Abstract
The efficiency of induction motor drives operating under variable conditions can be improved by predicting the optimum flux that minimizes the losses. In this study, a Loss-Minimization Controller (LMC) and a Search Controller (SC) are combined. The output from the controllers would drive the field oriented control inverter in order to achieve the optimum flux in the motor that minimizes the losses. For this purpose, a mathematical model for calculating the total power losses as a function of magnetic flux and a factor to obtain feedback as a function of optimum flux were discussed. An LMC-SC vector-controlled induction motor drive system was modelled, simulated and tested. The results have validated the effectiveness of this system in minimizing the motor operating losses, especially at light and medium loads. The proposed controller can be implemented in adjustable speed induction motor drive systems with variable loads, operating below rated speed.
Highlights
No less than 50% of the total energy generated worldwide is consumed in induction motors (Kumar et al, 2010)
Nowadays induction motor drives with cage-type machines are the most widely used machine especially in the industrial sector (Saravanan et al, 2012). This large share of energy consumed by induction motors has attracted researchers’ attention to maximize the Induction Motor (IM) efficiency especially at light loads where the induction motor operates at low efficiency (Raj et al, 2009)
The initial value of 0.03 for the flux-current ratio was passed onto the Search Controller (SC) to allow to check the optimality or otherwise of such a value
Summary
No less than 50% of the total energy generated worldwide is consumed in induction motors (Kumar et al, 2010). Scalar controlled drives depend on the previously mentioned variables in order to operate the induction motor at the optimal V/F ratio (Raj et al, 2009; Mary and Subburaj, 2013). In order to build a vector loss model controller a direct relationship between flux and stator current must be derived.
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