A 16kV Distribution Network Reconfiguration by Using Evolutionaring Programming for Loss Minimizing (original) (raw)
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Power losses issues persevered over few decades in the high demand utilization of energy electricity in developing countries. Thus, the radial structure of distribution network configuration is extensively used in high populated areas to ensure continuity of power supply in the event of fault. This paper proposes heuristic Genetic Algorithm known as SIGA (Selection Improvement in Genetic Algorithm) in consideration of genetic operator probabilities likewise the progression of switch adjustment in Distribution Network Reconfiguration (DNR) while satisfying the parameters constraints. The SIGA algorithm was embodied throughout the process in IEEE 33-bus distribution system in selection of five tie switches. As a consequence, the power losses were ranked in accordance to the minimum values and voltage profile improvement obtainable by the proposed algorithm. The results show that the SIGA performs better than GA by giving the minimized value of power losses.
Power losses issues persevered over few decades in the high demand utilization of energy electricity in developing countries. Thus, the radial structure of distribution network configuration is extensively used in high populated areas to ensure continuity of power supply in the event of fault. This paper proposes heuristic Genetic Algorithm known as SIGA (Selection Improvement in Genetic Algorithm) in consideration of genetic operator probabilities likewise the progression of switch adjustment in Distribution Network Reconfiguration (DNR) while satisfying the parameters constraints. The SIGA algorithm was embodied throughout the process in IEEE 33-bus distribution system in selection of five tie switches. As a consequence, the power losses were ranked in accordance to the minimum values and voltage profile improvement obtainable by the proposed algorithm. The results show that the SIGA performs better than GA by giving the minimized value of power losses.
A Genetic Algorithm Approach for Optimal Distribution System Network Reconfiguration
Journal of Energy Technologies and Policy, 2017
Electrical energy is an essential ingredient for the industrial and all-round development of any country. Power distribution systems are radial in configuration and this makes the networks hard to manage, thus, the need for optimization. This paper presents the optimization of network reconfiguration of distribution system using genetic algorithm to get the optimal switching scheme for network reconfiguration with objective function to reduce power loss and improve active power of the system. Load flow for the network reconfiguration problem was formulated as single objective optimization problem. The optimization model was simulated using MATLAB/SIMULINK and validated on standard IEEE 13-bus and 25-bus distribution test feeders. The result shows that active power increases by 91.1% (1.6469p.u.) while the power loss reduced by 99.4% (1.6372p.u.) for 13-bus system. For 25-bus system, active power increased by 27% (0.9154p.u.) and power loss reduced by 96.2% (4.3074p.u.) after optimiz...
2018
In this paper, we use an Evolutionary Algorithm (EA) and the branch exchange method to solve the optimal reconfiguration in radial distribution systems for power loss reduction that determine the optimal switches. The EA is a relatively powerful intelligence evolution method for solving optimization problems. It is a population based approach that is inspired from natural behaviour of species. In this paper EA is applied to a realistic distribution system (106 buses) located in the Medea city (Algeria). For the comparison purposes, our method is validated with the classical Branch and Bound (BB) method, widely used by the Distribution Companies. The results confirm the superiority of the EA.
Research Journal of Applied Sciences, Engineering and Technology, 2014
This study presents the implementation of Improved Genetic Algorithm (IGA) to minimize the power losses in the distribution network by improving selection operator pertaining to the least losses generated from the algorithm. The major part of power losses in electrical power network was highly contributed from the distribution system. Thus, the need of restructuring the topological of distribution network configuration from its primary feeders should be considered. The switches identification within different probabilities cases for reconfiguration purposes are comprehensively implemented through the proposed algorithm. The investigation was conducted to test the proposed algorithm on the 33 radial busses system and found to give the better results in minimizing power losses and voltage profile.
