Solar MPPT Techniques

1. Solar MPPT Techniques Geno Gargas ECE 548 Prof. Khaligh

2. Purpose of Presentation • Provide general description of solar MPPT techniques • Describe design of solar MPPT MATLAB model constructed • Present results of MATLAB simulation • Give analysis of results with recommendation for future work

3. I – Basics of MPPT • Solar panel characteristic has non-linear relationship with Temperature and Irradiance • MPP also moves non-linearly • MPPT can improve efficiency by 15-20%

4. Common MPPT methods Cheap and Easy Implementation • Fractional Open-Circuit Voltage • Fractional Short-Circuit Current Intermediate Price and Implementation • Perturb and Observe • Incremental Conductance Expensive and Difficult Implementation • Fuzzy Logic Control • Neural Networks Cheaper and Easier Increased Efficiency

5. Basic Perturb and Observe • Implemented through a DC/DC converter Logic Change duty cycle Observe consequences on power output Decide direction of next change in duty cycle

6. P & O Design Parameters • Balance Δd between size of the oscillation across MPP, and inability to not get confused • Two degrees of freedom: Δd and Ta Ta Constraints Δd where

7. II – Creation of MATLAB model • Boost converter with a typical 12V, 64W solar panel, using the P&O algorithm for MPPT 3 Subsystems Solar Panel Boost Converter MPPT controller

8. 1 - PV model design Important equations Equivalent Circuit • I/P -> Sun and Temperature • O/P -> Panel voltage • Uses controlled current sources MATLAB Model

9. PV model simulation • I-V and P-V characteristics of simulated PV model with various levels of irradiance • Very similar to characteristics of real solar panels

10. 2 – Boost converter design Parameters L = 20 mH Cout = 125 μF Rload = 10Ω Cin = 1000 μF Freq = 25 kHz • Large L to reduce size of current ripple • Simple PWM generator through use of ramp and comparator

11. Boost model simulation • Voltage and current of input vs. time for various duty cycles • Quick transient decay • Low ripple • Input source is model of solar panel

12. 3 – MPPT controller Logic Get Power and Duty values of K and K+1 periods Figure out direction of change in duty cycle Change duty cycle Repeat Timing Sequence Sample new values after transient decays Sample for direction of new Δd Sample values for use in next period Make change in Δd

13. Model of controller • Given values from comparing Pk+1 and Pk and Dk+1 and Dk • Performs logic and outputs new duty cycle

14. III - Simulation Test the system during three types of irradiance • Fast Changing (50 W/m2s) • Slow Changing (15 W/m2s) • No Change (0 W/m2s) Test with different Δd Large Δd (Δd = .02) Small Δd (Δd = .005)

15. Fast Changing Irradiance PV power Duty Cycle (Δd = .02) (Δd = .005)

16. Slow Changing Irradiance PV power Duty Cycle (Δd = .02) (Δd = .005)

17. No Change in Irradiance PV power (Δd = .02) (Δd = .005)

18. IV - Results Average Power in each simulation • These results were found using the mean statistical data provided by MATLAB in each simulation Average Maximum power available from solar panel • These results were found by simulating the panel at the average insolation for each form of change in irradiation, and finding the maximum point on the power curve.

19. Analysis Efficiency of MPPT algorithm for various parameters • Higher efficiency with small Δd, regardless of how the sun is changing • I observed that the smaller Δd takes much longer to get to the MPP from a step change in irradiance • The step change is a very rare occurrence, so this may not be an issue • Design the system for the smallest Δd possible for the best efficiency

20. Future Work • Design a controller that can vary the size of the perturbation with respect to how far from the MPP it is • Leave Δd at a small value and adjust the sampling time to see if that has any effect. • Simulate the MPPT controller for other converter types, possibly in line with a battery charge controller