AUTOMATIC VOLTAGE REGULATOR MODEL
OF THERMAL POWER PLANT WITH HYBRID
OPTIMISATION LEARNING METHOD
Guided By
Subadhra P.R.
Associate Professor
Submitted By
Vinmaya P
Power Electronics – Semester II
Roll No: 18
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Content
Page No
Introduction
1
System Modelling
1
Controller Design
1- 4
PID controller
4
Adaptive neural fuzzy inference system (ANFIS) controller
4
Simulation Models
5
Simulation Results
6 - 10
Conclusion
10
References
11
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Introduction:
Automatic voltage regulation (AVR) system primarily maintains a synchronous terminal voltage of a
generator at a predetermined level. As a result, the security of the power system relies highly on the AVR
system's stability AVR system comprises of voltage-controller, amplifier, exciter, generator, and sensor.
The voltage controller is an essential component of the AVR system to regulate voltage fluctuations. AVR
system’s step responses oscillate without a controller which degrades the voltage regulation effectiveness.
Among all the controllers, the proportional integral derivative (PID) controller in the AVR system is
preferred due to its straightforward and reliable behavior.
System Modelling:
Fig. 1. Schematic diagram of the AVR system.
Controller Design:
Fig. 2. Block Diagram of an AVR system
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TABLE I. AVR PARAMETERS
PID controller:
Conventionally, PID controllers are widely used because of their simplicity. For AVR,
the introduction of the PID controller is geared towards improving the response of the
system to changes that are harmful to the entire system. The PID was implemented using
the MATLAB Simulink platform and after fine-tuning the gains using the auto-tune
feature of the PID block in Simulink, the values of P, I, and D gains were set at 0.2112,
0.1885, and 0.0567 respectively. Fig. 3 shows the PID connection to the system.
TABLE II. PID CONTROLLER PARAMETERS
Adaptive neural fuzzy inference system (ANFIS) controller:
The ANFIS uses the learning capability of the neural network to derive the if-then
rules of the fuzzy system. Its basis is the fuzzy inference algorithm which uses the fuzzy
rules to infer an approximate new fuzzy set conclusion while taking the previous set as a
premise. A typical structure of an ANFIS is made up of five layers interconnected together
as illustrated in Fig.7 which is the structure implemented in this work. Its operation
follows the logic:
•
•
•
•
•
Maps the characteristics of the input to the membership function of the input
Maps the membership function of the input to the if-then rules
Maps the if-then rules to the characteristics of the output
Maps the output characteristics to the membership functions of the output
Maps the output membership functions to the output or decision linked with the
output.
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Simulation Models:
Fig. 3. Diagram of an AVR system without controllers
Fig. 4. Diagram of an AVR system with PID controllers
Fig. 5. Diagram of an AVR system with ANFIS controllers
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Simulation Results:
Upon loading the training data set, the graph of the index to the output sample is
represented in Fig. 6 indicating a training data pair of 4186 and a maximum output of
0.259.
Fig. 6. Training data pairs.
Fig. 7. Structure of ANFIS
Table 3 ANFIS Training parameters
The membership function number was set at 10 with a triangular membership
function type and hybrid training optimization method. Fig.8 shows the diagrammatic
representation of the if-then fuzzy rule set employed in designing the FIS system
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Fig. 8. Fuzzy rule sets.
The surface representation of the Mapping associated with the input and output of
the designed ANFIS is illustrated in Fig. 9
Fig. 9. Surface input to output mapping.
Setting the number of epochs to 50, the training was carried out and the subsequent error
chart is shown in F i g . 10. From 10, it can be deduced that since the points all tally
around the zero point, this indicates that the FIS training was successful and tallied
with the zero-error tolerance when tested and validated.
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Fig. 10. FIS Training error check.
Setting the simulation time to 10 s, Fig. 11 displayed the resultant error when a unit step
input is applied to the system as the reference input. This resultant error is displayed on
the vertical axis representing the difference between the reference voltage Vref and the
terminal voltage, VT.
Fig. 11. Steady State Error (Unit Step Input).
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Fig. 12. AVR Response without controller.
Fig. 13 depicts the tracking of the AVR system and its ability to respond to a unit reference
input and be stable. Comparing this response with the response of the AVR without the
PID controller depicted in Fig. 10, it can be agreed that the system with the PID controller
responded better to change or disturbances.
Fig. 13. AVR Response PID controller.
Consequently, the PID controller presented an error signal response as illustrated in
F i g . 1 4.
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Fig. 14. Steady State Error for AVR with PID Controller.
Fig. 15. AVR Response with ANFIS .
A clearer look at the error curve in Fig.16, gave a better curve when compared with the
error in Fig.12.
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Fig. 16. Steady State Error for AVR with ANFIS.
Parameters
AVR-only
AVR with ANFIS
AVR with ANFIS
Rise Time (s)
0.2343
0.9198
1.1994
Settling Time (s)
9.7393
3.2802
1.8818
Overshoot (%)
107.0917
4.4282
1.3206
Undershoot (%)
0
0
0
Steady State Error
0.1977
3.809e-06
4.269e-04
Peak
1.6615
1.0443
1.0136
Peak time (s)
0.7783
2.1585
2.6772
Table 4 Comparison based on different parameters
Conclusion:
•
The learning algorithms for ANFIS were done and the hybrid learning optimization
method was selected and adopted to train the FIS, which generated the results as tabulated
in the above.
•
Under zero tolerance and fix epochs of 50, the optimized FIS was trained, tested, and
validated from the grid partition.
•
ANFIS AVR setup exhibited good performance with stable output and better response to
the disturbance caused by transient voltage or change in load with a steady state error of
4.269e-04.
•
The average testing error of 0.0031962. The lower overshoot is the corresponding large
setting time which indicates that if a disturbance is introduced, it will take a longer time
for the system to reach a steady-state value.
•
It will also enhance the performance of an AVR by providing a good transient response,
adaptability to changing circumstances, and robustness to renewable based generator.
References:
[1] M. ALI, M. Soliman, K. Mahmoud, J. Guerrero, M. Lehtonen, M.M.F.
Darwish, Resilient design of robust multi-objectives pid controllers for automatic
voltage regulators: D-Decomposition approach, IEEE Access (1) (2021) vol. PP,
doi:10.1109/ACCESS.2021.3100415.
[2] S. Panda, B.K. Sahu, P.K. Mohanty, Design and performance analysis of PID
controller for an automatic voltage regulator system using simplified particle
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swarm optimization, J. Franklin
doi:10.1016/j.jfranklin.2012.06.008.
Inst.
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[3] H.E. Patoding, E.T.S. Lobo, M. Sau, P. Chrystian, Modeling control of
automatic voltage regulator with proportional integral derivative, Int. J. Res. Eng.
Technol. 04 (2015) 241–245.
[4] A.M. Mosaad, M.A. Attia, A.Y. Abdelaziz, Whale optimization algorithm to
tune PID and PIDA controllers on AVR system, Ain Shams Eng. J. 10 (4) (2019)
755–767, doi:10.1016/j.asej.2019.07.004.
[5] D.N. Cuesta Cuesta, F.M. Santa, Optimization of an automatic voltage
regulator AVR on a synchronous machine using fuzzy control, J. Phys. Conf. Ser.
2135 (1) (2021), doi:10.1088/1742-6596/2135/1/012004
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