This paper presents speed control of switched reluctance motor (SRM) with torque ripple reduction using direct Instantaneous torque control (DITC). The reference values of the instantaneous torque is generated from speed control by PI controller, from the comparison between the reference torques and the estimated torque using hysteresis controller in addition to selects different turn-off angles so that improve the performance of torque ripple, the latter achieves the switching signals needed for the converter to get the desired results. SRM have highly nonlinear for this the static characteristic has been determined using finite element method (FEM) under non-linear magnetic characteristics that makes them difficult to control but gives results closer to the truth. Results obtained from computer are compared for different turn-off angle which improve the performance the torque ripple of SRM

Dissolved Gas Analysis (DGA) in oil is one of the most common and reliable techniques for diagnosing and early detection of incipient faults in oil-immersed transformers. However, conventional methods often encounter conflicts and challenges in accurately interpreting DGA results. Addressing this, two novel graphical methods, Circle Method 1 and Circle Method 2, are proposed, represented by circles with movable axes at variable angles. These methods innovatively base their analysis on the convergence of samples with similar characteristics around their centers and the spacing of these centers for samples with differing characteristics. The primary purpose of these methods is to distinguish between different overlapping faults. To achieve this, advanced metaheuristic algorithms are employed to determine the optimal angles that yield the best distribution of samples. Utilizing insights from previous DGA methods and field experiences, initial fault zones within Circle Methods 1 and 2 are estimated, and precise boundaries between fault zones are established based on practical DGA data collected from multiple sources. Evaluation using IEC TC10 database of fault cases demonstrates that Circle Methods 1 and 2 exhibit superior diagnostic accuracy compared to existing methods, including the Mansour Pentagon, the Duval Pentagon, the Gouda Triangle, and the Cartesian Graphical Method. This innovation significantly enhances the interpretative capabilities of DGA, offering a more reliable tool for fault diagnosis in oil-immersed transformers.

Maximum power point tracking (MPPT) techniques for wind turbines have a significant effect on renewable energy production. Therefore, the association of the indirect torque control of the switched reluctance generator (SRG) with the wind turbine considering the MPPT technique has been developed in this work. The proposed strategy has a great impact on the production of renewable energy using an SRG machine. The main steps to achieve the object of this work are: First, the wind turbine was modeled and simulated according to the MPPT. In the second step, the indirect torque control strategy, based on the hysteresis current control for SRG 12/8, was realized. This was undertaken using a proportional integral regulator and the hysteresis controller for the torque in order to obtain the appropriate switching based on an asymmetric bridge converter. Moreover, the SRG has high nonlinear characteristics. Thus, the modeling results of this kind of machine are obtained by the use of the finite element method, with its dynamic study performed by the unique estimation of the electromagnetic torque in its generator mode functioning. Finally, the indirect torque control technique of the SRG has also been associated with the MPPT technique to maximize the efficiency power coefficient. The obtained results approve and validate the efficiency of a proposed MPPT of the wind turbine associated with the SRG. This illustrates, simultaneously, the remarkable effects of the turn-switching angles on the operating performances and the high quality of the produced energy. The importance of the effect of varying the turn-switching angles is also presented and discussed.

In this paper, a new modified model predictive control is proposed to improve the performance of the model predictive control for two-level voltage source inverters by alleviating computational burden and the disadvantages associated with the conventional model predictive control strategy. The objective of the proposed method is to reduce the number of candidate voltage vectors in each sector, thereby improving the overall performance of the control system, as well as achieving common-mode voltage reduction for two-level voltage source inverters. Two strategies are introduced to achieve this objective. Firstly, an algorithm is developed based on statistical computational processes to pre-define the candidate voltage vectors. This strategy involves ranking and considering the most frequently used voltage vectors. Consequently, by reducing the computational burden, the search space for the optimal voltage vectors is reduced. Furthermore, based on statistical results, a strategy is proposed to divide the sectors into three sectors instead of the six sectors in the conventional method. This approach effectively reduces the number of candidate voltage vectors. The modified model predictive control strategy aims to improve the efficiency of the control system by reducing the computational burden, and suppressing the common-mode voltage. The simulation and experiments are carried out to verify the effectiveness of the proposed strategy under various operational conditions. The results demonstrate that the modified model predictive control approach significantly reduces the computational burden and complexity of the control system while effectively suppressing the common-mode voltage; this contributes to improving the performance of two-level voltage source inverters and enhancing their applicability for connecting the renewable energies to the grid.