Fuzzy logic and neural network based advanced control and estimation techniques in power electronics and AC drives

The dissertation presents, examines and analyzes advanced control and estimation techniques in power electronics and ac drives. It constitutes four projects where such artificial intelligence tools were extensively used. A variable speed wind generation system was developed, where three fuzzy logic controllers were used for efficiency optimization and for performance enhancement control. The controller FLC-1 searches the generator speed on-line so that the aerodynamic efficiency of the wind turbine can be optimized. A second fuzzy controller, FLC-2, programmed the machine flux by on-line search so as to optimize the machine-converter system efficiency. A third fuzzy controller, FLC-3, performed robust speed control against turbine oscillatory torque and wind vortex. Next, a neural network was applied for estimation of feedback signals in an induction motor drive, which has some distinct advantages when compared to DSP based implementation. A feedforward neural network received the machine terminal signals at the input and calculated flux, torque and unit vectors at the output, which were then used in the control of a direct vector-controlled drive system. The application of fuzzy logic to the estimation of power electronic waveforms was taken into consideration for distorted line current waves in a TRIAC dimmer and in a three-phase diode rectifier feeding an inverter-machine load. Fuzzy logic estimation was applied to assess the rms current, fundamental rms current, displacement factor and power factor. Both the rule base and relational approaches were used for estimation of the above parameters. The estimated values were then compared with the actual values, indicating good accuracy. Finally, the development of a speed and flux sensorless vector-controlled induction motor drive was considered. The stator flux oriented drive started at zero speed in indirect vector control mode, transited to direct vector control mode as the speed developed, and then transited back to indirect vector control at zero speed. The vector control used stator flux orientation in both indirect and direct vector control modes with the stator resistance variation compensated by measurement of stator temperature. The problem of integration at low stator frequency was solved by cascaded low-pass filters with programmable time constants.