TWI391807B - A maximum power tracking system and method for photovoltaic power generation systems - Google Patents

Description

Maximum power tracking system and method for solar photovoltaic power generation system

The present invention relates to a solar photovoltaic power generation system, and more particularly to a maximum power tracking system for a solar photovoltaic power generation system.

In recent years, experts and scholars have successively proposed the research of the maximum power tracking control of many solar photovoltaic power generation systems, such as voltage and current methods based on solar photovoltaic arrays, power feedback method, linear approximation method, disturbance observation method, incremental conductance. Method, intelligent fuzzy control method, sliding mode and neural network method. The advantages and disadvantages of various maximum power tracking control methods are described as follows: The voltage and current method has a simple structure, low cost, easy implementation and no complicated calculation formula. It uses the open circuit voltage or short-circuit current of the solar cell measured when the solar sun intensity is high, and adjusts the output voltage or current of the solar cell to match the measured value, thereby achieving the effect of maximum power tracking. However, this method ignores the effects of the temperature of the solar photovoltaic module. Taking a single crystal germanium solar cell as an example, when the ambient temperature rises by 1 ° C, the open circuit voltage will drop by 0.4% - 0.5%. When the atmospheric environment changes drastically, this method cannot track another maximum power point in time. Moreover, the voltage and current method must periodically cut off or short-circuit the solar photovoltaic power generation system module to measure its open circuit voltage or short circuit current as a reference value, thus increasing the loss of the entire solar photovoltaic power generation system and reducing the overall efficiency.

The power feedback method is similar to the voltage feedback method. Since the voltage feedback method cannot automatically track the maximum power point under transient changes in atmospheric conditions, the power feedback method adds the judgment logic of the rate of change of the output power and voltage of the solar photovoltaic power generation system. Tracking in response to changes in atmospheric conditions. It is observable from the characteristic curve of output power and voltage that the slope of the maximum power point is zero (ie, dP/dV =0), so the slope value ( dP/dV ) is calculated by calculation to obtain the maximum power point.

Please refer to FIG. 1 , which is a graph showing the operating characteristics of the conventional power feedback method. In Figure 1, if the current operating point is to the left of the maximum power point, the slope of any two points is greater than zero; conversely, if it is to the right of the maximum power point, the slope is less than zero. Therefore, when the operating point changes, the position of the current operating point will be known from the obtained slope. When the slope value approaches zero, it means that it is quite close to the maximum power point. Compared with the voltage feedback method, the calculation process of this method is more complicated, but its energy loss is smaller, and the overall efficiency is higher than the voltage feedback method. However, because the sensing components in the actual circuit cannot achieve a fairly accurate measurement, the probability that the system actually works at zero slope of the power and voltage curves is minimal.

The starting point of the linear approximation method is to derive the formula under the concept of dP/dI =0. After derivation of the mathematical model, the relationship between the output power P _mppt and the input current I _mppt at the maximum power point is finally obtained.

Among them, A is an ideal parameter for solar cells, and the size is between 1 and 5. K is a Boltzmann constant with a value of about 1.3806 x 10-23 J/°K. T is the reference temperature of the solar cell, generally 298 °K. q is the amount of electron charge, which is 1.6022×10 ^-19 coulombs. R _s is the equivalent series resistance inside the solar cell.

The first formula above is a fairly approximate linear equation. When the output power and current of the externally captured solar photovoltaic system make the first formula, then the operating point operates at the maximum power point. It is obvious from the first formula that the parameter values of many solar cells are required in the linear approximation method, but these parameter values are not easy to obtain, and when the solar photovoltaic module changes due to aging or temperature, its parameters also change, and the linear approximation method at this time The parameter values must be re-measured to make it more computationally difficult.

