In this paper, a method based on bacterial foraging optimization algorithm (BFOA) is proposed for distribution network reconfiguration with the objective of loss minimization. A novel model to simplify a distribution network is presented. The feeder reconfiguration problem is formulated as a non-linear optimization problem, and BFOA is used to find the optimal solution. According to the characteristics of distribution network, some modifications are done to retain the radial structure and reduce the searching requirement. Test results of a 33 bus sample network have shown that the proposed feeder reconfiguration method can effectively ensure the loss minimization, and the BFOA technique is efficient in searching for the optimal solution. © 2011 Elsevier Ltd. All rights reserved.

Sathishkumar K

Department of Electrical Engineering

School of Electrical Engineering

Vellore Campus

Jayabarathi T

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

International Journal of Electrical Power & Energy Systems

Fulltext

International Transactions on Electrical Energy Systems

Loss minimization techniques for optimal operation and planning of distribution systems: A review of different methodologies

This paper presents a new evolutionary approach for reconfiguration of radial systems. The framework applied for optimization is Symbiotic Organism Search Algorithm (SOSA). The algorithm is impressed by the interactive behavior opted by the living organisms for surviving and to propagate in the ecosystem. This concept aims for optimal survivability in the ecosystem involving the harm and benefits received from other organisms. The aim is to find optimal reconfiguration and to reduce the real power loss in the distribution side. This approach is examined on 16-bus and 33-bus systems. The results show a significant reduction of real power loss. The time required for execution is less when compared to other approaches. Based on the results calculated with distribution load flow algorithm the SOSA gives better results in terms of real power loss reduction and it is best suitable for digital automation systems. © 2019 - IOS Press and the authors. All rights reserved

Journal of Intelligent & Fuzzy Systems

A novel distribution system reconfiguration for loss minimization using symbiotic organism search algorithm

The work gives a novel evolutionary technique for the re-configuration test IEEE systems. The framework applied for optimization is Symbiotic Organism Search Algorithm (SOSA). The aim is to find optimal reconfiguration and to improve the active loss in the consumer side. This approach is examined on 16 and 33 IEEE test circuits. The results shows a valid improvement of active losses. The time required for execution is less when compared to other approaches.

2019 IEEE 2nd International Conference on Power and Energy Applications (ICPEA)

A Novel Distribution Circuit Reconfiguration for Loss Minima With Symbiotic Organism Search Algorithm

International Journal of Ambient Energy

Optimal reconfiguration and renewable distributed generation allocation in electric distribution systems

Network reconfiguration which is constrained non linear optimization problem has been solved for loss minimization, load balancing, etc. for last two decades using various heuristic search evolutionary algorithms like binary particle swarm optimization, neuro-fuzzy techniques, etc. The contribution of this paper lies in considering distributed generation which are smaller power sources like solar photovoltaic cells or wind turbines connected in the customer roof top. This new connection in the radial network has made unidirectional current flow to become bidirectional there by increasing the efficiency but sometimes reducing stability of the system. Modified plant growth simulation algorithm has been applied here successfully to minimize real power loss because it does not require barrier factors or cross over rates because the objectives and constraints are dealt separately. The main advantage of this algorithm is continuous guiding search along with changing objective function because power from distributed generation is continuously varying so this can be applied for real time applications with required modifications. This algorithm here is tested for a standard 33 bus radial distribution system for loss minimization and test results here shows that this algorithm is efficient and suitable for real time applications. © 2015 The Authors. Published by Elsevier Ltd.

Energy Reports

Power system reconfiguration in a radial distribution network for reducing losses and to improve voltage profile using modified plant growth simulation algorithm with Distributed Generation (DG)

Loss allocation to consumers before and after reconfiguration of radial distribution networks

Abstract and Applied Analysis

Adaptive Bacterial Foraging Optimization

International Journal of Power and Energy Systems

AN IMPROVED TECHNIQUE FOR SERVICE RESTORATION IN DISTRIBUTION SYSTEMS USING NON-DOMINATED SORTING GENETIC ALGORITHM

IEEE Transactions on Power Delivery

Study of Reconfiguration for the Distribution System With Distributed Generators

Capacitors dispatch for quasi minimum energy loss in distribution systems using a loop-analysis based method

Power system reconfiguration and loss minimization for an distribution systems using bacterial foraging optimization algorithm

Journal	Data powered by TypesetInternational Journal of Electrical Power & Energy Systems
Publisher	Data powered by TypesetElsevier BV
ISSN	0142-0615
Open Access	No