AUTOMATIC GENERATION CONTROL USING FUZZY CONTROL CONTENTS 1.INTRODUCTION 2 2.FUZZY LOGIC 4 3. FUZZY LOGIC CONTROLLER 5 4. SYSTEM UNDER STUDY WITH FUZZY LOGIC CONTROLLER 9 5. APPLICATION OF FUZZY LOGIC TO AUTOMATIC GENERATION CONTROL 11 6. ALGORITHM OF FUZZY LOGIC TO AGC PROBLEM 11 7. FUZZY RULE BASE AND INTERFERENCE 13 8. AUTOMATIC GENERATION CONTROLLER 14 9.ADVANTAGES 18 10. CONCLUTION 19 11. REFRENCES 20 DEPT of E&E, VVCE, MYSORE Page 1
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AUTOMATIC GENERATION CONTROL USING FUZZY CONTROL
CONTENTS
1. INTRODUCTION 2
2. FUZZY LOGIC 4
3. FUZZY LOGIC CONTROLLER 5
4. SYSTEM UNDER STUDY WITH FUZZY
LOGIC CONTROLLER 9
5. APPLICATION OF FUZZY LOGIC TO AUTOMATIC
GENERATION CONTROL 11
6. ALGORITHM OF FUZZY LOGIC TO AGC PROBLEM 11
7. FUZZY RULE BASE AND INTERFERENCE 13
8. AUTOMATIC GENERATION CONTROLLER 14
9. ADVANTAGES 18
10.CONCLUTION 19
11.REFRENCES 20
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1. Introduction
Automatic generation control (AGC) plays a very important role in power
system as its main role is tomaintain the system frequency and tie line flow
at their scheduled values during normal period.Automatic generation control
with primary speed control action, a change in system load will result in a
steady state frequency deviation, depending upon governor drop
characteristics and frequency sensitivity of the load.
All generating units on speed governing will contribute to overall change in
gyration, irrespective of the location of the load change. Restoration of the
system frequency to nominal value requires supplementary control action
which adjusts the load reference set point. Therefore the primary objectives
of the automatic generation control are to regulate frequency to the nominal
value and to maintain the interchange power between control areas at the
scheduled values by adjusting the output of selected generators. This
function is commonly referred to as load frequency control. A secondary
objective is to distribute the required change in generation among the units
to minimize the operating costs.Generation in large interconnected power
system comprises of thermal, hydro, nuclear, and gas powergeneration.
Nuclear units owing to their high efficiency are usually kept at base load
close to theirmaximum output with no participation in system automatic
generation control (AGC). Gas powergeneration is ideal for meeting varying
load demand. However, such plants do not play very significantrole in AGC of
a large power system, since these plants form a very small percentage of
total systemgeneration.Gas plants are used to meet peak demands only.
Thus the natural choice for AGC falls on either thermal or hydro units.An
interconnected power system can be considered as being divided into
control areas which are connected by tie lines. In each control area, all
generator sets are assumed to form a coherent group. The power system is
subjected to local variations of random magnitudes and durations, Hence, it
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is required to control the deviations of frequency and tie-line power of each
control area. In actual power system operations, the load is changing
continuously and randomly. As the ability of the generation to track the
changing load is limited due to physical/technical considerations, there
results an imbalance between the actual and the scheduled generation
quantities. This imbalance leads to a frequency error i.e. the difference
between the actual and the synchronous frequency. The magnitude of the
frequency error is an indication of how well the power system is capable to
balance the actual and the scheduled Published in International Journal of
Advanced Engineering & Applications, Jan. 2010 58 generation. The presence
of an actual-scheduled generation imbalance gives rise initially to
systemfrequency excursions in accordance to the sign of the imbalance and
act to reduce the magnitude of actual scheduled generation imbalance.
A control signal made up of tie line flow deviation added to frequency
deviation weighted by a bias factor would accomplish the desired objective.
This control signal is known as area control error (ACE).ACE serves to
indicate when total generation must be raised or lowered in a control area.
In an interconnection, there are many control areas, each of which performs
its AGC with the objective of maintaining the magnitude of ACE (area Control
Error) “sufficiently close to 0” using various criteria. In order to maintain the
frequency sufficiently close to its synchronous value over the entire
interconnection, the coordination of the control areas’ actions is required. As
each control area shares in the responsibility for load frequency control,
effective means are needed for monitoring and assessing each area’s
performance of its appropriate share in load frequency control.
