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DTC, also known as Direct Torque Control, is a technique employed in variable frequency drives for regulating the torque and speed of three-phase AC electric motors. This method entails determining an approximation of the motor’s magnetic flux and torque by analyzing the voltage and current readings of the motor. In order to reduce flux ripples and ensure smooth speed operation, we have suggested the use of two fuzzy logic controllers based on DTC for induction motors. The proposed method offers a faster response compared to conventional DTC for induction motors. The MATLAB simulink software was used to illustrate the comprehensive results.

Numerous techniques for maximum power point tracking (MPPT) are extensively employed to extract the utmost energy from Photovoltaic (PV) panels. The P&O method is a highly effective technique among those who perturb and observe. Nevertheless, in the presence of partial shading conditions, it becomes imperative to employ an optimization algorithm that can accurately pinpoint the global maximum location (GML) amidst the numerous local maximum locations. The Whale-inspired algorithm is a renowned optimization method for efficiently identifying GML in comparison to numerous other optimization algorithms. An inverter is required to interfacing between PV and grid in single stage grid connected system as it will also work as a MPPT device by controlling voltage corresponding to GML. In this situation, the utilization of a whale-inspired optimization technique can enhance energy harvesting through an appropriate inverter controller. The combination of TS-Fuzzy and H∞ controllers through hybridization can lead to rapid response times and increased system stability. Hence, this paper introduces a new controller designed for the inverter to function effectively in a single-stage grid-connected PV system. The proposed controller can also provide the grid with high-quality power. This work showcases comprehensive findings derived from Hardware-in-the-Loop (HIL) testing conducted using OPAL-RT technologies.

Power generation from Photovoltaic (PV) modules is the best way to utilize the renewable energy in many places. The output of the PV modules will depends on many factors including changes in solar irradiance, temperature, partial shading conditions, operating voltage across terminals of module etc. Apart from operating voltage of PV modules, remaining all factors are depending on nature which we cannot change easily. Hence obtaining an effective voltage regulation of terminals to achieve maximum power from PV module is very important for best utilization. There are many existing maximum power point tracking (MPPT) mechanisms on PV systems but an effective integrating mechanism is required to achieve the best operation under all possible conditions. Artificial neural network (ANN) can able to produce best reference signal corresponding to maximum power location under various random changes very quickly. In other hand, sliding mode controller (SMC) is able to force the boost converter to deliver the best output voltage with fewer ripples under variable input voltage. Fastest response with fewer ripples in dc voltage can help to harvest maximum energy from PV panels. Therefore, integration of ANN with SMC can prepare the MPPT converter to deliver more energy under random changes in irradiance and temperature. The performance of ANN associated SMC for boost converter based MPPT of the PV system is presented in this paper. In order to obtain more realistic response, Hardware-in the-Loop (HIL) is established for proposed system with the help of two OPAL-RT modules. Extensive results by using HIL are presented and discussed under various case studies to enhance the proposed method.