Network Reconfiguration for Loss Reduction in Electrical Distribution System Using Genetic Algorithm
2013
Distribution system is critical links between the utility and the nuclear installation. During feeding electricity to that installation there are power losses. The quality of the network depends on the reduction of these losses. Distribution system which feeds the nuclear installation must have a higher quality power. For example, in Inshas site , electrical power is supplied to the nuclear reactor and other nuclear facilities from two incoming feeders (one from new abu–zabal substation and the other from old abu–zabal substation). Each feeder is designed to carry the full load, while the operator preferred to connect with a new abu–zabal substation, which has a good power quality. Bad power quality affects directly the nuclear reactor and has a negative impact on the installed sensitive equipment and instruments of the operation.This paper introduces an optimization technique based on genetic algorithms for distribution network reconfiguration to reduce the network losses to minimu...
Restructuration of distribution power system using Genetics Algorithms
2015 50th International Universities Power Engineering Conference (UPEC), 2015
This paper uses Genetic Algorithms (GAs) to solve the optimal reconfiguration in radial distribution systems for power loss reduction that determine the optimal switches. The GA is a relatively powerful intelligence evolution method for solving optimization problems. It is a population based approach that isinspired from natural behavior of species. In this paper GA is applied to a realistic distribution system (106 buses) located in the medea city (Algeria). For the comparison purposes, our method is validated with the classical Branch and Bound (B&B) method, widely used by the Distribution Companies. The results confirm the superiority of the GA.
International Journal of Intelligent Engineering and Systems, 2019
This paper presents a network reconfiguration which is a vital analysis process for optimizing and controlling distribution systems. The method is based on genetic algorithm by changing the status of the switches to improve the operational performance. The main objective is to minimize the system power losses and to keep bus voltage profile into limits with radial distribution to provide the consumers with quality electrical energy while minimizing the cost. For this optimization problem, an objective function is developed from an electrical branch to sort the fittest solution. Selection, Crossover and mutation are the necessary three operators in which some improvements are made for the effectiveness of the genetic approach. The method can be successfully applied for loss minimum problem. Numerical example simulated with MATLAB/GUI is demonstrated by 33-bus distribution network and tested using a default mode network. As results, premature convergence is avoided, it shows the validity of the proposed methodology while respecting all the constraints.
Electric Power Systems Research, 2010
In this paper a computational implementation of an evolutionary algorithm (EA) is shown in order to tackle the problem of reconfiguring radial distribution systems. The developed module considers power quality indices such as long duration interruptions and customer process disruptions due to voltage sags, by using the Monte Carlo simulation method. Power quality costs are modeled into the mathematical problem formulation, which are added to the cost of network losses. As for the EA codification proposed, a decimal representation is used. The EA operators, namely selection, recombination and mutation, which are considered for the reconfiguration algorithm, are herein analyzed. A number of selection procedures are analyzed, namely tournament, elitism and a mixed technique using both elitism and tournament. The recombination operator was developed by considering a chromosome structure representation that maps the network branches and system radiality, and another structure that takes into account the network topology and feasibility of network operation to exchange genetic material. The topologies regarding the initial population are randomly produced so as radial configurations are produced through the Prim and Kruskal algorithms that rapidly build minimum spanning trees.
Power distribution systems typically have tie and sectionalizing switches whose states determine the topological configuration of the network. The system configuration affects the efficiency with which the power supplied by the substation is transferred to the load. Whenever, as loads vary with time, switch operations may reduce losses in the system. In this paper a new feeder reconfiguration algorithm is build and present for the purpose of power loss reduction in distribution systems at different load profiles. The methodology developed combine optimization technique and control strategy to reconfigure the feeder. The network reconfiguration problem is formulated as single objective optimization problem with equality and inequality constraints. The proposed solution to this problem is based on a general combinatorial optimization algorithm known as genetic algorithm, and the load flow equations in distribution system, two methods of load flow solution with different accuracy are employed. The first one is a simplified method, which uses the approximation of active power and reactive power at the start of the implemented program. The second is a fast decoupled technique, which gives an exact solution at the end of the program. This paper is also intended to propose the control strategies to reconfigure the feeder, by using artificial neural networks with the mapping ability. The proposed algorithm has been implemented in technical MATLAB package and tested with two examples (hypothetical and practical). Tests show that GA is suitable algorithm as optimization technique, high accuracy, and avoid local minimum by searching in several regions and the ANN has the capability of the high speed control strategy decision. Thus, the outcome of the study in this paper shows an efficient technique to solve the problem of network reconfiguration for loss reduction in the distribution power system.