Morcos Metry

MPPT of Photovoltaic Systems Using Sensorless Current-Based Model Predictive Control

Variability in the solar irradiance level and ambient temperature of photovoltaic (PV) systems necessitates the use of maximum power point tracking (MPPT) of PV systems to ensure continuous harvesting of maximum power. This paper presents a sensorless current (SC) MPPT algorithm using model predictive control (MPC). The main contribution of this paper is the use of model-based predictive control principle to eliminate the current sensor that is usually required for well-known MPPT techniques such as perturb and observe (P&O). By predicting the PV system states in horizon of time, the proposed method becomes an elegant, embedded controller that allows faster response and lower power ripple in steady state than the conventional P&O technique under rapidly changing atmospheric conditions. This becomes possible without requiring expensive sensing and communications equipment and networks for direct measurement of solar irradiation changes. The performance of the proposed SC-MPC-MPPT with reduced load sensitivity is evaluated on the basis of industrial European Efficiency Test, EN 50530, that assesses the performance of PV systems under dynamic environmental conditions. The proposed control technique is implemented experimentally using dSPACE DS1007 platform to verify the simulation results.

An effective Model Predictive Control for grid connected Packed U Cells multilevel inverter

This paper presents a Model Predictive Control (MPC) for grid-tied Packed U Cells (PUC) multilevel inverter. The system under study consists of a single-phase 3-cell PUC inverter connected to the grid through filtering inductor. The proposed topology allows the generation of 7-level output voltage with reduction of passive and active components compared to the conventional multilevel inverters. The aim of the proposed MPC technique is to achieve grid-tied current injection, low Total Harmonic Distortion (THD) of the current, unity power factor, while balancing the capacitor voltages at maximum power point (MPP). The feasibility of this strategy is validated by simulation using Matlab/Simulink environment.

Maximum power point tracking of photovoltaic systems using sensorless current-based model predictive control

Variability of the solar resource necessitates that Maximum Power Point Tracking (MPPT) techniques be used in photovoltaic (PV) systems to ensure maximum electrical energy is harvested. This paper presents a MPPT algorithm using Model Predictive Control (MPC) that does not require the use of current sensors. The main contribution is the use of the model based predictive control (MPC-MPPT) to eliminate the current sensor that is usually required in the perturb and observe (P&O) MPPT technique. By predicting and controlling the future PV system operation in the time horizon, the proposed method is an elegant, embedded controller that has faster response than the conventional P&O technique under rapidly changing atmospheric conditions and without requiring expensive sensing and communications equipment and networks to directly measure solar insolation changes. Real time simulations run on a dSpace DS1007 platform compare of the proposed sensorless current MPC-MPPT (SC MPC-MPPT) technique to the full sensor version.

High efficiency MPPT by model predictive control considering load disturbances for photovoltaic applications under dynamic weather condition

Due to variability of solar energy resources, maximum power point tracking (MPPT) of photovoltaic (PV) is required to ensure continuous operation at the maximum power point (MPP) and maximize the energy harvest. Many standards are developed to ensure the safe and efficient power generation under dynamic weather conditions. This paper presents a high efficiency fixed-step model predictive control (MPC) technique to employ the MPPT for photovoltaic applications. The MPP operating point is determined by using perturb and observe (P&O) technique. The proposed fixed-step predictive model based MPPT presents significant advantages in dynamic response and power ripple at steady state. A characteristic of MPC is the use of system models for selecting optimal actuations, thus evaluating the effect of model parameter mismatch on control effectiveness is of interest. In this paper, the load model is eliminated from the proposed MPC formulation by using an observer-based technique. The performance of the proposed observer-based MPC-MPPT is evaluated on the basis of European Efficiency Test, EN 50530 that assesses the performance of PV systems under dynamic environment conditions. The proposed MPC-MPPT technique for a flyback converter is implemented using dSPACE DS1007.

A Model Predictive Control technique for utility-scale grid connected battery systems using packed U cells multilevel inverter

Grid-connected energy storage systems have been implemented in ac power systems as uninterruptable power supplies (UPS). Batteries and bi-directional power converters provide electrical power when off-grid and recharge when grid-connected. In this paper, a packed U cells (PUC) seven-level inverter has been selected as the grid-interface due to the lower cost and fewer number of components compared to other bi-directional topologies. Additionally, the PUC has higher power quality when compared to the traditional H-bridge. Compared to the traditional PI controller, Model Predictive Control (MPC) is attracting more interest due to its good dynamic response and high accuracy of reference tracking. Through the minimization of a user-defined cost function, the proposed MPC technique can simultaneously achieve unity power factor, low total harmonics distortion of the grid-side current and balance the PUC capacitors voltages at the grid side, and control bi-directional power flow in the batteries-PUC system. The presented topology and proposed control technique are verified by simulating a 600 W reduced-scale prototype. The theoretical principles are validated by implementing the controller on the prototype using dSPACE 1007 platform.

