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This research intends to establish a robust vector control (VC) of a 3-ϕ induction motor (IM). The classical 2-level inverter is displaced by a 5-level neutral-pointclamped (NPC) inverter. The 2-level inverter may only supply 8 voltage vectors, while the 5-level NPC inverter can furnish 125 voltage vectors. The objective is to bring about a command voltage vector that converges to the reference voltage vector as closely as possible; hence, guaranteeing a quick response on one hand and improving the dynamic performance on the other hand. A robust sliding mode controller (SMC) structure is used in all regulation loops. Satisfactory results are obtained for various speed zones. The quality and robustness of the global system are tested under resistive torque disturbance, reversal, high, and low-speed ranges in order to prove system stability. All the simulations have been performed under MATLAB/Simulink.This research intends to establish a robust vector control (VC) of a 3-ϕ induction motor (IM). The classical 2-level inverter is displaced by a 5-level neutral-pointclamped (NPC) inverter. The 2-level inverter may only supply 8 voltage vectors, while the 5-level NPC inverter can furnish 125 voltage vectors. The objective is to bring about a command voltage vector that converges to the reference voltage vector as closely as possible; hence, guaranteeing a quick response on one hand and improving the dynamic performance on the other hand. A robust sliding mode controller (SMC) structure is used in all regulation loops. Satisfactory results are obtained for various speed zones. The quality and robustness of the global system are tested under resistive torque disturbance, reversal, high, and low-speed ranges in order to prove system stability. All the simulations have been performed under MATLAB/Simulink.

This paper presents a physics-based mathematical model for a fuel cell hybrid vehicle system. The performance design of fuel cell hybrid electric vehicles (FCHEVs) is an area of interest in transportation applications. FCHEV is a combination of a proton exchange membrane fuel cell (PEMFC), with a battery and associated DC/DC and DC/AC converters. Suitable batteries are used in FCHEV acts as a backup system with efficient energy management. The battery is designed for fast power transfer during transient response and constant performance without hydrogen. The power management schemes discussed are consistent with the most common modern power management techniques used in fuel cell vehicle applications, including: the state engine control strategies, rule-based fuzzy logic strategies, classical proportional integral control strategies, frequency fuzzy logic decoupling/control minimization strategies. Therefore, we tested and evaluated the reliability of the model using the MATLAB/Simulink environment, with mixed results.

Global electricity consumption has raised in the last century due to many reasons such as the increase in human population and technological development. To keep up with this increasing trend, the use of fossil resources has increased. But these resources are not environmentally friendly, and for this reason, many countries and governments are encouraging the use of green sources. Among these sources, PV technology is widely promoted and used due to its improved efficiency and lower prices for photovoltaic panels. Therefore, the importance of forecasting power production for these plants is necessary. In this work, two hybrid models were proposed (CNN-LSTM and ConvLSTM) to effectively predict the power production of a self-consumption PV plant. To confirm the efficiency of the proposed models, the LSTM model was used as a baseline for comparison. The three models were trained on two datasets, a univariate dataset containing only the power output of the previous days, while the multivariate dataset contains more features (weather features) that affect the production of the PV plant. The time frames for the forecast ranged from one day to one week ahead of time. The results show that the proposed methods are more accurate than a normal LSTM model.

This paper presents a modelling study and focuses on an advanced higher order slip mode control strategy (Super Twisting Algorithm) for a variable speed wind turbine based on a permanent magnet synchronous generator to capture the maximum possible wind power from the turbine while simultaneously reducing the effect of mechanical stress, powered by a voltage inverter and controlled by vector PWM technique. This paper presents first and second order sliding mode control schemes. On the other hand, a challenging matter of pure SMC of order one can be summed up in the produced chattering phenomenon. In this work, this issue has been mitigated by implementing a new control. The proposed control, characterized by a precision in the case of a continuation of a significant reduction of the interference phenomenon, successfully addresses the problems of essential non-linearity of wind turbine systems. This type of control strategy presents more advanced performances such as behaviour without chattering (no additional mechanical stress), excellent convergence time, robustness in relation to external disturbances (faults in the network) and to non-modelled dynamics (generator and turbine) which have been widely used in power system applications by first order sliding mode control. In particular, second-order sliding regime control algorithms will be applied to the PMSG to ensure excellent dynamic performance. The suggested control is compared to the proportional-integral controller and sliding mode control of order one. The results of simulations under turbulent wind speed and parameter variations show the efficiency, robustness and significantly improved performance of the proposed control approach to distinguish and track quickly (about 10ms depending on the shading pattern) and at the same time saving the main priorities of the sliding mode of order one by reducing the existing chatter. The systems performances were tested and compared using Matlab/Simulink Software.

Commonly, three Hall effect sensors are used to detect the rotor position in brushless motors. Since one of these sensors may fail and lead to unstable operation, fault-tolerant control (FTC) of Hall sensor drives has attracted renewed research attention recently to ensure very high reliability. This article presents simple combinatory functions to detect fails in Hall effect sensors based only on Hall sensors signals, which require just a memory of 3 b. The proposed method guarantees more reliability and overcomes erroneous alarms that can be triggered by previous fault diagnosis methods using the information on the observed acceleration, which may be the subject of unexpected variations and lead consequently to false diagnosis. In addition, the article opens the discussion about the BLDC startup with a faulty sensor, in order to perform FTC algorithms ensuring a long use of the motor given the difficulty to repair Hall effect sensors inserted between the coils inside the stator. To confirm the theoretical analysis, experimental results are reported and limitations on the performances of the faulty system in terms of fault detection and signal reconstruction algorithm are discussed. The proposed schemes circuits can be inserted in conventional drive systems or by being placed between the existing driver and the motor.

In recent years, an expansive research exertion has been devoted to the control for aeorogenerators conforming of a Doubly Fed Induction Generator (DFIG) coupled to a wind turbine. Most preceding controllers based on standard models (whose magnetic induction characteristic is considered linear and iron losses have not been taken into account). Since such a magnetic characteristic is nonlinear in practice, this work develops a variable speed control strategy on the basis of a DFIG model in presence of nonlinear magnetic characteristic and iron losses. The main contribution of this paper is to present a high-performance maximum power point (MPPT) control of DFIG based on rotor flux optimization, designed to reach an optimal operational conditions. Of the foregoing, the control goal is twofold: i) minimization of the stator and rotor currents circulating by controlling the rotor flux for a maximum electromagnetic torque; ii) captured the maximum wind power to insure the attainment of MPPT. The performances of the proposed command has been studied theoretically and validated by simulation studies with Matlab/Simulink.

We need to solve the problem due to the nonlinearity and power fluctuation in the photovoltaic (PV) connected storage system and grid; for that, the authors develop an algorithm to obtain the maximum power point tracking (MPPT) via control of the duty cycle of DC/DC boost converter. Consequently, they design an MPPT based on the second-order sliding mode control. Next, generating the law control founded on the Lyapunov theory can augment the robustness and stability of the PV connected grid. Then, they add a battery energy storage system (BESS) with a control management algorithm in the DC/DC side to eliminate any fluctuation of the output power of the PV system because of the temperature and irradiation variation. On the grid side, they control the DC/AC inverter side by the three-phase voltage source inverter control (VSIC) as a charge controller for the grid parameters.