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Improved Restricted Control Set Model Predictive Control (iRCS-MPC) Based Maximum Power Point Tracking of Photovoltaic Module
摘要： This paper presents a robust two stage maximum power point tracking (MPPT) system of the photovoltaic (PV) module using an improved restricted control set model predictive control (iRCS-MPC) technique. The suggested work is improved in two aspects; a revision in conventional P&O algorithm is made by employing three distinct step sizes for different conditions, and an improvement in conventional MPC algorithm. The improved MPC algorithm is based on the single step prediction horizon that provides less computational load and swift tracking of maximum power point (MPP) by applying the control pulses directly to the converter switch. The computer aided experimental results for various environmental scenarios revealed that compared with the conventional method (conventional P&O + MPC), for the PV power and inductor current, the undershoot and overshoot is decreased to 68% and 35% respectively under stiff environmental conditions. In addition, the settling time needed to reach a stable state is significantly reduced in the proposed system. The viability of the solution suggested is verified in MATLAB/Simulink and by hardware experimentation.
关键词： maximum power point tracking (MPPT),photovoltaic systems,MPC,Boost converter,dc-dc power conversion,model predictive control (RCS-MPC)
更新于2025-09-11 14:15:04
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Model Predictive Control Via PV-Based VAR Scheme for Power Distribution Systems With Regular and Unexpected Abnormal Loads
摘要： This paper develops a model predictive controller (MPC) via photovoltaic (PV)-based volt-ampere reactive scheme to minimize the power loss and stabilize voltage ?uctuation when PV cells are connected to the power distribution line. The nominal power load data from California independent system operator is used to simulate dynamics of the system with DistFlow equations. Since power consumptions in fact may deviate from nominal values, an estimator is further developed to reconstruct the state variables and power loads from measurements. The integration of the MPC and estimator forms a closed-loop control framework and enables the system to quickly recover from undesired disturbances by effectively changing the real and reactive powers provided by PV cells. We use a bidirectional, single branch distribution circuit to demonstrate the performance of proposed scheme. The results show that our MPC indeed reduces power loss and keeps the voltage within a desired bound. Additionally, the estimator successfully detects and correctly estimates the abnormal change of power consumption and directs the MPC to compensate such unexpected power loads promptly.
关键词： Model predictive control (MPC),power distribution system,volt-ampere reactive (VAR) control,photovoltaic (PV)-cell
更新于2025-09-10 09:29:36
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[IEEE 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) - Paris, France (2018.10.14-2018.10.17)] 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA) - Application of Model Predictive Control in Modular Multilevel Converters for MTPA Operation and reduced Switching Losses
摘要： In this paper, a one-step Model Predictive Control system is executed in the Multilevel modular converter (MMC) to control the speed of an Interior Permanent Magnet (IPM) machine. Maximum Torque per Ampere (MTPA) and Field Weakening (FW) control technique is utilized for the greatest yield torque. The proposed Control strategy tracks the reference produced by MTPA and FW calculation by freely directing voltages from the MMC modules. The proposed method also studies the relation between the switching losses of MOSFETs and sampling frequency, cost function and a number of modules in an MMC. Finally, simulation results are provided to demonstrate the appropriateness of this control procedure.
关键词： Model Predictive Control,Modular Multilevel Converter (MPC),Maximum Torque per Ampere (MTPA),Interior Permanent Magnet motor (IPM),Switching losses,Field Weakening (FW)
更新于2025-09-04 15:30:14