Optimization of Reactive Power Expansion Planning

Jabr, Rabih A.

doi:10.1080/15325008.2011.567220

Cited by 13 publications

(4 citation statements)

References 32 publications

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“…A wide range of conventional optimization techniques have been used to handle the reactive power optimization problem, it has been found that all of these techniques were dealing mainly with continuous variable and they were suffering from convergence toward the local optimal solutions, these methods required some mathematical assumptions such as differential convexity property of the objective function [4], [5]. As the years went by, many researchers got interested in the field of reactive power optimization and proposed various optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Multi-Mean Scout Particle Swarm Optimization (MMSCPSO) based Reactive Power Optimization in Large-Scale Power Systems

Bananeza¹,

Mugemanyi²,

Nshimyumukiza³

et al. 2021

JERR

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The particle swarm optimization (PSO) is a population-based algorithm belonging into metaheuristic algorithms and it has been used since many decades for handling and solving various optimization problems. However, it suffers from premature convergence and it can easily be trapped into local optimum. Therefore, this study presents a new algorithm called multi-mean scout particle swarm optimization (MMSCPSO) which solves reactive power optimization problem in a practical power system. The main objective is to minimize the active power losses in transmission line while satisfying various constraints. Control variables to be adjusted are voltage at all generator buses, transformer tap position and shunt capacitor. The standard PSO has a better exploitation ability but it has a very poor exploration ability. Consequently, to maintain the balance between these two abilities during the search process by helping particles to escape from the local optimum trap, modifications were made where initial population was produced by tent and logistic maps and it was subdividing it into sub-swarms to ensure good distribution of particles within the search space. Beside this, the idle particles (particles unable to improve their personal best) were replaced by insertion of a scout phase inspired from the artificial bee colony in the standard PSO. This algorithm has been applied and tested on IEEE 118-bus system and it has shown a strong performance in terms of active power loss minimization and voltage profile improvement compared to the original PSO Algorithm, whereby the MMSCPSO algorithm reduced the active power losses at 18.681% then the PSO algorithm reduced the active power losses at 15.457%. Hence, the MMSCPSO could be a better solution for reactive power optimization in large-scale power systems.

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Section: Introductionmentioning

confidence: 99%

Multi-Mean Scout Particle Swarm Optimization (MMSCPSO) based Reactive Power Optimization in Large-Scale Power Systems

Bananeza¹,

Mugemanyi²,

Nshimyumukiza³

et al. 2021

JERR

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“…The development of power electronic based FACTS devices opened up new ways of controlling the power systems. They FACT devices have the capability of controlling both in steady state and dynamic control of power system [7].FACTS devices are incorporated for improving the power transfer capability, laudability of transmission lines, voltage stability, power loss reduction, damping of power oscillations, elimination of new transmission lines construction, and reactive power management etc., widely [8][9][10][11][12][13][14][15][16][17][18]. By not delimiting the system security, the pre-fixed thermal limits are considered to load conventional transmission systems using an important tool called FACTs device [19].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Unified Differential Evolution Algorithm for Optimal Operation of Power Systems with Static Security, Transient Stability and Statcom Device

Venkateswarlu*,

Vaisakh*

2019

IJRTE

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The complexity of a power system operating with transient stability/security constraints increases with increased interconnection of power transmission networks. Many of the power system’s secure operations are affected with the voltage/transient instability problems. Thereby, the modern power systems have considered solving optimal power flow (OPF) problems using voltage/transient stability constraints as a tedious and challenging task. Algebraic and differential equations of the voltage/stability constraints are included in non-linear optimal power flow optimization problems. In this work, the OPF problems with voltage/stability constraints are solved using a newly developed reliable and robust technique. Moreover, the impact of a FACTS device such as STATCOM device was investigated to test its impact in the enhancement of power system performance. An adaptive unified differential evolution (AuDE) technique is proposed to search in the non-convex and nonlinear problems to obtain the global optimal solutions. Compared to other existing methods and basic DE, the proposed AuDE algorithm has achieved better results under simulation conditions. The power system’s performance is considerably enhanced with STATCOM device. Efficiency of the proposed method in solving the transient and security constrained power systems for optimal operations were demonstrated using the numerical results obtained from IEEE 39-bus, 10-generator system and IEEE 30-bus, 6-generator system. Due to page limitation only 30-bus systems results are presented.

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“…A Covariance matrix based evolutionary strategy for reactive power planning as well as enhancement of voltage stability is presented in [29]. Reactive power expansion planning is presented in [30]. An idea for optimal VAR planning in distribution feeders are presented in [31] where the locations of capacitors are determined by a heuristic approach.…”

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confidence: 99%

GA based optimal planning of VAR sources using Fast Voltage Stability Index method

Bhattacharyya¹,

Rani²,

Vais³

et al. 2016

Archives of Electrical Engineering

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This paper presents a novel approach for reactive power planning of a connected power network. Reactive power planning is nothing but the optimal usage of all reactive power sources i.e., transformer tap setting arrangements, reactive generations of generators and shunt VAR compensators installed at weak nodes. Shunt VAR compensator placement positions are determined by a FVSI (Fast Voltage Stability Index) method. Optimal setting of all reactive power reserves are determined by a GA (genetic algorithm) based optimization method. The effectiveness of the detection of the weak nodes by the FVSI method is validated by comparing the result with two other wellknown methods of weak node detection like Modal analysis and the L-index method. Finally, FVSI based allocation of VAR sources emerges as the most suitable method for reactive power planning. Key words: active power loss, FVSI method, genetic algorithm, operating cost, reactive power planning, weak nodes IntroductionReactive power planning has been one of the most challenging problem to the power system engineers. Expansion of a transmission network is not always possible because of several reasons like cost, right of way etc. Again maintaining good quality of voltage for distribution of power to the consumer is also an important aspect. Sometimes a situation of system collapse is observed even with the slight increase in reactive power demand. Hence, it becomes absolutely necessary for the proper co-ordination and planning of all reactive power sources present in the network. In this paper, main concerns are proper planning and coordination of control variables like transformer tap changers, shunt VAR compensators, generators reactive VAR in an interconnected power system for minimum active power loss as well as minimum operating cost.A linear programming based optimization method is presented in [1] for optimal reactive power generation of large scale electric power networks. The solution of the reactive power problem by optimal placement of a capacitor is discussed in [2]. A modal analysis technique is described in [3] for the determination of weak nodes of a connected power network. The reactive power problem is solved in [4] by injecting reactive power at weak buses of a connected power system. Application of evolutionary programming in reactive power planning is discussed in [5]. A hybrid expert system and simulated annealing based algorithm for the planning of reactive power sources is presented in [6]. A technique for the determination of reactive margin is presented in [7]. Heuristic modal for secured operation of a power system is developed in [8]. A binary search technique and special heuristics are applied for optimal VAR planning in the case of a large scale power system in [9]. Static security constraints and nonprobabilistic uncertainties in load values are considered for optimal reactive power planning in [10]. Two rule based modules are used for voltage profile improvement to alleviate voltage limit violations and for minimization of ...

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Optimization of Reactive Power Expansion Planning

Cited by 13 publications

References 32 publications

Multi-Mean Scout Particle Swarm Optimization (MMSCPSO) based Reactive Power Optimization in Large-Scale Power Systems

Multi-Mean Scout Particle Swarm Optimization (MMSCPSO) based Reactive Power Optimization in Large-Scale Power Systems

Adaptive Unified Differential Evolution Algorithm for Optimal Operation of Power Systems with Static Security, Transient Stability and Statcom Device

GA based optimal planning of VAR sources using Fast Voltage Stability Index method

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