多机电力系统分散鲁棒励磁控制器的研究

提出了一种能够有效提高多机电力系统稳定性的分散鲁棒控制策略。采用发电机机端电压的相角和幅值表示发电机与系统其他部分的相互联系,并且针对由调速器的不稳定调节造成的发电机输入机械功率的波动,以及由电网中无功功率和有功功率的突然变化引起的发电机机端电压幅值与相角的扰动,应用鲁棒控制原理设计励磁控制器,从而实现多机电力系统的分散协调控制。仿真研究的结果表明了这种控制策略的有效性。     

A decentralized control system for stabilizing multimachine power systems

A decentralized control system is studied for stabilizing multimachine power systems. A longitudinal power system with three areas, each having one machine, is considered in this study. A decentralized control design method is proposed, which is based on the optimal regulator theory. First a centralized control system is designed without any consideration on whether state variables are all available or not. Second a pseudo-decentralized control system is designed by omitting control gains corresponding to state variables which give hardly any effects on the power system stability. It is found that only one variable of phase angle of each machine is absolutely necessary for the pseudo-decentralized control system. This leads to an idea based on power system engineering, that is to say, new variables of tieline power flow are introduced in the decentralized control system design to substitute for the phase angle of each machine. Thus a decentralized control system for power system stability can be designed using the new variables of tieline power flow. It is demonstrated from simulation studies that the decentralized control system improves even longitudinal power system stability as well as the centralized control system.     

单一电能质量扰动的分类识别研究

电能质量扰动的分类识别对电能质量综合治理具有重要意义,为此提出了一种基于粒子群优化极限学习机的电能质量扰动分类新方法。利用小波变换将扰动信号做10层分解,提取有效区分扰动信号类型层数的能量差、能量差平均值及能量差的标准差作为特征向量,并将扰动信号与正常信号的均方根作为补充,减少输入向量维度。提出采用极限学习机训练误差作为粒子群的适应度函数来优化隐含层神经元个数,在提升分类速度的基础上保持较高的分类精度。经仿真验证表明,该方法能够准确有效地识别常见的7种扰动类型,相比于传统的BP神经网络具有较高的分类速度。     

汽轮发电机组的非线性分散干扰抑制控制

将非线性鲁棒控制理论应用于多机电力系统的汽轮调速控制系统。在耗散系统理论框架下,用递推设计的方法,构造汽轮发电机组的能量存储函数,以避免求解HJI不等式,从而得到多机系统汽轮调速的非线性L₂增益干扰抑制控制规律。控制策略的所有变量都是可量测的,而且控制量只与本机组的状态量有关,与其他机组的状态量和输电网络的参数无关,因而具有分散性。4机系统仿真结果表明,该控制规律可以提高电力系统的暂态稳定性。     

Interconnected multi-machine powersystem stabilizer design using whaleoptimization algorithm

The role of Power System Stabilizer (PSS) in the power system is to provide necessary damping torque to the system in order to suppress the oscillations caused by a variety of disturbances that occur frequently and maintain the stability of the system. In this paper, a PSS design technique is proposed using Whale Optimization Algorithm (WOA) by considering eigenvalue objective function. Two bench mark multi machine test systems: three- generator nine- bus system, two- area four- generator inter connected system working on various operating conditions are considered as case studies and tested with the proposed technique. Extensive simulation results are obtained and effectiveness of proposed WOA-PSS are compared with well - known PSO and DE based stabilizers under several disturbances.     

Power systems operation using particle swarm optimization technique

This paper presents a proposed technique for solving different optimization problems using particle swarm optimization (PSO) technique as a modern optimization technique. The security constrained optimal active power dispatch is solved by a proposed optimal effective localized area (OELA) in large-scale power system at different operating conditions. However, the boundaries of this area can be increased or decreased depending on the amount and type of the operation problems as well as the control action requirements to remove these problems. Hence, minimum control variables are adjusted in a small-localized area to steer the system to secure and reliable operation condition. The optimal operation of ready reserve is introduced using an efficient proposed procedure considering the security constraints of the transmission lines power flows. Different emergency condition problems are solved using the OELA applied to different standard test systems.     

