Why use Fuzzy Logic in Power Electronics

1. Why use Fuzzy Logic in Power Electronics • Parameter variation that can be compensated with designer judgment • Processes that can be modeled linguistically but not mathematically • Setting with the aim to improve efficiency as a matter of operator judgment. • When the system depends on operator skills and attention • Whenever one process parameter affects another process parameter. • Effects that cannot be attained by separate PID control • Whenever a fuzzy controller can be used as an advisor to the human operator • Data intensive modeling (use of parametric rules)

2. This section will present two main applications of fuzzy logic in power electronics : • Intelligent Control Based Alternative Energy Performance Improvement • A Fuzzy Logic Based Photovoltaic Peak Power Tracker Controller • Additionally will provide discussions on : • Fuzzy Logic Based DC-Drive Control System • Fuzzy Logic Based Slip Gain Tuning

3. Motivation • Importance of alternative energy investments • Requirements of clean and safe renewable energy sources and safety problems of conventional power plants • Solar photovoltaic, thermal energy, wind power, and biomass generation are gaining more acceptance • Electric vehicles are also focus of intense research due to the necessity of pollution control and decrease of oil importation. • Need to supply an overwhelming industrial growth

4. Intelligent Modeling and Control • Fuzzy and neuro-fuzzy techniques have by now became efficient tools in modeling and control applications • Potential benefits in optimizing cost effectiveness. • Fuzzy logic is a methodology for the handling of inexact, imprecise, qualitative, fuzzy, verbal in-formation in a systematic and rigorous way • A neuro-fuzzy controller generates, or tunes, the rules or membership functions of a fuzzy controller with an artificial neural network approach

5. Why Optimization ofAlternative Energy Systems? • Installation costs are high • Availability of the alternative power is by its nature intermittent • Must be supplemented by additional sources to supply the demand curve • Efficiency constraints • Optimize the efficiency of electric power transfer, even for the sake of relatively small incremental gains, in order to amortize the installation costs within the shortest possible time.

6. Fuzzy BasedWind and Photovoltaic Systems • Had proven very successful implementation for searching maximum power point, optimizing generator set-up, and improving control robustness

7. Topics to be covered Fuzzy Optimization Principles • Wind Systems Characteristics • Fuzzy Programming of Generator Speed • Fuzzy Programming of Generator Flux Excitation • Fuzzy Control of Generator Speed Loop • Practical Implementation with DSP Control Photovoltaic Systems Characteristics • Solar Power Generation Using Fuzzy Control • Practical Implementation with RISC Control

8. Fuzzy Optimization Principles: Optimize benefit or minimize some effort ! • Practical problems: • Parameter variation: temperature, density, impedance • Non-linearities, dead-band, time delay • Cross-dependence of input and output variables

9. A Typical Convex Function Maximization • Pumping Systems: the water flow increases but the pressure decreases, and the power (y-axis) initially goes up, reaching a maximum and decreasing as the throttle continues opening.

10. Fuzzy Meta-Rule • There is an optimum pump speed for the corresponding maximum of power flow • Minimum input pump effort which would maximize the output power • The heuristic way of searching the maximum could be based on the following meta-rule: “If the last change in the input variable (x) has caused the output variable (y) to increase, keep moving the input variable in the same direction; if it has caused the output variable to drop, move it in the opposite direction.”

11. A rule-based fuzzy algorithm can be constructed on the basis of the above meta-rule. There are two input variables: last-change-of-throttle (LVT) and the change-of-power (VP) which can determine the new throttle change (NVT) necessary to bring the maximum of the power by the following rules: IF LVT = P AND VP = PB THEN NVT =PB IF LVT = P AND VP = PS THEN NVT =PM IF LVT = NAND VP = PB THEN NVT =NB IF LVT = N AND VP = PS THEN NVT =NM IF LVT = P AND VP = NB THEN NVT =NB IF LVT = P AND VP = NS THEN NVT =NM IF LVT = N AND VP = NB THEN NVT =PB IF LVT = N AND VP = NS THEN NVT =PM

12. The membership functions must be designed for the system

13. The actual implementation of the search algorithm may require some additional rules: • More fuzzy sets to improve accuracy • The scaling gains may change with some parameters or conditions • The peak power point may either be found in the same search direction or dependent of other parameter • The search for the peak power point may be trapped in a local minimum • Hardware quantization effect may influence the search and extra rules are necessary to tackle this situation

14. Wind Energy : Motivation • The world has enormous resources of wind power. With 10% of such potential all the electricity needs would be met • Over 1700 MW of wind generators installed worldwide. Current generation of 6 billion kWh of energy annually • Estimated that the generation will grow to 60 billion kWh by the turn of the millennium.

