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143
CHAPTER 6
ANFIS BASED NEURO-FUZZY CONTROLLER
6.1 INTRODUCTION
The quality of generated electricity in power system is dependent
on the system output, which has to be of constant frequency and must
maintain the scheduled power and voltage. The dynamic behaviour of the
system depends on disturbances and on changes in the operating point. The
unsteady nature of wind and frequent change in load demands may cause
large and severe oscillation of power for the considered wind-micro hydro-
diesel hybrid power system. In this Thesis, the supplementary controller for
LFC of the diesel generating unit takes care of sudden load changes and
maintains the system frequency, and the supplementary controller for BPC
takes care of the wind input variation and maintains the wind power
generation.
In the proposed work, Adaptive Neuro-Fuzzy Inference system
based Neuro-Fuzzy Controller (NFC) is designed individually for both
governor in diesel side and blade pitch control in wind side for performance
improvement of the wind-micro hydro-diesel hybrid system. This newly
developed control strategy combines the advantage of Neural Network and
Fuzzy Inference System and has simple structure that is easy to implement. In
order to keep system performance near its optimum, it is desirable to track the
operating conditions and use the updated parameters to control the system.
This work investigates the performance of wind-micro hydro-diesel hybrid
power system using ANFIS based NFC for LFC and BPC by simulation.
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This chapter is organized as follows. Section 2 describes the
introduction of Neuro-Fuzzy system. Section 3 demonstrates the Adaptive
Neuro-Fuzzy Inference System architecture. Section 4 describes the design of
ANFIS based Neuro-Fuzzy Controller for LFC and BPC of wind-micro
hydro-diesel hybrid power system. Section 5 demonstrates the simulation
results of the hybrid system with ANFIS based NFC. Section 6 shows the
analysis and performance comparison of the proposed controller. Summary of
the chapter is given in section 7.
6.2 NEURO-FUZZY SYSTEM
The techniques of fuzzy logic and neural networks suggest the
novel idea of transforming the burden of designing fuzzy logic systems to the
training and learning of connectionist neural networks and vice-versa. That is,
the neural networks provides connectionist structure and learning to the fuzzy
logic systems and the fuzzy logic systems provide the neural networks with a
structural framework with high–level fuzzy IF-THEN rule thinking and
reasoning. These benefits can be witnessed by the success in applying neuro-
fuzzy systems in areas like control of power system. Although the benefits of
combining fuzzy logic and neural networks are well known and have been
widely demonstrated, this work investigates its application in LFC and BPC
of an isolated wind-micro hydro-diesel hybrid power system, to improve the
system performance.
ANN have a massive parallel structure in the form of a directed
graph, composed of processing units( neurons) that are linked through
connections which may or may not have adjustable weights. Figure 6.1
shows a very simple structure of a Neural Network, composed of two layers
(input and output), each of them composed of three and two processing units
respectively.
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Figure 6.1 The general structure of a Neural Network
The advantages of neural networks over conventional systems are
their ability to perform non-linear input-output mapping, generalisation,
adaptability and fault tolerance (Lin and Lee 1996). On the other hand, the
main disadvantage of neural network is the broad lack of understanding of
how they actually solve a given problem. The main reason for this is that
neural networks do not break a problem down into its logical elements, but
rather solve it by a holistic approach, which can be hard to understand
logically. The main result of neural network learning process is reflected only
in a set of weights in which a full understanding of the functioning of the
neural network is an almost impossible task. To overcome this, hybrid Neuro-
Fuzzy system is proposed. Fuzzy logic which gives the benefit of enabling
systems more easily to make human-like decisions (Zadeh 1965), was
discussed in the previous chapters in detail.
The advantage gained from fuzzy logic approach is the ability to
express the amount of ambiguity in human thinking and subjectivity
(including natural language) in a comparatively undistorted manner. So, fuzzy
logic technique finds their applications in areas such as control (the most
widely applied area), pattern recognition, quantitative analysis etc.
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The main disadvantage of fuzzy systems, however, is that they do
not have much learning capability to tune their fuzzy rules and membership
functions. Normally, fuzzy rules are decided by experts or operators
according to their knowledge or experience. However, when the fuzzy system
model is designed, it is often too difficult (sometimes impossible) for human
beings to define all the fuzzy rules or membership functions.