分散励磁与超导储能装置的干扰抑制控制

提出了一种新的设计多机电力系统中励磁和超导储能装置的分散L₂增益干扰抑制控制器的方法。文中首先建立了含超导储能装置的多机电力系统的动态模型，继而利用递推方法设计励磁和超导储能装置的增益干扰抑制控制器，所得控制器可以利用本地测量量实现。计算机仿真结果说明了所设计的控制器可以提高系统的暂态稳定性，显著改善系统的动态性能。     

基于虚拟机动态迁移的电力仿真云计算平台资源调度策略

针对目前电力系统仿真计算实时性与计算精度要求高、平台可扩展性差以及资源利用率低等特点,首先给出了一种基于开源基础设施平台OpenStack和并行处理框架Hadoop的电力仿真云计算平台架构,能够以较低成本实现动态扩展、高效计算和海量存储等功能。其次,结合电力系统仿真任务特点,给出了一种基于多目标粒子群优化(PSO)算法的虚拟机迁移策略,实现电力仿真云计算平台资源调度。虚拟机迁移过程采用指数平滑预测模型确定热点,选择虚拟机时综合考虑迁移速度和效果两个因素,利用多目标PSO算法搜索目标节点,使得电力系统仿真计算在保证服务质量的同时兼顾高资源利用率和低运行成本的优势。最后,通过CloudSim进行仿真实验,将所提算法与贪心迁移算法和顺序放置非迁移算法进行对比。实验表明,所提算法在服务等级协议(SLA)违背率、剩余资源率、能耗以及虚拟机迁移次数等指标上均优于其他算法,验证了基于虚拟机动态迁移的多目标PSO算法在电力仿真云计算平台资源调度中的优势和可行性。     

电力系统非线性鲁棒自适应分散励磁控制设计

利用反步法设计了多机电力系统中的非线性鲁棒自适应励磁控制方案,控制目标是调节发电机功角和频率至稳态运行点的极小领域,并使闭环系统对发电机阻尼系数和电抗参数的不确定性具有自适应能力,且对模型误差和外部有界干扰具备鲁棒性,同时保证各控制器是分散化和本地化的。采用4机系统进行的数字仿真结果表明,实施此方案能有效地提高发电机的功角稳定性。     

飞轮储能系统多PI控制器参数优化

对飞轮储能系统(FESS)的有功和无功PI控制器参数优化进行了研究,提出了应用加入模拟退火思想的改进粒子群优化(AIPSO)算法优化FESS有功和无功多PI控制器参数.该算法在混沌初始化、迭代中加入混沌扰动和自适应调整惯性权重系数的改进粒子群优化算法基础上,引入模拟退火思想,既能限制位置更新,又能跳出局部最优解,具有更...     

Adaptive tuning of power system stabilizers using radial basis function networks

A novel approach for on-line adaptive tuning of power system stabilizer (PSS) parameters using radial basis function networks (RBFNs) is presented in this paper. The proposed RBFN is trained over a wide range of operating conditions and system parameter variations in order to re-tune PSS parameters on-line based on real-time measurements of machine loading conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious RBFN model. The simulation results of the proposed radial basis function network based power system stabilizer (RBFN PSS) are compared to those of conventional stabilizers in case of a single machine infinite bus (SMIB) system as well as a multimachine power system (MMPS). The effect of system parameter variations on the proposed stabilizer performance is also examined. The results show the robustness of the proposed RBFN PSS and its ability to enhance system damping over a wide range of operating conditions and system parameter variations. The major features of the proposed RBFN PSS are that it is of decentralized nature and does not require on-line model identification for tuning process. These features make the proposed RBFN PSS easy to tune and install.     

Utility interactive system using induction machines to supply mechanical power stored in solar batteries

Solar energy is a very attractive potential power source because of its clean and inexhaustible supply. However, the electric power that can be supplied by solar batteries is variable because it depends on weather conditions. Therefore, the supply is either stabilized by storing power in batteries, or it is added to the general utility network via an inverter. Such systems, however, have cost- and maintenance-related problems. This paper proposes a new utility interactive system that is composed of induction machine A connected to a solar battery through a general purpose inverter and another induction machine B interconnected to the utility line. The frequency of the inverter driving machine A is selected such that solar power can be maximally utilized. This system can supply a stable mechanical load with the required power by parallel operation of two induction machines even in rainy weather and, moreover, recharge the solar battery for the utility line in good weather. This proposed system is economical and useful as a small decentralized power supply because a power filter for rejecting high frequency noises and a detector for a power failure are not necessary. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 118 (2): 70–78, 1997     

基于SA-PSO算法优化CNN的电能质量扰动分类模型

针对传统电能质量扰动分类模型中扰动特征复杂、识别步骤繁琐的问题,提出了一种通过模拟退火(SA)算法与粒子群优化(PSO)算法相结合来优化卷积神经网络(CNN)的电能质量扰动分类模型。将CNN卷积层中的二维卷积核替换成一维卷积核;采用SA算法对PSO算法进行改进,规避PSO算法陷入局部最优的困境;采用改进后的PSO算法对CNN进行参数寻优;利用优化CNN提取和筛选合适的特征,根据这些特征利用分类器得到最终分类结果。通过算例分析得出,使用基于SA-PSO算法优化的CNN的电能质量扰动分类模型能精确地识别出电能质量扰动信号。     

A particle swarm optimization for reactive power and voltagecontrol considering voltage security assessment

This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. The method considers voltage security using a continuation power flow and a contingency analysis technique. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results     

Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization

Power system congestion is a major problem that the system operator (SO) would face in the post-deregulated era. Therefore, investigation of techniques for congestion-free wheeling of power is of paramount interest. One of the most practiced and an obvious technique of congestion management is rescheduling the power outputs of generators in the system. However, all generators in the system need not take part in congestion management. Development of sound formulation and appropriate solution technique for this problem is aimed in this paper. Contributions made in the present paper are twofold. Firstly a technique for optimum selection of participating generators has been introduced using generator sensitivities to the power flow on congested lines. Secondly this paper proposes an algorithm based on particle swarm optimization (PSO) which minimizes the deviations of rescheduled values of generator power outputs from scheduled levels. The PSO algorithm, reported in this paper, handles the binding constraints by a technique different from the traditional penalty function method. The effectiveness of the proposed methodology has been analyzed on IEEE 30-bus and 118-bus systems and the 39-bus New England system.      

应用粒子群优化算法求解可用输电能力

在电力市场环境下,诸多问题(例如实时电价、网络阻塞等)都需要最优潮流作为理想的工具.本文以最优潮流为基础,应用一种简单有效、且收敛性很好的演化计算算法--粒子群优化算法(PSO)进行可用输电能力(ATC)问题的求解.根据约束条件的越限量大小,动态地调整罚函数,在保证全局搜索能力的基础上改进了收敛速度.应用此算法对IEEE-30节点系统进行了可用输电能力计算,并与传统的最优潮流算法进行了比较,结果表明该算法的有效性,具有实用意义.     

Distribution Systems Reconfiguration using a modified particle swarm optimization algorithm

This paper presents the particle swarm optimization (PSO) algorithm for solving the optimal distribution system reconfiguration problem for power loss minimization. The PSO is a relatively new and powerful intelligence evolution algorithm for solving optimization problems. It is a population-based approach. The PSO is originally inspired from the social behavior of bird flocks and fish schools. The proposed PSO algorithm in this paper is introduced with some modifications such as using an inertia weight that decreases linearly during the simulation. This setting allows the PSO to explore a large area at the start of the simulation. Also, a modification in the number of iterations and the population size is presented. Comparative studies are conducted on two test distribution systems to verify the effectiveness of the proposed PSO algorithm. The obtained results are compared with those obtained using other techniques in previous work to evaluate the performance.     

Parallel inverter control using different conventional control methods and an improved virtual oscillator control method in a standalone microgrid

This paper develops a multi-timescale coordinated operation method for microgrids based on modern deep reinforcement learning. Considering the complementary characteristics of different storage devices, the proposed approach achieves multi-timescale coordination of battery and supercapacitor by introducing a hierarchical two-stage dispatch model. The first stage makes an initial decision irrespective of the uncertainties using the hourly predicted data to minimize the operational cost. For the second stage, it aims to generate corrective actions for the first-stage decisions to compensate for real-time renewable generation fluctuations. The first stage is formulated as a non-convex deterministic optimization problem, while the second stage is modeled as a Markov decision process solved by an entropy-regularized deep reinforcement learning method, i.e., the Soft Actor-Critic. The Soft Actor-Critic method can efficiently address the exploration–exploitation dilemma and suppress variations. This improves the robustness of decisions. Simulation results demonstrate that different types of energy storage devices can be used at two stages to achieve the multi-timescale coordinated operation. This proves the effectiveness of the proposed method.     

多机电力系统励磁控制的人工神经网络逆系统方法

基于各发电机的完整的实用三阶模型，通过将发电机内部的不可测变量转化为发电机端的可测变量，在未做任何近似或简化的情况下，实现了仅采用本地可测量的多机系统中各发电机励磁控制的精确线性化。在具体的励磁控制器设计中，选取机端电压作为被控量再附加电力系统稳定器(PSS)环节进行辅助控制，使得同时满足系统对电压稳定性和功角稳定性的控制要求；采用人工神经网络(ANN)逆系统方法实现精确线性化，避免了对系统精确数学模型的依赖，使文中方法更实用。针对一个2区域4机系统的仿真结果表明，安装ANN逆励磁控制器可以极大地提高被控机组的稳定性能，同时，全系统的稳定性也可以得到明显的提高。     

A new stabilizer design technique for multimachine power systems

A simple and systematic procedure for decentralized stabilizer design for multimachine systems is presented in this paper. The procedure first selects the most effective location of a stabilizer according to a machine stability index. The parameters of the stabilizer are then determined by a decentralized feedback control scheme. This process is carried out for one stabilizer at a time. The resultant overall improvement of the system stability, measured by a system stability index, is used to decide how many stabilizers are required for the system. The proposed technique is illustrated by a test system. Both eigenvalue analysis and nonlinear simulation are performed to investigate the effectiveness of the technique.