Assessment of Genetic Algorithm selection, crossover and mutation techniques in reactive power optimization (original) (raw)

Application of Genetic Algorithm for Reactive Power Disptach with Voltage Stability Constraints

This paper presents a Genetic Algorithm (GA) - based approach for solving optimal Reactive Power Dispatch (RPD) including voltage stability limit in power systems. The monitoring methodology for voltage stability is based on the L-index of load buses. Bus voltage magnitudes, transformer tap settings and reactive power generation of capacitor banks are the control variables. A binary-coded GA with tournament selection, two point crossover and bit-wise mutation is used to solve this complex optimization problem. The proposed algorithm has been applied to the IEEE 30-bus system to find the optimal reactive power control variables while keeping the system under safe voltage stability limit, and found to be more effective for this task.

Improved Genetic Algorithms ( IGA ) for Optimal Reactive Power Planning in Loss Minimisation Scheme

2004

The increasing demand of electrical power has caused the system network to be in stressed condition which led to transmission loss in the system. Consequently, voltage profile of the system decays accordingly leading to unpredictable voltage instability phenomenon. The issue of loss minimisation has been progressively investigated in the power system environment. Many techniques have been developed in the past in order to minimise transmission loss towards the improvement of voltage profile. This paper presents the application of Improved Genetic Algorithms (IGA) in order to optimise the reactive power planning (RPP) for loss minimisation in power system. In this study, an IGA engine is developed in order to implement the optimisation for the combination of reactive power dispatch and transformer tap changer setting. Two selection techniques namely the anchor spin and population spin were explored in order to investigate the merit of each technique. The selection technique is a vari...

An efficient Genetic algorithm based approach for Reactive Power Dispatch Problem

The International Conference on Electrical Engineering, 2010

The problem of reactive power dispatch (RPD) is to allocate reactive power generation so as to minimize the real power transmission losses and keep all voltage within the limits, while satisfying a number of equality and inequality constraints. This paper presents a new methodology for solving RPD. This methodology is consists of two phases. The first one employs the genetic algorithm (GA) to obtain a feasible solution subject to desired load convergence, while the other phase employs efficient GA to obtain the optimal solution. Also, some major improvements are added to the traditional genetic algorithm in order to improve the convergence and to find a better solution. Extensive testing of the proposed algorithm is done on standard IEEE-30 bus system and the results have been compared to those reported in the literature. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the RPD problem.

New Metaheuristic Algorithms for Reactive Power Optimization

Tehnicki vjesnik - Technical Gazette, 2019

Optimal reactive power dispatch (ORPD) is significant regarding operating the practice safely and efficiently. The ORPD is beneficial to recover the voltage profile, diminish the losses and increase the voltage stability. The ORPD is a complicated optimization issue in which the total active power loss is reduced by detecting the powersystem control variables, like generator voltages, tap ratios of tap-changer transformers, and requited reactive power, ideally. This study offers new approaches based on Shuffled Frog Leaping Algorithm (SFLA) and Tree Seed Algorithm (TSA) to solve the best ORPD. The results of the approaches are offered set against the current results studied in the literature. The recommended algorithms were tested by IEEE-30 and IEEE-118 bus systems to discover the optimal reactive power control variables. It was observed that the obtained results are more successful than the other algorithms.

Reactive Power Optimization Using Differential Evolution Algorithm

2013

In this Reactive power optimization is a nonlinear, multi-variable, multi-constrained programming problem, which makes the optimization process multifaceted. In this paper, based on the characteristics of reactive power optimization, a mathematical model of reactive power optimization, including comprehensive concern of the practical constraints and reactive power regulation means for optimization, is established. Reactive Power reduces power system losses by adjusting the reactive power control variables such as transformer tap-settings, generator voltages and other sources of reactive power such as capacitor banks. Reactive Power provides better system voltage control resulting in improved voltage profiles, system security, power transfer capability and overall system operation. Also Differential Evolution (DE) Algorithm has been studied, and the technique based on improved DE Algorithm for reactive power is going to be taken in this paper Optimization for the IEEE 14-bus and IEEE 57 bus system proves that the improved DE algorithm used for reactive power optimization is valuable. The algorithm is simple, convergent and of high quality for optimization, and thus appropriate for solving reactive power optimization problems, with some application view.

