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In this paper, focused on direct torque control (DTC) for permanent magnet synchronous generator direct driven standalone variable speed wind turbine. The machine side converter (MSC) employed three-level DTC with dither signal strategy proposed for minimizes the torque & flux ripple. The triangular signal used as a dither signal & injected in a hysteresis band controller of torque & flux comparator. The optimum magnitude of the triangular wave is 3% of its rated torque & flux used in order to reduce acoustic noise. The proposed strategy minimizes torque & flux ripple as compared to conventional two-level DTC technique. Three-level SVPWM strategy employed for a load-side converter (LSC) in order to reduce harmonic distortion.

Predictive maintenance is a system’s competency in distinguishing future scenarios where the machine is likely to fail and schedule repairs just prior to this happening. A heuristic technology to enable efficient predictive maintenance is digital twin technology. The development of a twin system between real-time machinery and the virtual world is made possible by digital twin technology, which is ideal for predictive maintenance. Induction motors, which are the core of industrial machinery, are sparsely represented in the digital twin domain. Therefore, this study created a digital twin of a squirrel cage induction motor, utilizing data-driven modeling and multiple physics, and integrated it with a custom predictive maintenance system. The purpose of this study is to implement digital twin technology for induction motors for fault diagnosis and predictive maintenance. This framework can extrapolate running parameters to presciently detect motor remaining useful lifetime as well as erratic fault diagnosis. The experimental setup for the 2.2 kW squirrel cage induction motor has been integrated into the digital workspace via the dSPACE MicroLabBox controller to allow frequent calibration and reference signal setup. The resultant digital framework deployed on MATLAB Simulink provided high accuracy without placing a great computational load on the processor. The proposed model’s commercial application may open the way for computational intelligence in Industry 4.0 adoption of induction motors.