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This presentation discusses using a fractional order PID (FOPID) controller tuned by a particle swarm optimization (PSO) technique for automatic voltage regulation (AVR). It begins with an introduction to AVR and conventional PID control. It then describes FOPID controllers and classical tuning methods. Next, it provides details on PSO and how to apply it to optimally tune FOPID controller parameters. Simulation results show the FOPID controller tuned with PSO achieves better dynamic and static performance compared to other tuning methods. In conclusion, a PSO-tuned FOPID controller is developed to improve stability for AVR systems.
prsntsn.pptx
- 1. A Presentation on Automatic Voltage Regulation Using FOPID Controller Tuned by Particle Swarm Optimization Technique SUPERVISOR (Mr. AKHILESH KUMAR MISHRA) Assistant Professor Presented By Tushar Verma (1201024508)
- 2. CONTENTS 1. Objective 2. Introduction 3. Automatic Voltage Regulation 4. PID Controller 5. FOPID Controller 6. Classical tuning methods of PID & FOPID Controller 7. Optimal Tuning of FOPID Tuning using PSO 8. Simulation Result 9. Conclusion
- 3. 1. OBJECTIVE • The aim of my work is to develop a controller based on Particle Swarm Optimization Technique to simulate an Automatic Voltage Regulator (AVR) for a synchronous generator in order to achieve better stability of the system and fulfil the requirements of good excitation control.
- 4. 2. INTRODUCTION • In this work bio-inspired optimization technique in controllers and their advantages over conventional methods is discussed using MATLAB/Simulink. • Also the advantage of FOPID controller over Conventional PID controller is discussed using MATLAB/Simulink. • The main aim is to apply PSO technique to design and tune parameters of FOPID controller to get an output with better dynamic and static performance.
- 5. 3. AUTOMATIC VOLTAGE REGULATION • The Automatic Voltage Regulator (AVR) is widely used in industrial application to obtain the stability and good regulation of different electrical apparatus. • The automatic voltage regulator or AVR, as the name implies, is a device intended to regulate voltage automatically: that is to take a varying voltage level and turn it into a constant voltage level.
- 6. Contd… • A simple AVR consists of:- 1. Amplifier, 2. Exciter, 3. Generator and 4. Sensor.
- 7. Contd… Fig : Block Diagram of AVR
- 8. Contd… • Table 3.1 Parameter range for the components used in AVR
- 9. 4. PID CONTROLLER • PID (proportional-integral-derivative) control is one of the earlier control strategies. Fig: Block Diagram of PID Controller
- 10. Contd… • PID controller has all the necessary dynamics: • Fast reaction on change of the controller input (D mode), • Increase in control signal to lead error towards zero (I mode) and • Suitable action inside control error area to eliminate oscillations (P mode). • The o/p of PID controller is given as:
- 11. Contd… • Table : Effect of each controllers Kp, Ti and Td on a closed-loop system
- 12. 5. FOPID Controller • Fractional-order calculus is an area of mathematics that deals with derivatives and integrals from non- integer orders. • In fact, in principle, they provide more flexibility in the controller design, with respect to the standard PID controllers, because they have five parameters to select (instead of three). • The concept of FOPID controllers was proposed by Podlubny in 1997 (Podlubny et al., 1997; Podlubny, 1999a).
- 13. Contd… • It is Clear, by selecting λ = 1 and μ = 1, a classical PID controller can be recovered. Using λ = 1, μ = 0, and λ = 0, μ = 1, respectively corresponds to the conventional PI & PD controllers. • All these classical types of PID controllers are special cases of the FOPID controller. •
- 14. Contd… • The mathematical representation of such a controller is as follows: • ADVANTAGES OF F-O CONTROLLER 1. If the parameter of a controlled system changes, a fractional order controller is less sensitive than a classical PID controller. 2. FOC has two extra variables to tune. This provides extra degrees of freedom to the dynamic properties of fractional order system.
- 15. 6.CLASSICALTUNINGMETHODSOF PID&FOPIDCONTROLLER • PID Tuning • The model of a plant is given as • The transfer is assumed as • Where, • K - Gain • ϴ - delay time • T – time constant
- 16. Contd… • Now the response of plant is taken as
- 17. Contd… • After computing the t1 and t2 times, the time delay (ϴ) and process time constant (T) can be obtained from the following equations:
- 18. Contd… • Ziegler-Nichols Tuning Method • Now the value of Kp, Ti, Td is obtained by the following table :
- 19. Contd… • Cohen-Coon Tuning Method • Cohen and Coon based the controller settings on the three parameters ϴ, T and K of the open loop step response.