Reactive Power Planning Based on Genetic Algorithms

2011

This paper addresses an optimal Reactive Power Planning (RPP) of power system. The Static Var Compensator (SVC) is introduced into power system in order to reactive power support and voltage control. The locations and the outputs of SVCs are determined using our proposed optimal reactive power planning model. The proposed method optimizes several objective functions at the same time within one general objective. The optimized objectives are minimization of total investment in reactive power support, average voltage deviation and minimization of total system loss. These objective functions are one of the most important objectives for every transmission and distribution systems. Genetic Algorithm (GA) is used to solve the optimization problem. The validity of the proposed method is tested on a typical power system.

Computational enhancement of genetic algorithm via control device pre-selection mechanism for power system reactive power/voltage control

2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491)

In this paper, the application of a novel and computationally enhanced genetic algorithm (CA) for solving the reactive power dispatch problem is presented. In order to attain a significant reduction in the computational time of GA, a systematic procedure of reactive power control device preselection mechanism is herein proposed to choose a-priori subsets of the available control devices, which maximally influence buses experiencing voltage limit violations. The GA reactive power dispatch module then accesses such judiciously pre-selected control device candidates to determine their optimal settings. A pragmatic scheme aimed at further curtailing the number of the final control actions entertained is also set forth. The farreaching simulation results obtained for two case study scenarios using the proposed algorithmic procedures on a German utility network of Duisburg, replicated on an operator-training simulator, are presented and fully discussed in depth.

Optimal power flow based on linear adapted genetic algorithm

2010

Reactive power planning is an optimization problem. Solving this problem requires finding an optimal solution that minimizes an objective function while satisfying certain constraints. This paper proposes an approach for optimum reactive power dispatch throughout the power network, using the Genetic Algorithms (GA). Varying the crossover probability rate P c and mutation probability rate P m , the GA's control parameters provide faster convergence than constant probability rates. The active power loss is minimized using six controlled system variables (generator voltages, transformer taps and shunt capacitors). The proposed method is applied to the practical Ward-Hale 6-bus system. The OPF is solved by a classical nonlinear optimization technique as well as the proposed linear adaptive genetic technique..

A hybrid genetic algorithm for optimal reactive power planning based upon successive linear programming

IEEE Transactions on Power Systems, 1999

A hybrid methodology is presented for the solution of the problem of the optimal allocation of reactive power sources. The technique is based upon a modified genetic algorithm (G.A.), which is applied at an upper level stage, and a successive linear program at a lower level stage. The objective is the minimization of the total cost associated to the installation of the new sources. The genetic algorithm is devoted to defining the location of the new reactive power sources, and therefore to handle the combinatorial nature of the fixed costs problem. At the lower level, the variable cost problem is solved by, calculating the magnitude of the sources to be installed at the previously determined locations by means of a linear prngram iterated successively with a fast decoupled load flow. Results are presented for the application of the proposed methodology when applied to the Venezuelan electric network.

Optimal reactive power dispatch using hybrid loop-genetic based algorithm

2016

Reactive power dispatch problem is one of the important issues of the power system in the operation and control. The Optimal Reactive Power Dispatch Problem is the minimization of active power loss in the power system by varying different system control variables, i.e. generator bus voltages or its reactive power output, transformer taps setting, shunt Var compensator etc. The minimization of active power loss will consequently reduce production cost for the requirement of reactive power support. In this paper a new algorithm is presented which is motivated by a hybrid approach called as hybrid Loop-Genetic based algorithm (HLGBA). The hybrid Loop-GA based algorithm (HLGBA) uses the benefits from global search, i.e. the genetic algorithm with less evolution process (only for global search space) and then from local search for refining the solution with the limited computation and time. This algorithm solve ORPD problem more efficiently. This new algorithm verified on standard IEEE_1...