Please refer to FIG. 2A and FIG. 2B together. FIGS. 2A and 2B are working characteristic diagrams of the conventional disturbance observation method, and FIG. 2A shows the forward disturbance voltage situation, and FIG. 2B shows Negative disturbance voltage situation. The Perturb and Observe Method is used to periodically increase or decrease the voltage at the output of the solar cell and compare it with the output voltage and output power before the change to determine the direction of the next disturbance. As shown in Figures 2A and 2B, if the output power of the solar cell is larger than before the disturbance (ie, P _{k +1} > P _k ), the voltage will maintain the same direction of disturbance; conversely, if the output power of the solar cell is more disturbed Before the small (ie P _{k +1} < P _k ), the voltage disturbance direction must be changed in the next cycle. By repeating the disturbance and comparison, the output power of the solar photovoltaic system can be approximated to the maximum power point.

The perturbation observation method has a simple structure, and the parameter value required for measurement is small, and the relevant parameters of the solar cell are not required to be measured in advance, so it is most widely used in maximum power tracking control. However, the magnitude of the disturbance will affect the transient response and steady-state response of the tracking. If the disturbance is set larger, the transient response is faster, but the steady-state response has a larger oscillation. Conversely, when the disturbance amount is set small, the oscillation amplitude of the system becomes smaller at steady state, and it is easier to track to the maximum power point; however, the transient response is slower. The disturbance process also causes the operating point to oscillate near the maximum power point. Therefore, the accuracy of this method is low under steady state, the power loss is also greatly increased, and when the solar radiation amount is greatly changed, the disturbance observation method may not be timely. Adjusting the tracking direction leads to tracking errors.

The Incremental Conductance Method is based on the output power and voltage curve of the solar photovoltaic system. The slope is zero at the maximum power point (ie, dP / dV =0) to improve the disturbance observation method in the atmosphere. Tracking accuracy and dynamic response under drastic changes in the environment. The mathematical relationship between the output voltage ( V ), output current ( I ) and output power ( P ) of the solar cell is:

It is known from the above formula 2 that when the obtained dI/dV slope is -( I/V ), it means that the current operating point is equal to the maximum power point, but it is limited by the fact that the sensing element cannot reach a fairly precise amount. Therefore, in the actual circuit, the solar photovoltaic power generation system works at a very small probability that the slope of the power and voltage curves is zero.

The general incremental conductance method utilizes a fixed step adjustment whose fixed step size will affect the performance of the maximum power tracking. If the step value is set larger, the tracking speed is faster, but in steady state, the output power of the solar photovoltaic system will change drastically, which not only increases the system loss but also is difficult to trace to the maximum power point. Conversely, when the step value is set small, the output power variation amplitude of the system becomes smaller at steady state, and it is easier to track to the maximum power point, but the relative tracking time becomes longer. Therefore, the size of the steps depends on the accuracy and tracking speed requirements, so the relevant designers should strike a balance between transient response and steady-state response.

However, in order to improve the defects of the incremental conductance method, some experts have proposed a modified incremental conductance method, which uses variable step tracking and adds a fixed voltage tracking method in the tracking process. Therefore, the method can be based on the solar photovoltaic power generation system. The existing characteristic curve automatically adjusts the size of the step. However, the tracking process of this method is quite complicated, thus increasing the system cost, and the system will be caused once the control switch of the tracking system is switched from the fixed voltage tracking method to the modified incremental conductivity method mode or its tracking start point setting error. Unstable.

Other high-end intelligent maximum power tracking methods, such as intelligent fuzzy control, sliding mode and neural network method, which focus on the nonlinear characteristics of solar photovoltaic module arrays, can provide the alternative to solar photovoltaic modules. Power tracking method. However, these algorithms need to establish the exclusive maximum power point control law according to the output characteristics of the solar photovoltaic module, and the calculation process is quite complicated, so the practicality of these control methods is limited.

One aspect of the present invention is to provide a maximum power tracking system for a solar photovoltaic system to increase the speed at which a solar photovoltaic system tracks a maximum power point.

A maximum power tracking system for a solar photovoltaic power generation system according to an embodiment of the present invention includes a solar photovoltaic module, a differential arithmetic unit, an extension analysis unit, and a control unit. The differential operation unit is configured to generate a power-to-voltage slope signal according to the operating point state of the solar photovoltaic module. The extension analysis unit has a plurality of extension matter element models, and the extension matter element model can be used to analyze the input signal such as the error of the power versus the voltage slope and the error rate of change, thereby generating a tracking signal. The control unit operates the solar photovoltaic system at the maximum power point based on the tracking signals described above.

