Optimal Reconfiguration of Radial Distribution Systems Using a Fuzzy Mutated Genetic Algorithm

Prasad, K.; Ranjan, Rakesh; Sahoo, Niranjan; Chaturvedi, Ashvini

doi:10.1109/tpwrd.2005.844245

Cited by 125 publications

(51 citation statements)

References 4 publications

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“…Case (A) if S i 6 ¼ {}, where {} stands 6 ¼ for null set In this case, at first, the transition probabilities between the X i and each individual in S i are calculated as indicated in Equation (19):…”

Section: Application Of Hpso To Distribution Feeder Reconfigurationmentioning

confidence: 99%

“…A methodology that combines two heuristic procedures to determine the group of switches to be open in order to minimize the total power losses in distribution systems has been described in Reference [20]. In References [1,12,14,19], the authors have combined the optimization techniques with heuristic rules and fuzzy logic for higher efficiency and robust performance. Baran and Wu [23] have made an attempt to improve the method of Civanlar et al [3] by introducing two approximation formulas for power flow in the transfer of system loads.…”

Section: Introductionmentioning

confidence: 99%

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An efficient hybrid evolutionary algorithm based on PSO and ACO for distribution feeder reconfiguration

Niknam

2009

Int Trans Elec Energy Syst

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SUMMARYA new formulation based on norm2 method for the multi objective distribution feeder reconfiguration (DFR) is introduced in order to minimize the real power loss, deviation of the nodes' voltage, the number of switching operations, and to balance the loads on the feeders. In the proposed method, since the objective functions are not the same and commensurable, the objective functions are considered as a vector and the aim is to maximize the distance (norm2) between the objective function vector and the worst objective function vector while the constraints are met. The status of the tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a multi objective and nondifferentiable optimization problem so a hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called HPSO, is proposed to solve it. The feasibility of the HPSO algorithm and the proposed DFR is demonstrated and compared with the solutions obtained by other approaches and evolutionary methods such as genetic algorithm (GA), ACO and the original PSO, over different distribution test systems.

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Section: Application Of Hpso To Distribution Feeder Reconfigurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An efficient hybrid evolutionary algorithm based on PSO and ACO for distribution feeder reconfiguration

Niknam

2009

Int Trans Elec Energy Syst

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“…References [6][7][8] proposed three different methods derived from a genetic algorithm (GA) respectively. Prasad et al [6] improved random evolution rules, making it possible to deal with discrete variables, and avoided islands and loops by improving encoding. Mendoza et al [7] proposed accentuated crossover and directed mutation, reducing searching space and memory occupation.…”

Section: Introductionmentioning

confidence: 99%

A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

et al. 2017

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This work presents a method for distribution network reconfiguration with the simultaneous consideration of distributed generation (DG) allocation. The uncertainties of load fluctuation before the network reconfiguration are also considered. Three optimal objectives, including minimal line loss cost, minimum Expected Energy Not Supplied, and minimum switch operation cost, are investigated. The multi-objective optimization problem is further transformed into a single-objective optimization problem by utilizing weighting factors. The proposed network reconfiguration method includes two periods. The first period is to create a feasible topology network by using binary particle swarm optimization (BPSO). Then the DG allocation problem is solved by utilizing sensitivity analysis and a Harmony Search algorithm (HSA). In the meanwhile, interval analysis is applied to deal with the uncertainties of load and devices parameters. Test cases are studied using the standard IEEE 33-bus and PG&E 69-bus systems. Different scenarios and comparisons are analyzed in the experiments. The results show the applicability of the proposed method. The performance analysis of the proposed method is also investigated. The computational results indicate that the proposed network reconfiguration algorithm is feasible.

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“…Intelligent based optimization methods have also been utilized in finding optimum tie switch combinations in network reconfiguration problem. Authors have used Genetic Algorithms (GA) [11], fuzzy [12][13][14], neural network [15,16], fuzzy-GA [17], Particle Swarm Optimization (PSO) [18], Matroid Theory [19], Hybrid Evolutionary algorithm [20], Ant Colony Search (ACS) algorithm [21,22], Harmony Search Algorithm [23], and Bacterial Foraging Algorithm [24]. Das [12] and Savier and Das [25] have proposed fuzzy based multiobjective approach for loss reduction and considered voltage limit and line limits as well in fuzzy set.…”

Section: Introductionmentioning

confidence: 99%

Gravitational Search Algorithm and Selection Approach for Optimal Distribution Network Configuration Based on Daily Photovoltaic and Loading Variation

Ing

Mokhlis

Illias

et al. 2015

Journal of Applied Mathematics

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Network reconfiguration is an effective approach to reduce the power losses in distribution system. Recent studies have shown that the reconfiguration problem considering load profiles can give a significant improvement on the distribution network performance. This work proposes a novel method to determine the optimal daily configuration based on variable photovoltaic (PV) generation output and the load profile data. A good combination and coordination between these two varying data may give the lowest power loss in the system. Gravitational Search Algorithm (GSA) is applied to determine the optimum tie switches positions for 33-Bus distribution system. GSA based proposed method is also compared with Evolutionary Programming (EP) to examine the effectiveness of GSA algorithm. Obtained results show that the proposed optimal daily configuration method is able to improve the distribution network performance in term of its power loss reduction, number of switching minimization and voltage profile improvement.

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Optimal Reconfiguration of Radial Distribution Systems Using a Fuzzy Mutated Genetic Algorithm

Cited by 125 publications

References 4 publications

An efficient hybrid evolutionary algorithm based on PSO and ACO for distribution feeder reconfiguration

An efficient hybrid evolutionary algorithm based on PSO and ACO for distribution feeder reconfiguration

A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

Gravitational Search Algorithm and Selection Approach for Optimal Distribution Network Configuration Based on Daily Photovoltaic and Loading Variation

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