Adaptive incremental conductance MPPT algorithm

Abstract

The invention discloses an adaptive incremental conductance MPPT algorithm which is based on the incremental conductance algorithm and uses multiple step models combined with allowable errors to accurately control the converting circuit duty ratio D and to increase tracking efficiency and accurately respond to rapidly changing lighting conditions. The adaptive incremental conductance MPPT of the invention is characterized by quick tracking and accurate response under sudden changes in illumination with little fluctuation and without stable oscillation. Compared with the existing MPPT, the invention has better tracking performance, accurate response, no oscillation, high tracking efficiency, easy implementation, and reduced energy loss. The invention solves the problems that the existing algorithms often have oscillation, inaccurate response, low tracking efficiency and high difficulty in implementation. The improved step size model and precise directional control in the algorithm of the present invention are effective.

Description

technical field [0001] The invention relates to the technical field of maximum power point tracking of a photovoltaic system, in particular to an adaptive incremental conductance MPPT algorithm. Background technique [0002] In order to improve the maximum power point tracking efficiency of photovoltaic systems, many MPPT algorithms have been proposed and improved. The incremental conductance algorithm is one of the most widely used algorithms, and the classic incremental conductance MPPT with variable step size and fixed step size, but These algorithms suffer from steady-state oscillations, inaccurate responses, low tracking efficiency, and energy loss, especially when the light intensity changes rapidly. [0003] In a photovoltaic system, due to the nonlinear characteristics of photovoltaic panels, as well as changes in the external environment, such as temperature, light intensity, and load changes, the output power of the system will be lower than the ideal output power....

AI Technical Summary

Problems solved by technology

[0011] (1) Open-circuit voltage algorithm and short-circuit current algorithm need to constantly test the current open-circuit voltage or short-circuit current of photovoltaic panels due to changes in the environment, which is not only difficult to fully realize, but also leads to energy loss;

[0012] (2) Disturbance and observation algorithm: After tracking a maximum power point, the subsequent tracking will be disturbed in two directions, so oscillation will occur, resulting in energy loss;

[0013] (3) The performance of the incremental conductance algorithm is greatly affected by the step size control model. Too small step size will lead to slow tracking speed, and too large step size will cause oscillation, and it cannot respond accurately when the light suddenly increases;

[0014] (4) The characteristics of photovoltaic panels will also change when the neural network algorithm changes in temperature and sunlight, so the neural network needs to be continuously trained by the changing photovoltaic characteristics in order to track accurately, but this implementation is difficult;

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