Thereby, the maximum power tracking system of the solar photovoltaic power generation system of the present embodiment prejudges the extension matter element model to which the input signal should be classified, and then tracks the maximum power point thereof, thereby having a fast transient response and Smooth steady state response.

Another aspect of the present invention is to provide a maximum power tracking method for a solar photovoltaic power generation system to increase the speed at which a solar photovoltaic power generation system tracks a maximum power point.

A maximum power tracking method for a solar photovoltaic power generation system according to an embodiment of the present invention includes the steps of: obtaining a plurality of regional categories according to a power-voltage ( PV ) curve of a solar photovoltaic module, and corresponding plurality of slope errors Value e and multiple error change values . Using the plurality of region categories, the plurality of slope error values e, and the plurality of error variation values , establish multiple extension matter element models R :

Wherein, R _k is the k th Extension Element Model matter element model in R, F ₀ is a matter element section, c _1, c ₂ is F characteristic value of _0, V _plk k-th region category Corresponding slope error value e , the value of V _p2k is the slope error variation corresponding to the kth region category The value range. In this way, an extension element input model can be established to receive an input signal. The input signals are analyzed using the plurality of extensional matter element models R described above to cause the solar photovoltaic system to operate at a maximum power point.

Thereby, the maximum power tracking method of the solar photovoltaic power generation system of the present embodiment predicts the extension matter element model to which the input signal should be classified, and then tracks the maximum power point thereof, thereby having a fast transient response and smoothing. Steady state response.

In order to solve the shortcomings of the existing Maximum Power Point Tracking (MPPT) method, and at the same time improve the dynamic response and steady state response of the maximum power tracking of the solar photovoltaic module, the present invention is in the following embodiments. Based on the unique ingenuity, combined with disturbance observation method, extension theory and matter-element theory, a maximum power tracking system and tracking method for solar photovoltaic power generation system are proposed.

Please refer to FIG. 3, which is a flow chart showing the steps of the maximum power tracking method of the solar photovoltaic power generation system according to an embodiment of the present invention. In the third embodiment, the maximum power tracking method of the solar photovoltaic power generation system of the present embodiment includes the following steps: First, as shown in step 101, multiple regions are obtained according to a power-voltage ( PV ) curve of a solar photovoltaic module. Category, and corresponding multiple slope error values e and multiple error variation values .

Then, as shown in step 102, using the plurality of region categories, the plurality of slope error values e, and the plurality of error variation values , establish multiple extension matter element models R :

Wherein, R _k is the k th Extension Element Model matter element model in R, F ₀ is a matter element section, c _1, c ₂ is F characteristic value of _0, V p1k k-th region category Corresponding slope error value e , the value of V _p2k is the slope error variation corresponding to the kth region category The value range.

Next, as shown in step 103, an extension object input model is established to receive an input signal.

Finally, as shown in step 104, the input signals are analyzed using the plurality of extensional matter element models R described above to cause the solar photovoltaic system to operate at a maximum output power point.

Thereby, the present embodiment enables a solar photovoltaic power generation system to quickly operate at the maximum power point of the solar photovoltaic power generation system, thereby making the most efficient use of the power generated by the solar photovoltaic power generation system.

The operating principle of this embodiment is explained as follows: In the matter-element theory, R is the basic element describing the thing, which is called the matter element, N is the name of the thing, C is the characteristic of the thing, and V is the characteristic value of the thing. Then the matter element expression is:

R = (N, C, V) ...... (5)

In the definition of the classical domain and the local domain, it is assumed that the point f is any point above the interval F = < a _q , b _q > Then, the matter element R ₀ corresponding to F ₀ =< a _p ,b _p > can be expressed as:

Where c is the characteristic of F ₀ and V _p is the magnitude of c , ie the classical domain. The matter element R F corresponding to _F can be expressed as:

Similarly, c is the eigenvalue of F , and V _q is the magnitude of c , the section domain.

