(1) Fuzzy logic control method
Fuzzy logic control method is an MPPT control method based on fuzzy logic. Fuzzy logic is a type of artificial intelligence. The control algorithm based on fuzzy logic is usually called fuzzy control. Its realization can be divided into the following three steps: fuzzification, control rule evaluation and defuzzification. The input of the fuzzy logic controller is usually the error E and the error change ΔE. Since dP/dU is 0 at the maximum power point, the input variables E and ΔE in the photovoltaic system can be determined by the following two formulas:
In the formula, P(n) and U(n) are the output power and output voltage of the photovoltaic array, respectively. It can be seen that when the photovoltaic array is working at the maximum power point, the error E(n) is 0. Some documents have proposed other methods for determining the error E and the error change AE, for example, according to the maximum power point dP/dI is 0 A similar expression can also be obtained.
The biggest feature of fuzzy control is to express expert experience and knowledge as language control rules, and then use these rules to control the system. Fuzzy logic control follows quickly, and there is basically no fluctuation after reaching the maximum power point, that is, it has better dynamic and steady-state performance. . However, defining fuzzy sets, determining the shape of membership functions, and formulating rule tables require more intuition and experience from designers.
(2) Neural network method
Neural network method is a MPPT control method based on neural network. Neural network is a new type of information processing technology. The most common and commonly used multilayer neural network structure is shown in Figure 1. The network in the figure has 3 layers of neurons: input layer, hidden layer and output layer. The number of layers and the number of neurons in each layer are determined by the complexity of the problem to be solved. According to the number of neurons in each layer, the network is defined as a 2-5-1 network. When applied to a photovoltaic array, the input signal can be the parameters of the photovoltaic array such as open circuit voltage Uoc, short circuit current Isc or external environment parameters such as light intensity and temperature, or it can be a composite of the above parameters, and the output signal can be optimized The output voltage, the duty cycle signal of the converter, etc.
There is a weight gain Wij between each node in the neural network. Choosing the appropriate weight can convert any continuous function of the input into any desired function to output, so that the photovoltaic array can work at the maximum power point. In order to obtain the accurate maximum power point of the photovoltaic array, the determination of the weight must be obtained through the training of the neural network. This training must use a large amount of input/output sample data, and most photovoltaic arrays have different parameters, so targeted training is required for systems that use different photovoltaic arrays, and this training process may take months or even For several years, this is also a disadvantage of its application in photovoltaic systems. After training, based on the network, not only can the input and output training samples be completely matched, but also the input and output patterns of interpolation and a certain number of extrapolation can be matched. This is not possible with simple look-up table functions. The advantage of neural network method lies in it.
(3) One-Cycle Control method (OCC)
Figure 2 shows the maximum power tracking control circuit of the photovoltaic array using the single-cycle control method. The one-cycle controller consists of a resettable integrator, a comparator, an RS flip-flop and other linear components.
The output power Po of the photovoltaic array can be represented by the output voltage Pg of the photovoltaic array, the effective value Uo of the grid-connected voltage of the photovoltaic array, the sampling resistor Rs, the clock period Ts of the trigger, and the parameters of single-cycle control. The relationship between the output power Po and the output voltage Ug of the photovoltaic array is:
The above formula is to coordinate the selection of the five single-cycle control parameters K, Kg, Uc, R1, C1, and the inverter output power can be adjusted by the value of the photovoltaic array output voltage to achieve the tracking of the maximum power point of the photovoltaic array And input a sinusoidal current of unit power factor to the grid. The single-cycle control can avoid the two-level power conversion of the traditional photovoltaic system. The combination of a unit power circuit and a single-cycle controller realizes two functions: maximum power point tracking and DC/AC conversion
(4) Sliding mode control method
The principle of sliding mode control is to use the discontinuity of control, relying on its high frequency conversion to force the closed-loop system to reach and maintain the designed sliding surface. The control rules of the system can be summarized as follows, and the control quantity of the controller can be taken:
In the formula, u is the switch MN of the power switching device that controls the output energy of the solar cell. When u is 1, it means that the switching device is turned on. When u is 0, it means the switching device is off. For the established system and switching MN formula , Can make the system start from any initial state, and finally stabilize at the switching function S=0.
The sliding mode control method can significantly improve the tracking speed of the photovoltaic system, but the change step length of the modulation depth of the switching device and the selection of u will affect the dynamic and steady-state characteristics of the system tracking. When u increases, the tracking speed can be accelerated , But at this time the fluctuation of the output power and voltage of the photovoltaic array will also increase.