Neural network-based estimation of power electronic waveforms

Artificial neural network techniques are indicating a lot of promise for application in power electronic systems. So far, these applications are mainly confined to control, identification, and diagnostic problems, but the application in estimation is fairly new. The paper explores the application of neural networks for estimation of power electronic waveforms. The distorted line current waveforms in a single-phase thyristor AC controller and a three-phase diode rectifier that feeds an inverter-machine load have been taken into consideration, and neural networks have been trained to estimate the total RMS current, fundamental RMS current, displacement factor, and power factor. The performance of the neural network-based estimators has been compared with the actual values, and excellent performance is indicated. Neural network-based estimation has the usual advantages of very fast and simultaneous response of all the outputs, noise, and fault-tolerant performance and can be easily implemented in dedicated analog or digital hardware chips, which can coexist with digital signal processor (DSP) and/or application-specific integrated circuit (ASIC) chips. The estimation techniques can be extended to more complex waveforms in power electronics.