Please refer to FIG. 4, which is a schematic diagram of the correlation function of the extension matter element model of FIG. This embodiment combines the extension theory with the matter-element theory, and uses the extension set to extend the fuzzy set from (0,1) to (-∞,∞) to define the set value of any data in the domain, and then establishes An extension matter element model sufficient to describe the characteristics of a solar photovoltaic module.

The method of defining the distance and associated function values is as follows: Let F ₀ =< a _p , b _p > be any interval on a F , then the value of the elementary correlation function is:

among them:

The eleventh formula is the distance between the f and F intervals. The graph of the correlation function at the beginning of Fig. 4, the value of the correlation function can calculate the degree of association of point f belonging to F ₀ Indicates the extent to which f belongs to F ₀ , K ( f )<0 is called the degree that f does not belong to F ₀ , and the region where -1< K ( f )<0 is called the extension domain. In the extension domain, f can be attributed to the range of F ₀ by conditional transformation. Using the concept of extension correlation, the correlation of the feature element model can be established, and then the working point of the P - V curve of the solar photovoltaic module can be known to perform the maximum power tracking of the fast solar photovoltaic system.

Please continue to refer to Figure 5, which is a graph of the slope variation of Figure 3 and the power-voltage ( P - V ) operating characteristics of the solar module. Specifically, as can be seen from FIG. 5, the slope error value e of the power-voltage ( P - V ) operating characteristic curve of the solar photovoltaic module at the maximum power point (MPP) is zero. The closer to MPP, the smaller e becomes; on the contrary, the farther from MPP, the larger e , and the difference between the two error values It will change due to different control directions. When the voltage at the end of the solar photovoltaic module increases, Negative value; conversely, when the terminal voltage of the solar photovoltaic module is reduced, It is positive. By outputting the output voltage V _pv and the output current I _{pv of the} solar photovoltaic power generation system, the output power P _pv is calculated, and then the slope error value e ( k ) is calculated as:

And the amount of error value change ( k ) is:

And select e and It is the two eigenvalues of the maximum power point of the system.

Please continue to refer to Figure 6, which is a schematic diagram of the dynamic analysis of the slope of the power-voltage ( PV ) operating characteristic curve of the solar photovoltaic module in Figure 3. In this embodiment, a solar photovoltaic module is selected for simulation, and a boost converter is used to adjust the duty cycle of the driving signal (Duty Cycle, D), and the solar photovoltaic solar radiation is 200 W/m ² -1000 W/m ² . The range is simulated and the sixth figure is drawn to demonstrate how to build the extension matter element model. This embodiment divides the solar photovoltaic power generation system into 12 categories and defines e and The scope of the classic domain of each category in the extension theory is as follows:

Next, in this embodiment, the slope error e and the error variation of each category obtained according to the PV characteristic curve of the solar photovoltaic module are obtained. The characteristics of the extension of the meta-model classic domain < a _p , b _p >, as shown in the following table 2:

The nodes < a _q , b _q > of each eigenvalue can be defined by the maximum and minimum values of the classical domain:

After establishing a plurality of extension matter element models according to the above demonstration, the present embodiment can use the extension matter element models to analyze the slope error e and the error variation of the PV characteristic curve obtained by the solar photovoltaic power generation system in an arbitrary sunshine state. As an input signal, a tracking signal is generated to adjust the power generation of the solar photovoltaic module to operate at the maximum power point.

Please refer to FIG. 7 again. FIG. 7 is a flow chart showing the steps of the maximum power tracking method of the solar photovoltaic power generation system according to another embodiment of the present invention. This embodiment describes how to calculate the slope error e and the error variation of the PV characteristic curve obtained by the solar photovoltaic power generation system in an arbitrary sunshine state after establishing a plurality of extension matter element models according to the above embodiment. To make it work at the maximum power point. The steps of this embodiment are as follows: First, as shown in step 201, a plurality of extension matter element models of the solar photovoltaic power generation system are started according to the above embodiment. Then, as shown in step 202, the current voltage V _pv and current I _{pv of the} solar photovoltaic power generation system are detected. Then, as shown in step 203, calculates the output current of the solar photovoltaic power generation system according to the voltage V _pv P _pv and current I _pv. Next, as shown in step 204, the slope error value e and the error variation value of the PV curve are calculated. . Then, as shown in step 205, the error variation value is determined according to the value of the correlation function. Whether it belongs to the classic domain range of a certain extension matter model. If so, as shown in step 206, the correlation function is calculated using the classical domain; if not, as shown in step 207, the association function is calculated using the local domain.

