Hydrogen is acknowledged as a potential and appealing energy carrier for decarbonizing the sectors that contribute to global warming, such as power generation, industries, and transportation. Many people are interested in employing low-carbon sources of energy to produce hydrogen by using water electrolysis. Additionally, the intermittency of renewable energy supplies, such as wind and solar, makes electricity generation less predictable, potentially leading to power network incompatibilities. Hence, hydrogen generation and storage can offer a solution by enhancing system flexibility. Hydrogen saved as compressed gas could be turned back into energy or utilized as a feedstock for manufacturing, building heating, and automobile fuel. This work identified many hydrogen production strategies, storage methods, and energy management strategies in the hybrid microgrid (HMG). This paper discusses a case study of a HMG system that uses hydrogen as one of the main energy sources together with a solar panel and wind turbine (WT). The bidirectional AC-DC converter (BAC) is designed for HMGs to maintain power and voltage balance between the DC and AC grids. This study offers a control approach based on an analysis of the BAC’s main circuit that not only accomplishes the function of bidirectional power conversion, but also facilitates smooth renewable energy integration. While implementing the hydrogen-based HMG, the developed control technique reduces the reactive power in linear and non-linear (NL) loads by 90.3% and 89.4%.

This study proposes a new approach for the detection of broken rotor bar (BRB) fault in three phase induction motor drive using Hilbert transform (HT) and artificial neural networks (ANNs), where the machine is controlled by direct torque control (DTC). HT is preferred to develop the stator current envelope. The sideband frequency and its amplitude of the samples are the input for the ANN. By using fast Fourier transform, the amplitude and frequency components are extracted and the severity of fault is determined by comparing the magnitude of an average of sideband frequency with the fundamental frequency. High accuracy identification of fault is found by ANN, where the results are trained and tested to a minimum mean square error that will detect the number of BRB in the induction motor. DTC is adopted for a suitable control technique in the industrial drives system to maintain good performance in torque control. The performance of the proposed method is verified by using MATLAB/SIMULINK and experimental tests.

This paper presents a high accuracy detection of Broken Rotor Bar(BRB) fault by Artificial Neural Network (ANN) through advanced signal processing tool as Hilbert Transform (HT) where three phase Induction Motor Drives(IMD) is operated under Direct Torque Control (DTC) topology with steady state. The major significance of all diagnostic methods is, need information about the characteristic’s frequencies and amplitude. The diagnosing of machine fault requires the spectrum into isolated various frequency components. The Discrete Fourier Transform (DFT) cannot produce good output at low slip. So, in this paper ANN and HT are proposed. DTC method is efficient technique in industrial drives with variable torque applications. The stator current envelope can be formed by HT. Then samples of amplitude and side band frequency are given as ANN inputs. In order to diagnose the quantity of BRB in IM, the findings are qualified and checked to the minimal Mean Square Error (MSE)