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CHAPTER I
INTRODUCTION
1.1 Background of the Study
Most power systems comprises of complex networks, where many generating
stations and load centers are interconnected through long power transmission and
distribution networks. Utility distribution networks, critical commercial
operations and sensitive industrial loads all suffer from various types of outages
and interruptions which can lead to significant financial loss, loss of production,
idle work forces etc. Today due the changing trends and restructuring of power
systems, the consumers are looking forward to the quality and reliability of power
supply at the load centers.
A voltage problem is an occurrence manifested as a nonstandard voltage that
results in a failure or a disoperation of end use equipment. With shifting trend
towards distributed and dispersed generation, the issue of power quality is taking
new dimensions. The concept of custom power was introduced to distribution
systems for improving the system performance. The aim therefore, in this work, is
to identify the prominent concerns in the area and thereby to recommend
measures that can enhance the quality of the power, keeping in mind their
economic viability and technical consequences.
The (DVR) with PI transformation control is an effective custom power device
for the enhancement of power quality due to its quick response, high reliability
and nominal cost. The (DVR) is used to compensate deep voltage sags, voltage
unbalances and harmonics. It is efficiently capable of protecting sensitive loads
against the voltage variations or disturbances.
Aspects on power quality can be classified into three categories that is, voltage
stability, continuity of supplying power, and voltage waveform. Based on this
classification, several examples of power quality level definitions were presented
by Shuhonget al., (2009).They investigated that most power quality problems in
distribution systems are related to voltage sags. Its principle and control strategies
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Bhaskar et al., (2010) presented the performance of a DVR in mitigating
voltage sag.
Omar and Rahim (2009) presented the low voltage dynamic voltage restorer
(DVR) based on application of space Vector Pulse Width Modulation (SVPWM)
for three phases Voltage Source Converter (VSC) and it is the standard PWM
techniques to utilize the DC-AC power conversion. Omar and Rahim (2009)
proposed a control system based on park technique which is a scaled error of the
between source side of the DVR and its reference for sags correction.
Chandrasekaran, et al., (2008) presented a generalized control algorithm for
voltage disturbance extraction and mitigation. The suggested control algorithm
employs the synchronous stationary frame (SSF) for sag detection and
instantaneous p-q theory (IPQT) technique for harmonic extraction to effectively
track and extract the most common voltage disturbances. Jazayeri and
Abdollahzadeh (2009) Introduced estimation algorithm in the DVR control
system. Hanif and Ahmad Choudhry (2010) demonstrated the DVR using the
leading series voltage injection (LSVI) concept for mitigating voltage sag and
swells as well as share power in a manner which suits a specific customer. In case
of abundant availability of generated power from a distributed energy resource,
maximum power can be shared, while in case of scarcity, minimum power sag
mitigation can be managed.
Tiwari and Gupta (2010) presented dynamic voltage restorer against the
voltage sag. DVR is a custom power device used to correct the voltage sag by
injecting voltage as well power into the system. The mitigation capability of these
devices is generally influence by the maximum load; power factor and maximum
voltage dip to be compensated. Voltage Dip on a feeder is the main task for DVR
system operation and appropriate desired voltage sag compensation.
Several research papers and reports addressed the subject of improving power
in distribution system by the custom power devices. The following research
papers and reports present a brief review of the work undertaken so far control
strategies and methods for the DVR. These models can also aid instructors in
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1.2 Objectives
The objective of this project was to evaluate the performance of Dynamic
Voltage Restorer Control by using Adaptive Neuro-Fuzzy Inference System,
Proportional Integral and Fuzzy Inference System methods in order to determine
the value of power injecting to the system when recovery of voltage had been
taken.
1.3 Problem Statements
The increased use of sophisticated electronics, high efficiency variable speed
drive, and power electronic controller, power quality has become an increasing
concern to utilities and customers. Voltage sags is the most common type of
power quality disturbance in the distribution system. It can be caused by fault in
the electrical network or by the starting of a large induction motor. Although the
electric utilities have made a substantial amount of investment to improve the
reliability of the network, they cannot control the external factor that causes the
fault.
Meanwhile during short circuits, bus voltages throughout the supply network
are depressed, severities of which are dependent of the distance from each bus to
point where the short circuit occurs. After clearance of the fault by the protective
system the voltages return to their new steady state values. Part of the circuit that
is cleared will suffer supply disruption or blackout. Thus in general a short circuit
will cause voltage sags throughout the system and may cause blackout to a small
portion of the network.
1.4 Scope of Work
This thesis used the Matlab-Simulinkmodel of dynamic voltage restorer which
was used to power quality at distribution system using an ANFIS method.
1.5 Organization of Thesis
This thesis is compiled in five chapters as per the details. Chapter I highlight
the brief introduction, summary of work carried out by various researchers. The
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in this chapter. The next is chapter II which describes how the concept of custom
power was introduced to improve the power quality and the brief introduction of
different kinds of custom power devices. Discussion on the dynamic voltage
restorer in detail and outlines the control technique used in the simulation of
(DVR) was also included in the chapter II. Then chapter III presents the results
pertaining to various cases and the comparison of results obtained for various
method. Afterward is chapter IV the clarification of PI Controller and Adaptive
Neuro-Fuzzy Inference System (ANFIS) and the training of ANFIS by using the
ANFIS editor GUI. The last is chapter V which the closing and consist of
