Mouna Ben Hamed

MPPT controller for a photovoltaic power system based on fuzzy logic

This paper proposes a maximum power point tracking algorithm based on fuzzy logic control for photovoltaic systems, According to the nonlinear characteristic of photovoltaic array [1], its necessary to find a solution to track the maximum power of the PV system in order to improve its efficiency. The fuzzy logic controller presented in this work provide fast response and good performance against the climatic and load change and uses directly the DC/DC converter duty cycle as a control parameter. Simulation results show that the proposed algorithm can effectively improve the efficiency of photovoltaic array output.

FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine

As per modern electrical grid rules, Wind Turbine needs to operate continually even in presence severe grid faults as Low Voltage Ride Through (LVRT). Hence, a new LVRT Fault Detection and Identification (FDI) procedure has been developed to take the appropriate decision in order to develop the convenient control strategy. To obtain much better decision and enhanced FDI during grid fault, the proposed procedure is based on voltage indicators analysis using a new Artificial Neural Network architecture (ANN). In fact, two features are extracted (the amplitude and the angle phase). It is divided into two steps. The first is fault indicators generation and the second is indicators analysis for fault diagnosis. The first step is composed of six ANNs which are dedicated to describe the three phases of the grid (three amplitudes and three angle phases). Regarding to the second step, it is composed of a single ANN which analysis the indicators and generates a decision signal that describes the function mode (healthy or faulty). On other hand, the decision signal identifies the fault type. It allows distinguishing between the four faulty types. The diagnosis procedure is tested in simulation and experimental prototype. The obtained results confirm and approve its efficiency, rapidity, robustness and immunity to the noise and unknown inputs.

Multiple Lunberger observer for an induction motor represented by decoupled multiple model

This paper addresses the analysis and design of the state estimation of the induction motor (IM) speed as a non linear system, subject of several disturbances as load disturbances and parameters variation represented by a multiple model. Thus, in this paper, we propose the synthesis of a Lunberger multiple observer. This study consists of estimating the asynchronous machine speed through the determination of each local observer earning. The obtained sub observers are interpolated using the validity concept. Experimental results for 1kw IM modeling are operated on a dSpace system with DS1104 controller board based on digital signal processor (DSP) TMS320F240. Obtained results shows the performances of the proposed multiple observer.

A real time implementation of an improved MPPT controller for photovoltaic systems

This paper proposes and develops a novel maximum power point tracking (MPPT) approach. This method is designed to be applied to systems that are fed with a photovoltaic (PV) generator. It is based on the bijectivity of the PV generator characteristic; as if the optimal current is reached, this means that the Vpv generator is obligatory at its optimal value leading to an optimal power. Based on this principle, as maximum power is obtained either based on the Ipv current control or the Vpv voltage control. In this paper, the MPPT algorithm is based only on the Ipv current control. The proposed MPPT algorithm is implemented on a dSpace DS1104 controller board. To demonstrate the efficiency and the validity of the developed algorithm in real time, an experimental setup around a buck converter and a resistive load is successfully implemented and experimentally studied. Obtained experimental results prove the validity of the proposed MPPT algorithm.

Application of parity space approach in fault detection of DC motors

This paper deals with a fault detection and isolation (FDI) of a DC motor described by linear dynamic models. Dynamic space parity approach is used to detect sensor and actuator faults. This approach is based on obtaining an input-output description of a given system and transforming it into the parity equations. Therefore, the theoretical symptoms matrix is established. The later is compared to an experimental one to isolate the faults. The validity and usefulness of the fault detection and isolation algorithms are thoroughly verified with experiments on 1kw DC motor using a dSpace system with DS1104 controller board based on digital signal processor (DSP) TMS320F240.

Real-time implementation of a maximum power point tracking algorithm based on first order sliding mode strategy for photovoltaic power systems

This paper focuses on a maximum power point tracking (MPPT) algorithm based on the first order sliding mode approach. This work is aimed at systems that are fed with a photovoltaic (PV) generator. The main objective is to act on the panel to ensure that the energy collected is always at its maximum. Because a PV panel frequently suffers from non-linearity of its P-V curves, we propose to work with MPPT controllers based on the first order sliding mode approach; indeed, this approach in general is recognized as one of the efficient tools to design robust controllers. It has received much more attention within the last two decades, and many researchers are dealing with this type of robust controller to ensure optimal operating point of a PV system. The proposed MPPT algorithm has been implemented using a DSPACE DSP (digital signal processor) controller. To demonstrate the efficiency and the validity of the developed algorithm in real time, an experimental setup around a boost converter and a resistive load is experimentally studied and successfully implemented. The real-time validation and the experimental results show that the proposed algorithm can effectively improve the efficiency of PV array output under climatic variation and load change. It is a favorable algorithm that is easy to realize.