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OBJECTIVE
The objective of the controller is to generate and delivered power in an
interconnected system as economically and reliably as possible while
maintaining the voltage and frequency within permissible limits.
FUZZY LOGIC
The past few years have witnessed a rapid growth in the number and variety
of applications of fuzzy logic (FL). FL techniques have been used in image-
understanding applications such as detection of edges, feature extraction,
classification, and clustering. Fuzzy logic poses the ability to mimic the
human mind to effectively employ modes of reasoning that are approximate
rather than exact. In traditional hard computing, decisions or actions are
based on precision, certainty, and vigor. Precision and certainty carry a cost.
In soft computing, tolerance andimpression are explored in decision making.
The exploration of the tolerance for imprecision and uncertainty underlies
the remarkable human ability to understand distorted speech, decipher
sloppy handwriting, comprehend nuances of natural language, summarize
text, and recognize and classify images. With FL, we can specify mapping
rules in terms of words rather than numbers. Computing with the words
explores imprecision and tolerance. Another basic concept in
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FL is the fuzzy if–then rule. Although rule-based systems have a long history
of use in artificial
intelligence, what is missing in such systems is machinery for dealing with
fuzzy consequents or
fuzzy antecedents. In most applications, an FL solution is a translation of a
human solution.
Thirdly, FL can model nonlinear functions of arbitrary complexity to a desired
degree of accuracy.FL is a convenient way to map an input space to an
output space. FL is one of the tools used to model a multi-input,multi-output
system.
III. FUZZY LOGIC CONTROLLERS
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The design of Fuzzy Logic Controller can be divided into three areas namely, the allocation of
the areas of inputs, the determination of the rulesassociated with the inputs and outputs and the
defuzzification of the output into a real value.
A fuzzy inference system (FIS) essentially defines a nonlinear mapping of the
input data vector into a scalar output, using fuzzy rules. The mapping
process involves input/output membership
functions, FL operators, fuzzy if–then rules, aggregation of output sets, and
defuzzification.
An FIS with multiple outputs can be considered as a collection of
independent multi-input, single-output systems. A general model of a fuzzy
inference system (FIS) is shown above. The FLS maps crisp inputs into crisp
outputs. It can be seen from the figure that the FIS contains four
components: the fuzzifier, inference engine, rule base, and defuzzifier. The
rule base contains linguistic rules that are provided by experts. It is also
possible to extract rules from numeric data. Once the rules have been
established, the FIS can be viewed as a system that maps an input vector to
an output vector. The fuzzifier maps input numbers into corresponding fuzzy
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memberships. This is required in order to activate rules that are in terms of
linguistic variables.
The fuzzifier takes input values and determines the degree to which they
belong to each of the fuzzy sets via membership functions. The inference
engine defines mapping from input fuzzy sets into output fuzzy sets. It
determines the degree to which the antecedent is satisfied for each rule.
If the antecedent of a given rule has more than one clause, fuzzy operators
are applied to obtain one number that represents the result of the
antecedent for that rule. It is possible that one or more rules may fire at the
same time. Outputs for all rules are then aggregated. During aggregation,
fuzzy sets that represent the output of each rule are combined into a single
fuzzy set.
Fuzzy rules are fired in parallel, which is one of the important aspects of an
FIS. In an FIS, theorder in which rules are fired does not affect the output.
The defuzzifier maps output fuzzy sets into a crisp number. Given a fuzzy set
that encompasses a range of output values, the defuzzifier returns one
number, thereby moving from a fuzzy set to a crisp number. Several
methods for defuzzification are used in practice, including the centroid,
maximum, mean of maxima, height, and modified height defuzzifier. The
most popular defuzzification method is the centroid, which calculates and
returns the center of gravity of the aggregated fuzzy set. FISs employ rules.
However, unlike rules in conventional expert systems, a fuzzy rule localizes a
region of space along the function surface instead of isolating a point on the
surface. For a given input, more than one rule may fire. Also, in an FIS,
multiple regions are combined in the output space to produce a composite
region.
The AGC based on FLC is proposed in this study. One of its main advantages
is that controller parameters can be changed very quickly by the system
dynamics because no parameter estimation is required in designing
controller for nonlinear systems. Therefore a FLC, which representsa model-
free type of nonlinear control algorithms, could be a reasonable solution.