Sensitivity analysis to model parameter errors of MPPT by model predictive control for photovoltaic applications

Due to variability of solar energy resources, maximum power point tracking (MPPT) of photovoltaic (PV) is required to ensure continuous operation at the maximum power point (MPP) and maximize the energy harvest. This paper presents a digital model predictive control technique to employ the MPPT for flyback converter for photovoltaic applications. The MPP operating point is determined by using perturb and observe (P&O) technique. The proposed two-steps predictive model based MPPT presents significant advantages in dynamic response and power ripple at steady state. A characteristic of MPC is the use of system models for selecting optimal actuations, thus evaluating the effect of model parameter mismatch on control effectiveness is of interest. In this paper the load model is eliminated from the proposed MPC formulation by using an observer based technique. The sensitivity analysis results indicate a more robust controller to uncertainty and disturbances in the resistive load.

Sensorless current model predictive control for maximum power point tracking of single-phase subMultilevel inverter for photovoltaic systems

Stochastic dynamic behavior of solar energy necessitates the use of robust controllers for photovoltaic (PV) power electronics interfaces to maximize the energy harvest by continuous operation at maximum power point (MPP). This paper proposes a sensorless current model predictive control maximum power point tracking (SC-MPC-MPPT) algorithm. By predicting the future behavior of the power conversion stage, the proposed controller features fast and stable performance under dynamic ambient condition and negligible oscillation around MPP at steady state. Moreover, it does not require expensive sensing and communication equipment and networks to directly measure the changing solar insolation level. The power conversion stage includes an upstream boost dc/dc power conversion to a dc-link capacitor, and a downstream seven-level sub-Multilevel Inverter (sMI) from the dc-link capacitor to the grid. The sMI is using three power arms cascaded with an H-bridge inverter. This topology brings considerable benefits such as reduced number of power switches and their gate drivers when compared to the traditional multilevel inverters. Model Predictive Control (MPC) is employed for current regulation of the sMI, thus eliminating the need of cascaded classical control loops and modulator. The proposed SC-MPC-MPPT technique for a boost converter is implemented experimentally using the dSPACE DS1007 platform.

Model predictive control for PV maximum power point tracking of single-phase submultilevel inverter

Dynamic behavior of solar energy resource entails the need of robust controllers that can converge to the maximum power point (MPP) to maximize energy harvest. This paper explores an improved Perturb and Observe (P&O) technique that combines a fixed step model predictive controller (MPC), to speed up the control loop, applied to a boost converter. The proposed MPC Maximum Power Point Tracking (MPPT) technique is of higher efficacy and robustness over conventional MPPT. The improved MPC-MPPT is tested for the first time on an MPC strategy of seven-level subMultilevel Inverter (sMI) using three power arms cascaded with the H-bridge inverter. Such topology brings about many sizable benefits such as reduced number of power switches and their gate drivers when compared to the traditional multilevel inverter. MPC is also used as the control strategy for the sMI to eliminate complexities in the space vector pulse width modulation (SVPWM) and overcome the weaknesses of the inner control loop performance.

Model predictive control for maximum power point tracking of quasi-Z-source inverter based grid-tied photovoltaic power system

Stochastic dynamic behavior of solar energy necessitates the use of robust controllers for photovoltaic (PV) power electronics interfaces. Such robust controller maximizes the energy harvest through continuous operation using a maximum power point tracker (MPPT). A model predictive control MPPT (MPC-MPPT) is proposed in this paper for a quasi-Z-source inverter (qZSI) based grid-connected PV power system. MPC is a robust suboptimal controller and is proposed in this paper as an elegant, embedded controller. Such controller has shown better dynamic performance than the conventional perturb and observe (P&O) technique, particularly under rapidly changing meteorological conditions. The qZSI is a single-stage topology that can guarantee MPPT and control the injected power to the grid simultaneously. The proposed method simulation results are presented in this paper.

A variable step-size MPPT for sensorless current model predictive control for photovoltaic systems

Variability of the solar energy resources requires highly effective maximum power point tracking (MPPT) to ensure maximum energy harvesting from the photovoltaic (PV) modules. To accomplish this, a MPPT controller typically requires accurate knowledge of the voltage and current from the PV module, and must converge quickly with minimal hunting around the maximum power point (MPP). Conventional MPPT techniques use fixed step-size perturbation which need to be optimized for one of two objectives: reducing the convergence settling time, or reducing the steady state ripple. Also, the required sensors increase system cost and can cause reliability issues, particularly for the current sensors which can exhibit thermal drift and degrade over time. This paper presents a highly efficient, variable-step sensorless current MPPT controller using an observer-based model derived from the principles of model predictive control (MPC) to adaptively determine the perturbation step-size. The proposed variable step, sensorless current, model predictive control maximum power point tracking (VS-SC-MPC-MPPT) continuously adjusts the perturbation step size using the predicted dynamic model to enable fast convergence and small limit cycle, without the need of expensive measuring devices. The performance of the VS-SC-MPC-MPPT in this paper is compared to previously developed MPC-MPPT methods. The provided investigation aims to demonstrate higher system efficacy with lower cost. The feasibility of the proposed controller is verified though computer simulation and real time simulation using dSPACE DS1007.