Integrating Statistics and Manufacturing Data into Simulation of Permanent Magnet Motor Drives

Focus:

Simulating motor drives using Spice, Simulink or other tools is a great way to verify a concept or basic system performance. And through the use of Monte Carlo (MC) and worst-case analysis (WCA), a reasonable estimate of the performance probability distribution can be made. However, MC and WCA techniques are based on assumptions of a normal probability distribution and linear correlations between various system parameters. These techniques are not sufficient in predicting realistic system performance. In this paper, we propose techniques to modify MC and WCA through the integration of manufacturing data to explain and predict abnormal correlations between various system parameters such as those that occur when a production ramp up takes place. These techniques are tested and verified on a permanent magnet brushless motor drive system.

What you’ll learn:

• How to account for abnormal correlations between system parameters when simulating motor drive systems
• How to enhance simulation accuracy when performing Monte Carlo and worst-case analyses of permanent magnet motor drives

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Author & Publication:

Rakesh Dhawan and Amitabh Mallik, How2Power Today, Jul 30 2010

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