A novel codification for meta-heuristic techniques used in distribution network reconfiguration

Swarnkar, Anil; Gupta, Nikhil; Niazi, K. R.

doi:10.1016/j.epsr.2011.03.020

Cited by 64 publications

(64 citation statements)

References 23 publications

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“…These algorithms converge, on average, in 34, 43 and 30.20 generations, respectively, whereas the BRKGA converges in 21.28 generations. These results show that the BRKGA is among the best metaheuristics for DNR problems and its performance can be substantially improved with the application of heuristic mechanisms such as restart procedures to prevent convergence to local optima (Gonçalves and Resende 2010) and heuristic spark (Swarnkar et al 2011). …”

Section: -Node Test Systemmentioning

confidence: 92%

“…The codification used in this work is derived from graph theory and uses a set of rules to correct infeasible individuals and, thus, generate only feasible vectors during decodification. This set of rules was proposed in Swarnkar et al (2011) to be used in conjunction with any metaheuristic technique. In a problem of DNR for loss reduction, feasibility of a solution means it is radial, without isolated nodes from the network.…”

Section: Codification For Dnr Problemsmentioning

confidence: 99%

“…Electric power distribution systems are located after the transmission and subtransmission systems and are generally structured in mesh but operated in radial configuration to reduce short-circuit fault levels and to facilitate the implementation of effective coordination and protection schemes (Swarnkar et al 2011). The problem consists in altering the open or closed status of normally closed (NC) sectionalizing switches and normally open (NO) tie-line switches to optimize a given objective of the network while satisfying various operational constraints and maintaining the radiality of the distribution system.…”

Section: Introductionmentioning

confidence: 99%

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A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

2017

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This work presents a biased random-key genetic algorithm (BRKGA) to solve the electric distribution network reconfiguration problem (DNR). The DNR is one of the most studied combinatorial optimization problems in power system analysis. Given a set of switches of an electric network that can be opened or closed, the objective is to select the best configuration of the switches to optimize a given network objective while at the same time satisfying a set of operational constraints. The good performance of BRKGAs on many combinatorial optimization problems and the fact that it has never been applied to solve DNR problems are the main motivation for this research. A BRKGA is a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. Solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval (0,1), thus enabling an indirect search of the solution inside a proprietary search space. The genetic operators do not need to be modified to generate only feasible solutions, which is an exclusive task of the decoder of the problem. Tests were performed on standard distribution systems used in DNR studies found in the technical literature and the performance and robustness of the BRKGA were compared with other GA implementations.

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Section: -Node Test Systemmentioning

confidence: 92%

Section: Codification For Dnr Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

2017

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“…In the present work, the codification proposed in [32] is used to solve the problem. This is a rule-based codification to check and correct infeasible radial topologies.…”

Section: Node Voltage Constraintmentioning

confidence: 99%

“…3) All the common branch vectors of a prohibited group vector cannot participate simultaneously to form an individual. The definitions of loop vector, common branch vector and prohibited group vector may be referred from [32].…”

Section: Node Voltage Constraintmentioning

confidence: 99%

Optimal distributed generation allocation in radial distribution systems considering customer-wise dedicated feeders and load patterns

Kanwar

Gupta

Niazi

et al. 2015

J. Mod. Power Syst. Clean Energy

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Distribution system planners usually provide dedicated feeders to its different class of customers, each of whom has its own characteristic load pattern which varies hourly and seasonally. A more realistic modeling should be devised by considering the daily and seasonal variations in the aggregate load patterns of different class of customers. This paper addresses a new methodology to provide integrated solution for the optimal allocation of distributed generations and network reconfiguration considering load patterns of customers. The objectives considered are to maximize annual energy loss reduction and to maintain a better node voltage profile. Bat algorithm (BA) is a new bio-inspired search algorithm which has shown an advance capability to reach into the promising region, but its exploration is inadequate. The problem is solved by proposing the improved BA (IBA). The proposed method is investigated on the benchmark IEEE 33-bus test distribution system and the results are very promising.

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Behavior analysis of new bio‐inspired metaheuristics to solve distribution network reconfiguration problem under different radiality constraints representation

Gerez

Costa

Filho

2021

Int J Numerical Modelling

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As distribution networks operate in their majority in a radial way, this condition becomes one of the most important constraints of distribution network reconfiguration (DNR) problem. There are diverse ways to represent this in DNR, but a lack of studies that provide an analysis of its behavior is noted, specially regarding its influence in metaheuristics and bio‐inspired metaheuristics. This paper presents a discussion between two alternatives to represent this in DNR in conjunction with three new bio‐inspired metaheuristics. One is based on the formation of the incidence matrix of the system and its determinant; the other is a modified approach of a method that establishes a set of forbidden switches through the analysis of the system fundamental loops. To solve DNR, three new bio‐inspired metaheuristics are presented: monarch butterfly optimization (MBO), gray wolf optimizer (GWO), and marine predators algorithm (MPA). A comparison is performed with selective particle swarm optimization (SPSO). To test the alternatives, three systems are used (33‐, 69‐, and 84‐ bus), with results showing the influence of the radiality approach in the results of bio‐inspired metaheuristics.

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A novel codification for meta-heuristic techniques used in distribution network reconfiguration

Cited by 64 publications

References 23 publications

A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

Optimal distributed generation allocation in radial distribution systems considering customer-wise dedicated feeders and load patterns

Behavior analysis of new bio‐inspired metaheuristics to solve distribution network reconfiguration problem under different radiality constraints representation

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