Comparison of MPPT algorithms for DC-DC boost converters based PV systems using robust control technique and artificial intelligence algorithm

This paper proposes two methods of maximum power point tracking algorithm for photovoltaic systems, based on the first hand on fuzzy logic control and on the other hand on the first order sliding mode control. According to the nonlinear characteristic of photovoltaic array, its necessary to find a solution to track the maximum power of the PV system in order to improve its efficiency. The fuzzy logic controller was presented in many works. It provides fast response and good performance against the climatic and load change and uses directly the DC/DC converter duty cycle as a control parameter. Moreover, the sliding mode control approach is recognized as one of the efficient tools to design robust controllers it has been receiving much more attention within the last two decades and many research are dealing with this type of robust controllers. A detailed comparison between the fuzzy logic and slinging mode controllers was presented in this work. Simulation results show that the proposed algorithms can effectively improve the efficiency of a photovoltaic array output.

Adaptive neuro-fuzzy inference system into induction motor: Estimation

This paper presents the application of an adaptive neuro-fuzzy inference system (ANFIS) for an induction motor for speed estimation. Due to the drawbacks of the mechanical sensors, ANFIS (neuro-fuzzy inference adaptive system) speed observer is developed and it is based on artificial intelligence technique combining the concepts of fuzzy inference systems and neuron networks. The ANFIS rotor speed estimator depends only on measurable stator quantities (voltages and currents) that are easily accessible, hence the easy implementation in practice and thus reduces the cost since there is no need to the speed sensor. In addition, this work deals also with the vector controlled induction motor using stator field orientation (SFO). It is well known that the vector control strategy is based on the simultaneous determination of the magnitude and argument of the flux vector. This control method gives an effective solution that provides decoupling between the flux and torque of an induction motor, hence overcoming the complex control obstacle of this type of machines. Simulations to evaluate the performance of the estimator considering the vector drive system were done from the Matlab/Simulink software.

Chapter 2.11 – Exergetic and Energetic Performance Evaluation of a Flat Plate Solar Collector in Dynamic Behavior

Abstract This work aimed to evaluate the energy and the exergy performance of a flat plate solar collector under transient conditions. A theoretical model based on the first and second laws of thermodynamics was developed to predict the thermal behavior of the system. An exergy analysis was performed to determine the location, type, and magnitude of exergy destroyed and losses over the collector. The mathematical model obtained was solved numerically using the MATLAB computational program and Stat Ease Expert Design software. A detailed parametric study was conducted to assess the effect of various parameters. The results showed that the values for exergy efficiency were low compared with energetic ones. Furthermore, the main cause of exergy destruction was the absorption of radiation by the absorber plate. The mass flow rate, inlet water temperature, and absorber emissivity are critical parameters for a solar collector. However, the number of tubes and the diameter of the pipes have negligible effects on the collector’s performance. Thus, more accurate results and useful applications of the exergy method in transient behaviors were obtained to design solar collectors.

MPPT Controllers Based on Sliding-Mode Control Theory and Fuzzy Logic in Photovoltaic Power Systems: A Comparative Study

In this chapter we will deal with a comparative study between two control methods for maximum power point tracking (MPPT) algorithms in photovoltaic (PV) systems. The two MPPT controllers considered in this chapter are: the Fuzzy Logic Controller (FLC) and the Sliding Mode Controller (SMC). The MPPT controller based on the fuzzy-logic-algorithm uses directly the DC-DC converter duty cycle as a control variable and it provides a fast response and good performances against the climatic and load changes. The SMC exhibits a very fast response for tracking the maximum power point (MPP) for photovoltaic systems. The input parameters are the voltage and the current, the duty cycle is used to generate the optimal MPP under different operating conditions. Simulation results show that both algorithms can effectively perform the MPPT hence improving the efficiency of PV systems.