This paper proposes a speed-sensorless stator field orientation control (SFOC) of IM drives based on a delayed-state Kalman filter (DSKF) optimally tuned with the differential evolution (DE) algorithm. The DSKF estimates the stator flux components in the stationary reference frame, using the derivatives of the stator flux components as mathematical model and the stator voltage equations as observation model. The DE algorithm gives the values of the covariance matrices of model and measurement errors to obtain optimal performance from the DSKF. The DE is a reliable and versatile function optimizer that has not yet been widely implemented in the field of electrical drives. It performs extremely well for the problem under analysis. The DE outperformed the best known GAs on proposed optimization problem. The paper investigates the responses to a load speed reversal as well as to a training test at low speed and the experiments show the low-speed performance of the sensorless control scheme using the new optimized DSKF.
Differential Evolution Optimization of DSKF Algorithm for Sensorless SFO Control of IM Drives / Salvatore, N; Cascella, Gl; Caponio, A; Stasi, Silvio; Neri, F.. - (2008), pp. 1149-1154. (Intervento presentato al convegno 34th Annual Conference of the IEEE Industrial Electronics Society, IECON 2008 tenutosi a Orlando, Florida, U.S.A. nel November 10-13, 2008) [10.1109/IECON.2008.4758116].
Differential Evolution Optimization of DSKF Algorithm for Sensorless SFO Control of IM Drives
Cascella, GL;STASI, Silvio;
2008-01-01
Abstract
This paper proposes a speed-sensorless stator field orientation control (SFOC) of IM drives based on a delayed-state Kalman filter (DSKF) optimally tuned with the differential evolution (DE) algorithm. The DSKF estimates the stator flux components in the stationary reference frame, using the derivatives of the stator flux components as mathematical model and the stator voltage equations as observation model. The DE algorithm gives the values of the covariance matrices of model and measurement errors to obtain optimal performance from the DSKF. The DE is a reliable and versatile function optimizer that has not yet been widely implemented in the field of electrical drives. It performs extremely well for the problem under analysis. The DE outperformed the best known GAs on proposed optimization problem. The paper investigates the responses to a load speed reversal as well as to a training test at low speed and the experiments show the low-speed performance of the sensorless control scheme using the new optimized DSKF.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
