An improved generalized differential evolution algorithm for multi-objective reactive power dispatch

Ramesh, S.; Kannan, S.; Baskar, S.

doi:10.1080/0305215x.2011.576761

Cited by 38 publications

(17 citation statements)

References 22 publications

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“…In the first category, different volt/VAR control methods have been proposed to improve network conditions. The daily optimal volt/VAR control integrating distributed generators (DGs) under steady-state sinusoidal operation condition was investigated in [8,9]. Viawan and Karlsson [10] suggested a coordination strategy for volt/VAR control in the presence of a DG and conventional control equipment, such as an on-load tap-changer (OLTC) and capacitors.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Programming to Volt/VAR/Total Harmonic Distortion Control in Distribution Networks Including Wind Turbines

Jashfar

Hosseini-Biyouki

Esmaeili

2015

Electric Power Components and Systems

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Section: Introductionmentioning

confidence: 99%

A Stochastic Programming to Volt/VAR/Total Harmonic Distortion Control in Distribution Networks Including Wind Turbines

Jashfar

Hosseini-Biyouki

Esmaeili

2015

Electric Power Components and Systems

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“…Basu (2011) used a two-objective differential evolution to optimize both fuel cost and emission level for an economic environmental dispatch problem. Ramesh et al (2011) proposed an improved generalized differential evolution (I-GDE3) based on the concepts of simulated binary crossover based recombination and dynamic crowding distance. It can optimize the reactive power dispatch problem with multiple and competing objectives, real power loss and voltage deviation.…”

Section: Introductionmentioning

confidence: 99%

Decomposition-based multi-objective differential evolution particle swarm optimization for the design of a tubular permanent magnet linear synchronous motor

Wang

Chen

Cai

et al. 2013

Engineering Optimization

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This article proposes a decomposition-based multi-objective differential evolution particle swarm optimization (DMDEPSO) algorithm for the design of a tubular permanent magnet linear synchronous motor (TPMLSM) which takes into account multiple conflicting objectives. In the optimization process, the objectives are evaluated by an artificial neural network response surface (ANNRS), which is trained by the samples of the TPMSLM whose performances are calculated by finite element analysis (FEA). DMDEPSO which hybridizes differential evolution (DE) and particle swarm optimization (PSO) together, first decomposes the multi-objective optimization problem into a number of single-objective optimization subproblems, each of which is associated with a Pareto optimal solution, and then optimizes these subproblems simultaneously. PSO updates the position of each particle (solution) according to the best information about itself and its neighbourhood. If any particle stagnates continuously, DE relocates its position by using two different particles randomly selected from the whole swarm. Finally, based on the DMDEPSO, optimization is gradually carried out to maximize the thrust of TPMLSM and minimize the ripple, permanent magnet volume, and winding volume simultaneously. The result shows that the optimized TPMLSM meets or exceeds the performance requirements. In addition, comparisons with chosen algorithms illustrate the effectiveness of DMDEPSO to find the Pareto optimal solutions for the TPMLSM optimization problem.

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“…Objective function The minimization of real power loss (Ploss) is considered as an important objective function [10] which is expressed as:…”

Section: Optimal Reactive Power Dispatch Problemmentioning

confidence: 99%

“…The biogeography based optimization algorithm is used to find the solution for ORPD problem of IEEE 30 bus system [9]. Ramesh et al have obtained the solution for the multi-objective problem for IEEE 300 bus system using differential evolution [10]. From the literature, it can be understood that the reactive power maintenance is an important ancillary service.…”

Section: Introductionmentioning

confidence: 99%

Generator reactive power pricing for practical utility system using power flow tracing method

Danalakshmi¹,

Kannan²,

Gnanadass³

2018

IJET

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The shift from regulated to restructured power system results in an increased competition among the electricity market. In restructured power system, the separation of transmission services from generation and distribution makes it necessary to find the contribution of power from individual generator to individual load. The power flow tracing method is used to obtain the generator power output to a particular load. The reactive power has to be maintained in order to sustain the voltage level throughout the system for reliable and secure operation. Hence the reactive power cost allocation has become imperative in the power system. In this paper, the tracing method is integrated with the optimal reactive power dispatch problem to trace the generator minimal reactive power for sustaining the real power transaction and enhancing the system security by meeting the demand. The Differential Evolution is used for optimal reactive power dispatch. The cost allocation to the generators for the reactive power service based on the opportunity cost method is obtained for 62 Bus Indian Utility Systems.

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An improved generalized differential evolution algorithm for multi-objective reactive power dispatch

Cited by 38 publications

References 22 publications

A Stochastic Programming to Volt/VAR/Total Harmonic Distortion Control in Distribution Networks Including Wind Turbines

A Stochastic Programming to Volt/VAR/Total Harmonic Distortion Control in Distribution Networks Including Wind Turbines

Decomposition-based multi-objective differential evolution particle swarm optimization for the design of a tubular permanent magnet linear synchronous motor

Generator reactive power pricing for practical utility system using power flow tracing method

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