Mohammed Bekhti

Bio: Mohammed Bekhti is an academic researcher from University of Science and Technology of Oran Mohamed-Boudiaf. The author has contributed to research in topics: Power budget & Antenna (radio). The author has an hindex of 2, co-authored 8 publications receiving 11 citations.

Experimental investigation of artificial intelligence applied in MPPT techniques

Abstract: Nano-satellites are key features for sharing the space data and scientific researches. They embed subsystems that are fed from solar panels and batteries. Power generated from these panels is subject to environmental conditions, most important of them are irradiance and temperature. Optimizing the usage of this power versus environmental variations is a primary task. Synchronous DC-DC buck converter is used to control the power transferred from PV panels to the subsystems while maintaining operation at maximal power. In this paper, artificial intelligence techniques: neural networks and adaptive neural fuzzy inference systems (ANFIS) are used to accomplish the tracking task. Simulation and experimental results demonstrate their efficiency, robustness and tracking quality.

Sliding mode controller for buck DC-DC Converter

Abstract: This paper is a simple and systematic approaches to the design of sliding mode controller for buck DC-DC Converters. Various aspects of the design, including the practical problems and the proposed solutions, are detailed. However, these control strategies can't compensate for large load current and input voltage variations. In this paper, a new control strategy by compromising both schemes advantages and avoiding their drawbacks is proposed, analyzed and simulated.

General Purpose Pulse Width Modulation Based Sliding Mode Controller for Buck DC-DC

Abstract: This paper is a simple and systematic approaches to the design and analysis a pulse width modulation (PWM) based sliding mode controller for buck DC-DC Converters. Various aspects of the design, including the practical problems and the pr oposed solutions, are detailed. However, these control strategies can 't ompensate for large load current and input voltage variations. In this paper, a new control strategy by compromising both schemes advan tages and avoiding their drawbacks is proposed, analyzed and simulated. Keywords—buck; DC/DC converters; sliding mode control, pulse width modulation

Solar Arrays and Battery Power Sources Conceptual Design for Low Earth Orbit Microsatellites

Abstract: The power system is a vital subsystem in a spacecraft As long as the spacecraft has power, it can perform its mission Almost all other failures can be worked out by ground operations from ground stations but a power loss is very fatal for the spacecraft In the early years of spaceflight, the power system was also the limiting factor in any mission duration Many studies show that solar cell power (short-circuit current and open-circuit voltage) are degraded by space environment radiation The power system is designed such that the end of life (EOL) power is adequate for the mission’s requirements Beginning of life (BOL) power is set by the estimate of the radiation damage over the spacecraft’s lifetime It is well known in the literature, the radiation damage to solar cells is caused by high-energy protons from solar flares and from trapped electrons in the Van Allen belt The purpose of this paper is to investigate the power system design trades involved in the mission analysis of a low earth orbit (LEO) satellite at an altitude of 700 km Based on the power requirements of the payload and the constant power requirements for the remainder of the spacecraft (platform subsystems), the solar arrays and batteries for the spacecraft will be sized

Design and Optimization of Microsatellite Power System

Abstract: In the framework of studying and Optimization of the power subsystem for a microsatellite, our purpose is to cover all aspects leading to the optimization of power which ensures accomplishment of the microsatellite mission in orbit. The power subsystem of a microsatellite is responsible to provide a necessary energy for the different operations of spacecraft under all spatial conditions that may be met during its mission, this until the end of its envisaged lifetime. It must to include the photovoltaic generator, the batteries, the battery charge regulator (BCR), power conditioning module, distribution module and protection. There are many technical problems, such as the failure of the photovoltaic panels causing a dysfunction of the satellite, for that the literature suggests solutions about the algorithms of optimization, the most important is the research of the maximum power point tracking (MPPT) when the photovoltaic generator and the load are connected through a static converter. The maximum power point varies according to several factors such as, irradiance and temperature.