Next, as shown in step 208, the identification category of the current state of the solar photovoltaic power generation system, that is, which extension element model it belongs to, is output. Next, as shown in step 209, the tracking signal is adjusted according to the extension matter element model to which it belongs, that is, the duty cycle of the driving signal of the boost converter is adjusted. Finally, as shown in step 210, the solar photovoltaic system is operated at the maximum power point.

In the above embodiment, since the PV characteristic curves of the solar photovoltaic module are divided into 12 categories, the matter-element model of the slope error and the error variation of each category can be expressed as:

And by the above steps 202-204, an extension matter element input model can be established:

When calculating the degree of association shown in steps 205 to 207, the weights W _j1 , W _j2 of each feature can be further selected to represent the importance of each feature value. In the present embodiment, W _j1 = 0.85 and W _j2 = 0.15 are selected. Then the relevance of each category is:

In step 209, the present embodiment selects a maximum value from the calculated standard correlation degrees to identify the attribution category of the input slope error and the slope error variation, and adjusts the step size Δ of the duty cycle of the boost converter. D and error adjust the polarity to track the maximum power point. In order to improve stability and adaptability, the new duty cycle D _{new is} calculated as follows:

D _new = D _old +Δ D +(Δ D × p ×( K ( f )-1)) (18)

Where D _old is the duty cycle value calculated in the previous cycle, and K(f) is the maximum value among the degrees of association belonging to each category.

Please refer to FIG. 8. FIG. 8 is a functional block diagram of a maximum power tracking system of a solar photovoltaic power generation system according to an embodiment of the present invention. The maximum power tracking system 300 of the solar photovoltaic power generation system of the present embodiment includes a solar photovoltaic module 310, a differential operation unit 320, an extension analysis unit 330, and a control unit 340. The differential operation unit 320 is configured to generate an input signal according to the state of the solar photovoltaic module 310. The extension analysis unit 330 has a plurality of extension matter element models for analyzing the input signals by using the extension matter element models to generate a tracking signal. The control unit 340, such as a boost converter, is configured to drive the solar photovoltaic module 310 to operate at a maximum power point based on the tracking signal. The feature of this embodiment is that the extension control unit 330 does not need a large number of databases, does not require a learning process, is simple to calculate, and is easy to implement in a system wafer.

Please refer to FIG. 9 again. FIG. 9 is a block diagram showing the structure of the maximum power tracking system of the solar photovoltaic power generation system according to another embodiment of the present invention. In FIG. 9, the solar photovoltaic module 310 is implemented by a solar photovoltaic module array 311; the differential operation unit 320 is implemented by a differential operation circuit 321; and the control unit 340 is converted by a boost type. The device 341 is implemented. Specifically, between the solar photovoltaic module array 311 and the load, the maximum power point (MPP) tracking is performed by a circuit configuration of a boost converter 341. The differential operation circuit 321 is configured to calculate the slope error e and the slope error variation of the P - V characteristic curve based on the input power and the voltage signal. The extension analysis unit 330 changes the duty cycle of the boost converter according to the input signal, thereby enabling the solar photovoltaic system to operate at the maximum power point. In other words, the solar photovoltaic power generation system calculates the correlation function value of the input signal via the extension analysis unit 330, determines the category to which the input signal belongs (ie, the area where the operating point is located), and performs the boost type conversion according to the maximum power tracking method described above. The fine-tuning of the duty cycle is followed by returning the tracking signal to the boost converter 341 to adjust the duty cycle of the power semiconductor switch for MPPT control to achieve maximum power tracking.

