This study aims to identify the critical parameters for implementing a sustainable artificial intelligence (AI) cloud system in the information technology industry (IT). To achieve this, an AHP-ISM-MICMAC integrated hybrid multi-criteria decision-making (MCDM) model was developed and implemented. The analytic hierarchy process (AHP) was used to determine the importance of each parameter, while interpretive structural modeling (ISM) was used to establish the interrelationships between the parameters. The cross-impact matrix multiplication applied to classification (MICMAC) analysis was employed to identify the driving and dependent parameters. A total of fifteen important parameters categorized into five major groups have been considered for this analysis from previously published works. The results showed that technological, budget, and environmental issues were the most critical parameters in implementing a sustainable AI cloud system. More specifically, the digitalization of innovative technologies is found to be the most crucial among the group from all aspects, having the highest priority degree and strong driving power. ISM reveals that all the factors are interconnected with each other and act as linkage barriers. This study provides valuable insights for IT industries looking to adopt sustainable AI cloud systems and emphasizes the need to consider environmental and economic factors in decision-making processes.

This paper proposes a Power factor Corrected (PFC) Bridgeless Buck-Boost converter fed BLDC motor drive. The Bridgeless configuration eliminates the Diode Bridge Rectifier in order to reduce the number of components and the conduction loss. The position sensors used in BLDC drives have drawbacks of additional cost, mechanical alignment problems. These bottle necks results in sensorless technique. The Sensorless technique mostly relies on measurement of Back EMF to determine relative positions of stator and rotor for the correct coil energising sequence can be implemented. This paper introduces the offline Finite Element method for sensorless operation. The proposed sensorless scheme estimates the motor position at standstill and running condition. The obtained Power Factor is within the acceptable limits IEC 61000-3-2. The proposed drive is simulated in MATLAB/Simulink the obtained results are validated experimentally on a developed prototype of the drive.

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Improvements in the material characteristics of bandgap semiconductors allow the use of high-temperature, high-voltage, and fast switch rates in power devices. Another good reason for creating new Si power converter devices is that previous models perform poorly. The implementation of novel power electronic converters means high energy efficiency but a more logical use of electricity. At this moment, titanium dioxide and gallium nitride are the most prospective semiconductor materials because of their great features, established technology, and enough supply of raw components. This study is focused on providing an in-depth look at recent developments in manufacturing Si-C- and high-powered electronic components and showcasing the whole scope of the newly developing product generation.

A sensorless speed control method for doubly-fed induction machine (DFIM) operating with constant frequency but in variable speed mode is presented in this project work. The control method is based on rotor speed estimation technique by a reactive power model reference adaptive system (MRAS) observer. The presented technique does not depend on any kind of flux evaluation and also independent to the resistance variation of either stator or rotor. The MRAS observer has a capacity for speed catching operation. PI controller is designed and also optimized using algorithm for better dynamic behaviour of the machine. MATLAB Simulink model and the simulation results are shown to check the effectiveness of the observer and also of the controller.