基于分解的智能优化算法在电力系统无功优化中的仿真研究

电力系统无功优化是一个多目标优化问题,在传统无功优化模型的基础上,建立了以有功网损最小和无功补偿成本最少为目标函数的多目标无功优化仿真模型.采用MOEAD算法求解多目标无功优化模型.仿真结果表明,采用MOEAD算法求解多目标无功优化问题,能够有效降低有功网损,减少无功补偿成本,而且计算速度快、计算性能好.     

基于多目标粒子群算法的高维多目标无功优化

提出一种高维多目标电力系统无功优化模型。相比于传统的电力系统无功优化模型,该模型能够在无功优化中同时兼顾系统的有功损耗、电压水平、静态电压稳定性以及供电能力。针对已有的求解多目标无功优化模型的算法应用于求解所提模型时存在的局限性,进一步引入一种基于帕雷托熵的高维多目标粒子群优化算法并加以改进,使得该算法能够有效求解高维多目标优化问题。最后,利用IEEE-39节点系统验证了所提模型和求解算法的正确性和有效性。仿真结果表明,在传统的多目标无功优化模型中引入系统供电能力,能够在不恶化其他目标函数优化效果的情况下,使系统的供电能力得到提高。     

计及控制设备动作次数约束的三阶段动态无功优化算法

为了避免控制设备频繁操作,动态无功优化模型需考虑无功补偿装置投切开关及变压器抽头的允许动作次数约束。但是,动态无功优化属于大规模、多时段、强耦合的混合整数非线性规划问题,对其直接求解是困难的。建立了以有功网损最小为目标函数的动态无功优化模型,并提出一种实用的三阶段动态无功优化算法,该算法的核心是一种具有多项式计算复杂度的前推-回推式动态规划算法。将计及控制设备动作次数约束的动态无功优化问题的求解分解为多个时间断面的连续无功优化计算、理想无功补偿装置无功补偿功率曲线和变压器变比曲线的阶梯化以及在确定各个时段的无功补偿容量和变压器变比情况下的连续无功优化计算3个阶段。对IEEE 30节点系统和某实际区域电网进行测试,结果验证了所提算法的合理性和实用性。     

低压配电网无功补偿分散配置优化方法

针对低压配电网集中配置无功补偿装置无法解决电压质量问题,提出了一种低压配电网无功补偿分散配置优化方法。优化模型的目标函数为最小化配电网的网损,约束条件包括各个节点电压的上下限和无功补偿点补偿容量的上下限。先以所有负荷节点作为候选补偿点,采用内嵌二次罚函数处理离散变量的非线性原对偶内点法求解优化模型,得到理想分散无功补偿配置方案,同时获得配电网的大致无功补偿容量需求,在此基础上综合考虑无功补偿装置的投资费用以及便于装置的运行管理等要求,选定几个重要补偿点再次求解优化模型,获得最终的实用分散无功补偿配置方案。对几个实际低压配电网台区进行的分析计算验证了方法的有效性和实用性。     

基于粒子群无功优化的网损降低技术研究

在传统无功优化模型上,建立了以网损最小和电压稳定裕度最大为目标函数的多目标优化模型,通过对目标函数加权建立妥协模型,使多目标函数问题转化为单目标函数问题,并采用粒子群算法求解无功优化问题,通过对IEEE30节点系统的算例分析,表明了采用多目标无功优化模型和粒子群算法能够在保证各节点电压不越限的情况下,有效地降低系统网损...     

0.4kV配电网无功补偿多点配置研究

针对低压配电网无功补偿问题,建立了以最小网损为目标函数的无功补偿优化模型,运用改进内点法对优化模型进行求解.并对在配变低压侧根部一点配置补偿装置无法解决的低压配电网网损等现象,提出了在配变低压侧三相线路上多点分布配置无功补偿装置的优化方式,先以所有负荷节点做为待补偿点用改进内点法对模型进行求解,得到理想配置方案后,考虑到补偿的经济性及检修方便等因素,选定几个最终补偿点进行无功补偿装置配置.对实际的低压配电网进行了分析计算验证方法的可行性和实用性.     

基于黄金分割的混沌粒子群优化算法在配电网无功规划中的应用

首先以有功损耗功率最小作为目标函数,将节点电压越限和发电机无功出力越限作为罚函数,建立无功补偿在配电网中优化配置的数学模型。然后设计基于黄金分割的混沌粒子群优化算法对上述模型进行求解。该算法通过黄金分割评判准则,按照适应度的高低,将粒子群分成标准粒子和混沌粒子两部分,同时解决了粒子群优化过程中容易陷入局部最优和混沌算法重复搜索部分解的问题,从而可以更有效地搜索到全局最优解,成功地提高了无功优化问题的求解速度,使算法能更好地适应问题的求解。算例结果表明,该方法技术上可行且效果较好。     

