This paper gives a comprehensive review of advanced cooling schemes and their applications to the permanent magnet synchronous motors (PMSMs), as well as investigating the electrical motor’s topologies its thermal design issues, materials and performances. Particularly, the electromagnetic and electric performances, machine sizing, together with the structural design, are given. In addition, the work addresses the motor’s material design and properties along with its insulation performance, which is the main goal of optimization. Mainly, thermal mapping with analysis is provided according to the different cooling methods, including air-cooling, water-cooling, oil-cooling, heat-pipe-cooling, potting silicon gelatin cooling, and as well as cooling strategies for tubes and microchannels. The most common special features and demands of the PMSMs are described in the appearance of the motor’s failures caused by uncontrolled temperature rise. In addition, heat sources and energy losses, including copper loss, core loss versus motor speed, and output power, are analyzed. The review of the proposed cooling methods that will achieve the required heat transfer of the PMSM is presented with numerical simulations and measurements data. A review of numerical methods and results, including the finite element methods (FEM), such as the Ansys CFD software, to obtain a high-accuracy thermal mapping model of the PMSM system is given. The revived methods and design requirements due to PMSM temperature profile and cooling flow at different rotor speeds and torque loads are investigated. Finally, the motor design recommendations, including the newly developed cooling solutions, which enable it to effectively redistribute the temperature and heat transfer, increasing the efficiency of the PMSM machine, are laid out.

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.

The use of a Doubly Fed Asynchronous Machine (DFAM) provides attractive characteristics and offers operational flexibility in many variable speed generation applications, such as in a hydroelectric pumped storage plant. In a variable speed hydroelectric pumped storage plant, the start-up process of DFAM is identical to the conventional singly fed asynchronous machine, wherein a significant amount of energy is wasted. This paper introduces an energy-efficient start-up strategy in DFAM based hydroelectric pump-turbine. The back-to-back voltage source converter connected to the rotor side is amenable for speed control (real power), braking (regenerative/dynamic), and starting the unit. Further, in this starting technique, the stator circuit of the machine is injected with a low voltage DC supply at starting instead of short-circuiting the windings. This DC injection reduces the slip losses and cuts down the magnetizing current requirement. The magnitude of the required DC supply is estimated based on the machine’s reactive power requirement. Also, the switching of stator winding between the short circuit connection, DC injection, and grid supply is carried out using a changeover switch and determined by the speed of the rotor. The proposed starting strategy is investigated with 250 MW DFAM in Matlab/Simulink environment and experimented with a 2.2 kW DFAM prototype. Test results show that the proposed starting method can conserve more than 26.1 percent of electrical energy in the example application compared to the conventional V/f start-up strategy.