统优化算法难以得到理想的结果。文中提出对决策变量直接采用浮点数编码技术，并根据给定的概率分布进行杂交操作和实施参数变异的改进遗传算法（RGA），用以求解此问题，最后用具体算例对该方法进行了验证。通过与二进制编码遗传算法所得结果进行对比分析，表明此法计算结果正确合理，收敛速度快，求解精度高。这也说明RGA不失为一种行之有效的优化方法，具有应用潜力。     

考虑环网检测的配电网拓扑重构遗传算法

提出了一种基于遗传算法的配电网自动优化重构方法。由于配电网拓扑约束的限制(连通辐射状网络),遗传算法在解决配电网重构问题过程中,可能产生大量不可行解。针对该问题,首先提出了一种快速"环网和孤立节点"检测算法,可检测进化过程中产生的解是否满足配电网拓扑约束的要求;其次,提出了一种基于拓扑搜索的初始种群自动形成算法,该算法除可用于初始种群的形成外,还可用于生成新的解以替代遗传进化过程中产生的不可行解。为了提高遗传算法的收敛性能,提出了一种定向变异的遗传算子,该算子不仅可保证经变异运算后产生的个体满足配电网拓扑约束的要求,而且可保证该个体为本次变异操作可产生的最优解。该算法的提出提高了遗传算法解决重构问题的自动化程度和收敛性能。以IEEE 33节点、PGE 69节点和119节点系统为例对方法进行了测试,验证了该方法的有效性。     

基于事例推理模糊神经网络的中压配电网短期节点负荷预测

根据认知科学理论，在并行分布处理(PDP)模型基础上，提出了一种基于事例推理的模糊神经网络(CBRFNN)。分析了CBRFNN的原理，定义了CBRFNN的基本结构，并提出一种混合(有监督／无监督)学习算法，使得CBRFNN具备了很好的泛化能力。CBRFNN中的所有节点通过快速、增量式的学习过程动态生成，并可通过网络自组织来有效抵御坏数据的影响。所提方法很好地解决了中压配电网短期节点负荷预测这类信息不完备、不精确问题。     

Algorithm tuners for PSO methods and genetic programming techniques for learning tuning rules

In recent years, improvements in processing power have allowed the application of optimization methods to complicated large optimization problems. Among these methods, heuristic optimization techniques such as particle swarm optimization (PSO) have been a particular focus of attention because of their simplicity, performance, and easy software implementation. However, there is no solid theoretical foundation for analyzing the convergence of these algorithms, and in practice, their rate of convergence is often determined by the choice of parameters. For this reason, the algorithm's parameters must be tuned appropriately for each new optimization problem we want to solve, and in some cases the parameters must be varied as the algorithm is updated. In this paper, we combine a feedback element as an algorithm tuner with an original algorithm; the resulting algorithm is applied to the optimization problem in question, and we use genetic programming (GP) to generate tuning rules to automatically tune the algorithm, according to its current state, as the algorithm is updated. More specifically, we adopt PSO as a heuristic optimization method, and we augment PSO by using GP as a meta‐algorithm to solve the learning problem of automatically generating tuning rules for the parameters in the PSO algorithm. This leads to the proposed method for generating parameter tuning rules to solve optimization problems more efficiently. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms

This article applies the grey wolf optimizer and differential evolution (DE) algorithms to solve the optimal power flow (OPF) problem. Both algorithms are used to optimize single objective functions sequentially under the system constraints. Then, the DE algorithm is utilized to solve multi-objective OPF problems. The indicator of the static line stability index is incorporated into the OPF problem. The fuzzy-based Pareto front method is tested to find the best compromise point of multi-objective functions. The proposed algorithms are used to determine the optimal values of the continuous and discrete control variables. These algorithms are applied to the standard IEEE 30-bus and 118-bus systems with different scenarios. The simulation results are investigated and analyzed. The achieved results show the effectiveness of the proposed algorithms in comparison with the other recent heuristic algorithms in the literature.