The true maximum power point tracking method requires real-time measurement of voltage and current, and judges the changes in the operating point of the photovoltaic array through the voltage and current values, thereby realizing the optimization of the operating point. The advantage of this type of method is that it is not affected by factors such as light intensity, temperature, and battery aging. It is mainly divided into disturbance observation method, incremental conductance method, fuzzy control method, neural network control method and so on.
1) Disturbance observation method
Due to its simple structure and easy implementation, the perturbation observation method is currently one of the commonly used methods to implement MPPT.
The principle is to increase or decrease the output voltage or current of the photovoltaic array at a certain interval of time, and observe the direction of power change thereafter to determine the next control signal.
2) Incremental conductance method
Incremental conductance method is to realize the tracking of the maximum power point by adjusting the voltage of the working point to gradually approach the voltage of the maximum power point. The incremental conductance method avoids the blindness of the disturbance observation method. It can determine the relationship between the operating point voltage and the maximum power point voltage.
3) Intermittent variable step size search method
The intermittent variable step length search method is a new method that organically combines the constant voltage method and the variable step length disturbance observation method 3). The method is specifically divided into the following three stages, as shown in Figure 1. In the figure, ① represents the first stage-the start-up stage, represents the second stage-the timing T1 variable step search stage, and 3 represents the third stage-the timing T2 fixed duty cycle stage.
4) Power stepping method
In the single-stage three-phase or single-phase photovoltaic grid-connected power generation system, the stable operating point of the photovoltaic array is in the right area including the maximum power point, and the left area of the maximum power point is the unstable operating area. In the unstable area, the trajectory of the output voltage of the photovoltaic array can be changed by controlling the grid-connected power, so that it can enter the stable working area. Therefore, using the dynamic model of the single-stage grid-connected power generation system, and according to the motion characteristics of the output voltage of the photovoltaic array, the maximum power point tracking method can be reasonably designed to achieve real-time tracking and control of the maximum power point of the photovoltaic array.
5) Fuzzy control method
The fuzzy control method does not require precise mathematical models, has a fast response, and is less affected by changes in the external environment. Therefore, the use of fuzzy logic control method for maximum power point tracking will obtain a more ideal tracking effect. Fuzzy control usually has three stages: fuzzification, fuzzy inference, and output defuzzification.
6) Neural network control method
Neural networks usually have three layers: input, hidden, and output layers. The nodes of each layer are changed and determined by the user. Input variables can be PV array parameters, such as open circuit voltage, short circuit current, light intensity, and temperature. Generally, there is only one output variable, that is, the duty cycle of the DC/DC converter, and the driving power converter runs at or near the maximum power point.
Since most photovoltaic arrays have their own characteristics, the neural network is only suitable for a certain photovoltaic array that has been tested, and the characteristics of the photovoltaic array will change over time, which also means that the neural network is specific to a certain photovoltaic array. A specific photovoltaic cell module, not universal
