Reliability enhancement using optimal distribution feeder reconfiguration

Kavousi‐Fard, Abdollah; Akbari-Zadeh, Mohammad-Reza

doi:10.1016/j.neucom.2012.08.033

Cited by 116 publications

(52 citation statements)

References 32 publications

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“…However, ODS outperforms DS and DE in terms of simulation time. Table 9 shows that, for the multi-objective formulation, the average value of objective function obtained by ODS algorithm is close to the minimum value to a greater extent as compared to the average and 3,5,6,7,8,10,11,12,13,16,19,20,21,22,23,26,28,31 ,32 1041400 8069900 DS 0 -0 6999200 3, 6, 7, 11, 14, 15, 16, 20, 22, 23, 24, 27, 28, 29, 32 822162 7821362 ODS 0 -0 6768300 2,3 ,4,5,9, 12, 13, 15, 17, 20, 21, 23, 24, 25, 27, 28, 29, 31, 32 1041400 7809700 minimum values of DS algorithm and DE. This proves ODS to be better in terms of robustness.…”

Section: Description Of the Test Systemmentioning

confidence: 91%

“…Many studies have focused on network reconfiguration [16][17][18][19][20]. Kavousi-Fard and Akbari-Zadeh [21] proposed a multi-objective improved shuffled frog leaping algorithm for network reconfiguration in order to optimise reliability indices and active power loss. An artificial immune systems optimisation has been utilised by Alonso et al [22] for multi-objective distribution system reconfiguration.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal allocation of distributed generation and remote control switches for reliability enhancement of a radial distribution system using oppositional differential search algorithm

Ray¹,

Bhattacharya²,

Bhattacharjee³

2015

J. eng.

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Reliability enhancement of power distribution system has attained much significance in the present competitive electricity market. Accordingly, methodologies to assess and improve distribution system reliability are also gaining much importance. This study proposes oppositional differential search (ODS) algorithm to solve reliability optimisation problem of radial distribution system. The objective of this study is to obtain the optimum number, size and location of distributed generation as well as optimal number and location of remote control switch simultaneously in radial distribution system in order to improve system reliability at a compromised cost. A multi-objective function has been formulated here. Differential search (DS) algorithm imitates the seasonal migration behaviour of an organism in search of efficiency of food areas. Opposition-based DS (ODS) algorithm has been used here to improve the quality of solution in minimum time. The proposed opposition-based DS (ODS) algorithm utilises opposition-based learning for Superorganism initialisation and also for iteration wise update operation. Simulation results obtained by ODS algorithm have been compared with that of DS algorithm and differential evolutionary algorithm. Simulation results reveal that ODS algorithm provides considerably superior performance, in terms of quality of solution obtained and computational efficiency.

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Section: Description Of the Test Systemmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Optimal allocation of distributed generation and remote control switches for reliability enhancement of a radial distribution system using oppositional differential search algorithm

Ray¹,

Bhattacharya²,

Bhattacharjee³

2015

J. eng.

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show abstract

“…The complete data of the system for load flow can be found in [29]. Also, for the reliability studies, the required data are taken from [24]. The nominal voltage level of the system is 12.66 kV.…”

Section: A Case Study Onementioning

confidence: 99%

“…In other words, after the first time allocation of the switches in the network, there is no need of any extra investment. Some of the main benefits of the optimal DFR can be named as: power loss reduction [16]- [19], voltage profile enhancement [20], [21], increasing load balance [22], reducing the system total costs [23], and reliability enhancement [24], [25]. While the DFR strategy can always be considered as an available strategy for improving the power system situation, the existence of uncertainty in the DFR problem can reduce the dependability of the analysis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Stochastic Framework Based on Cloud Theory and θ-Modified Bat Algorithm to Solve the Distribution Feeder Reconfiguration

Kavousi‐Fard

Niknam

Fotuhi‐Firuzabad

2015

IEEE Trans. Smart Grid

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Distribution feeder reconfiguration (DFR) is a precious operation strategy that can improve the system from different aspects including total cost, reliability, and power quality. Nevertheless, the high complexity of the new smart grids has resulted in much uncertainty in the DFR problem that necessities the use of a sufficient stochastic framework to deal with them. In this way, this paper proposes a new stochastic framework based on cloud theory to account the uncertainties associated with multiobjective DFR problem from the reliability point of view. Cloud theory is constructed based on fuzzy theory and probability idea. In comparison with the Monte Carlo simulation method, cloud models can give more information on the uncertainties associated with the problem. This special aspect of cloud models makes it possible to integrate the fuzziness and randomness of qualitative concepts through the cloud drops and then transforms them to the quantitative model. In order to solve the proposed problem, a fast and powerful optimization technique is required. To deal with this issue, a new optimization algorithm designated as θ -bat algorithm is proposed in this paper. The feasibility and satisfying performance of the proposed method are examined on the 32-bus and 69-bus IEEE distribution test system.Index Terms-Cloud theory, distribution feeder reconfiguration (DFR), θ -modified bat algorithm (θ -MBA), uncertainty.

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“…In conventional power systems, network reconfiguration adds to the accompanying advantages, which a few researchers have tested by employing smart routines, like decreasing system power losses [13,14], effective restoring system [15], enhancing system reliability [16], and balancing load [17]. The primary contrast between these networks is the presence of numerous vitality merchants with different conditions for offering energy in a focused cost [18].…”

Section: Introductionmentioning

confidence: 99%

Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm

Kaur

Ghosh

2016

Applied Soft Computing

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Reliability enhancement using optimal distribution feeder reconfiguration

Cited by 116 publications

References 32 publications

Optimal allocation of distributed generation and remote control switches for reliability enhancement of a radial distribution system using oppositional differential search algorithm

Optimal allocation of distributed generation and remote control switches for reliability enhancement of a radial distribution system using oppositional differential search algorithm

A Novel Stochastic Framework Based on Cloud Theory and θ-Modified Bat Algorithm to Solve the Distribution Feeder Reconfiguration

Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm

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