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 2022-01-09 09:01

Turkish Journal of Electrical Engineering amp; Computer Sciences

http:// journals. tubitak. gov. tr/ e l ektrik/

Research Article

Turk J Elec Eng amp; Comp Sci (2017) 25: 4615 – 4623

⃝c TUuml; BI˙TAK

doi:10.3906/elk-1703-159

Reactive power optimization in a power system network through metaheuristic algorithms

Chandragupta Mauryan KUPPAMUTHU SIVALINGAM1,lowast;, Subramanian RAMACHANDRAN1, Purrnimaa Shiva Sakthi RAJAMANI2

1Department of Electrical and Electronics Engineering, Faculty of Electrical Electronics Engineering,

Sri Krishna College of Technology, Coimbatore, India

2Department of Electrical and Electronics Engineering, Faculty of Electrical Electronics Engineering, Hindustan College of Engineering and Technology, Coimbatore, India

Received: 13.03.2017 bull; Accepted/Published Online: 13.06.2017 bull; Final Version: 03.12.2017

Abstract: Reactive power optimization (RPO) in a power system is a rudimentary necessity for the reduction of the loss of power. For the requirement of a unity power factor in the RPO system, the reduction of the system losses is ensured. The pivotal requirements of a power system are inclusive of a perfect compensation technique and methodology for stable reactive power compensation. The proposed concept in this paper utilizes the different reactive power optimization algorithms and performs a comparison. The process is accomplished by the use of IEEE 6-bus, 14-bus, and 30-bus systems to test the optimization technique. The conclusive information reinforces the outperformance of the based optimization algorithm to the other algorithm, thereby providing high stability to the system. The algorithm ensures the confinement of the voltage profile of the system within the permissible limits.

Key words: Reactive power optimization, power loss minimization, optimization, voltage profile

Introduction

The stark factor that has seized the attention of a large number of researchers is the concerns in reactive power optimization (RPO) in a power system. In the necessity of the reduction of the system losses, the RPO is made to work towards the improvement of the power factor of the system. Due to the unity power factor in the system, the reduction of losses is obtained, resulting in the maintenance of stability in the system. The RPO is used for the maintenance of stability and safe operating zone in the system in addition to reduction of power loss. For the optimization of reactive power in a system, the optimization is done on the basis of the voltage profile of the bus bar and power factor. By various optimization techniques the minimization of loss of power in a system is achieved. Real power generation constraint and reactive power generation constraints are taken into account in the reduction of the power loss. The quantity of reactive power depends on the phase shift between the voltage and current wave. Reactive power improves voltage stability and avoids voltage collapse. By regulating the reactive power, voltage stability, system efficiency, energy cost, and power losses of a power system network can be controlled effectively. Over long distance power transmission, additional reactive power loss occurs due to the large reactive impedance. To avoid excessive reactive power transmission and consumption, it should be as close as possible to each other; if it is not compensated properly, then it will cause an inappropriate voltage profile.

lowast;Correspondence: ssst.m1m2m3@gmail.com

Related works

A multitude of proposals for the compensation of reactive power in the system have been composed in various papers. The utility of wind farms across the network with the New England 39-bus systems with 12 doubly fed induction generators is studied. Assessment of voltage security parameters is performed. The comparison of the loss of power and the reactive power generation of individual wind farms has been attained with and without the imposition of voltage stability constraints on the OPF solution. The constrained OPF increased active power losses in the network. Furthermore, the determination of the contribution to the reactive power by each wind farm and the optimization on the basis of selectivity is completed for the evaluation of contributions towards system active power losses by each individual wind farm [1]. Moreover, only by the use of wind farms with the least contribution to system active power losses can a reduction in the number of reactive power optimized wind farms be realized. There is another proposed solution applied for the multitude kinds of optimal reactive power flow problems that are constrained in operation.

The constraints like power balance, line flow, and V b limits are revealed by Aniruddha Bhattacharya et al. [2] as the biogeography-based optimization (BBO) technique. The BBO technique for the global optimum is done by two steps: migration and mutation. Active power loss is reduced by BBO to obtain solutions for the optimal reactive power flow problems on standard IEEE 57-bus and IEEE 30-bus power systems. To optimize the reactive power, an avant-garde proposal put forth by Su et al. [3] is the improved cloud particle swarm optimization BP neural network [4]. It is easy to track the local minimum and the slow convergences in the distribution network reactive power optimization are the functions in cloud particle swarm optimization (CPSO).

