Power Research

P. Dinakara Prasad Reddy ¹, V. C. Veera Reddy ², T. Gowri Manohar ³, B. Chandra Sekhar ⁴

Affiliations
1 Research Scholar(JNTUK), Department of EEE, S V University College of Engineering, S V University, Tirupati-517502, IN
2 Professor, EEE Department, AITS, Tirupati-517502, IN
3 Professor, S V University College of Engineering, S V University, Tirupati-517502, IN
4 Senior Research Fellow, Energy Efficiency & Renewable Engineering Division, Central Power Research Institute, Bangalore-560080, IN

Abstract

Distributed Generator (DG) resources are small electric generating plants that can provide power to homes, businesses or industrial facilities in distribution feeders. By optimal placement of DG we can reduce power loss and improve the voltage profile. However, the values of DGs are largely dependent on their types, sizes and locations as they were installed in distribution feeders. The main contribution of the paper is to find the optimal locations of DG units and sizes. Index vector method is used for optimal DG locations. In this paper new optimization algorithm i.e. Grey wolf optimization algorithm is proposed to determine the optimal DG size. This paper uses three different types of DG units for compensation. The proposed methods have been tested on 15-bus, 34-bus, and 69-bus radial distribution systems. MATLAB^TM, Version 8.3 software is used for simulation.

Keywords

Grey Wolf Optimization Algorithm, Index Vector Method, Distributed Generation Placement, Radial Distribution System

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M. Damodar Reddy ¹, V. C. Veera Reddy ²

Affiliations
1 Associate Professor, Department of E.E.E., S V University, Tirupati, Andhra Pradesh, IN
2 Professor, Department of E.E.E., S V University, Tirupati, Andhra Pradesh, IN

Abstract

This paper delineates a novel method of network reconfiguration for the compensated network. A two-stage methodology is used to reduce the losses and to improve the voltage profile of the balanced radial distribution networks. In the first stage, capacitors are placed optimally for the reactive power compensation of the original network. Fuzzy approach is used to find the optimal capacitor locations and Particle Swarm Optimisation (PSO) method is used to find the sizes of capacitors. In the second stage, an improved fuzzy multi-objective algorithm is used for the network reconfiguration of the compensated network. The proposed method is tested on 33-bus and 69-bus test systems and the results are presented.

Keywords

Novel Method, Compensated Network, Fuzzy Approach, PSO Method, Network Reconfiguration, Fuzzy Multi-Objective Algorithm.

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