Some Studies and Investigations of Distribution System Monitoring and Fault Location in Smart Grid

Abstract

In today s era of smart grid system, distribution systems are changing drastically due to the expansion and inclusion of large number of distributed generating units into the power system at distribution level. Power distribution networks cover wide areas and usually consist of many numbers of nodes, thousands of end user loads, many distribution transformers and short lines with different resistances and inductances. Diagnosis of open circuit and short circuit faults is one of the most important tasks in power system. Many of methods like artificial neural network, fuzzy, decision tree and support vector methods are presented. These are knowledge based methods which use characteristics obtained from the time domain signals of current, voltage as well as information of fault location, type of load, settings of protective devices and previously registered faults. newlineFor successful implementation of such a fault location approach, a considerable amount of information of faults in nodes is required, which is difficult in today s fast growing distribution networks. Therefore, some advanced distribution systems have started installing fault locators in the feeder. In order to keep costs down, low-cost diagnosis techniques are essential. Fortunately, using modern technologies for data sensing, recording, signal processing and analysis, techniques combined with intelligent algorithms or artificial intelligence provides some possible solutions to the automated fault diagnosis and monitoring of the system. To face the challenges of modernized grids, conventional fault diagnosis methodologies require drastic change for making use of these advanced infrastructure and technologies. This will be helpful to achieve automation in fault diagnosis tasks, improved power quality, reliability, resilience and self healing property of the power system. newlineThis thesis proposes the use of smart sensors and advanced communication technology that will be available in future smart grids to carry out automated monitoring and fault diagnosis tasks