Radial Network Reconfiguration Using Genetic Algorithm Based on the Matroid Theory

Enacheanu, Bogdan; Raison, Bertrand; Caire, Raphaël; Devaux, Olivier; Bienia, Wojciech; Hadjsaïd, Nourédine

doi:10.1109/tpwrs.2007.913303

Cited by 199 publications

(70 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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)

show abstract

Section: Introductionmentioning

confidence: 99%

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

2015

Teh. vjesn.

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…Mendoza et al [7] proposed accentuated crossover and directed mutation, reducing searching space and memory occupation. Enacheanu et al [8] combined GA with graph theories to select an efficient mutation, making all the resulting individuals feasible.…”

Section: Introductionmentioning

confidence: 99%

A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

“…Therefore, the reconfiguration process is widely used to attain one or combination of those objectives [1][2][3][4][5][6][7][8][9][10][11][12]. This can be usually done by altering the open/closed status of switches of the distribution system.…”

Section: Introductionmentioning

confidence: 99%

“…With regard to the complexity of the optimization problem, artificial intelligence techniques are now widely used for the solution of reconfiguration problem such as artificial neural networks [1], genetic algorithm (GA) [2,3] and refined GA (RGA) [4], simulated Abbreviations: V i , ith node voltage; Ij, jth branch current; NR, number of branches; N b , number of buses; P loss , active power loss; P in , active input power to the network; P loss , total active load powers; S i , apparent power in the ith branch; S max i , maximum capacity (KVA) in the ith branch; LBI, load balancing index; Var(x), variance of arrays of X matrix; r i , ith branch resistance; Z(i,j), impedance between ith and jth nodes; P(i), probability of selecting ith position. annealing (SA) [5], plant growth simulation (PGA) [6], fuzzy logic [7] and ant colony optimization (ACO) [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A new fuzzy optimal reconfiguration of distribution systems for loss reduction and load balancing using ant colony search-based algorithm

Saffar

Hooshmand

Khodabakhshian

2011

Applied Soft Computing

View full text Add to dashboard Cite

Radial Network Reconfiguration Using Genetic Algorithm Based on the Matroid Theory

Cited by 199 publications

References 23 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

A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

A new fuzzy optimal reconfiguration of distribution systems for loss reduction and load balancing using ant colony search-based algorithm

Contact Info

Product

Resources

About