Dead-times and switch voltage drops represent the most important sources of distortion of the (average) output voltage in PWM inverters. Their effect is a function of the parameters of the drive system and of the operating conditions, and is often intolerable in many drives applications, thus requiring a proper compensation strategy. Many techniques are implemented in industrial drives and reported in literature, even very recently. Differently from standard approaches the proposed methodology is based on a detailed physical model of the power converter (including output capacitance), described by a small set of parameters. A novel self-commissioning identification procedure is proposed, adopting Multiple Linear Regression. The technique is tested on a commercial drive in comparison to state-of-the-art techniques. Also back-EMF estimation improvements in a PMSM sensorless drive system are shown to provide additional validation of the method.
Self-commissioning of inverter dead-time compensation by multiple linear regression based on a physical model
Calligaro, S.;PETRELLA, Roberto
2014-01-01
Abstract
Dead-times and switch voltage drops represent the most important sources of distortion of the (average) output voltage in PWM inverters. Their effect is a function of the parameters of the drive system and of the operating conditions, and is often intolerable in many drives applications, thus requiring a proper compensation strategy. Many techniques are implemented in industrial drives and reported in literature, even very recently. Differently from standard approaches the proposed methodology is based on a detailed physical model of the power converter (including output capacitance), described by a small set of parameters. A novel self-commissioning identification procedure is proposed, adopting Multiple Linear Regression. The technique is tested on a commercial drive in comparison to state-of-the-art techniques. Also back-EMF estimation improvements in a PMSM sensorless drive system are shown to provide additional validation of the method.File | Dimensione | Formato | |
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