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 1 GOVERNMENT ENGINEERING COLLEGE THRISSUR 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 2 GOVERNMENT ENGINEERING COLLEGE THRISSUR 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 3 GOVERNMENT ENGINEERING COLLEGE THRISSUR 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. 4 GOVERNMENT ENGINEERING COLLEGE THRISSUR 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 5 GOVERNMENT ENGINEERING COLLEGE THRISSUR 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 6 GOVERNMENT ENGINEERING COLLEGE THRISSUR 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. 7 GOVERNMENT ENGINEERING COLLEGE THRISSUR 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). 8 GOVERNMENT ENGINEERING COLLEGE THRISSUR 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. 9 GOVERNMENT ENGINEERING COLLEGE THRISSUR 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. 10 GOVERNMENT ENGINEERING COLLEGE THRISSUR 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 11 GOVERNMENT ENGINEERING COLLEGE THRISSUR swarm optimization, J. Franklin doi:10.1016/j.jfranklin.2012.06.008. Inst. 349 (8) (2012) 2609–2625, [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 12 GOVERNMENT ENGINEERING COLLEGE THRISSUR