基于内点法和改进粒子群算法的无功优化混合策略

基于内点法与粒子群算法,提出了一种混合策略来求解电力系统无功优化问题。根据优化变量的不同性质将无功优化问题分解为离散优化和连续优化两个子问题,采用改进的粒子群优化算法和内点法交替求解,使两者的优化结果互为基础,提高了混合策略的整体寻优效率;根据粒子运动趋势及目标函数中网损与节点电压无功的相关性,对基本粒子群算法进行改进,自适应调整惯性权重和罚因子;以IEEE30节点系统和某实际地区电网作为试验系统,验证了该算法的正确性和有效性。     

用于改进潮流计算中PV-PQ节点类型转换逻辑的非线性规划模型

由于无功容量的限制,电力系统潮流计算中经常要进行PV-PQ节点类型转换,传统转换逻辑容易引起数值振荡,甚至错误识别节点类型而导致潮流发散。针对该问题,文中提出一种非线性规划模型以改进PV-PQ节点类型转换逻辑,采用现代内点算法求解。模型以无功容量作为约束条件,通过目标函数控制系统电压,避开了节点类型转换。同时,无功的优化配置可以增强维持系统电压的能力,增大无功裕度;结合现代内点法求解,模型表现出了处理大规模和重负荷系统的优良性能。对IEEE-118系统和一个实际系统的仿真验证了该模型的优势。     

基于粒子群算法的配电网无功补偿优化规划

无功补偿优化配置在电力系统规划设计中有着重要的作用.文中建立了针对10 kV配电网的无功补偿优化配置的数学模型,以配网年电能损失费用与折合为等年值的无功补偿设备的投资费用之和最小为目标函数,以无功平衡、电压合格等为约束条件.求解方法上,采用了改进的粒子群算法,使算法能更好地适应问题的求解.算例结果表明,该方法技术上可行且具有较好的经济性.     

Fuzzy Genetic Algorithm Approach for the Optimal Placement of Flexible AC Transmission Systems Devices in a Power System

Abstract—This article presents a novel approach for optimal flexible AC transmission systems devices planning in an interconnected power system under different loading conditions. The static VAR compensator and thyristor-controlled series capacitor are two types of flexible AC transmission systems devices considered for optimal power system operation. In the proposed approach, a fuzzy membership function is used to determine weak nodes in the power system for the placement of static VAR compensators as a flexible AC transmission systems device. The thyristor-controlled series capacitor is the other type of flexible AC transmission systems devices for which its positions are determined by the reactive power flow in lines. The genetic algorithm is used for the optimal setting of the power system variables, including flexible AC transmission systems devices. The proposed technique is compared with other optimization methods using different globally accepted evolutionary algorithms where the nodes or point of VAR compensation is determined by eigenvalue analysis, and the amount of flexible AC transmission systems devices is determined by evolutionary techniques, such as the genetic algorithm, differential evolution, and particle swarm optimization. The superiority of the proposed fuzzy-based optimization approach is established by the results and comparative analysis with other methods.     

基于改进PSO算法在含风电场的电力系统无功优化控制

含风电场的电力系统无功优化是一种具有多状态、多约束条件的非线性规划问题.针对其存在易陷入局部最优解的缺点,提出了改进的PSO算法.该算法改变了初始化方法和粒子更新方法,并在算法后期引入变异因子.在放射状配电网络系统的仿真计算中,改进PSO算法与遗传算法相比较,结果表明,改进PSO算法可在较短时间内取得更好的优化效果.     

Worst-case identification of reactive power margin and local weakness of power systems

This paper has the main objectives of evaluating the worst-case VAR margin of power systems and identifying the most vulnerable busbars. One possible method of achieving these objectives is to progressively increase system-wide reactive power (VAR) demands on power systems and to perform loadflow after each VAR increase. The process is continued up to the point where loadflow diverges. This method is inefficient and subjective, and would most likely fail to reach critical stability due to numerical problems. A more sophisticated method is to directly locate critical stability by solving an optimization problem. By evaluating the system VAR margin, traditional optimization approaches usually pre-specifies a disturbance scenario, which distributes the VAR increases for stressing the power system. However, different disturbance scenarios will stress the power system towards different critical points, which will lead to different VAR margins. To estimate the system's capability to withstand VAR disturbance, the worst disturbance scenario should be identified. Traditional optimization approaches did not usually lead to the worst case. Worst-case identification of disturbance scenario is treated in this paper as a separate optimization problem with the VAR disturbance scenario taken as the decision variables. Apart from providing the worst-case VAR margin and the associated disturbance scenario, the proposed method also highlights local weakness of the study power system and relative effectiveness of control measures. The paper presents a systematic method of worst-case identification by incorporating genetic algorithm (GA) and nonlinear programming techniques in two levels. In order to achieve an accurate and reliable estimation, the method performs feasibility checks during optimization on VAR disturbance scenarios, generator reactive limits, and voltage constraints at regulated busbars.     

