Radial Distribution Network Reconfiguration for Loss Reduction and Load Balancing using Plant Growth Simulation Algorithm

Rao, R. Srinivasa; Sivanagaraju, S.; Nagar, Hayath

doi:10.15676/ijeei.2010.2.4.2

Cited by 26 publications

(7 citation statements)

References 22 publications

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“…This process requires a modification of the system radial topology. One of the main objectives is to improve the load balance for all branches, which can be attained using the system load balance index (SLBI), which is defined by SLBI = where S j is the apparent power flow in branch j, and S j max is the maximum apparent power capacity of branch j [33]. Our objective function is to minimize SLBI.…”

Section: Load Balancing Indexmentioning

confidence: 99%

Power Distribution System Reconfiguration using Fast Non-Dominated Sorting Genetic Algorithm and Graph Theory

Eldurssi

O’Connell²

2012

Power and Energy Systems and Applications

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Distribution system reconfiguration (DSR) is a multiobjective, non-linear problem. This paper introduces a new, fast, non-dominated sorting genetic algorithm (FNSGA) for solving the DSR problem in normal operation by satisfying all objectives simultaneously with a relatively short computational time. The goals of the project are to minimize real power losses, and improve the voltage profile and load balancing index with minimum switching operations. Instead of dealing with a single objective function while the others are formulated as constraints, as in the traditional methods, the FNSGA is applied to optimize all the objectives according to the operator's wishes. Moreover, an adapted mutation operation is applied instead of a random one to speed up convergence. Radial topology is satisfied using graph theory by formulating the branch-bus incidence matrix (BBIM) and checking the rank of each topology. The algorithm is applied to two different systems, the IEEE16 bus and the IEEE 32 bus test systems. The results show the efficiency of this algorithm as compared to other methods in terms of both achieving all the goals and minimizing the computational time with reasonable population and generation sizes.

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Section: Load Balancing Indexmentioning

confidence: 99%

Power Distribution System Reconfiguration using Fast Non-Dominated Sorting Genetic Algorithm and Graph Theory

Eldurssi

O’Connell²

2012

Power and Energy Systems and Applications

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“…A substantial amount of time has been invested by contemporary researchers in studying the influence of network reconfiguration in achieving loss reduction [13] and voltage stability improvement [14]. It can be seen from the works presented in [15][16][17][18] that most of the strategies are focused on reducing the feeder active power losses and enhancement of voltage stability for constant loading condition.…”

Section: Introductionmentioning

confidence: 99%

Optimal feeder reconfiguration in distributed generation environment under time-varying loading condition

Odyuo¹,

Sarkar²,

Sumi³

2021

SN Appl. Sci.

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The development and planning of optimal network reconfiguration strategies for electrical networks is greatly improved with proper application of graph theory techniques. This paper investigates the application of Kruskal's maximal spanning tree algorithm in finding the optimal radial networks for different loading scenarios from an interconnected meshed electrical network integrated with distributed generation (DG). The work is done with an objective to assess the prowess of Kruskal's algorithm to compute, obtain or derive an optimal radial network (optimal maximal spanning tree) that gives improved voltage stability and highest loss minimization from among all the possible radial networks obtainable from the DG-integrated mesh network for different time-varying loading scenarios. The proposed technique has been demonstrated on a multiple test systems considering time-varying load levels to investigate the performance and effectiveness of the suggested method. For interconnected electrical networks with the presence of distributed generation, it was found that application of Kruskal's algorithm quickly computes optimal radial configurations that gives the least amount of power losses and better voltage stability even under varying load conditions. Article Highlights Investigated network reconfiguration strategies for electrical networks with the presence of Distributed Generation for time-varying loading conditions. Investigated the application of graph theory techniques in electrical networks for developing and planning reconfiguration strategies. Applied Kruskal’s maximal spanning tree algorithm to obtain the optimal radial electrical networks for different loading scenarios from DG-integrated meshed electrical network.

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“…The quadratic-loss allocation scheme such as branch current flow and ensures that every consumer has allocated losses at branches for which current it contributes [3]. The load balance index based approach the optimal network reconfiguration of the distribution system using a Plant Growth Simulation algorithm is reported in [4]. The particle swarm optimization (PSO) algorithm based approach to distribution feeder reconfiguration with presence of DG units [5].The refined genetic algorithm (RGA) based reconfiguration of the distribution network in order to minimize the power loss in the system is reported in [6].…”

Section: Introductionmentioning

confidence: 99%

Network Reconfiguration with Placement of DGs to Minimize the Power Loss in RDSs using CABC Algorithm

Mohandas¹

2019

IJCA

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The network reconfiguration is reshaping of the network system with supporting of sectionals/tie switches so as to reduce the power loss and to improve the voltage profile of the system. This paper presents the reconfiguration of a network system with location of distributed generation (DG) is to reduce power loss and in order to improve the voltage stability of the radial distribution systems (RDS). In this approach, the objective function is formulated based on the various technical issues such as power losses, thermal limit, voltage profile and stability of the system. The network reconfiguration problem is a nonlinear optimization problem; a chaotic artificial bee colony (CABC) algorithm is implemented to find the optimal solution of this approach. It is one of the enhanced versions of artificial bee colony algorithm. Two different cases are considered of this approach such as (i) only reconfiguration and (ii) reconfiguration with DGs. The efficiency of the proposed algorithm is validated by testing it on 33-node and 69-node radial distribution systems. The simulation results of this proposed approach are compared with other methods available in the earlier report.

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Radial Distribution Network Reconfiguration for Loss Reduction and Load Balancing using Plant Growth Simulation Algorithm

Cited by 26 publications

References 22 publications

Power Distribution System Reconfiguration using Fast Non-Dominated Sorting Genetic Algorithm and Graph Theory

Power Distribution System Reconfiguration using Fast Non-Dominated Sorting Genetic Algorithm and Graph Theory

Optimal feeder reconfiguration in distributed generation environment under time-varying loading condition

Network Reconfiguration with Placement of DGs to Minimize the Power Loss in RDSs using CABC Algorithm

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