A comprehensive assessment of MPPT algorithms to optimal power extraction of a PV panel

The electrical energy produced by photovoltaic (PV) process is inexhaustible, developable everywhere and clean. Whatever the conditions, it is desirable to extract the biggest amount of power from the solar panel. This is achieved with the use of a Maximum Power Point Tracking (MPPT) algorithm. Fluctuations in weather conditions (irradiation and temperature) strongly degrade the performance of the photovoltaic module's energy conversion and therefore all the power cannot be transferred to the load. The objective is to study and compare different approaches of MPPT algorithms to evaluate the power extracted under the standard test conditions and varying weather conditions. Results of the performance with all these algorithms are compared under different operating conditions. The results show that the Fuzzy Logic Controller (FLC) is the fastest in terms of stabilization and is followed respectively by Fractional Short-Circuit Current (FSCC), Fractional Open-Circuit Voltage (FOCV), Perturb and Observe (P&O), Incremental Conductance (INC) and Hill Climbing (HC) algorithms. The FLC also gives the best results in extracting, followed by P&O INC, HC, FSCC and FOCV algorithms

Collecting and analysis of data from autonomous sensors with applications to smart buildings

International audienc

Current Harmonic Estimation in Power Transmission Lines Using Multi-layer Perceptron Learning Strategies

This main contribution of this work is to propose a new approach based on a structure of MLPs (multi-layer perceptrons) for identifying current harmonics in low power distribution systems. In this approach, MLPs are proposed and trained with signal sets that are generated from real harmonic waveforms. After training, each trained MLP is able to identify the two coefficients of each harmonic term of the input signal. The effectiveness of the new approach is evaluated by two experiments and is also compared to another recent MLP method. Experimental results show that the proposed MLPs approach enables to identify effectively the amplitudes of harmonic terms from the signals under noisy condition. The new approach can be applied in harmonic compensation strategies with an active power filter to ensure power quality issues in electrical power systems

Received Revised

Prediction in real-time image sequences is a key-feature for visual servoing applications. It is used to compensate for the time-delay introduced by the image feature extraction process in the visual feedback loop. In order to track targets in a three-dimensional space in real-time with a robot arm, the target’s movement and the robot end-effector’s next position are predicted from the previous movements. A modular prediction architecture is presented, which is based on the Kalman filtering principle. The Kalman filter is an optimal stochastic estimation technique which needs an accurate system model and which is particularly sensitive to noise. The performances of this filter diminish with nonlinear systems and with time-varying environments. Therefore, we propose an adaptive Kalman filter using the modular framework of mixture of experts regulated by a gating network. The proposed filter has an adaptive state model to represent the system around its current state as close as possible. Different realizations of theses state model adaptive Kalman filters are organized according to the divide-and-conquer principle: they all participate to the global estimation and a neural network mediates their different outputs in an unsupervised manner and tunes their parameters. The performances of the proposed approach are evaluated in terms of precision, capability to estimate and compensate abrupt changes in targets trajectories, as well as to adapt to time-variant parameters

A Nonlinear H-Infinity Control Approach for Three-Phase Voltage Inverters

The article proposes a nonlinear H-infinity (optimal) control approach to the problem of control of threephase inverters. The dynamic model of the three-phase inverter undergoes approximate linearization, round a local operating point. This local equilibrium is re-calculated at each time instant and consists of the present value of the state vector of the inverter and of the last value of the control input thatwas exerted on it. The linearization is based on Taylor series expansion and the computation of the associated Jacobian matrices. The modelling error due to truncation of higher order terms from this expansion is compensated by the robustness of the control scheme. Next, an H-infinity feedback controller is designed. The feedback gain is computed after solving an algebraic Riccati equation at each iteration of the control algorithm. Through Lyapunov stability analysis it is proven that the control loop satisfies an H-infinity tracking performance criterion, which signifies elevated robustness to model uncertainty and external perturbations. Moreover, under moderate conditions the global asymptotic stability of the control loop is proven

A comparative study of MPPT algorithms to optimal power extraction of a photovoltaic panel