A new controller for DC-DC converters based on particle swarm optimization

Abstract: Display Omitted We propose a new algorithm to control DC-DC converters.The new algorithm is based on Particle Swarm Optimization.The proposed controller does not require any parameter search like classical controllers.We compare the proposed controller with the PI Anti-Windup controller.The proposed controller exhibits better performance in terms of settling time and steady state oscillations. We propose a new controller for DC-DC converters based on particle swarm optimization (PSO). This new converter controller uses the PSO optimization method to directly control, by itself, the output voltage of a boost DC-DC converter. In order to validate and qualify the proposed converter controller, we analyzed and implemented some variants of the PSO algorithm, namely the standard PSO and the global local best PSO. The proposed converter controller was then compared with a variant of the classic PI controller with anti-windup, for different operational conditions. The three controllers compared in this work were implemented in the microcontroller TMS320F28027 by using the code composer studio from Texas Instruments. The results show that the proposed controller exhibits better behavior in terms of settling time and overshoot. Unlike most popular DC-DC converter controllers, the proposed controller does not require any sort of optimal parameter determination.

Extended High Gain Observer based Control Design for Buck-Boost Converters

Abstract: The evolution of the modern technologies and the development of power electronics has led to the extensive use of DC-DC Buck-Boost converters in the application of power amplifier, battery management system, self-regulating power supplies etc, as they have higher output voltage and low operating duty cycle. The problem of degradation in adeptness of the Buck-Boost converters in presence of a load and lumped parameter uncertainties are analyzed. An Extended High Gain Observer is formulated to estimate the output voltage, load current, and its first derivative which is considered to be nonlinear and bounded. The reference current is compared with the estimated load current, forming a sliding surface. Sliding Mode Controller ensures convergence of estimated load current trajectories to reference current trajectories in finite time. Finally, an inner control loop generates a duty ratio resulting in output voltage which tracks the required reference voltage. The simulation results have been implemented in MATLAB/Simulink to validate fast estimation of load current, output voltage and disturbance rejection in a closed-loop process.

Artificial Neural Networks Applications in Partially Shaded PV Systems

Abstract: Renewable energy sources have attracted attention in the last few years as an efficient and sustainable alternative to conventional fossil fuels. Among these sources, solar power emerges as an abundant and feasible energy resource for powering various forms of energy-demanding sectors, such as industrial applications and transportation. Solar photovoltaic (PV) systems directly transmute the energy in the solar electromagnetic radiation to electrical energy. However, a significant problem in solar PV systems is partial shading. A noticeable energy loss happens when a small portion of the PV system is subject to shading. There has been increasing attention to applying Artificial Intelligence (AI) techniques to mitigate partial shading. One of the most promising AI techniques is Artificial Neural Networks (ANNs) used extensively in analysing partially shaded PV systems. This work reviews the applications of ANNs in various aspects of partially shaded PV systems. The application of ANNs in Maximum Power Point Tracking (MPPT), fault detection, fault mitigation, system modelling, and performance optimization of solar PV systems undergoing partial shading are summarized and discussed. Finally, future research directions are presented to further improve these techniques and move them toward practical application.

Stator winding fault detection of induction generator based wind turbine using ANN

Abstract: This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.

A maximum power point tracking based on levy flight optimization

Abstract: This paper proposes a Levy flight global maximum power point tracking for solar photovoltaic (PV) system under partial shading conditions. The proposed method comes with merits such as simplicity, fast response and free of oscillation. This algorithm uses random search over the exploration space and compares the previous and current states to obtain the best solution. For evaluation and comparative analysis, performance of the proposed method is also measured against Perturb and Observe (P&O) and Particle Swarm Optimization (PSO). All three algorithms are simulated in MATLAB/Simulink environment. Simulation results are satisfactory over the conducted tests under uniform and non-uniform irradiance. The proposed algorithm is able to track global maximum power point (GMPP) under partial shading conditions with fast tracking time and zero ripple at steady-state.