Fuzzy Logic (Zadeh 1965) and Artificial Neural Networks (Haykin
1998) are complementary technologies in the design of intelligent systems.
The combination of these two technologies into an integrated system appears
to be a promising path toward the development of the intelligent systems
capable of capturing qualities characterising the human brain. The neural
network can improve the transparency, making them closer to fuzzy systems,
while fuzzy systems can self adapt, making them closer to neural networks
(Lin and Lee 1996).
Neural fuzzy systems (Jang et al. 2005 and Lin and Lee 1996) have
attracted the growing interest of researchers in various scientific and
engineering areas, especially in the area of control; hybrid Neuro-Fuzzy
systems seem to be attracting increasing interest.
6.3 ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)
Adaptive Neuro-Fuzzy Inference System (ANFIS) is an Artificial
Intelligence technique which creates a fuzzy inference system based on the
input-output model data pairs of the system. ANFIS combines neural network
and fuzzy system together. ANFIS can be employed in a wide variety of
applications of modelling, decision making, signal processing and control.
ANFIS is a class of adaptive network that is functionally equivalent to Fuzzy
Inference System. Since ANFIS design starts with the pre-structured system,
the membership function of input and output variables contain more
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information that Neural Network has to drive from sampled data sets.
Knowledge regarding the systems under design can be used right from the
start. Hence, the proposed ANFIS controller is more efficient. The rules are in
the linguistic forms and so intermediate results can be analyzed and
interpreted easily (Ashok Kusagur et al. 2010).
ANFIS is a multi layer adaptive neural network based Fuzzy
Inference System. ANFIS algorithm is composed of fuzzy logic and neural
networks with 5 layers to implement different node functions to learn and
tune parameters in a Fuzzy Inference System (FIS) structure using a hybrid
learning mode. In the forward pass of learning, with fixed premise
parameters, the least squared error estimate approach is employed to update
the consequent parameters and to pass the errors to the backward pass. In the
backward pass of learning, the consequent parameters are fixed and the
gradient descent method is applied to update the premise parameters. Premise
and consequent parameters will be identified for membership function (MF)
and FIS by repeating the forward and backward passes. ANFIS is fuzzy
Sugeno model put in the framework of adaptive systems to facilitate learning
and adaption (Jang 1993). Such framework makes Fuzzy Logic Controller
more systematic and less relying on expert knowledge.
To present the ANFIS architecture, let us consider two fuzzy rules
based on a first order sugeno model:
Rule 1 : IF (x is A1) and (y is B1) THEN (f1=p1x+ q1y +r1)
Rule 2 : IF (x is A2) and (y is B2) THEN (f2=p2x + q2y +r2)
where x and y are the inputs , Ai and Bi are the fuzzy sets, fi are the outputs
within the fuzzy region specified by the fuzzy rule, pi, qi and ri are the design
parameters that are determined during the training process.
148
Figure 6.2 illustrates the equivalent ANFIS architecture for this
sugeno model (Jang et al. 2005), where nodes of the same layer have similar
functions.
Figure 6.2 ANFIS architecture
Out of the five layers, the first and fourth layers consist of adaptive
nodes while the second, third and fifth layers consist of fixed nodes. The
adaptive nodes are associated with their respective parameters, get duly
updated with each subsequent iteration while the fixed nodes are devoid of
any parameters.
Layer 1: Fuzzification layer:
Every node i in this layer 1 is an adaptive node. The outputs of
layer 1 are the fuzzy membership grade of the inputs, which are given by:
O i1 = Ai (x), For i= 1,2 (6.1)
O i1 = Bi-2 (y), For i= 3,4 (6.2)
149
where x and y is the inputs to node i, where A is a linguistic label (small,
large) associated with this node. Where Ai(x), Bi-2(y) can adapt any fuzzy
membership function. The membership function for A can be any appropriate
parameterized membership function such as the generalised bell function.
A(x) 2b
i
i
1
x c1a
(6.3)
where { ai, bi., ci } is the parameter set. In fact, any continuous and piecewise
differentiable functions, such as commonly used trapezoidal (or)
triangular-shaped membership functions, are also qualified candidates for
node functions in this layer. Parameters in this layer are referred to as premise
parameters.
