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The main contribution of this review work is to show how various control techniques are used to manage the speed of Permanent Magnet Synchronous Motor (PMSM). The PMSM's are mostly used in electric vehicles, electric traction and high performance industrial drive applications. In this article conventional sensorless techniques are compared with machine learning techniques such as fuzzy logic, artificial neural network and neuro-fuzzy controllers to control the speed of PMSM drive based on vector control approach. The benefits of machine learning techniques used in sensorless PMSM drive are easy to design, less execution time and fast access speed control. The various controlling techniques used in controller along with its complexity, advantages and drawbacks are discussed in this article. The above mentioned controlling techniques are implemented and simulated by using MATLAB R2019b/Simulink software based on sensorless Model Reference Adaptive System (MRAS) with the help of Field Oriented Control (FOC) strategy of PMSM drive. By comparing the all sensorless controlling techniques in simulation study, it is identified that the combination of neuro-fuzzy controller gives the best speed control performance than other controllers. [ABSTRACT FROM AUTHOR]

A key focus of the Sustainable Development Goals (SDGs) is to harvest electricity from renewables. The fields of renewable energy sources (RES) and harvesting methods have seen tremendous growth. Multiport power electronic converter circuits integrate multiple energy sources. This integration aids in powering the load with different required voltage ratings. A simple and reliable control scheme for this system can maintain high power transfer efficiency and reliability. To integrate RES, non-isolated (NI) DC-DC multiport converters (MPC) are introduced; however, the main objective is to concentrate on NI MPC which provide a reliable output. In this paper non-isolated MPC topologies are reviewed and analyzed based on difference parameters considering circuit configuration; operating principal; number of components; complexity; and efficiency. This comprehensive review can be used as a guide for the suitable selection of converter topology to RES integration. [ABSTRACT FROM AUTHOR]

The demand for accurate health status assessment of bearings in rotating electrical machines is rising. However, traditional fault diagnosis methods based on deep learning are not effective in efficiently and intelligently determining the health condition of the bearings and also requires large memory, hence constraining their use in low-cost microcontrollers. In this work, combination of short time Fourier transform (STFT) with Ostu’s threshold method and MobileNet-V2 with U-Net (MV2UNet) is proposed for rolling bearing fault health status. The proposed method obtains the STFT images of the raw vibration signals from the laboratory experimental setup. Then the Ostu’s threshold method is used to make the labels for STFT images. Finally, the proposed method is trained with these labelled images to detect the health status of each bearing fault. The proposed model achieved the Dice coefficient is 0.9834 and the intersection over union is 0.9479. The main advantage of the proposed model has low memory requirements, which makes it well-suited for implementation in affordable hardware devices like microcontrollers. Finally, the proposed method result is compared with U-Net, DeepLabV3+ with MobileNet-V2 and DeepLabV3+ with ResNet-50. [ABSTRACT FROM AUTHOR]

The increased use of electronics for control and use of nonlinear type loads by consumers in the present power system network are injecting harmonics into the power signal, due to which the power quality issue has become more challenge for the present researchers. In this work, the reduced order generalized integrator (ROGI) and fuzzy logic control (FLC) are collectively used to reduce the current harmonics in the power system. FLC‐SVM technique is used to maintain the process of SAF—Shunt active filter with fixed switching frequency. This proposed control technique consists of control loop for current to have fast and effective control. The Proportional Resonant controller is used to have control on voltage for a slow and effective control process which is also used to compensate reactive power. The shunt active filter is designed with the help of ROGI by integrating PI controller for fuzzy‐based SVM to reduce the current harmonics on the load side. The results are achieved in MATLAB/SIMULINK software. [ABSTRACT FROM AUTHOR]

Present advancements in the power systems paved way for introducing the smart grid (SG). A smart grid is beneficial to consumers which enables the bi-directional flow of information between the utility and customer. Demand-side management (DSM) techniques are crucial as load-side management techniques to attain the better stability of the grid. Home energy management systems (HEMS) play a indispensable part in the DSM. Countless traditional optimization techniques are utilized to implement HEMS, but the limitations of traditional Math heuristic methods gave rise to a concept-based optimization techniques called the Meta heuristic methods. Recent advancements introduced smart optimization techniques powered by Artificial Intelligence (AI). This article elucidates the applications of AI-based optimization techniques and their advantages over other methods. Various Machine learning (ML) and Deep Learning (DL) algorithms and their utilization for HEMS are discussed in brief. [ABSTRACT FROM AUTHOR]