Artificial Neural Networks and Clustering Techniques Applied in the Reconfiguration of Distribution Systems

Salazar, Harold; Gallego, Ramón Alfonso; Romero, Rubén

doi:10.1109/tpwrd.2006.875854

Cited by 126 publications

(58 citation statements)

References 24 publications

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“…For a large network, the difficulty of network reconfiguration is finding in the status (open or closed) the optimal configuration and the ability to handle switches to assure sections of the network. The primary aim of network reconfiguration is to determine the topology in which the active power loss is minimized to be as low as possible [14]. To reach optimal configuration, any proposed algorithm for optimization should be run a few times with a different random parameter set each time [15], where the random values of the parameters may give better or worse results to the reference objective [16].…”

Section: Reconfiguration Methodsmentioning

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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Section: Reconfiguration Methodsmentioning

confidence: 99%

“…Step 7: Update (velocity using Equation (13), position using Equation (14) and weight coefficient using Equation (15)). …”

Section: Algo1mentioning

confidence: 99%

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

et al. 2017

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“…In this sense, the literature related with the DSR problem assuming fixed demand levels includes publications reporting the use of heuristic algorithms [1][2], metaheuristics as Genetic Algorithms [3], Simulated Annealing [4], Tabu Search [5], Ant Colonies [6], GRASP [7], Artificial Neural Networks [8] and Artificial Immune Systems [9]. On the other hand, references [10][11][12][13] address the DSR problem assuming variable demand levels.…”

Section: Introductionmentioning

confidence: 99%

Distribution System Reconfiguration with variable demands using the Opt-aiNet algorithm

Souza

Romero

Pereira

et al. 2016

2016 13th International Conference on the European Energy Market (EEM)

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Abstract-This paper describes the application of the Opt-aiNet algorithm to the reconfiguration problem of distribution systems considering variable demand levels. The Opt-aiNet algorithm is an optimization technique inspired in the immunologic bio system and it aims at reproducing the main properties and functions of this system. The reconfiguration problem of distribution networks with variable demands is a complex problem that aims at identifying the most adequate radial topology of the network that complies with all technical constraints in every demand level while minimizing the cost of power losses along an extended operation period. This work includes results of the application of the Opt-aiNet algorithm to distribution systems with 33, 84, 136 and 417 buses. These results demonstrate the robustness and efficiency of the proposed approach.

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“…Distribution system reconfiguration and capacitor placement are the two different practices adapted for distribution system loss reduction for more than five decades. Distribution system reconfiguration is the process of changing the open/close status of the switches present in the distribution system in order to reduce the power loss [1][2][3][4][5][6]. The reconfiguration problem later have been viewed as mutli-objective optimization problem by making the power flow constraints as multi-objectives [7][8].…”

Section: Introductionmentioning

confidence: 99%

Opposition Based Differential Evolution Algorithm for Capacitor Placement on Radial Distribution System

Muthukumar¹,

Thanushkodi²

2014

Journal of Electrical Engineering and Technology

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-Distribution system is a critical link between customer and utility. The control of power loss is the main factor which decides the performance of the distribution system. There are two methods such as (i) distribution system reconfiguration and (ii) inclusion of capacitor banks, used for controlling the real power loss. Considering the improvement in voltage profile with the power loss reduction, later method produces better performance than former method. This paper presents an advanced evolutionary algorithm for capacitor inclusion for loss reduction. The conventional sensitivity analysis is used to find the optimal location for the capacitors. In order to achieve a better approximation for the current candidate solution, Opposition based Differential Evolution (ODE) is introduced. The effectiveness of the proposed technique is validated through 10, 33, 34 and85-bus radial distribution systems.

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Artificial Neural Networks and Clustering Techniques Applied in the Reconfiguration of Distribution Systems

Cited by 126 publications

References 24 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

Distribution System Reconfiguration with variable demands using the Opt-aiNet algorithm

Opposition Based Differential Evolution Algorithm for Capacitor Placement on Radial Distribution System

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