Please refer to FIG. 10A to FIG. 10C together. FIG. 10C is a waveform diagram of the output power dynamic response of the solar photovoltaic module according to an embodiment of the present invention, and FIG. 10A is a conventional solar power module output power of the fixed step method. Dynamic response waveform diagram, Fig. 10B is a waveform diagram of the output power dynamic response of the solar photovoltaic module according to the conventional incremental conductance fixed step method. It can be seen from the figures 10A to 10C that the dynamic response of the output power of the solar photovoltaic module of the present embodiment has a relatively fast transient response and smooth steady state when the solar radiation decreases from 1000 W/m ² to 800 W/m ² . response.

Please refer to FIG. 10D to FIG. 10F together. FIG. 10F is a waveform diagram of the output power dynamic response of the solar photovoltaic module according to another embodiment of the present invention, and FIG. 10D is a conventional solar radiation module output of the disturbance step observation fixed step method. Power dynamic response waveform diagram, Fig. 10E is a waveform diagram of the output power dynamic response of the solar photovoltaic module according to the conventional incremental conductance fixed step method. It can be seen from the figures 10D to 10F that the dynamic response of the output power of the solar photovoltaic module of the present embodiment has a relatively fast transient response and smooth stability when the solar radiation is increased from 400 W/m ² to 500 W/m ² . State response.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention, and the present invention can be modified and modified without departing from the spirit and scope of the present invention. The scope is subject to the definition of the scope of the patent application attached.

101~210. . . step

300. . . Solar Power Generation Series' Maximum Power Tracking System

310. . . Solar photovoltaic module

311. . . Solar photovoltaic module array

320. . . Differential arithmetic unit

321. . . Differential operation circuit

330. . . Extension analysis unit

340. . . control unit

341. . . Boost converter

The above and other objects, features, advantages and embodiments of the present invention will become more apparent and understood.

Figure 1 is a graph showing the operating characteristics of a conventional power feedback method.

Fig. 2A is a graph showing the operational characteristics of the conventional disturbance observation method, which shows the forward disturbance voltage.

Figure 2B is a graph of the operational characteristics of the conventional perturbation observation method, which depicts the negative disturbance voltage.

Fig. 3 is a flow chart showing the steps of the maximum power tracking method of the solar photovoltaic power generation system according to an embodiment of the present invention.

Fig. 4 is a schematic diagram of the correlation function of the extension matter element model of Fig. 3.

Fig. 5 is a graph showing the slope variation of Fig. 3 and the power-voltage (P-V) operating characteristic of the solar photovoltaic module.

Fig. 6 is a schematic diagram showing the dynamic analysis of the slope of the power-voltage (P-V) operating characteristic curve of the solar photovoltaic module of Fig. 3.

Fig. 7 is a flow chart showing the steps of the maximum power tracking method of the solar photovoltaic power generation system according to another embodiment of the present invention.

Fig. 8 is a functional block diagram of a maximum power tracking system of a solar photovoltaic power generation system according to an embodiment of the present invention.

Figure 9 is a block diagram showing the structure of a maximum power tracking system of a solar photovoltaic power generation system according to another embodiment of the present invention.

Fig. 10A is a dynamic response waveform diagram of the output power of the solar photovoltaic module when the solar radiation amount is changed from 1000 W/m ² to 800 W/m ² in the conventional disturbance observation fixed step method.

Figure 10B is a diagram showing the dynamic response waveform of the output power of the solar photovoltaic module when the solar radiation changes from 1000 W/m ² to 800 W/m ² in the conventional incremental conductance fixed step method.

Fig. 10C is a waveform diagram showing the dynamic response of the output power of the solar photovoltaic module when the solar radiation amount is changed from 1000 W/m ² to 800 W/m ² according to an embodiment of the present invention.

The 10th figure is a dynamic response waveform diagram of the output power of the solar photovoltaic module when the solar radiation amount changes from 400 W/m ² to 500 W/m ² in the conventional disturbance observation fixed step method.