- 20. Contd… FOPID Tuning • Ziegler-Nichols Type Tuning Rules • First set of tuning rules • The first set of tuning rule is given as • P=-0.0048+0.2664L+0.4982T+0.0232L2-0.0720T2-0.0348TL
- 21. Contd…
- 22. 7. Optimal Tuning of FOPID Tuning using PSO • Introduction to Particle Swarm Optimization (PSO) 1. Origins 2. Concept 3. PSO Algorithm
- 23. Contd… 1. Origin • Inspired from the nature social behavior and dynamic movements with communications of insects, birds and fish.
- 24. Contd… • In 1986, Craig Reynolds described this process in 3 simple behaviors: 1.Separation • avoidcrowdinglocalflockmates
- 25. Contd… 2. Alignment • Move towards the average heading of local flock mates
- 26. Contd… 3. Cohesion • Move toward the average position of local flock mates
- 27. Contd… 2. Origin • Uses a number of agents (particles) that constitute a swarm moving around in the search space looking for the best solution. • Each particle in search space adjusts its “flying” according to its own flying experience as well as the flying experience of other particles.
- 28. Contd… • Each particle adjusts its travelling speed dynamically corresponding to the flying experience of itself and its colleagues. • Each particle keeps track: • its best solution, personal best, pbest • the best value of any particle, global best, gbest
- 29. Contd… • Each particle modifies its position according to • Its current position • Its current velocity • The distance between its current position and pbest • The distance between its current position and pbest
- 31. Contd…
- 32. Contd… Particle update rule p = p + v With v = v + c1 * rand * (pBest – p) + c2 * rand * (gBest – p) where • p: particle’s position • v: path direction • c1: weight of local information • c2: weight of global information • pBest: best position of the particle • gBest: best position of the swarm • rand: random variable
- 33. Contd… 1. Create a ‘population’ of agents (particles) uniformly distributed over X 2. Evaluate each particle’s position according to the objective function 3. If a particle’s current position is better than its previous best position, update it 4. Determine the best particle (according to the particle’s previous best positions)
- 34. Contd… 5. Update particles’ velocities 6. Move particles to their new positions: 7. Go to step 2 until stopping criteria are satisfied
- 35. Contd…
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- 44. 8. SIMULATION RESULT • The figure shows the Simulink model of AVR using PID controller
- 45. Contd… • the figure shows the step response of AVR using PID controller which is tuned by the Ziegler Nichols open loop tuning method.
- 46. Contd… • The figure shows the Simulink model of AVR using PID controller
- 47. Contd… • the figure shows the step response of AVR which is tuned by Cohen Coon open loop tuning method.
- 48. Contd… • The figure shows the Simulink model of AVR using FOPID controller which is tuned by the conventional tuning method i.e. Ziegler Nichols open loop tuning method for the first set of tuning rules
- 49. Contd… • The figure shows the step response of AVR using fractional order PID controller which is tuned by the ZN tuning method for the first set of tuning rules
- 50. Contd… • The figure shows the Simulink model of AVR using FOPID controller which is tuned by the bio inspired optimization method i.e. Particle Swarm Optimization Technique.
- 51. Contd… • The figure shows the step response of AVR using fractional order PID controller which is tuned by the Particle Swarm Optimization Technique based on bio inspired method
- 52. Contd… • The figure show the comparative model of AVR using PID controller tuned by ZN & CC open loop tuning method and Fractional Order PID controller tuned by ZN & Particle Swarm Optimization method respectively.
- 53. Contd… • The figure shows the comparative step response of AVR obtained by the PID controller tuned by ZN & CC open loop tuning method and FOPID controller tuned by conventional tuning method i.e. ZN tuning method for the first set of tuning rule
- 54. Contd… • Now the following table shows the comparative analysis of PID controller tuned by ZN & CC and FOPID controller tuned by ZN & PSO for AVR. From the table 8.1 it is clear that the settling time of the response for AVR obtained by FOPID controller tuned by Particle Swarm optimization is better than all the responses obtain by different methods performed and it desirable condition.
- 55. Contd…
- 56. 9. Conclusion • Hence a controller is developed based on Particle Swarm Optimization Technique to simulate an Automatic Voltage Regulator (AVR) for a synchronous generator in order to achieve better stability of the system and fulfil the requirements of good excitation control.
- 57. Thank You