Implementation of Maximum Power Point Tracking Using Kalman Filter for Solar Photovoltaic Array on FPGA

2013 ◽  
Vol 2 (2) ◽  
pp. 152-158 ◽  
Author(s):  
Varun Ramchandani ◽  
Kranthi Pamarthi ◽  
Naveen Varma ◽  
Shubhajit Roy Chowdhury
Keyword(s):  
Kalman Filter ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Solar Photovoltaic ◽  
Maximum Power Point ◽  
Photovoltaic Array ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Research on Maximum Power Point Tracking Method for Photovoltaic System

2012 ◽  
Vol 220-223 ◽  
pp. 2091-2094
Author(s):  
Yue Shen Lai ◽  
Sheng Dong Hou ◽  
Gang Wang
Keyword(s):  
Maximum Power Point Tracking ◽  
Mathematic Model ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Computer Simulation Model ◽  
Maximum Power Point ◽  
Photovoltaic Array ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Photovoltaic Array is nonlinear,and the power generated by it is influenced by sun light,temperature,load and so on In order to improve the system efficiency, firstly, analyzed the physical and mathematic model of photovoltaic array, through the MATLAB Simulink application software simulation tools, set up a computer simulation model of photovoltaic modules.Secondly,this paper gives the improved Perturbation and Observation Method and the Boost circuit used in the controller.after the analysis of some common maximum power point tracking algorithms and DC-DC circuits. Experiments shows that the controller method can rapidly to track the maximum power point,and increase the cell efficiency.

scholarly journals A Reinforcement Learning-Based Maximum Power Point Tracking Method for Photovoltaic Array

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Roy Chaoming Hsu ◽  
Cheng-Ting Liu ◽  
Wen-Yen Chen ◽  
Hung-I Hsieh ◽  
Hao-Li Wang
Keyword(s):  
Reinforcement Learning ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Weather Data ◽  
Maximum Power Point ◽  
Photovoltaic Array ◽  
Pv Array ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

A reinforcement learning-based maximum power point tracking (RLMPPT) method is proposed for photovoltaic (PV) array. By utilizing the developed system model of PV array and configuring the environment for the reinforcement learning, the proposed RLMPPT method is able to observe the environment state of the PV array in the learning process and to autonomously adjust the perturbation to the operating voltage of the PV array in obtaining the best MPP. Simulations of the proposed RLMPPT for a PV array are conducted. Experimental results demonstrate that, in comparison to an existing MPPT method, the RLMPPT not only achieves better efficiency factor for both simulated weather data and real weather data but also adapts to the environment much fast with very short learning time.

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