Figure 10E is a diagram showing the dynamic response waveform of the output power of the solar photovoltaic module when the solar radiation amount changes from 400 W/m ² to 500 W/m ² in the conventional incremental conductance fixed step method.

FIG. 10F is a waveform diagram showing the dynamic response of the output power of the solar photovoltaic module when the amount of solar radiation changes from 400 W/m ² to 500 W/m ² according to an embodiment of the present invention.

201~210. . . step

Claims (9)

A maximum power tracking method for a solar photovoltaic power generation system, comprising: obtaining a plurality of regional categories according to a power-voltage ( P - V ) curve of a solar photovoltaic module, and corresponding plurality of slope error values e and plural Slope error variation Using the plurality of region categories, the plurality of slope error values e and the plurality of slope error variations , establish a plurality of extension matter element models R : Wherein, a plurality of R _k for the k-th Extension Element Model of a matter element model in R, F ₀ is a matter element section, c _1, c ₂ is the characteristic value F _0, V p1k k-th The range of the slope error value e corresponding to the region type, V _p2k is the slope error variation value corresponding to the kth region category a value range; receiving an input signal; and analyzing the input signal using the plurality of extension matter element models R to operate the solar photovoltaic system at a maximum power point. The maximum power tracking method of the solar photovoltaic power generation system according to claim 1, further comprising calculating the plurality of slope error values e and the corresponding plurality of slope error variation values The plurality of correlation functions are analyzed by the plurality of correlation functions to analyze the input signal. The maximum power tracking method of the solar photovoltaic power generation system according to claim 2, wherein the correlation function is K(e) : among them, Is the distance between the slope error value e of the input signal and the classical domain e ₀ , Is the distance between the slope error value e of the input signal and the region e ₁ , It is the slope error value e of the input signal, the distance between the classical domain e ₀ and the region e ₁ . The maximum power tracking method of the solar photovoltaic power generation system according to claim 3, wherein the slope error value e of the input signal is different from the classical domain e ₀ for: Wherein, c is the minimum interval value e ₀ of the boundary, d is the maximum boundary value of the interval e _0. The maximum power tracking method of the solar photovoltaic power generation system according to claim 1, wherein the input model is a matter-element input model: A maximum power tracking system for a solar photovoltaic power generation system, comprising: a solar photovoltaic module having a power-voltage ( P - V ) curve; and a differential computing unit for operating state of the solar photovoltaic module Generating an input signal; an extension analysis unit having a plurality of extension matter element models for analyzing the input signal by using the plurality of extension matter element models, thereby generating a tracking signal, wherein the plurality of extensions The meta-model uses the power-voltage ( P - V ) curve to obtain a plurality of region categories, and a corresponding plurality of slope error values e and a plurality of slope error variations. The plurality of extension element models are established; and a control unit is configured to operate the solar photovoltaic module at a maximum power point according to the tracking signal; wherein: the plurality of extension element models R are: Wherein, a plurality of R _k for the k-th Extension Element Model of a matter element model in R, F ₀ is a matter element section, c _1, c ₂ is the characteristic value F _0, V p1k k-th The range of the slope error value e corresponding to the region type, V _p2k is the slope error variation value corresponding to the kth region category The value range; and the input signal is a matter-element input model: The maximum power tracking system of the solar photovoltaic power generation system according to claim 6, wherein the plurality of extensional matter element models calculate the plurality of slope error values e and the corresponding plurality of error variation values The plurality of correlation functions are analyzed by the plurality of correlation functions to analyze the input signal. The maximum power tracking system of the solar photovoltaic power generation system of claim 7, wherein the correlation function is K(e) : among them, Is the distance between the slope error value e of the input signal and the classical domain e ₀ , Is the distance between the slope error value e of the input signal and the region e ₁ , It is the slope error value e of the input signal, the distance between the classical domain e ₀ and the region e ₁ . The maximum power tracking system of the solar photovoltaic power generation system according to claim 8, wherein the slope error value e of the input signal and the extension distance of the classical domain e ₀ for: Wherein, c is the minimum interval value e ₀ of the boundary, d is the maximum boundary value of the interval e _0.