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

Saffar, Ali; Hooshmand, Rahmat‐Allah; Khodabakhshian, Amin

doi:10.1016/j.asoc.2011.03.003

Cited by 86 publications

(56 citation statements)

References 15 publications

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“…In this paper, a new index of average voltage is proposed to manage all buses voltages and satisfy most of the electrical utility constraints. The index is given in Equation (11).…”

Section: Average Voltage Conceptmentioning

confidence: 99%

“…Gupta et al [9] and Duan et al [10] presented their methods to reduce power loss based on the Enhanced Genetic Algorithm (EGA). Saffar et al [11] proposed a new combined method between fuzzy and ant colony search-based algorithms where power loss reduction and load balancing were the main objectives. Carpaneto and Chicco [12] proposed the application of a hyper-cube ant colony optimization framework on the test system to find the optimal configuration with the objective of power loss minimization.…”

Section: Introductionmentioning

confidence: 99%

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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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“…In this paper, a new index of average voltage is proposed to manage all buses voltages and satisfy most of the electrical utility constraints. The index is given in Equation (11).…”

Section: Average Voltage Conceptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…Simulations indicate that the multidimensional information source designed and implemented in real time shows that the results have improved compared to previous approaches. In [21], load balancing and automatic load balancing for cloud services are surveyed with fuzzy logic and ant colony, and a method is developed to support cloud agents for an optimal configuration in using dependencies on cloudbased applications that require high security. This method uses the model-driven principles of UML and the Bayesian networks to store, analyze, and optimize the configuration in the cloud space.…”

Section: Literature Reviewmentioning

confidence: 99%

Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

Sadeghi¹

2018

ijacsa

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Abstract-Load balancing, reliability, and traffic are among the service-oriented issues in software engineering, and cloud computing is no exception to this rule and has put many challenges ahead of experts in this field. Considering the importance of the load balancing process in cloud computing, the purpose of this paper is to provide an appropriate solution for load balancing load in complex cloud systems using an adaptive fuzzy neural system. This system consists of four layers, and a particular operation is performed on each layer. The results of the experiments show that the system has better performance in the criteria mentioned above (balancing, traffic and reliability).

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“…Ghasemi and Moshtagh (2014) have found the optimal reconfiguration of distribution networks by a novel codification and modified heuristic approach for reducing power losses cost and increasing cost-benefit from voltage improvement. Saffar et al (2011) have suggested an optimal reconfiguration of distribution systems using a new fuzzy-ant colony search based algorithm. The goals of optimization are the loss reduction and load balancing.…”

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

Cited by 86 publications

References 15 publications

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

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

Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

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

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