Analysis of a Grid-Tied Photovoltaic Inverter By using Nonlinear fuzzy based Predictive Controller (original) (raw)
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This paper presents a combination between a fuzzy logic control (FLC) and a predictive direct power control for multifunctional grid connected photovoltaic (PV) system, to solve the oscillation problem in the DC link voltage of the inverter caused by the fast irradiation changing. The whole system consists of a PV system which interface a DC-AC inverter, a FLC maximum power point tracking (MPPT) algorithm has been adopted to operate the DC-DC converter at the MPP. The predictive control strategy is applied to the DC-AC inverter with FLC in its voltage control loop to improve the power exchange between the grid and the PV system. Simulation results have been verified through MATLAB/Simulink software for the purpose of giving the effectiveness of the suggested control against existed controllers. This is an open access article under the CC BY-SA license.
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The electrical power generation based on the sun's rays through a series of solar cells linked together is considered as clean energy that reduces gas emissions are harmful compared to those produced by fossil fuels. Thus, lead to decrease the effects on the components of the environment. Also, the unknown impacts on the system behaviorwill inevitably affect the final response of the system, so it requires a more robust and effective control methodology. This paper presents the current control based the model predictive control (MPC) to generate optimal pulse of converter gates to handle the photovoltaic system (PV) unit based on maximum power point tracking (MPPT). The MPC has features that can offer stable regulation for an uncertainty effects during different dynamic conditions. The DC-DC boost converter based on the Fuzzy logic controller (FLC) is used to boost up and handle the photovoltaic voltage to satisfy the total efficiency by applying the MPC-current control on the AC inverter. In this study, the MPC is used to control the system in order to meet suitable performance according to the minimization procedures for the optimization problem. MATLAB is used to achieve the simulation results and show that, the MPC is more efficient and gives better output performance comparing with PI and FLC control.
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This paper presents a model predictive direct power control strategy for a grid-connected inverter used in a photovoltaic system as found in many distributed generating installations. The controller uses a system model to predict the system behavior at each sampling instant. The voltage vector that generates the least power ripple is selected using a cost function and applied during the next sampling period; thus, flexible power regulation can be achieved. In addition, the influence of a one-step delay in the digital implementation is investigated and compensated for using a model-based prediction scheme. Furthermore, a two-step horizon prediction algorithm is developed to reduce the switching frequency, which is a significant advantage in higher power applications. The effectiveness of the proposed model predictive control strategy was verified numerically by using MATLAB/Simulink and validated experimentally using a laboratory prototype.
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Renewable energy sources, especially photovoltaic (PV) ones, are gaining more and more interest due to the predicted lack of conventional sources over the coming years. That shortage is not the only concern, as environmental issues add to this concern also. Thus, this study proposes two-stage PV grid connected system, which is supported with extended Kalman filter (EKF) for parameter estimation. In the first stage, maximum power point tracking (MPPT) for the boost converter is accomplished using new MPPT method in which the switching state of the converter is directly generated after the measurement stage, so it is called direct switching MPPT technique. This technique is compared with the conventional finite control set model predictive control (FCS-MPC) method, where the design of the cost function is based on minimizing the error between the reference and the actual current. The reference current is obtained by employing perturb and observe (P&O) method. In the second stage, the ...
Model Predictive Current Control of Grid Connected PV Systems
Indonesian Journal of Electrical Engineering and Computer Science, 2016
This paper deals with the design and simulation of an efficient solar photovoltaic system with a maximum power point tracking system (MPPT). Maximum power point (MPP) is obtained by using Perturb and Observe (P&O) algorithm. The output from solar panel is fed to the DC-DC (Boost) converter which steps up the output voltage. It is then fed to a 3-phase inverter. The inverter used is a 3-phase two-level inverter implemented with a Model Predictive Control strategy. Model of the system is considered in order to predict the control variables. Optimum switching state is selected by minimizing the cost function for each sampling period. This is achieved through modelling and MATLAB simulation of various stages that constitute the overall system.
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Single-phase grid-connected inverters with LCL filter are widely used to connect photovoltaic systems to the utility grid. Among the existing control schemes, predictive control methods are faster and more accurate but also more complicated to implement. Recently, the Model Predictive Control (MPC) algorithm for single-phase inverter has been presented, where the algorithm implementation is straightforward. In the MPC approach, all switching states are considered in each switching period to achieve the control objectives. However, since the number of switching states in single-phase inverters is small, the inverter output current has a high Total Harmonic Distortions (THD). In order to reduce this, this paper presents an improved MPC for single-phase grid-connected inverters. In the proposed approach, the switching algorithm is changed and the number of the switching states is increased by means of virtual vectors. Simulation results show that the proposed approach lead to a lower THD in the injected current combined with fast dynamics. The proposed predictive control has been simulated and implemented on a 1 kW single-phase HERIC (highly efficient and reliable inverter concept) inverter with an LCL filter at the output.
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This study designed a system consisting of a photovoltaic system and a DC-DC boost converter with buck-boost inverter. A multi-error method, based on model predictive control (MPC), is presented for control of the buck-boost inverter. Incremental conductivity and predictive control methods have also been used to track the maximum power of the photovoltaic system. Due to the fact that inverters are in the category of systems with fast dynamics, in this method, by first determining the system state space and its discrete time model, a switching algorithm is proposed to reduce the larger error for the converter. By using this control method, in addition to reducing the total harmonic distortion (THD), the inverter voltage reaches the set reference value at a high speed. To evaluate the performance of the proposed method, the dynamic performance of the converter at the reference voltage given to the system was investigated. The results of system performance in SIMULINK environment were ...
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This paper presents simplified predictive current controller for 3-ph PWM voltage source inverter connected to the utility grid via filter inductance. By controlling inductance voltage drop, the inverter voltage and hence the injected current can be controlled. The proposed predictive control technique operates with constant switching frequency using space-vector modulation (SVM). In each switching period, the proposed control algorithm computes the required inverter average voltage vector in α–β reference frame in order to follow up the reference current at the end of the switching period. The proposed technique has no need for coordinate transformations or PI controllers. The proposed controller is verified by simulation using MATLAB/Simulink, and is experimentally verified using a fixed point microcontroller (Maple microcontroller). Detailed implementation of the proposed hardware is presented. Both simulations and experimental results are presented in the paper.
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In this paper, a comparative review for maximum power point tracking (MPPT) techniques based on model predictive control (MPC) is presented in the first part. Generally, the implementation methods of MPPT-based MPC can be categorized into the fixed switching technique and the variable switching one. On one side, the fixed switching method uses a digital observer for the photovoltaic (PV) model to predict the optimal control parameter (voltage or current). Later, this parameter is compared with the measured value, and a proportional–integral (PI) controller is employed to get the duty cycle command. On the other side, the variable switching algorithm relies on the discrete-time model of the utilized converter to generate the switching signal without the need for modulators. In this regard, new perspectives are inspired by the MPC technique to implement both methods (fixed and variable switching), where a simple procedure is used to eliminate the PI controller in the fixed switching m...