Power Distribution System Planning Using Q-GIS

Power Distribution System Planning Using Q-GIS

Shabbir Uddin (Sikkim Manipal Institute of Technology, Majitar, India), Sandeep Chakravorty (Indus University, Ahmedabad, India), Karma Sonam Sherpa (Sikkim Manipal Institute of Technology, Majitar, India) and Amitava Ray (Jalpaiguri Government Engineering College, Jalpaiguri, India)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/IJEOE.2018040103

Abstract

This article focuses on the usage and advantage of incorporating Geographical Information System for advancing the power distribution system. Geographical Information System-based electricity distribution system planning strategies are applied to determine optimum routing. Existing and proposed layouts have been drawn using GIS-based software Q-GIS 2.12.3. This software helps attach data with the corresponding geographic. A comparison between the Newton-Raphson load flow study of existing and proposed layouts of distribution systems has been performed to find the technical viability of the proposed route. The information obtained from the power flow study is voltage at each load and the real power flowing in each line. The voltages found by the load flow analysis of existing and proposed layouts are compared to show the voltage increase. The developed system is tested on a 12 bus system substation of Sikkim Manipal Institute of Technology, Sikkim, India.
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1. Introduction

The distribution system is a portion of power systems which is devoted to delivering electrical energy to the end user. The distribution system planning is important to fulfill the growing demand of electricity in a best possible way. The optimal distribution system planning problem has become an issue due to the number of variables for a network arrangement which is, in practice, related to its geographical features. The design of electrical distribution networks is a constant development process from both the consumer and management point of view, also in growth of load demand and in research and development.

Power Generation followed by transmission at high voltage (440, 220 or 132 kV), and distribution networks of medium voltage (e.g. 33KV or 11KV) and of lower voltage (0.4 KV), have a very complex network. This network has the purpose of transmitting power from the points of generation to the points of consumption. Lowering cost and losses in electrical distribution power system is the major reason for introducing new tools, like Geographic Information System (GIS) that carries out complex power system studies by combining it to other power system analysis software for designing and examining electrical distribution network (Hassan and Akhtar, 2012). GIS could help in assessing distribution system losses (Triplett, Rinell and Foote, 2010) and improve the consistency of Energy Outage System (Davor, Slavko and Snjean, 1994; Rezaee, Nayeripour, Roosta and Niknam, 2009; Liu and Qiu, 1998).

Choosing an optimum location of a distribution sub-station and grouping the various load points to be fed from a particular distribution sub-station has always been a concern to the distribution planners. Authors have used a Fuzzy c-means clustering method applied to various loads which are at different location to form a cluster so that a sub-station could be placed for each cluster for the distribution of power. Context Aware Decision Algorithm based on the Analytical Hierarchy process (AHP) is then applied on each cluster comprising of load points to be fed and an optimum feeder layout is obtained depending on some reliability factors (Shabbiruddin and Chakravorty, 2011).

An expert system was proposed for power distribution system planning. Here in this paper authors have presented a hybridization of K-means clustering method with fuzzy context aware decision algorithm for choosing the optimum location of distribution substation and its feeder layout. K-means clustering has been applied to various loads which are at different location to form a cluster with load points in closer proximity so that a substation could be placed for each cluster for the distribution of power. Fuzzy Context Aware Decision Algorithm based on the AHP is then applied on each cluster to decide on the feeder layout connecting the load points in each cluster (Shabbiruddin, Ray, Sherpa and Chakravorty, 2016).

GIS platform is used to locate the load points in terms of coordinates. Soft computing based clustering algorithm is further used to divide the load points into different clusters with suitable optimum location of each substation (Shabbiruddin, Sherpa, Chakravorty and Ray 2016).

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