A photovoltaic power generation system environment adaptive mppt method and system

Abstract

The invention discloses a photovoltaic power generation system environment self-adaptive type MPPT (maximum power point tracking) method. A voltage correcting parameter delta U and PI are introduced to control a link, a duty ratio D of a direct-current chopper is regulated in real time, maximum power point voltage Um optimal-searching speed is increased, the power loss is reduced, and the maximum power point voltage Um is changed in real time along with the environmental conditions, the incremental conductance method (I&S) optimal-searching step length is reduced, and the photovoltaic battery MPPT is quickly and accurately realized, the defects that a conventional MPPT algorithm is easy to affect by the environment, the robustness is poor and the power loss is relatively large are improved, and the defects that the intelligent type MPPT algorithm is complex and easy to be locally optimal are also avoided.

Description

technical field [0001] The invention belongs to the technical field of photovoltaic cells, and relates to a novel photovoltaic cell maximum power tracking (MPPT) method, in particular to an environment-adaptive MPPT method for a photovoltaic power generation system. Background technique [0002] The energy crisis around the world has promoted the development of photovoltaic power generation systems. In order to make full use of solar energy, maximize the energy conversion efficiency of photovoltaic cells, and maximize the performance of photovoltaic devices, photovoltaic power generation systems generally use the maximum power point tracking (MPPT) method to control the output of photovoltaic devices. The power is controlled so that it always works at the maximum power point. According to the characteristics of the MPPT method and the mechanism process of the specific realization, the MPPT method is divided into three categories: ① indirect control method based on parameter ...

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Problems solved by technology

[0003] ① Indirect control methods based on parameter selection methods mainly include constant voltage method, open-circuit voltage proportional coefficient method, short-circuit current proportional coefficient method, difference calculation method, etc. The tracking time of the area, but it has a strong dependence on the output characteristics of the photovoltaic module, it can only approximate the maximum power point, and the efficiency is low

The original experience of the fuzzy logic control method and the control theory draw a list to control the system, the tracking speed is fast, the fluctuation is small after reaching the maximum power point, and it has good dynamic and steady-state performance, but it needs to be accurately set in advance. Design links such as shape tables are difficult, and the test perimeter is long. In order to improve the speed of the method, a high-performance controller is required, and the hardware cost is high; the training process of the neural network MPPT method requires a large amount of sample data, and there are many types of components. Most parameters Different, so different systems need to carry out their own targeted training for several months or years; synovial membrane control MPPT method lies in the discontinuity of the control, the system tracking speed is fast, but the continuous adjustment of the switch will affect the dynamic and stable of the system. state performance, when the step is large, the output voltage and power fluctuate greatly

[0006] Therefore, the indirect and direct MPPT control methods have simple control methods, few measured parameters, and follow the changes of the external environment. However, in practical applications, the disturbance near the maximum power point is large, and it is easily affected by the environment. The robustness is poor and the power loss is large.

The artificial intelligence method has good robustness, flexibility and suitability, but the method is complex, easy to fall into local optimum, and the equipment cost is high

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