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There are many possibilities to apply fuzzy logic to the control system. A
fuzzy system knowledge base consists of fuzzy IF-THEN rules and
membership functions characterizing the fuzzy sets .The result of the
inference process is an output represented by a fuzzy set, but the output of
the fuzzy system should be a numeric value. The transformation of a fuzzy
set into a numeric value is called defuzzification.
In addition, input and output scaling factors are needed to modify the
universe of discourse. Their role is to tune the fuzzy controller to obtain the
desired dynamic properties of the process-controller closed loop .In this
paper, the inputs of the proposed Fuzzy
controllers are area control error (ACE), and change ratein area control error
(ACE) as shown in Figure, whichis indeed error (e) and the derivation of the
error(⋅e) ofthe system, respectively. This gives us a fairly good indicator of
the general tendency of the error.According to the conventional automatic
control theory, the performance of the PI controller is determined by its
proportional parameter and integral parameter. The proportional term
provides control actionequal to some multiple of the error, while the integral
term forces the steady state error to zero. Whenever the steady-state error
of the control system is eliminated, it can be imagined substituting the input
ACE of the fuzzy controller with the integration of error. This will result in the
fuzzy controller behaving like a parameter timevaryingPI controller; thus the
steady-state error isremoved by the integration action. However,
thesemethods will be hard to apply in practice because of the difficulty of
constructing fuzzy control rules. Usually, fuzzy control rules are constructed
by summarizing the manual control experiences of an operator. The operator
intuitively regulates the executer to control the process by watching the
error and the change rate of the error between output of the system and the
set-point value given by the technical requirement. It is no practical wayfor
the operator to observe the integration of the error of the system. Therefore
it is impossible to explicitly abstract fuzzy control rules from the operator’s
experience. Hence, it is better to design a fuzzy controller that possesses the
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fine characteristics of the PI controller by using only ACE and ACE. One way
is to have an integrator serially connected to the output of the fuzzy
controller, as shown in Fig. 3 . The control input to the plant can be
approximated by
uβ∫utdt(3)
Where β is the integral constant, or output scaling factor. Hence, the fuzzy
controller becomes a parameter timevarying PI controller. The controller is
called as PI–type fuzzy controller, and the fuzzy controller without the
integrator as the PD–type fuzzy controller. In a PI–type fuzzy control system,
the steady-state error is zero, but when the integral factor is small the
response of the system is slow, and when it is too large there is a high
overshoot and serious oscillation term forces the steady state error to zero.
Whenever thesteady-state error of the control system is eliminated, itcan be
imagined substituting the input ACE of the fuzzy controller with the
integration of error. This will result inthe fuzzy controller behaving like a
parameter time varying PI controller; thus the steady-state error is removed
by the integration action.
However, these methods will be hard to apply in practice because of the
difficulty of constructing fuzzy control rules. Usually, fuzzy control rules are
constructed by summarizing the manual control experiences of an operator.
The operator intuitively regulates the executer to control the process by
watching the error and the change rate of the error between output of the
system and the set-point value given by the technical requirement. It is no
practical wayfor the operator to observe the integration of the error of the
system. Therefore it is impossible to explicitly abstract fuzzy control rules
from the operator’s experience. Hence, it is better to design a fuzzy
controller that possesses the fine characteristics of the PI controller by using
only ACE and ACE.
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One way is to have an integrator serially connected to the output of the
fuzzy controller, as shown in Fig below. The control input to the plant can
beapproximated by u β∫utdt
where βis the integral constant, or output scaling factor. Hence, the fuzzy
controller becomes a parameter timevaryingPI controller. The controller is
called as PI–type fuzzy controller, and the fuzzy controller without the
integrator as the PD–type fuzzy controller. In a PI–type fuzzy control system,
the steady-state error is zero, but when the integral factor is small the
response of the system is slow, and when it is too large there is a high
overshoot and serious oscillation.
The type of the FLC obtained is called Mamdani -type which has fuzzy rules
of the form If ACE is Ai and ACE is Bi THEN u is Ci i=1,…, n. Here, Ai, Bi, Ci,
are the fuzzy sets. The triangle membership functions for each fuzzy
linguistic values ofthe ACE and ACE are shown in Table 2 [8], in which NB,