Application of fuzzy neural networks in the estimation of distorted waveforms

Neural networks and fuzzy logic are showing good promise for application in power electronics and motion control systems. They have been applied in the feedback control of power converters and drives, the estimation of waveforms and signals and performance enhancement control. Fuzzy logic and neural networks are appropriate where the plant model is ill-defined, nonlinear and has parameter variation problems. Besides, such technologies have distinct advantages when compared to a digital signal processor (DSP) based implementation, such as fast response, robustness and immunity to harmonic noise. They are model-free estimators since they "learn from experience" with numerical and linguistic data. The present work uses a fuzzy neural network (FNN) where a neural network topology emulates fuzzy reasoning. Such a neural network permits automatic identification of fuzzy rules and tunes the membership functions. The distorted line current waves in a three-phase diode rectifier feeding an inverter-machine load have been taken into consideration and an FNN has been applied to estimate RMS current and fundamental RMS current. The results of the estimation are compared with actual values, and indicate good accuracy. Although the paper considers a relatively simple estimation problem, the FNN technique can be extended to more complex waveforms and to the estimation of signals of scalar or vector-controlled drives.