Layer 2: Rule layer:
Every node in this layer is a fixed node labelled M, whose output is
the product of all the incoming signals. The outputs of this layer can be
represented as:
O i2 = wi = Ai (x) . Bi (y), i=1,2 (6.4)
Each node output represents the firing strength of a rule.
Layer 3: Normalization layer:
Every node in this layer is a fixed node labeled N. The ith node
calculates the ratio of the ith rule’s firing strength to the sum of all rule’s firing
strength.
3 ii
1 2
wOi w i 1,2(w w )
(6.5)
150
For convenience, outputs of this layer are called normalized firing strengths.
Layer 4: De-fuzzification layer:
Every node i in this layer is an adaptive node. The output of each
node in this layer is simply the product of the normalized firing strength and a
first order polynomial.
O i4 = iw fi = iw (pi x+ qi y +ri ) i=1, 2 (6.6)
where iw is a normalized firing strength from layer 3 and { pi , qi , ri } is the
parameter set of this node. Parameters in this layer are referred to as
consequent parameters.
Layer5: Summation neuron:
This single node in this layer is a fixed node labelled , which
computes the overall output as the summation of all incoming signals.
i i5 ii i i
i 1 2
w fO w f
(w w ) (6.7)
Thus we have constructed an adaptive network that is functionally
equivalent to a Sugeno fuzzy model.
From the ANFIS architecture shown in Figure 6.2, it is observed
that when the values of the premise parameters are fixed, the overall output
can be expressed as a linear combination of the consequent parameters. More
precisely, the output f in Figure 6.2 can be rewritten as
1 2i 2 1 1 2 2
1 2 1 2
w wf . f .f w .f w .fw w w w
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1 1 1 1 1 1 2 2 2 2 2 2(w .x)p (w .y)q (w )r (w .x)p (w .y)q (w )r (6.8)
which is linear in the consequent parameters p1, q1, r1, p2, q2 and r2.
The ANFIS structure is tuned automatically by least square
estimation and the back propagation algorithm (hybrid learning).
6.4 DESIGN OF ANFIS BAESD NEURO-FUZZY
CONTROLLER
The development of the control strategy to control the frequency
deviation of the wind-micro hydro-diesel hybrid power system using the
concept of ANFIS control scheme is presented here. The proposed
neuro-fuzzy method combines the advantages of neural networks and fuzzy
system to design a model that uses a fuzzy theory to represent knowledge in
an interpretable manner and the learning ability of a neural network to
optimize its parameters. ANFIS is a specific approach in Neuro-fuzzy
development which was first introduced by Jang (1993). To start with, we
design the controller using ANFIS scheme. The model considered here is
based on Takagi- Sugeno fuzzy inference model. The block diagram of the
proposed ANFIS based NFC for LFC and BPC of wind-micro hydro- diesel
hybrid power system is shown in Figure 6.3.
Figure 6.3 Block diagram of ANFIS based Neuro-Fuzzy Controller
152
The inputs to the ANFIS based Neuro-Fuzzy controller for LFC of
the hybrid system on diesel side are error E( Fs) and change in error E
Fs)’.
The fuzzification unit converts the crisp data into linguistic
variables, which is given as input to the rule based block. The set of 49 rules
are written on the basis of previous knowledge/experiences in the rule based
block. Hybrid learning algorithm is used to train the neural network to select
the proper set of rule base. For developing the control signal, the training is
very important step in the selection of the proper rule base. Once the proper
rules are selected and fired, the control signal required to obtain the optimal
output is generated. The output of NN unit is given as input to the
de-fuzzification unit and the linguistic variables are converted back into the
crisp form.
This chapter proposes a systematic approach for establishing a
concise ANFIS that is capable of online self-organizing and self-adapting its
internal structure for learning the required control knowledge that satisfies the
desired system performance. The proposed ANFIS based NFC uses a hybrid
learning algorithm to identify consequent parameters of Sugeno type fuzzy
inference system. This algorithm applies a combination of least square
method and back propagation gradient descent method for training fuzzy
inference system membership function parameters to emulate a given training
data set.
Steps to design the ANFIS based NFC are given below.
1. Draw the simulink model for LFC and BPC of an isolated
wind-micro hydro-diesel hybrid power system with Takagi-
Sugeno inference model Fuzzy Logic Controller.