15. Recent evolution of power semiconductors and variable frequency drive technology has aided the acceptance of variable speed generation systems. • Drawback of wind power is that its availability is somewhat statistical in nature • Must be supplemented by additional sources to supply the demand curve • In spite of the additional cost of power electronics and control, the total energy capture in a variable speed wind turbine (VSWT) system is larger and, therefore the life-cycle cost is lower than with fixed-speed drives.

16. A Voltage-Fed Double PWM Converterfor Wind Generation System

17. Double-PWM Converter Advantages • Line-side power factor is unity with no harmonic current injection (satisfies IEEE 519). • The cage-type induction machine is rugged, reliable, economical, and universally popular. • Machine current is sinusoidal - no harmonic copper loss. • Rectifier can generate programmable excitation for the machine. • Continuous power generation from zero to highest turbine speed is possible. • Power can flow in either direction permitting the generator to run as a motor for start-up (required for vertical turbine). Similarly, regenerative braking can quickly stop the turbine. • Autonomous operation of the system is possible with a start-up capacitor charging the battery.

18. Vertical Axis Wind Turbine Advantages : • Machinery located on the ground, convenient for maintenance • Can accept wind from any direction without any special yaw mechanism and, therefore, is preferred for high power output. Disadvantages : • Turbine is not self-starting • Large pulsating torque which depends on wind velocity, turbine speed, and other factors related to the design of the turbine

19. Turbine Power Coefficient (Cp)

20. Turbine Dynamic Model

21. Turbine Torque and Power Characteristics

22. Motivations for Using Fuzzy Control in Wind Systems • To change the generator speed adaptively, so as to track the power point as the wind velocity changes (without wind velocity measurement) • To reduce the generator rotor flux, boosting the machine efficiency when the optimum generator speed set-up is attained (in steady-state) • To have robust speed control against turbine torque pulsation, wind gusts and vortices

23. Fuzzy System Based Wind Energy System

24. Optimization Heuristics

25. Fuzzy Logic Controller FLC-1

26. Membership Functions for FLC-1

27. Set of Rules for FLC-1

28. Scaling Gains Computation

29. Flux Optimization Heuristics

30. Block diagram of fuzzy control FLC-2

31. Membership functions for FLC-2

32. Set of Rules for FLC-2

33. Fuzzy-PI Control Heuristics

34. Block diagram of fuzzy control FLC-3

35. Membership Functions for FLC-3

36. Set of Rules for FLC-3

37. Experimental Evaluation

38. DSP Based Hardware

39. Control Coordination

40. StaticCharacteristics

41. Wind Velocity Increase and the Corresponding Operation of FLC-1

42. Operation of Fuzzy Controller FLC-2

43. Robust performance of fuzzy controller FLC-3 against pulsating turbine torque

44. Efficiency improvement by controllers FLC-1 and FLC-2 at different wind velocities(1.0 pu = 31.5 mph)

45. Line side voltage and current waves showing unity power factor operation

46. Fuzzy Based Wind System : Conclusion • A complete fuzzy logic based 3.5 kW variable speed wind generation system has been designed and performance was evaluated thoroughly in laboratory • The power circuit was based on induction generator and double-sided IGBT PWM converters, and the control hardware was based on dual TMS320C30 DSPs • The system uses three fuzzy controllers: a controller that tracks the generator speed with the varying wind velocity to optimize the turbine aerodynamic efficiency, the second controller programs the machine flux at light load to optimize the generator-converter system efficiency, and the third controller gives robust performance of the generator speed control system.

47. The advantages of the fuzzy control are that the control algorithms are universal, give fast convergence, parameter insensitive and they accept noisy and inaccurate signals. • The performance of the system was found to be excellent with all the controls. A higher power unit for field installation can use the same controller but the power circuit rating is to be boosted.