A Novel Method for Maximum Power Point Tracking of the Grid-Connected Three-Phase Solar Systems Based on the PV Current Prediction
نویسندگان
چکیده
In this paper, it is first attempted to provide a small signal model of the photovoltaic (PV) system, DC-DC boost converter, and pulse width modulation (PWM) generator. Then, technique provided for maximum power point tracking (MPPT) in grid-connected solar systems based on variable adaptive perturbation observation with predictive control PV current. An innovative aspect proposed current method use controller achieve value impedance, which has been used converter. The obtain coming basis model. goal make converter inductor track reference. Voltage ripple minimization added cost function simultaneously as system constraint optimize performance. This reduces amount voltage fluctuations around point. capable detecting rapid changes radiation. A sudden simultaneous increase detected by algorithm then duty cycle becomes increasing instead decreasing. simulation carried out MATLAB Simulink environment real-time 26.6 kW three-phase system. results are compared techniques linear proportional integral derivative (PID) controller-based control. show that total harmonic distortion (THD%) inverter reduced 0.16% PID method. addition, THD% 0.1% output variation less than 5 V.
منابع مشابه
Fuzzy Based Maximum Power Point Tracking in Grid Connected Pv Systems under Partially Shading Conditions
To convert solar energy more viable, the efficiency of solar array systems should be maximized. An easier approach to maximizing the efficiency of solar array systems is Maximum Power Point Tracking (MPPT). MPPT is used in photovoltaic (PV) systems to maximize the output power of photovoltaic array, irrespective of the irradiation and temperature conditions. Many conventional MPPT fails to atta...
متن کاملIntelligent Systems A novel maximum power point tracking method for PV systems using artificial neural network
This paper presents a novel maximumpower point trackingmethod of a stand-alone photovoltaic system using artificial neural network. The proposed method estimates the maximum power of a solar module in different conditions. The main advantage of the proposed methodology, comparing to conventional methods is more accuracy. Also compared to other neural network based methods this model can be trai...
متن کاملA New Maximum Power Point Tracking Method for PEM Fuel Cells Based On Water Cycle Algorithm
Maximum Power Point (MPP) tracker has an important role in the performance of fuel cell (FC) systems improvement. Tow parameters which have effect on the Fuel cell output power are temperature and membrane water. So contents make the MPP change by with variations in each parameter. In this paper, a new maximum power point tracking (MPPT) method for Proton Exchange Membrane (PEM) fuel cell is pr...
متن کاملMaximum Power Point Tracking of the Photovoltaic System Based on Adaptive Fuzzy-Neural Method
The aim of this paper was to present an optimized method in order to use maximum capacity of the photovoltaic panels. In this regard, we presented a method for the maximum power point tracking in the photovoltaic systems by using the neural networks and adaptive controller. In the proposed system, we estimated an error by using neural network. If this error is lower than the allowable systems e...
متن کاملA Novel Algorithm to Find Maximum Power Point for Solar Systems under Partial Shading
In this paper, a new two-stage control algorithm to reach the maximum power point in photovoltaic (PV) systems under partially shaded conditions is presented. This algorithm tracks the maximum power point without any need to measure the open circuit voltage, short circuit current and making use of any extra switches. To achieve maximum power performance, the method firstly selects the relevant ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Chinese Journal of Electronics
سال: 2023
ISSN: ['1022-4653', '2075-5597']
DOI: https://doi.org/10.23919/cje.2021.00.218