153
2. Simulate it with seven membership functions for the two
inputs, error ( Fs) and change in error ( Fs)’ and with rule
base shown in Figure 6.5. Simulation steps are same as
explained in previous chapters for FLC.
3. Collect the training data while simulating the system with
Fuzzy Logic Controller to design the ANFIS based NFC.
4. The two inputs, i.e. error ( Fs on diesel side and PGW on
wind side) and change in error ( Fs’, PGW ’) and the output
signal gives the training data.
5. Use ANFIS edit to create the FIS file (lfcanfi23.fis).
6. Load the training data collected from step2 and load the
lfcanfi23.fis file.
7. Choose the hybrid learning algorithm.
8. Train the collected data with generated FIS up to particular
number of Epochs.
Figure 6.4 shows the FIS editor of Sugeno type Fuzzy Inference
System with two inputs (error and change in error) and one output. Each input
is having seven linguistic variables with triangular membership functions. 49
rules are framed and it is shown in Figure 6.5. The rules are viewed by rule
viewer as shown in Figure 6.6.
154
Figure 6.4 FIS editor (Sugeno model) with two inputs and one output
Figure 6.5 Rule editor of Fuzzy Sugeno model
155
Figure 6.6 Rule viewer of Fuzzy Sugeno model
After running this FIS file with simulink model, the training dataare collected and loaded by the ANFIS editor. The ANFIS editor windowshown in Figure 6.7 has all the provisions for loading the data and FIS filefrom the workspace and also for training and testing the data for better controlperformance.
Figure 6.7 ANFIS editor window
156
Figure 6.8 shows the loading and training of data to ANFIS
structure. The ANFIS structure is trained with hybrid learning up to 50
epochs, with error tolerance of zero.
Figure 6.8 Training of data with hybrid learning method
The ANFIS information obtained after simulation is as follows.
ANFIS info:
Number of nodes: 131
Number of linear parameters: 49
Number of nonlinear parameters: 42
Total number of parameters: 91
Number of training data pairs: 101
Number of checking data pairs: 0
Number of fuzzy rules: 49
157
Start training ANFIS...
1 3.32714e-006
2 0.000173762
Figure 6.9 shows the ANFIS structure for the designed NFC for
LFC and BPC of wind-micro hydro-diesel hybrid power system.
Figure 6.9 ANFIS architecture of Neuro-Fuzzy Controller for LFC and
BPC of wind-micro hydro-diesel hybrid power system
6.5 SIMULATION RESULTS
Simulations are performed with the proposed ANFIS based NFC
for LFC and BPC of the hybrid power system with system parameters given
in Appendix 1. The ANFIS based NFC, improves the performance of the
hybrid system by the learning and training approach. For the same system
parameters, simulink model of the hybrid power system is simulated with
FLC (Mamdani model) and conventional PIC for performance comparison.
All the responses such as change in frequency, change in wind power, change
in diesel power and change in hydro power during various load disturbances
158
are observed and investigated in terms of settling time, overshoot and steady
state error value to get the optimum performance of the wind-micro hydro-
diesel hybrid power system.
Simulation is carried out for 1%, 2%, 3%, 4% and 5% step load
change ( PL=0.01 p.u., 0.02 p.u., 0.03 p.u., 0.04 p.u. and 0.05 p.u.) at
t = 0 sec. The change in frequency of the system, change in wind power
generation, change in diesel power generation and change in hydro power
generation for 4% (0.04 p.u.) step load change is shown in Figures 6.10, 6.11,
6.12 and 6.13 respectively.
Figure 6.10 Change in frequency of the hybrid system with ANFIS
based NFC, FLC and PIC for a step load change of 4%
0 1 2 3 4 5-0.02
-0.01
0
0.01
Time in secs.s)
PICFLCANFIS
159
Figure 6.11 Change in wind power generation of the hybrid system with
ANFIS based NFC, FLC and PIC for a step load change of
4%
Figure 6.12 Change in diesel power generation of the hybrid system with
ANFIS based NFC, FLC and PIC for a step load change of
4%
0 1 2 3 4 5 6 7 8-0.015
-0.01
-0.005
0
0.005
0.01
0.015
Time in secs.
PICFLCANFIS
0 1 2 3 4 5 6 70
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Time in secs.
