Global environmental changes, nuclear power risks, losses in the electricity grid, and rising energy costs are increasing the desire to rely on more renewable energy for electricity generation. Recently, most people prefer to live and work in smart places like smart cities and smart universities which integrating smart grid systems. The large part of these smart grid systems is based on hybrid energy sources which make the energy management a challenging task. Thus, the design of an intelligent energy management controller is required. The present paper proposes an intelligent energy management controller based on combined fuzzy logic and fractional-order proportional-integral-derivative (FO-PID) controller methods for a smart DC-microgrid. The hybrid energy sources integrated into the DC-microgrid are constituted by a battery bank, wind energy, and photovoltaic (PV) energy source. The source-side converters (SSCs) are controller by the new intelligent fractional order PID strategy to extract the maximum power from the renewable energy sources (wind and PV) and improve the power quality supplied to the DC-microgrid. To make the microgrid as cost-effective, the (wind and PV) energy sources are prioritized. The proposed controller ensures smooth output power and service continuity. Simulation results of the proposed control schema under Matlab/Simulink are presented and compared with the super twisting fractional-order controller.

Automatic speech recognition (ASR) is one of the ways used to transform acoustic speech signals into text. Over the last few decades, an enormous amount of research work has been done in the research area of speech recognition (SR). However, most studies have focused on building ASR systems based on adult speech. The recognition of children’s speech was neglected for some time, which means that the field of children’s SR research is wide open. Children’s SR is a challenging task due to the large variations in children’s articulatory, acoustic, physical, and linguistic characteristics compared to adult speech. Thus, the field became a very attractive area of research and it is important to understand where the main center of attention is, and what are the most widely used methods for extracting acoustic features, various acoustic models, speech datasets, the SR toolkits used during the recognition process, and so on. ASR systems or interfaces are extensively used and integrated into various real-life applications, such as search engines, the healthcare industry, biometric analysis, car systems, the military, aids for people with disabilities, and mobile devices. A systematic literature review (SLR) is presented in this work by extracting the relevant information from 76 research papers published from 2009 to 2020 in the field of ASR for children. The objective of this review is to throw light on the trends of research in children’s speech recognition and analyze the potential of trending techniques to recognize children’s speech.

The present work addresses the modelling, control, and simulation of a microgrid integrated wind power system with Doubly Fed Induction Generator (DFIG) using a hybrid energy storage system. In order to improve the quality of the waveforms (voltages and currents) supplied to the grid, instead of a two level-inverter, the rotor of the DFIG is supplied using a three-level inverter. A new adaptive algorithm based on combined Direct Reactive Power Control (DRPC) and fuzzy logic controls techniques is applied to the proposed topology. In this work, two topologies are proposed. In the first one, the active power injected into the grid is smoothened by using an economical hybrid battery and supercapacitor energy storage system. However, in the second one, the excess wind energy is used to produce and store the hydrogen, and then a solid oxide fuel cell system (SOFC) is utilized to regenerate electricity by using the stored hydrogen when there is not enough wind energy. To avoid overcharging, deep discharging of batteries, to mitigate fluctuations due to wind speed variations, and to fulfil the requirement of the load profile, a power management algorithm is implemented. This algorithm ensures smooth output power in the first topology and service continuity in the second. The modelling and simulation results are presented and analysed using Matlab/Simulink.