Static VAR Compensator Damping Controller Design Based on Flower Pollination Algorithm for a Multi-machine Power System

A new optimization technique known as the flower pollination algorithm is suggested in this article for robust tuning of a static VAR compensator to mitigate power system oscillations. The flower pollination algorithm is based on the properties of flowering plants. The tuning of a static VAR compensator controller is established as an optimization process. The flower pollination algorithm is applied to find out the optimal parameters of a static VAR compensator by diminishing certain performance index. The behavior of the suggested algorithm has been appreciated with the behavior of the genetic algorithm and bacteria foraging to certify the notability of the suggested flower pollination algorithm in designing a static VAR compensator. Simulation results of the flower pollination algorithm based static VAR compensator provide greater attenuation for various loading conditions than the optimized static VAR compensator by genetic algorithm and the optimized static VAR compensator by bacteria foraging. The results of the developed flower pollination algorithm based static VAR compensator are observed via time-domain analysis, eigenvalues, and various indices. Also, the economic value of this work is confirmed.     

量子遗传算法优化RBF神经网络及其在热工辨识中的应用

量子遗传算法是基于量子计算原理的概率优化方法,在量子门更新过程中,旋转角的大小直接影响优化的结果和进化的速度。文中针对模糊量子遗传算法(FQGA)容易导致系统陷入局部最优的缺点,将量子衍生交叉算法的思想引入FQGA,提出了一种新的量子遗传算法。同时利用该方法构造径向基函数神经网络进行非线性系统辨识。其特点是通过这种新的量子遗传算法,实现对RBF神经网络权值、宽度和中心位置等有关参数的估计。其速度快、精度高。通过RBF神经网络有效地完成了对非线性系统的辨识。对典型非线性函数辨识的测试表明：该方法有效地提高了量子遗传算法的计算精度和收敛速度。同时利用该方法设计了一种通用的热工对象模型辨识神经网络算法,编制了专用的模型识别软件,对某电厂循环流化床锅炉一次风对床温的动态特性进行辨识,结果表明该方法是一种精度比较高的辨识算法。     

A Gravitational Search Algorithm Based Static VAR Compensator Switching Function Optimization Technique for Minimum Harmonic Injection

Switching function optimization for minimum source harmonic injection for a static VAR compensator is presented. The static VAR compensator is configured with a fixed capacitor and insulated-gate bipolar transistor controlled reactor. The switching function is optimized for minimum source harmonic injection considering the desired fundamental voltage across load terminals. A gravitational search algorithm is employed for this purpose. It is observed that the proposed switching scheme with two different switching angles per half-cycle provides lower source harmonic injection compared to conventional switching, hence improving the source current harmonics. The switching angles are computed off-line using the gravitational search algorithm for varying modulation indices considering the minimum total harmonic distortion of the reactor voltage. The switching angles are stored in a processor as a function of the modulation index for on-line application using a piecewise mixed model approximation technique for low memory usage. It is observed that the proposed switching improves the source current total harmonic distortion on an average of 4 to 5% over most of the operating range compared to conventional switching without optimization. Various simulation and experimental results are presented on different loads to validate the proposed concept.     

A VAR planning method with multiple power-flow states

The goal of reactive power (VAR) planning is to find the minimum cost installation plan of new reactive power sources so that the system voltage is maintained within an acceptable level. The consideration of multiple contingency states, together with the discrete nature of VAR facilities, creates a large-scale nonlinear mixed-integer programming (MIP) problem. To overcome the discrete nature of VAR facilities, an approximate method for the MIP problem is employed since the method is linear-programming based and thus efficient for large-scale problems. To treat the multiple contingencies, a resource directive decomposition approach is used in the proposed algorithm. If the number of installed VAR sources is fixed, the overall problem can be reduced to independent subproblems. Then subproblems are coordinated to give a VAR installation pattern in which installation cost becomes less than before. The algorithm proposed is tested for a 135-node real-size system and the results show the validity and effectiveness of the algorithm.     

基于典型运行方式的成都电网电压无功最优控制

成都地区电网作为受端网络在运行中面临电压-无功协调控制的难题,原有的电压-无功运行模式只能满足局部电压控制的要求,尚不能实现全局最优。本文根据成都电网电压-无功运行的实际需要,以网损最小为目标,建立了无功优化的数学模型。针对成都电网的几种典型运行方式,分析了丰大、枯大、节假日和主要电厂停机等条件下的电压-无功最优控制问题;在分析的基础上,提出了实现电压-无功优化控制的策略,并对成都电网的规划建设提出了建议,研究结论对成都电网调度运行也发挥了指导作用。     

基于改进量子遗传算法的电力系统无功优化

提出一种基于改进量子遗传算法的电力系统无功优化方法。该方法借鉴量子计算的一些概念,采用量子比特对控制变量编码,这种编码方式能表示出许多可能的线性叠加态,从而更好地维持种群的多样性。同时利用搜索到的最佳个体信息更新量子门,加快了该方法的收敛速度,采用群体灾变策略防止该方法陷入“早熟”。分别采用线性规划算法、复合形算法、改进禁忌搜索算法、标准遗传算法、自适应遗传算法和该方法对IEEE 6和IEEE 30节点系统进行无功优化,实验结果表明,该方法全局寻优能力强、收敛速度快。     

一种地区电网多目标无功优化的新方法--改进模拟退火算法

本文介绍了改进模拟退火算法在地区电网无功优化中的应用。实际计算表明,与常规优化方法比较,模拟退火算法收敛性好,适应性强,是实现无功优化的好方法。另外,本文给出一种新的多目标模型,测试结果验证了此法的可行性。