PICFLCANFIS
160
Figure 6.13 Change in hydro power generation of the hybrid system
with ANFIS based NFC, FLC and PIC for a step load
change of 4%
From the simulation results, settling time for change in frequency,
change in wind, diesel and hydro power generation of the hybrid power
system for the proposed ANFIS based NFC, conventional PIC and FLC for a
step load change of 1%, 2%, 3%, 4% and 5% are observed and tabulated in
Table 6.1.
0 1 2 3 4 5
-2
-1
0
1
2
3
4
x 10-3
Time in secs.
PICFLCANFIS
161
162
On analysing the performance from the Table 6.1, it is observed
that the proposed ANFIS based NFC damps out the deviations in frequency,
wind, diesel and hydro power with less settling time for various load
disturbances (from 0.01 p.u. to 0.05 p.u.). The proposed ANFIS based NFC
maintains steady response and is more reliable than the fixed parameter PIC
and FLC, regardless of changes in load power variations. The proposed
controller generates a control signal to the governor, which in turn controls
the diesel power generation to maintain the system frequency and power
generation of the renewable hybrid system.
The amplitude of the second oscillation for dynamic responses
(deviations in frequency, wind, diesel and hydro power) of the hybrid system
for various load disturbances are observed and tabulated in Tables 6.2 and 6.3
for ANFIS based NFC, FLC and PIC.
Table 6.2 Amplitude of oscillations for deviations in frequency and
wind power for NFC, FLC and PIC against various load
disturbances
Loadchange
(p.u.)
Change in frequency(Hz) Change in wind power(p.u. KW)
ANFIS based
NFCFLC PIC
ANFIS based
NFCFLC PIC
0.01 -0.0004056 -0.001114 -0.0009308 -0.0004831 -0.0005421 -0.0009607
0.02 -0.001202 -0.001821 -0.001993 -0.0009222 -0.001189 -0.001897
0.03 -0.001664 -0.002252 -0.002824 -0.001265 -0.001632 -0.002857
0.04 -0.002 -0.00328 -0.0037 -0.001519 -0.001874 -0.003847
0.05 -0.002716 -0.004 -0.004499 -0.0002019 -0.002214 -0.004804
163
Table 6.3 Amplitude of oscillations for deviations in diesel and hydro
power for NFC, FLC and PIC against various load
disturbances
Loadchange(p.u.)
Change in diesel power(p.u. KW) Change in hydro power(p.u.KW)
ANFIS basedNFC
FLC PIC ANFIS basedNFC
FLC PIC
0.01 0.01091 0.01156 0.01165 0.0000911 0.0002187 0.0001843
0.02 0.02145 0.02249 0.02334 0.0002214 0.0003901 0.000354
0.03 0.03107 0.03384 0.03502 0.0003842 0.0004734 0.0005562
0.04 0.04302 0.04474 0.04658 0.0004944 0.0006289 0.0007336
0.05 0.05334 0.0556 0.05816 0.0005382 0.0007826 0.0008961
On analysing the performance from the Tables 6.2 and 6.3, the
amplitude of oscillations of the proposed ANFIS based NFC is less
compared to FLC and PIC. Simulation of the hybrid power system with the
proposed ANFIS based NFC shows improved system performance when
compared to conventional PIC and FLC.
6.6 ANALYSIS AND PERFORMANCE COMPARISON
An investigation on the dynamic responses of frequency change
and change in wind, diesel and hydro power generation have been carried out
for LFC and BPC of the hybrid power system using the proposed ANFIS
based NFC for different load changes and compared with FLC and
conventional PI Controller. Figure 6.14 shows the comparison of responses
(frequency response, change in wind, diesel and hydro power) in terms of
settling time for ANFIS based NFC, FLC and PIC against a step load change
of 4%(0.04 p.u.).
164
Figure 6.14 Comparison of dynamic responses of the hybrid system forNFC, FLC and PIC in terms of settling time (seconds)against a step load change of 4%
The bar chart in Figures 6.15, 6.16, 6.17 and 6.18 illustrates the
performance comparison of the hybrid system for change in frequency,change in wind power, change in diesel power and change in hydro power
respectively for three controllers (ANFIS based NFC, FLC and PIC) in termsof settling time against various load disturbances.