The improvement of the cloud particle swarm optimization is done on the basis of cloud digital feature

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基于元启发式算法的电力系统无功优化

Chandragupta Mauryan KUPPAMUTHU SIVALINGAM1,lowast;, Subramanian RAMACHANDRAN1, Purrnimaa Shiva Sakthi RAJAMANI2

1 印度哥印拜陀斯里克里希纳理工学院 ，电气电子工程学院，电气与电子工程系

2 印度哥印拜陀工程技术学院电气电子工程系电气与电子工程系

收稿: 13.03.2017 bull; 网上公开发表: 13.06.2017 bull; 最后浏览: 03.12.2017

摘要:

电力系统中的无功优化是降低电力损耗的基本必要条件。根据RPO系统对单位功率因数的要求，保证了系统损耗的降低。电力系统的关键要求包括完善的无功补偿技术和稳定无功补偿方法。本文提出的概念利用了不同的无功优化算法，并进行了比较。该过程通过使用IEEE6总线、14总线和30总线系统来测试优化技术来完成。结论性信息增强了基于优化算法的性能，从而为系统提供了高稳定性。该算法保证了系统电压分布在允许范围内。

关键词: 无功功率优化、功率损耗最小化、优化、电压曲线

前言

一个电力系统中的无功优化（RPO）是引起众多研究者关注的重要因素。在降低系统损耗的必要性下，提出了RPO，以提高系统的功率因数。由于系统功率因数的统一，使系统损耗降低，从而维持了系统的稳定性。RPO用于维护系统的稳定性和安全操作区，同时降低功率损失。针对系统无功优化问题，根据母线电压分布和功率因数进行了优化。通过各种优化技术，使系统功率损失最小化。在降低电力损耗时，考虑了实际发电约束和无功发电约束。无功功率的大小取决于电压波和电流波之间的相移。无功功率提高了电压稳定性，避免了电压崩溃。通过调节电网的无功功率、电压稳定性、系统效率、能源成本和功率损耗，可以有效地控制电网的运行。在长距离输电中，由于无功阻抗较大，会产生额外的无功损耗。为了避免过大的无功功率传输和消耗，应尽可能地接近对方；如果补偿不当，将导致不适当的电压分布。

相关工作

本文对系统无功补偿提出了许多建议。研究了新英格兰39总线系统和12台双馈感应发电机在整个电网中的应用。对电压安全参数进行评估。在有无电压稳定约束的情况下，对单个风电场的功率损耗和无功发电进行了比较。受限的opf增加了网络中的有功功率损耗。此外，还完成了各风电场对无功功率贡献的确定和基于选择性的优化，以评估各风电场对系统有功功率损失的贡献。只有利用对系统有功损耗贡献最小的风电场，才能减少无功优化风电场的数量。针对运行受限的多种最优无功潮流问题，提出了另一种求解方法。Aniruddha Bhattacharya等人揭示了诸如功率平衡、线路流量和Vb限制等约束条件。作为基于生物地理学的优化（BBO）技术，BBO技术的全局优化分为迁移和变异两个步骤。在标准的IEEE57总线和IEEE30总线电力系统中，BBO降低了有功功率损耗，以获得最佳无功功率流问题的解决方案。为了优化无功功率，Su等人提出了一个前卫的建议。云粒子群优化（CPSO）的功能是配电网无功优化中容易跟踪局部最小值和缓慢收敛的问题。

基于云的数字特征，对云粒子群优化算法进行了改进，从解空间变换的角度出发，将局部搜索和全局搜索相结合。在一个IEEE30总线上完成了试验模拟的转换。仿真结果表明，采用改进的算法可以获得更好的全局解，从而进一步提高了收敛速度和精度。为了实现将柔性交流输电系统（FACTS）设备以最优方式放置在互联电力系统中的进化技术，Bhattacharyya等人设计了一种遗传算法（GA）。袁等提出了并行免疫粒子群优化算法。采用粒子群算法和离散粒子群算法并行优化，提高了系统的收敛能力。局部收敛问题的有效解决方法是利用免疫算子，而复杂编码问题的合理解决方法是将离散变量与连续变量并行优化得到的混合体。通过对IEEE14总线、IEEE30总线和IEEE118总线系统进行并行免疫粒子群优化的仿真，可以观察到更快的收敛效果和更高的稳定性。它也是大规模电力系统无功优化的首选方案。

