Artificial Immune Systems applied to the reconfiguration of electrical power distribution networks for energy loss minimization

Oliveira, Leonardo W. de; Oliveira, Edimar J. de; Gomes, Felipe V.; Silva, Ivo Chaves da; Marcato, André L. M.; Resende, Paulo V.C.

doi:10.1016/j.ijepes.2013.11.008

Cited by 72 publications

(66 citation statements)

References 33 publications

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“…Simulations are carried out on the IEEE 33-bus test system, comparing the initial configuration with the optimal one and without a comparison to other algorithms. Similar methods are followed in [69] and [70].…”

Section: Immune Algorithms (Ia)mentioning

confidence: 99%

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Quintero-Duran

Candelo-Becerra

Sousa

2017

JESTR

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Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topic.

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Section: Immune Algorithms (Ia)mentioning

confidence: 99%

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Quintero-Duran

Candelo-Becerra

Sousa

2017

JESTR

View full text Add to dashboard Cite

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“…Distribution network reconfiguration is a complex constrained optimization process in the operation of a power system. It is an alternative used to reduce technical loss in distribution feeders [4]. Therefore, reconfiguration is an efficient and necessary approach to saving electrical power and enhancing the performance of a distribution system [5].…”

Section: Introductionmentioning

confidence: 99%

“…The Cuckoo Search Algorithm (CSA) proposed by Nguyen and Truong [13] for network reconfiguration had two objectives, which were to enhance the voltage profile and reduce power loss. de Oliveira et al [4] presented a methodology with the aim of minimizing energy loss by applying Artificial Immune (AI) on a 33-bus test network.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Distribution Network Reconfiguration Analysis under Different Load Demands

et al. 2017

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Abstract:The strategies of distribution network reconfiguration are applicable for minimizing power loss and saving electrical energy in the distribution system. Network reconfiguration is usually represented by constant load demand so ignoring the variability of load demand causes uncertainty and misleading results in the minimization of power loss. This paper consists of two parts: first, the reconfiguration was accomplished using an optimization framework based on constant load to find sets of optimal switches. The minimization of active power loss was taken as an objective function while bus voltage, branch current and system radiality were taken as system constraints. The study was applied to a 33-bus test distribution network, which is exceedingly used as test examples for solving reconfiguration problems. Second, lists of the configurations set obtained from the first part, as well as other different optimization methods proposed earlier under constant load demand were taken as test switches. Additionally, the network in the presence of distributed generators was taken to analyze the reconfiguration under an active network. Two types of load demands; the variable load and voltage-dependent load, are proposed to represent the practical load demands. This paper presents a new method for good analysis as it defines the effect of loading levels and loading patterns on a distribution system performance for passive and active networks. The proposed approach tries to find the actual power loss under different characteristics of loads. Therefore, the probable benefit of this approach is the contribution to providing more flexibility for electrical utilities in terms of distribution system operation, while also opening new prospects in the automation of smart distribution systems.

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“…The Quantum-inspired Binary Firefly algorithm is used to find the optimal network reconfiguration. A methodology for the reconfiguration of radial electrical distribution systems based on the bio-inspired meta-heuristic Artificial Immune System (AIS) to minimize energy losses was presented by De Oliveira et al (2014), in which radiality and connectivity constraints as well as different load levels for planning the system operation were considered. Sedighizadeh et al (2013) have proposed an efficient Hybrid Big BangBig Crunch (HBB-BC) optimization algorithm to solve the multi-objective reconfiguration of balanced and unbalanced distribution systems in a fuzzy framework.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Symbiotic Organisms Search for Optimal Fuzzified Joint Reconfiguration and Capacitor Placement in Electric Distribution Systems

2017

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Network reconfiguration and capacitor allocation are two valuable tools to optimally manage distribution systems. In the present work, an efficient algorithm is proposed for the joint problem of reconfiguration and capacitor placement in distribution systems. The multiobjective problem is formulated to minimize objective functions of total network power losses, bus voltage violation, and branch loading deviations subject to different technical and operational constraints. To enhance exploration ability, the Symbiotic Organisms Search algorithm is strengthened with Particle Swarm Optimization capabilities to develop the Hybrid Symbiotic Organisms Search algorithm, which is applied to solve the proposed problem. Objective functions are fuzzified and aggregated by a geometric mean operator to have the same scales. The proposed joint optimization problem is solved with the introduced Hybrid Symbiotic Organisms Search algorithm in two different case studies of balanced and unbalanced test systems. According to obtained results that are compared with some well-known evolutionary algorithms, the superiority of the Hybrid Symbiotic Organisms Search algorithm is confirmed in terms of solution optimality and convergence speed.

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Artificial Immune Systems applied to the reconfiguration of electrical power distribution networks for energy loss minimization

Cited by 72 publications

References 33 publications

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

A New Method for Distribution Network Reconfiguration Analysis under Different Load Demands

Hybrid Symbiotic Organisms Search for Optimal Fuzzified Joint Reconfiguration and Capacitor Placement in Electric Distribution Systems

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