Figure 6.15 Comparison of frequency response for NFC, FLC and PICin terms of settling time (seconds) against various loaddisturbances
0
0.01
0.02
0.03
0.04
0.05
Change infrequency
(Hz)
Change inwind
power(p.u.KW)
Change indieselpower
(p.u.KW)
Change inhydropower
(p.u.KW)
Sett
ling
tim
e in
sec
onds
ANFIS based NFC
FLC
PIC
0
1
2
3
4
1% 2% 3% 4% 5%
Sett
ling
time
in s
econ
ds
Load disturbance in percentage
ANFIS based NFC
FLC
PIC
165
Figure 6.16 Comparison of change in wind power response for NFC,
FLC and PIC in terms of settling time (seconds) against
various load disturbances
Figure 6.17 Comparison of change in diesel power response for NFC,
FLC and PIC in terms of settling time (seconds) against
various load disturbances
0
2
4
6
8
10
1% 2% 3% 4% 5%
Sett
ling
time
in s
econ
ds
Load disturbance in percentage
ANFIS based NFC
FLC
PIC
0
1
2
3
4
1% 2% 3% 4% 5%
Sett
ling
time
in s
econ
ds
Load disturbance in percentage
ANFIS based NFC
FLC
PIC
166
Figure 6.18 Comparison of change in hydro power response for NFC,
FLC and PIC in terms of settling time (seconds) against
various load disturbances
The bar chart in Figures 6.19, 6.20, 6.21 and 6.22 illustrates the
performance comparison of three controllers (ANFIS based NFC, FLC and
PIC) in terms of amplitude of oscillations against various load disturbances.
Figure 6.19 Comparison of change in frequency response of the hybrid
system for NFC, FLC and PIC in terms of oscillations (Hz)
against various load disturbances
0
0.5
1
1.5
2
2.5
3
3.5
1% 2% 3% 4% 5%
Sett
ling
time
in s
econ
ds
Load disturbance in percentage
ANFIS based NFC
FLC
PIC
0
0.001
0.002
0.003
0.004
0.005
1% 2% 3% 4% 5%
Am
plitu
de o
f osc
illat
ion
in H
z
Load disturbance in percentage
ANFIS based NFC
FLC
PIC
167
Figure 6.20 Comparison of change in wind power response of the hybrid
system for NFC, FLC and PIC in terms of oscillations
(p.u. KW) against various load disturbances
Figure 6.21 Comparison of change in diesel power response of the
hybrid system for NFC, FLC and PIC in terms of
oscillations (p.u. KW) against various load disturbances
0
0.001
0.002
0.003
0.004
0.005
1% 2% 3% 4% 5%
Am
plitu
de o
f osc
illat
ion
in p
.u.K
W
Load disturbance in percentage
ANFIS based NFC
FLC
PIC
0
0.01
0.02
0.03
0.04
0.05
0.06
1% 2% 3% 4% 5%
Am
plitu
de o
f osc
illat
ion
in p
.u.K
W
Load disturbance in percentage
ANFIS based NFC
FLC
PIC
168
Figure 6.22 Comparison of change in hydro power response of the
hybrid system for NFC, FLC and PIC in terms of
oscillations (p.u. KW) against various load disturbances
From the performance comparison, it is observed that the overshoot
and settling time of the ANFIS based NFC is lower than those of FLC and
conventional PIC. The results obtained using ANFIS based NFC proposed in
this work outperform both conventional PIC and FLC by its hybrid learning
algorithm.
6.7 SUMMARY
An attempt is made in this work to develop a control strategy that
combines the advantages of neural networks and fuzzy inference system for
LFC and BPC of an isolated wind-micro hydro-diesel hybrid power system.
The hybrid learning algorithm applied by the proposed ANFIS structure trains
the Fuzzy Inference System membership function parameters for better
dynamic performance of the hybrid system. The designed ANFIS based NFC
for LFC and BPC of the hybrid system is investigated for various load
disturbances by simulation. It is observed from the simulation results that the
proposed controller is effective and provides significant improvement in
0
0.0002
0.0004
0.0006
0.0008
0.001
1% 2% 3% 4% 5%
Am
plitu
de o
f osc
illat
ion
in p
.u.K
W
Load disturbance in percentage
ANFIS based NFC
FLC
PIC
169
system performance by combining the benefits of fuzzy logic and neural
networks. The proposed ANFIS based Neuro-Fuzzy Controller maintains the
system reliable for sudden load changes and proves its superiority.
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