The demand for electrical energy has been increasing in recent years as well as the constraints related to its production. Thus, more electrical energy will be produced by photovoltaic process which is the conversion of sunlight into electricity because it is inexhaustible energy, developable everywhere, clean and requires little maintenance. The drawbacks of this source of energy are the intermittency of the photovoltaic source and the power supplied by the photovoltaic generator depends on unpredictable meteorological conditions. Among the solutions to remedy this, it is possible to consider the storage of energy in batteries and the implementation of a strategy of the maximum power point tracking (MPPT) to extract at any time the maximum power. Indeed, the improvement of the efficiency of the photovoltaic generator requires optimal operation of the DC-DC converters used as an interface between the photovoltaic generator and the load to be powered. The principle of this control is based on the automatic variation of the duty cycle of the converters by bringing it to the optimal value so as to maximize the power delivered by the photovoltaic module. This work studies and compares the most popular MPPT algorithms. The Perturb and Observe (P&O) algorithm is based on a periodic disturbance of the voltage at the terminals of the photovoltaic module and the comparison of the output power of the photovoltaic module with that of the previous disturbance cycle. The INcremental of Conductance (INC) algorithm uses the knowledge of the conductance value and the increment of conductance to derive the position of the operating point from the point of maximum power. The Hill Climbing (HC) algorithm is based on the duty cycle in each sampling period that is determined by comparing the current power to the previous one. The algorithm for measuring a fraction of the open-circuit voltage (FCO) is based on the linear relationship between the open circuit voltage and the voltage at the peak power point. The algorithm for measuring a short circuit current fraction (FCC) is a technique based on the linear relationship between the short-circuit current and the current at the point of maximum power. Finally, the fuzzy logic control algorithm (FLC) works with inaccurate inputs that do not require a precise mathematical model. All of these techniques have been implemented under the MatLab/Simulink environment to manage the duty cycle. The comparison of the results obtained under different operating conditions shows that the FLC algorithm is the fastest in terms of stabilization time with a response time of 0.005 seconds. It shows good oscillation behavior around the operating point. The latter is followed by the P&O algorithm with a response time of 0.06 seconds, by the INC algorithm with a time of 0.07 seconds, by the Hill Climbing (HC) algorithm with a response time of 0.08 seconds, by the FCC and FCO algorithms with a response time of 0.10 and 0.11 seconds, respectively. The power of the different MPPT algorithms is evaluated at the maximum power point with a 40 W photovoltaic module. The fuzzy logic control algorithm (FLC) gives the best results by extracting 39.8 W, followed by P&O algorithms (38 W), INC (37.5W), HC (36W), FCC (35W) and FCO (34W)

PREDICTING UNKNOWN MOTION FOR MODEL INDEPENDENT VISUAL SERVOING

International audiencePrediction in real-time image sequences is a key-feature for visual servoing applications. It is used to compensate for the time-delay introduced by the image feature extraction process in the visual feedback loop. In order to track targets in a three-dimensional space in real-time with a robot arm, the target's movement and the robot end-effector's next position are predicted from the previous movements. A modular prediction architecture is presented, which is based on the Kalman filtering principle. The Kalman filter is an optimal stochastic estimation technique which needs an accurate system model and which is particularly sensitive to noise. The performances of this filter diminish with nonlinear systems and with time-varying environments. Therefore, we propose an adaptive Kalman filter using the modular framework of mixture of experts regulated by a gating network. The proposed filter has an adaptive state model to represent the system around its current state as close as possible. Different realizations of theses state model adaptive Kalman filters are organized according to the divide -and-conquer principle: they all participate to the global estimation and a neural network mediates their different outputs in an unsupervised manner and tunes their parameters. The performances of the proposed approach are evaluated in terms of precision, capability to estimate and compensate abrupt changes in targets trajectories, as well as to adapt to time-variant parameters. The experiments prove that, without the use of models (e.g., the camera model, kinematic robot model, and system parameters) and without any prior knowledge about the targets movements, the predictions allow to compensate for the time-delay and to reduce the tracking error

A direct power control of the doubly-fed induction generator based on the SVM Strategy

This paper proposes a direct power control scheme for the doubly-fed induction generator (DFIG) for variable speed wind-power generation. The machine is connected as a generator. Its rotor is fed by a two-level inverter. We propose to control the DFIG with a technique based on the direct power control (DPC) performances. A combination of a space-vector modulation (SVM) technique and active and reactive power controllers is made to replace hysteresis controllers used in the classic DPC drive resulting in a fixed switching frequency of the power converter. The performances obtained by using this control strategy are shown under MATLAB Simulink

Three-level NPC Converter-based Neuronal Direct Active and Reactive Power Control of the Doubly Fed Induction Machine for Wind Energy Generation

In this paper, neuronal direct power control (DPC) strategy is applied for a doubly fed induction generator (DFIG) based wind energy generation system. Used in three level neutral point clamped (NPC) rectifiers, to directly control the active and reactive power, switching vectors for rotor side converter were selected from the optimal switching table using the estimated stator flux position and the errors of the active and reactive power, also the grid side is controlled by direct power control based a grid voltage position to ensure a constant DC- link voltage. This approach is validated by using MATLAB/SIMULINK software and simulation results can prove the excellent performance of this control as improving power quality and stability of wind turbine

Double star induction motor direct torque control with fuzzy sliding mode speed controller

In this paper, we present a speed fuzzy sliding mode control for a direct torque controlled double star induction motor (DSIM). Direct torque control (DTC) uses only a couple of hysteresis comparators to perform both torque and flux dynamic control. The proposed control scheme utilizes a fuzzy sliding mode controller (FSMC) for the speed control. The FSMC is formed with the robustness of sliding mode control (SMC) and the smoothness of fuzzy logic (FL). Few fuzzy rules are used for this strategy control. The sliding mode control is used to achieve robust performance against parameter variations and external disturbances. The problem with this conventional controller is that it has large chattering on the torque and the drive is very noisy. In order to reduce chattering, the sign function is substituted with a fuzzy control law which draws the system state variables into prespecified bounds of sliding mode surface. The computer simulations are conducted to demonstrate the satisfactory tracking performance and robustness of the control with reduced chattering problem