Discrete Particle Swarm Optimization to Network Reconfiguration for Loss Reduction and Load Balancing

Sivanagaraju, S.; Rao, J. Viswanatha; Raju, P. Sangameswara

doi:10.1080/15325000701735389

Cited by 59 publications

(34 citation statements)

References 6 publications

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“…These methods find admirable solutions for the medium size systems and are not suitable for large systems [6]. In recent years, new heuristic optimization algorithms like Genetic Algorithm (GA) [7 ÷ 11], Nondominated Sorting Genetic Algorithms (NSGA) [12], matroid theory [13], other meta-heuristics techniques like plant growth [14], Particle Swarm Optimization (PSO) [15], tabu search [16] and ant colony search [17,18] have been proposed for DSR problem. They are aimed to deal with large system with fast execution time [19].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective distribution feeder reconfiguration by considering energy not supplied with distributed generation

2015

Teh. vjesn.

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Original scientific paper Nowadays, the operational performance of the Distribution system can be improved by exchanging the functional links between the elements of the system, called reconfiguration. This paper aims at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria. The newness of the method is in the fact that optimization is evaluated on active power distribution systems i.e by incorporating the distributed generators directly to the main distribution system. This paper presents a proficient method for solving the multi-objective reconfiguration of radial distribution systems with regard to distributed generators. The conformist distribution feeder reconfiguration (DFR) problem cannot meet the reliability requirements. The recommended approach considers reliability (Energy Not Supplied), voltage deviation and power loss problem simultaneously. By taking the reliability objective into account, the DFR problem becomes more complicated than before and it needs to be solved with a precise algorithm. Therefore this study utilizes Non Dominated Sorted Genetic Algorithm-II (NSGA-II) for DFR and Gravitational Search Algorithm (GSA) for DG placement is to find the optimal location and sizing of DGs which minimizes real power loss, Energy Not Supplied (ENS) and improves voltage profile of the network. Keywords: distributed generation; Distribution Feeder Reconfiguration (DFR); Energy not Supplied (ENS); GSA; NSGA-II Rekonfiguracija multi-objektivnog distribucijskog napojnog voda s obzirom na neisporučenu energiju distribuirane proizvodnjeIzvorni znanstveni članak Danas se učinkovitost distribucijskog sustava može poboljšati zamjenom funkcionalnih poveznica između elemenata sustava, to jest rekonfiguracijom. Cilj je rada postići takvu optimizaciju rekonfiguracijom distribucijskog sustava uzimajući u obzir različite kriterije. Novina ove metode je u činjenici da se optimizacija procjenjuje na aktivnim sustavima raspodjele energije tj. ugrađivanjem distribuiranih generatora direktno u glavni distribucijski sustav. U radu se predstavlja napredna metoda za rješavanje multi-objektivne rekonfiguracije sustava radijalne distribucije u odnosu na distribuirane generatore. Konformistički promatran problem rekonfiguracije distribucijskog napojnog voda (DFR) ne može zadovoljiti zahtjevima za pouzdanošću. Preporučeni pristup uzima simultano u obzir problem pouzdanosti (neisporučena energija), devijacije u naponu i gubitka energije. Uzimajući u obzir pouzdanost, DFR problem postaje složeniji nego ranije i treba se riješiti preciznim algoritmom. Stoga se u ovom radu koristi Non Dominated Sorted Genetic Algorithm-II (NSGA-II) za DFR i Gravitational Search Algorithm (GSA) za optimalnu lokaciju i veličinu DG-ija jer se tako minimalizira stvarni gubitak energije, neisporučene energije (ENS) i poboljšava profil napona mreže.Ključne riječi: distribuirana proizvodnja; neisporučena energija (ENS); GSA; NSGA-II; rekonfiguracija distribucijskog napojnog voda (DFR)

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Section: Introductionmentioning

confidence: 99%

Multi-objective distribution feeder reconfiguration by considering energy not supplied with distributed generation

2015

Teh. vjesn.

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“…jb is not changed, otherwise it is changed. The change depends on the integer or binary representations: 2 In addition, a last-resource large number of iterations N (a) For the integer encoding strategies, rounding the velocity results to integer values is considered in [31] and the enhanced integer coded PSO method [32] has been used to minimize the total branch losses by maintaining the radial network structure and without violating the constraints. In these cases, the change can be implemented by searching for a feasible configuration represented by the integer coding used, for example adjacent to the configuration that has to be changed.…”

Section: Discrete and Binary Pso Codingmentioning

confidence: 99%

Multi-Objective Distribution Network Reconfiguration Based on Pareto Front Ranking

et al. 2016

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Electrical distribution system reconfiguration is frequently addressed as a multi-objective problem, typically taking into account the system losses together with other objectives, among which reliability indicators are widely used. In the multi-objective context, Pareto front analysis enables the operator handling conflicting and even noncommensurable objectives without needing the use of additional hypotheses or weights. This paper provides advances on the application of Pareto front analysis to multi-objective distribution network reconfiguration. Starting from previous results in which genetic algorithms were effectively adopted to find the best-known Pareto front, a version of the multiobjective binary particle swarm optimization (MOBPSO) customized for distribution network reconfiguration has been developed by exploiting the internal ranking of the solutions (based on a multi-criteria decision making method in the selection of the local best) and the network topology. Furthermore, the Pareto front mismatch metric (already used by the authors to compare different methods for small networks for which the complete Pareto front can be calculated) has been generalized to be used with large systems for which only the best-known Pareto front is found. Applications to a test network and to a real urban distribution network are discussed, showing the consistent superiority of the customized MOBPSO version with respect to the application of genetic algorithms and of a more classical version of the particle swarm optimization method.

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“…In [2], an exhaustive survey of the modern heuristic methods for the DNR is presented. Artificial intelligence techniques were also applied to DNR problem extensively, for example, simulated annealing (SA) [3], artificial neural networks (ANN) [4], genetic algorithms (GA) [5], ant colony search algorithm (ACSA) [6], fuzzy logic [7] and particle swarm optimization [8].…”

Section: Introductionmentioning

confidence: 99%

Combination of capacitor placement and reconfiguration for loss reduction in distribution systems using selective PSO

Khalil

Gorpinich

Elbanna

2013

22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013)

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Capacitor placement and distribution network reconfiguration (DNR) are two useful methods in reducing the power losses of distribution networks. This paper proposes a selective particle swarm optimization (SPSO) to solve the optimal capacitor placement problem, the optimal feeder reconfiguration problem, and the problem of a combination of the two. The problem is posed as an optimization problem with an objective to maximize the loss reduction and improve the voltage profile. The optimization procedure is subject to some technical (maximum permissible branch current, maximum and minimum voltage limits and maximum permissible size of capacitors) and operational constraints (load connectivity and radial network structure). The proposed algorithm has been implemented and tested on two test systems from the literatures. The results obtained using the proposed algorithm are compared with the results obtained using different methods given in literature. The simulation results demonstrate the effectiveness of the proposed algorithm. In addition, simulation results show that the results of simultaneous capacitor placement and DNR is more effective than considering them separately.

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Discrete Particle Swarm Optimization to Network Reconfiguration for Loss Reduction and Load Balancing

Cited by 59 publications

References 6 publications

Multi-objective distribution feeder reconfiguration by considering energy not supplied with distributed generation

Multi-objective distribution feeder reconfiguration by considering energy not supplied with distributed generation

Multi-Objective Distribution Network Reconfiguration Based on Pareto Front Ranking

Combination of capacitor placement and reconfiguration for loss reduction in distribution systems using selective PSO

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