Distribution Network Reconfiguration Based on Simulated Annealing Immune Algorithm

Chen, Jie; Zhang, Fan; Zhang, Yuexi

doi:10.1016/j.egypro.2011.10.037

Cited by 18 publications

(6 citation statements)

References 1 publication

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“…A simulated annealing (SA) immune algorithm is used for optimization of distributed generation reconfiguration in [303], [304] for efficient use of distribution system. An advanced immune algorithm is used in [236] to optimize the clustering performance of TT-SVM pattern classifier.…”

Section: Bee Colony Optimizationmentioning

confidence: 99%

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

et al. 2021

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Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its preeminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.

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Section: Bee Colony Optimizationmentioning

confidence: 99%

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

et al. 2021

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“…From every pair of parents two new offspring are created, which need to keep the part of the parent's genetic material, preferably the best one. In most of the approaches that employ evolution algorithms for the network reconfiguration problem, this is usually the point at which the problems appear [5,8,10,11,16,17,19,20,22]. These problems are related to the methods used in the crossover process, which result in offspring that violate radiality constraint or have isolated parts of the network.…”

Section: Crossover Processmentioning

confidence: 99%

“…The proposed algorithm introduces several improvements related to the generation of the initial set of possible solutions as well as crossover and mutation steps in the genetic algorithm. Although genetic algorithms are often used in the optimal reconfiguration of a distribution networks [15][16][17][18][19][20][21][22][23][24], most of the approaches [16][17][18][19][20][21][22][23] don't provide an effective means of creating an initial population, as well as effective operators to implement a crossover and mutation process over the set of population individuals. Due to this, during the evolution process, a large number of generated individuals is often rejected and power flow calculations are often conducted for unfeasible individuals (network topologies), that don't provide the radial network topology or include the isolated parts of the network.…”

Section: Introductionmentioning

confidence: 99%

Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

et al. 2020

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This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time.

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“…A hybrid Simulated Annealing Immune (SAI) algorithm was presented in [95]. Chen et al considered a loopencoding method to avoid unfeasible solutions for the radial reconfiguration problem.…”

Section: Immune Algorithms (Ia)mentioning

confidence: 99%

“…Chen et al mixed concepts from SA and Immune (SAI) algorithms to avoid local stagnation in [95]. The objective function was to reduce power losses in distribution systems.…”

Section: Simulated Annealing (Sa)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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Distribution Network Reconfiguration Based on Simulated Annealing Immune Algorithm

Cited by 18 publications

References 1 publication

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

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

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