Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm

Kaur, Manvir; Ghosh, Swaroop

doi:10.1016/j.asoc.2016.09.019

Cited by 40 publications

(21 citation statements)

References 37 publications

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“…base case (without reconfiguration and DG allocation and sizing), reconfiguration only, DG allocation only, DG allocation 37 33,34,35,36,37 33,34,35,36,37 33,34,35,36,37 33,34,35,36,37 33,34,35,36,37 DG (MW) (Bus No.) 0.8044 (14) 0.9142 (12) 0.8049 (14) 0.7628 (14) 0.7798 (14) 0.5897 (14) 0.1070 181.1063 241.2001 (30) 1.1059 241.1072 241.1251 (24) 0.1895 (18) 0.5724 171.3917(30) 291.287 291.2024 (29) 0.6158 (29) 0.5586 (31) 0.4173 (32) 0.7602 (18) 0.8463 (18) 0.765 (18) 0.7127 (18) 0.5315 (18) 0.5840 (33) Power 28 6,14,10,32,37 7,13,11,32,27 33,13,9,28,30 33,34,11,31 after reconfiguration, reconfiguration after DG allocation, simultaneous reconfiguration and DG sizing, and simultaneous reconfiguration and DG allocation and sizing were considered. The optimization problem was formulated with multiple objectives of minimization of power loss along with maximization of system VSI and was evaluated for IEEE 33-and 69-bus radial distribution networks.…”

Section: Discussionmentioning

confidence: 99%

“…In [13,14], hybrid algorithm based on PSO and ACO was used to solve feeder reconfiguration. Recently, other population-based metaheuristic optimizations, such as simulated annealing (SA) [15], hybrid algorithm based on SA and Tabu search [16], enhanced gravitational search algorithm (EGSA) [17], runner root algorithm [18], genetic algorithm (GA) [19][20][21], discrete firefly algorithm [22], modified plant growth simulation algorithm [23], and fuzzy firefly algorithm [24], have been applied to the network reconfiguration problem. Some researchers have integrated both DG allocation and DNR problem to optimize the efficiency of distribution network.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic optimization techniques for voltage stability enhancement of radial distribution network with simultaneous consideration of network reconfiguration and DG sizing and allocations

Rawat¹,

Vadhera²

2019

Turk J Elec Eng & Comp Sci

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In this paper, heuristic optimization techniques, such as integrated particle swarm optimization (IPSO), teaching-learning-based optimization (TLBO), and Jaya optimization, were applied effectively for the first time to optimize the radial distribution network (RDN) by simultaneously considering reconfiguration of the network and allocation and sizing of the distributed generations (DG). The objectives were to maximize the voltage stability and to minimize the power loss of the network without violating the system constraints. In standard PSO technique, the movement of current particle depends upon global best position and its own best position up to current step. However, if the particle lies too close to any of these positions, the guiding role highly decreases and even vanishes. To resolve this problem and to find the global best solution, IPSO was utilized to optimize the network reconfiguration and DG allocation and sizing problem in the RDN. Also, the optimization techniques, such as TLBO and Jaya optimization, which do not require any tuning of parameters, unlike other heuristic optimization techniques, were implemented successfully in this paper. Seven test cases were generated from different combinations of network reconfiguration and DG allocation and sizing. Moreover, for comparison, the optimization techniques, such as particle swarm optimization (PSO), adaptive cuckoo search algorithm (ACSA), harmony search algorithm (HSA), and fireworks algorithm (FWA), were also applied to IEEE 33-and 69-bus distribution test networks. The comparison results prove overall superiority of Jaya optimization when applied on the two IEEE bus systems with seven test cases undertaken.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heuristic optimization techniques for voltage stability enhancement of radial distribution network with simultaneous consideration of network reconfiguration and DG sizing and allocations

Rawat¹,

Vadhera²

2019

Turk J Elec Eng & Comp Sci

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show abstract

“…Hung applied FA in OFDM systems [29] and Kamarian et al used FA for thermal buckling optimization of composite plates [32], while Jafari and Akbari used FA to optimize micrometre-scale resonator modulators [31], and Othman et al used a supervised FA to achieve optimal placement of distributed generators [49]. Also, Singh et al combined FA with least-squares method to estimate power system harmonics [61], and Kaur and Ghosh used a fuzzy FA for network reconfiguration of unbalanced distribution networks [34].…”

Section: Applications Of Fa and Its Variantsmentioning

confidence: 99%

Nature-Inspired Algorithms and Applied Optimization

Yang¹

2018

Studies in Computational Intelligence

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“…The reconfiguration method used in this paper is the traditional branch and bound method that casts doubt on the efficiency of the method for large networks. In Reference 14, the unbalanced DNR was performed using a Firefly algorithm. The main innovation of this reference is the changes made to the Firefly algorithm and utilizes a fuzzy algorithm.…”

Section: Introductionmentioning

confidence: 99%

Reconfiguration of distribution network using discrete particle swarm optimization to reduce voltage fluctuations

Behbahani

Jalilian

Amini

2020

Int Trans Electr Energ Syst

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Summary Reconfiguration of the distribution network (DN) is the changing of status of normally open and close switches for a specific purpose. In this paper, a situation is studied in which a fluctuating load (such as a welder) violates the network power quality (PQ) standards. Therefore, in order to mitigate the emission of the generated flicker, an optimization problem is developed for improving the PQ of the network comprising voltage flicker level and voltage profile in addition to the network power loss as a traditional objective of the DN reconfiguration. The optimization procedure is performed by changing the network configuration using tie and sectionalizing switches. Discrete particle swarm optimization is utilized to optimize the network configuration. The proposed method is evaluated in two DNs. IEEE standard 69‐bus DN and 95‐bus practical Iranian DN are considered as case study networks. The results show the effectiveness of the proposed method for improving the network PQ, especially in case of voltage flicker. The presented studies and procedure will guide the utility engineers in making decisions associated with the choice of reasonable mitigation technique to overcome the identified PQ problems.

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Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm

Cited by 40 publications

References 37 publications

Heuristic optimization techniques for voltage stability enhancement of radial distribution network with simultaneous consideration of network reconfiguration and DG sizing and allocations

Heuristic optimization techniques for voltage stability enhancement of radial distribution network with simultaneous consideration of network reconfiguration and DG sizing and allocations

Nature-Inspired Algorithms and Applied Optimization

Reconfiguration of distribution network using discrete particle swarm optimization to reduce voltage fluctuations

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