| dc.description.abstract | Power Distribution System forms a vital part of any Electric Power System. It acts as a
final power delivery stage to the consumers. Hence, its study is important for the
development of an efficient and reliable Power System. As utilities world over are
competing for improvement in service to consumers, the area of Fault Diagnosis in
distribution systems has attracted many researchers in the last two decades. Techniques
involving algorithmic approaches, Artificial Intelligence (AI) was reported the most. AI
techniques involving Artificial Neural Networks (ANNs) have proved their ability in
recent years, in a variety of applications.
Locating Faults on a Distribution System poses a major challenge to utility operators, due
to the unique characteristics of the distribution system. These characteristics are worthy
of discussion in the present work. Most of the previous work on Fault Location
concentrated on estimatmg the faulty line segments through statuses of the circuit
breakers and relays with the aid of algorithmic approaches. In this thesis, a new approach
to fault location is adopted, in the general absence of circuit breaker and relay statuses in
each section of the distribution feeder. Methods for estimation of fault location are
developed using ANN techniques. Voltage and current measurements at the substation
are used to estimate the location of fault imder various practical conditions of the
distribution systems. Initially, a general three-phase fault detection algorithm is described
with results of a practical distribution system, for detecting faulty line segments. Also, an
efficient method of Short Circuit Analysis is described, which is used to simulate all
types of fault conditions on a distribution systems.
The work concentrates mostly on the fault pattern processing techniques and their right
applicability in a Fault Locating System. While Function Approximation (FA) is the main
theory used in the technique, Classification techniques take on a major supportive role to
the FA problem. Fault Location in distribution systems is explained as a function
approximation problem, which is complex to solve due to the various practical
constraints particular to distribution systems. Incorporating Classification techniquesreduces this complex FA problem to simpler ones, which then gives efficient fault
location estimations. The function that is approximated is the relation between threephase
voltage and current measurements at the substation during fault, and the line
reactances o f the fault points jfrom the substation. This function is approximated by
FeedForward Neural Networks (FFNN). The voltage and current measurements are the
inputs and the line reactances o f fault points are the outputs o f the neural network.
Similarly, for solving the classification problems such as fault type classification, source
short circuit level classification, and fault bus classification. Support Vector Machines
(SVM) are employed. The work presented in this thesis consists o f using the FFNNs and
the SVMs in a combination, for locating faults in radial distribution systems. Four
schemes employing such a combination of FFNNs and SVMs are proposed for estimation
o f fault location, along with the estimation o f the fault type and the source short circuit
level during fault condition.
Levenberg Marquardt learning strategy, which is robust and fast, is used for training
FeedForward Neural Networks. A very fast and simple to implement algorithm.
Sequential Minimal Optimization is used as the learning strategy for the case o f Support
Vector Classification. These learning methods have the advantage that they can be
implemented in practice with much ease, and at the same time provide very efficient
results.
Practical distribution systems of 52 nodes and 133 nodes are considered for studies. The
results presented show that the proposed approach of fault location gives accurate results
in terms of the estimated fault location. Practical situations in distribution systems, such
as protective devices placed only at the substation, all types o f faults, a wide range of
varying source short circuit levels, varying loading patterns, and long feeders with
multiple laterals, are considered for studies. The results demonstrate the feasibility of
applying the proposed method in practical distribution system fault diagnosis. | |
