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.

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

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. 

Artificial Neural Networks Applications in Partially Shaded PV Systems

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.

http://ijeecs.iaescore.com/index.php/IJEECS/article/download/21503/13813

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

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.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/20687/13199

A maximum power point tracking based on levy flight optimization

Solar panels have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP), which depends on the environmental factors, such as temperature and irradiation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP despite the inevitable changes in the environment. Various methods for maximum power point tracking (MPPT) were developed and finally implemented in solar power electronic controllers to increase the efficiency in the electricity production originate from renewables. In this paper we compare using Matlab tools Simulink, two different MPP tracking methods, which are, fuzzy logic control (FL) and sliding mode control (SMC), considering their efficiency in solar energy production.

Maximum power point tracking algorithm based on sliding mode and fuzzy logic for photovoltaic sources under variable environmental conditions

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.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/18875/12947

Experimental investigation of artificial intelligence applied in MPPT techniques

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.

Saidia Della Krachai

Papers

Experimental investigation of artificial intelligence applied in MPPT techniques

Sliding mode controller for buck DC-DC Converter