Differential evolution applied for reliability optimization of radial distribution systems

Arya, L. D.; Choube, S.C.; Arya, Rajesh

doi:10.1016/j.ijepes.2010.08.022

Cited by 39 publications

(17 citation statements)

References 16 publications

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“…The effectiveness of the proposed bacterial foraging algorithm has been demonstrated by solving reliability optimization problem on a sample 8 node radial distribution system [14], which is shown in fig 5. The system has seven distributor segments. The system has been modified due to presence of DG at load points LP-3, LP-6 and LP-8.…”

Section: Simulation Resultsmentioning

confidence: 99%

Bacterial Foraging Algorithm for Reliability Performance of Radial Distribution System with DG

Biju¹,

Mariappan²

2013

IJCA

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Assessment of customer power supply reliability is an important part of distribution system operation and planning. Distribution system reliability assessment is able to predict the interruption profile of a distribution system based on system topology and component reliability data. In this paper, a methodology has been developed for reliability enhancement of radial distribution system having distributed generation (DG). DG is being adopted in distribution networks with one of the objectives being enhancement of system reliability. By determining the optimum values of failure rates/repair times, reliability of the distribution system has been optimized subjected to constraints on customer and energy based indices i.e. SAIFI, SAIDI, CAIDI, AENS, and ASAI. The objective function includes (i) cost of modification for failure rates/repair times (ii) additional cost of expected energy supplied by DG. A recently developed Bacterial foraging algorithm (BFA) is employed to solve the optimization problem. The proposed algorithm has been implemented on an eight node distribution network. The obtained results are compared with PSO, CAPSO and DE algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

Bacterial Foraging Algorithm for Reliability Performance of Radial Distribution System with DG

Biju¹,

Mariappan²

2013

IJCA

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“…Initialization: Generate a population of size 'M' for failure rate k and repair time r each, where each vector of respective population consists of failure rate and repair time of each component respectively. These values are obtained by sampling uniformly between lower and upper limits as given by relation (8) and (9). Step 3.…”

Section: Solution Methodology Using Bb Psomentioning

confidence: 99%

“…The paper published describes a two stage methodology for reliability enhancement of a radial distribution system using sensitivity analysis. Arya et al [9] proposed a methodology for reliability optimization of radial distribution systems by applying differential evolution. The optimal values of repair times and failure rates of each section are determined here.…”

Section: List Of Symbolsmentioning

confidence: 99%

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Reliability Optimization of Radial Distribution Systems Employing Differential Evolution and Bare Bones Particle Swarm Optimization

Kela¹,

Arya²

2014

J. Inst. Eng. India Ser. B

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This paper describes a methodology for determination of optimum failure rate and repair time for each section of a radial distribution system. An objective function in terms of reliability indices and their target values is selected. These indices depend mainly on failure rate and repair time of a section present in a distribution network. A cost is associated with the modification of failure rate and repair time. Hence the objective function is optimized subject to failure rate and repair time of each section of the distribution network considering the total budget allocated to achieve the task. The problem has been solved using differential evolution and bare bones particle swarm optimization. The algorithm has been implemented on a sample radial distribution system. List of symbolsSAIFI System average interruption frequency index SAIDI System average interruption duration index CAIDI Customer average interruption duration index AENS Average energy not supplied k k , r k Failure rate and average repair time of k th distributor segment respectively L i Average load connected at i th load point k sys,i System failure rate at i th load point U sys,i System annual outage duration at i th load point N i Number of customers at load point i J Objective function k i,min and r i,min Reachable minimum values of failure rate and repair time of i th distributor segment k i,max and r i,max Maximum allowable failure rate and repair time of i th distributor segment SAIFI t, SAIDI t , CAIDI t and AENS t Target values of the respective indices CBUDGET The total budget available for preventive maintenance and corrective repair a K, b K Cost coefficients X ij 0 j th parameter of X i vector X j,min and X j,max Lower and upper bounds on variable X j rand j Random digit in the range [0, 1] r Scale factor X base (k) Base vector V ðkÞ i Mutant vector t i (k) Trial vector Cr Cross over rate or cross over probability j rand Random integer between [1,D] X ðkÞ i

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“…Time-sequential Monte-Carlo simulation technique that can handle time dependence of the input parameters was employed in the analysis. In [16], a methodology for reliability enhancement of radial distribution system by determining optimal values of repair times and failure rates of each section was presented. In many countries voltage and commercial qualities are regulated by means of performance standards [4].…”

Section: Introductionmentioning

confidence: 99%

Quality performance based regulation through designing reward and penalty scheme for electric distribution companies

Simab¹,

Haghifam²

2012

International Journal of Electrical Power & Energy Systems

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Differential evolution applied for reliability optimization of radial distribution systems

Cited by 39 publications

References 16 publications

Bacterial Foraging Algorithm for Reliability Performance of Radial Distribution System with DG

Bacterial Foraging Algorithm for Reliability Performance of Radial Distribution System with DG

Reliability Optimization of Radial Distribution Systems Employing Differential Evolution and Bare Bones Particle Swarm Optimization

Quality performance based regulation through designing reward and penalty scheme for electric distribution companies

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