为了提高优化问题的性能，提出了混合软计算技术。在这方面，进化规划和有效的粒子群优化（epepso）在解决非线性问题上可能会非常有效。epepso具有更高的质量、更好的优化成本和更高的收敛速度。多目标函数有助于分析由Liang等人实施的电压控制方法。然而，上述算法缺乏对有限时间段的最佳拟合。这里提出的工作提供了一系列提供RPO的优化技术的全面比较。

无功功率优化

目标函数

式（1）中给出的目标函数的最小化导致了更好的无功优化。由于RPO是与两个电压系统之间的电压分布和余弦角相关的一个因素，因此主要目标是最小化相同的值。

. 如式（1）所示， 系统被认为与特定区域一起运行。

MinPL = sum; GK [V 2 V 2 minus; 2ViVj cos (alpha;i minus; alpha;j)] (1)

i

j

给定网络中线性系数用K来表示，上述功能的最小化受到若干限制，式（2）给出了系统的发电量和需求限

制。

0 = P gi minus; Pdi minus; Vi sum; Vj(Gij cos theta;ij Bij sin theta;ij) (2)

（3）式阐述了系统补偿方案中的无功需求和无功发电。.

0 = Qgi minus; Qdi minus; Vi sum; Vj (Gij sin theta;ij minus; Bij cos theta;ij) (3)

Qci min lt; Qci lt; Qcimaxiisin;nc Qgi min lt; Qci lt; Qcimaxiisin;ng Tk min lt; Tk lt; Tkmaxkisin;nt

Vi min lt; Vi lt; Vimaxiisin;n

交互人工智能蜂群 (IABC)

    1. 算法流程图

开始

根据HMOABC规则初始

化蜂群和参数

是

A

是否满足停止

要求

停止

否

基于归一化目标值和多样化选择的蜂群分类选择

为旁观者提供解决方案

评价整个目标，基于快速非显性排序对蜂群进行排序

A

基于归一化目标值和多样化选择的蜂群分类选择

为旁观者提供解决方案

利用牛顿引力定律，找出雇佣蜜蜂和旁观者之间的关系。

为雇佣的蜂群提供新的解决方案

图1 IABC算法的流程图。

    1. 约束条件

为了验证针对RPO问题提出的优化技术，对6、14和30总线系统进行了测试。使用以下措施比较精度的性能：控制变量数据、电压分布、实际功率损失、迭代次数和CPU时间。一般来说，ABC算法提供了更好的目标函数结果。雇佣蜜蜂之间的关系由雇佣蜜蜂的原始设计来考虑。通过轮盘选择选择雇佣蜂和旁观者蜂之间的关系。因此，其强度不足以最大化开发能力，即找到食物来源的机会是最小的。基于ABC算法的框架，预计将出现交互式ABC算法。所提出的IABC算法的流程图如图1所示。

仿真结果及讨论

该算法在IEEE14总线系统中得到了验证。将所得结果与其他优化方法的结果进行了比较。表1和表2给出了不同方法的RPO问题的控制变量比较，并表明IABCO实现的最小实际功率损失是所有其他方法中最小的，强调了其优越的解决质量。

表1 6总线系统不同方法比较

控制变量

经典粒子群算法

改进粒子群算法

量子粒子群算法

ABCO

(拟议方案1)

IABCO

(拟议方案2)

V1

1.07

1.02

0.97

1.05

1.07

V2

1.12

1.05

1.00

1.07

1.10

V3

0.97

1.00

1.00

1.02

1.05

V4

1.01

1.02

0.95

1.05

1.07

V5

1.00

1.00

0.97

1.02

1.05

V6

0.95

0.97

0.95

1.01

1.05

Q1

0.192

0.934

0.916

0.895

0.872

Q2

0.601

0.598

0.523

0.510

0.504

Q4

0.05

0.05

0.05

0.05

0.05

Q6

0.055

0.055

0.055

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