Design and implementation of a nonlinear pi predictive controller for a grid-tied photovoltaic inverter

This paper presents the design, implementation, and performance testing of a nonlinear proportionalintegral (PI) predictive controller for a grid-tied inverter used in photovoltaic systems. A conventional cascade structure is adopted to design the proposed controller, where the outer loop is used to regulate the dc-link voltage, and the inner loop is designed as a current controller for adjusting the active and reactive powers injected into the grid. For each loop, the controller is derived based on combining a continuous-time nonlinear model predictive control and nonlinear disturbance observer techniques. It turns out that the composite controller reduces to a nonlinear PI controller with a predictive term that plays an important role in improving tracking performance. The salient feature of the proposed approach is its ability to approximately preserve the nominal tracking performance during the startup phase. Both simulation and experimental results are provided to demonstrate the effectiveness of the proposed approach in terms of nominal performance recovery, disturbance rejection, and current control

A Robust Continuous-Time MPC of a DC–DC Boost Converter Interfaced With a Grid-Connected Photovoltaic System

The main function of the dc–dc converter in a grid-connected photovoltaic (PV) system is to regulate the terminal voltage of the PV arrays to ensure delivering the maximum power to the grid. The purpose of this paper is to design and practically implement a robust continuous-time model predictive control (CTMPC) for a dc–dc boost converter, feeding a three-phase inverter of a grid-connected PV system to regulate the PV output voltage. In CTMPC, the system behavior is predicted based on Taylor series expansion, raising concerns about the prediction accuracy in the presence of parametric uncertainty and unknown external disturbances. To overcome this drawback, a disturbance observer is designed and combined with CTMPC to enhance the steady-state performance in the presence of model uncertainty and unknown disturbance such as the PV current, which varies nonlinearly with the operating point. An interesting feature is that the composite controller reduces to a conventional PI controller plus a predictive term that allows further improvement of the dynamic performance over the whole operating range. The effectiveness of the proposed controller was tested numerically and validated experimentally with the consideration of the grid-connected PV inverter system and its controller

Experimental Validation of a Robust Continuous Nonlinear Model Predictive Control Based Grid-Interlinked Photovoltaic Inverter

This paper presents a robust continuous nonlinear model predictive control (CNMPC) for a grid-connected photovoltaic (PV) inverter system. The objective of the proposed approach is to control the power exchange between the grid and a PV system, while achieving unity power factor operation. As the continuous nonlinear MPC cannot completely remove the steady-state error in the presence of disturbances, the nonlinear disturbance observer-based control is adopted to estimate the offset caused by parametric uncertainties and external perturbation. The stability of the closed-loop system under both nonlinear predictive control and disturbance observer is ensured by convergence of the output-tracking error to the origin. The proposed control strategy is verified using a complete laboratory-scale PV test-bed system consisting of a PV emulator, a boost converter, and a grid-tied inverter. High performance with respect to dc-link voltage tracking, grid current control, disturbance rejection, and unity power factor operation has been demonstrated

Offset-free feedback linearisation control of a three-phase grid-connected photovoltaic system

In this study, a state feedback control law is combined with a disturbance observer to enhance disturbance rejection capability of a grid-connected photovoltaic (PV) inverter. The control law is based on input-output feedback linearisation technique, while the existing disturbance observer is simplified and adopted for the system under investigation. The resulting control law has a proportional-integral (PI)/almost PI-derivative-like structure, which is convenient for real-time implementation. The objective of the proposed approach is to improve the DC-bus voltage regulation, while at the same time control the power exchange between the PV system and the grid. The stability of the closed-loop system under the composite controller is guaranteed by simple design parameters. Both simulation and experimental results show that the proposed method has significant abilities to initiate fast current control and accurate adjustment of the DC-bus voltage under model uncertainty and external disturbance

DSOGI-PLL based power control method to mitigate control errors under disturbances of grid connected hybrid renewable power systems

The control of power converter devices is one of the main research lines in interfaced renewable energy sources, such as solar cells and wind turbines. Therefore, suitable control algorithms should be designed in order to regulate power or current properly and attain a good power quality for some disturbances, such as voltage sag/swell, voltage unbalances and fluctuations, long interruptions, and harmonics. Various synchronisation techniques based control strategies are implemented for the hybrid power system applications under unbalanced conditions in literature studies. In this paper, synchronisation algorithms based Proportional-Resonant (PR) power/current controller is applied to the hybrid power system (solar cell + wind turbine + grid), and Dual Second Order Generalized Integrator-Phase Locked Loop (DSOGI-PLL) based PR controller in stationary reference frame provides a solution to overcome these problems. The influence of various cases, such as unbalance, and harmonic conditions, is examined, analysed and compared to the PR controllers based on DSOGI-PLL and SRF-PLL. The results verify the effectiveness and correctness of the proposed DSOGI-PLL based power control method

Enhancement of Power Quality in Grid Connected Photovoltaic System Using Predictive Current Control Technique

Now- a days the increased use of power electronic devices has resulted in power quality problems such as voltage sag, swell, harmonics and voltage flicker. Non-linear loads affect system power quality. PV systems are grid connected via an interfacing converter. Single phase shunt active power filter (APF) can be used to develop the power quality in terms of current harmonic mitigation and reactive power compensation. In this paper a PV interfacing inverter which acts as a shunt an APF is controlled using predictive current control (PCC) technique for current harmonics mitigation. The MATLAB Simulink model is used to study the performance of system

Adaptive Backstepping-based H∞ Robust controller for Photovoltaic Grid-connected Inverter

To improve the robustness and stability of the photovoltaic grid-connected inverter system, a nonlinear backstepping-based H∞ controller is proposed. A generic dynamical model of grid-connected inverters is built with the consideration of uncertain parameters and external disturbances that cannot be accurately measured. According to this, the backstepping H∞ controller is designed by combining techniques of adaptive backstepping control and L2-gain robust control. The Lyapunov function is used to design the backstepping controller, and the dissipative inequality is recursively designed. The storage functions of the DC capacitor voltage and grid current are constructed, respectively, and the nonlinear H∞ controller and the parameter update law are obtained. Experimental results show that the proposed controller has the advantage of strong robustness to parameter variations and external disturbances. The proposed controller can also accurately track the references to meet the requirements of high-performance control of grid-connected inverters

Advanced control techniques for modern inertia based inverters

”In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids. The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques”--Abstract, page iv

Enhanced Power Quality in Single-Phase Grid-Connected Photovoltaic Systems: An Experimental Study

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The main aim of the research work presented in this paper consists of proposing an effective control scheme for a grid-connected single-phase photovoltaic (PV) system to enhance not only the power quality at the point of common coupling (PCC) but also to operate with a maximum power point tracking (MPPT) controller. Moreover, an orthogonal signal generator (OSG) module for effective grid synchronization, a current reference generation controller, and a PWM generating block have also been designed and included in this paper. The proposed control strategy allows the MPPT controller to switch to faulty mode and maintains the voltage according to network requirements using an adaptive neuro-fuzzy inference system (ANFIS)-based control whenever a fault occurs at the PCC. The performance of the analyzed control strategy, which is based on the static compensation of the DC-link voltage fluctuations in a grid-connected inverter powered by PV, is further explored through simulations in MATLAB, and the results are included in this paper. Moreover, the control scheme is implemented experimentally using a dSPACE DS 1104 control board and then assessed on a small laboratory-scale single-phase PV system that is subjected to some fault scenarios. The simulation and experimental results have shown improved power quality and robustness against grid fluctuations, resulting in better dynamic performance.Peer reviewe