EKF-based estimation and control of electric drivetrain in offshore pipe racking machine

A typical challenge for electric drivetrains is to reduce the number of sensors required for control action or system monitoring. This is particularly important for electric motors operating in offshore conditions, since they work in hostile environment which often damages data acquisition systems. Therefore, this paper deals with verification and validation of the extended Kalman filter (EKF) for sensorless indirect field-oriented control (IFOC) of an induction motor operating in offshore conditions. The EKF is employed to identify the speed of the induction motor based on the measured stator currents and voltages. The estimated speed is used in the motor speed control mode instead of a physical encoder signal. In addition, we utilize a stationary frame model of the induction machine to assess the fidelity level of the EKF-based estimation of rotor fluxes. The experimental setup is used to validate accuracy of the EKF-based state estimation and motor speed control. The importance of the current work is demonstrated on an example of a full-scale offshore drilling equipment. Real-world speed and load profiles sustained by the gripper arm of the vertical pipe racking machine are scaled down and reproduced by the experimental setup. The proposed EKF algorithm accurately estimates both speed and electromagnetic torque experienced by the reference full-scale electric drivetrain, creating a potential to reduce the number of data acquisition devices in similar type of equipment.