Dynamic multi-group self-adaptive differential evolution algorithm for reactive power optimization

Zhang, Xuexia; Chen, Weirong; Dai, Chaohua; Cai, Wenzhao

doi:10.1016/j.ijepes.2009.11.009

Cited by 80 publications

(16 citation statements)

References 14 publications

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“…Unfortunately, these methods suffer from severe limitations in handling non-linear, discrete -continuous functions and constraints [1]. Lately, a wide variety of stochastic search methods have been developed to solve global optimisation problems such as genetic algorithm (GA) [10 -12], particle swarm optimisation (PSO) [13 -16], differential evolution (DE) [17,18] and so on.…”

Section: Introductionmentioning

confidence: 99%

Hybrid shuffled frog leaping algorithm and Nelder–Mead simplex search for optimal reactive power dispatch

Khorsandi¹,

Alimardani²,

Vahidi³

et al. 2011

IET Gener. Transm. Distrib.

108

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The optimal reactive power dispatch (ORPD) problem is a non-linear mixed-variable optimisation problem. This study employs a new evolutionary algorithm that expands the original shuffled frog leaping algorithm (SFLA) to solve this problem. In order to fully exploit the promising solution region, a local search algorithm known as Nelder -Mead (NM) algorithm is integrated with SFLA. The resultant NM-SFLA is very efficient in solving ORPD problem. The most important benefit of the proposed method is higher speed of convergence to a better solution. The proposed method is applied to ORPD problem on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus power systems and compared with four versions of particle swarm optimisation algorithm, two versions of differential evolutionary algorithm and SFLA. The optimal setting of control variables including generator voltages, transformer taps and shunt VAR compensation devices for active power loss minimisation in a transmission system is determined while all the constraints are satisfied. The simulation results show the efficiency of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Hybrid shuffled frog leaping algorithm and Nelder–Mead simplex search for optimal reactive power dispatch

Khorsandi¹,

Alimardani²,

Vahidi³

et al. 2011

IET Gener. Transm. Distrib.

108

View full text Add to dashboard Cite

show abstract

“…Comparative Study of Firefly algorithm and Particle Swarm Optimization for Noisy Non-Linear Optimization Problems was reported in 2012 by Saibal K. Pal et al [22]. There are various noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms.…”

Section: Introductionmentioning

confidence: 99%

Application of Grade Algorithm Based Approach along with PV Analysis for Enhancement of Power System Performance

Kannan¹,

Subramaniam²,

Manokar³

2016

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This paper presents an application of GRADE Algorithm based approach along with PV analysis to solve multi objective optimization problem of minimizing real power losses, improving the voltage profile and hence enhancing the performance of power system. GRADE Algorithm is a hybrid technique combining genetic and differential evolution algorithms. Control variables considered are Generator bus voltages, MVAR at capacitor banks, transformer tap settings and reactive power generation at generator buses. The optimal values of the control variables are obtained by solving the multi objective optimization problem using GRADE Algorithm programmed using M coding in MATLAB platform. With the optimal setting for the control variables, Newton Raphson based power flow is performed for two test systems, viz, IEEE 30 bus system and IEEE 57 bus system for three loading conditions. Minimization of Real power loss and improvement of voltage profile obtained are compared with the results obtained using firefly and particle swarm optimization (PSO) techniques. Improvement of Loadability margin is established through PV curve plotted using continuation power flow with the real power load at the most affected bus as the bifurcation parameter. The simulated output shows improved results when compared to that of firefly and PSO techniques, in term of convergence time, reduction of real power loss, improvement of voltage profile and enhancement of loadability margin.

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“…Dynamic adaptive differential evolution algorithm proposed in [10] and improved particle swarm optimization algorithm proposed in [11], which reactive power optimization by different methods of distribution network, but the selection of weight and convergence precision were not accuracy. Conventional adaptive particle swarm optimization use the strategies which can't reflect the process of actual optimization search of linear inertia weight reduction.…”

Section: Introductionmentioning

confidence: 99%

Research of Distribution Network Reactive Power Optimization Based on Improved Cloud Particle Swarm Optimization BP Neural Network

Su¹,

Zhang²

2013

IJCEE

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  Abstracts-In view of cloud particle swarm optimization is easily trapped in local minimum and existed slowly convergence in the distribution network reactive power optimization, the cloud particle swarm optimization is improved based on cloud digital features (Ex, En, He), local search and global search which are based on the solution space transform are combined, the random particle is crossed by cloud generator of X condition，the cross operation of particle according to cloud genetic, variation operation of the particle according to the normal cloud `particle. apparent improvement are got in time and storage by using the algorithm, in order to enhance convergence accelerate, train BP neural network by improved cloud particle swarm, the simulation experiment are done in IEEE30-bus system and YUMEN power grid in northwest china, simulation results show that a better global solution can be attained by using the improved algorithm, convergence speed and accuracy are accelerated and improved.Index Terms-Distribution network, reactive power optimization, cloud particle swarm algorithm, cloud model, BP neural network.

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Dynamic multi-group self-adaptive differential evolution algorithm for reactive power optimization

Cited by 80 publications

References 14 publications

Hybrid shuffled frog leaping algorithm and Nelder–Mead simplex search for optimal reactive power dispatch

Hybrid shuffled frog leaping algorithm and Nelder–Mead simplex search for optimal reactive power dispatch

Application of Grade Algorithm Based Approach along with PV Analysis for Enhancement of Power System Performance

Research of Distribution Network Reactive Power Optimization Based on Improved Cloud Particle Swarm Optimization BP Neural Network

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