Photovoltaic power generation system maximum power point tracking control method considering time delay

Abstract

The invention discloses a photovoltaic power generation system maximum power point tracking control method considering time delay, and belongs to the technical field of photovoltaic cell maximum power point tracking control. The method solves the problems that a traditional maximum power point tracking control method is low in tracking precision and low in tracking speed and cannot deal with complex working environments, battery array parameter change and time delay in a system. An averaging method based on time delay is used for averaging a system to obtain an equivalent system, a constant variation formula method based on an ordinary differential equation is used for carrying out quantitative stability analysis on the equivalent system, a quantitative relation between system stability and controller parameters is established, a final controller form is obtained, and the stability of the system is improved. The MPPT tracking control of the photovoltaic cell is realized in a real sense, the tracking precision and the tracking speed are improved, and the power loss of the photovoltaic cell is effectively reduced. The method can be applied to maximum power point tracking control of the photovoltaic power generation system with time delay.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic cell maximum power point tracking control, and in particular relates to a maximum power point tracking (MPPT) control method for a photovoltaic power generation system taking time lag into consideration. Background Art

[0002] Traditional fossil energy has been supporting the rapid development of society since modern times. However, human demand for energy has increased dramatically with the explosive growth of population and the vigorous development of industry and technology. Due to the crisis of traditional energy and the pollution caused to the environment, new energy technologies such as solar energy, wind energy, hydropower, and biomass have appeared in the new vision and have been further developed and used. As a representative of new energy, solar energy has many advantages such as cleanliness and sustainability, and its development momentum is very rapid. At present, the direct application of solar energy is mainly concentrated in photovoltaic power generation. However, photovoltaic cells, as a working component that relies on solar energy, have inherent instability. Their power generation capacity is significantly affected by sunshine conditions. Therefore, at different time periods within a day or under different weather conditions, their power generation capacity will fluctuate significantly. This instability of photovoltaic power generation caused by the natural environment has brought great challenges to the conversion efficiency and resource utilization of photovoltaic cell power systems. Therefore, the energy management system is required to manage and distribute electric energy in real time according to the changes in power and system load of photovoltaic cells, avoid unreasonable consumption, and maximize energy storage and utilization.

[0003] The key to the photovoltaic cell energy management system lies in the maximum power point tracking technology. By monitoring the current and voltage of the photovoltaic cells and energy storage batteries in the solar photovoltaic power generation system, the gain of the DC / DC converter is changed in real time according to the optimization algorithm, so that the photovoltaic cells always work at the maximum power point, and then the power is distributed through the distributor. Photovoltaic cell maximum power point tracking control technology can predict the possible maximum power output point of the photovoltaic cell under the current working conditions based on the real-time detection of the photovoltaic array output power and a certain control algorithm. The popularization and application of maximum power point tracking technology can comprehensively control the voltage and output voltage of solar photovoltaic cells in a short period of time, and comprehensively promote the overall improvement of the management level of photoelectric conversion efficiency. Traditional maximum power point tracking control algorithms include constant voltage tracking method, perturbation observation method, etc. The constant voltage tracking method is simple to implement and has stable performance, but its limitation is that it is only applicable to independent photovoltaic systems with low power, stable sunshine conditions, and small external stability changes. Its control effect is only similar to MPPT control, and it does not truly realize MPPT; the perturbation observation method control system has a simple structure and the control algorithm is easier to implement, but the working point of the photovoltaic cell always changes around the maximum power point and cannot be stabilized at the maximum power point, that is, the tracking accuracy is low, resulting in a certain power loss. In addition, the traditional maximum power point tracking control algorithm also has problems such as slow tracking speed, model dependence, inability to cope with complex working environments, changes in the parameters of the battery array itself, and time lag in the system. Therefore, it is necessary to find an intelligent MPPT control method with real-time search capabilities. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of low tracking accuracy, slow tracking speed and inability to cope with complex working environment, parameter changes of the battery array itself and time lag in the system in the traditional maximum power point tracking control method, and propose a maximum power point tracking control method for a photovoltaic power generation system taking time lag into consideration.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a maximum power point tracking control method of a photovoltaic power generation system considering time lag, the method specifically comprising the following steps:

[0006] Step 1: Based on the current-voltage relationship of a single photovoltaic cell, an ideal mathematical model of a photovoltaic power generation system consisting of multiple photovoltaic cells connected in series is established;

[0007] Step 2: Taylor expansion is performed on the ideal mathematical model of the photovoltaic power generation system to obtain a quadratic static objective function, and the quadratic Hessian matrix and the duty cycle in the quadratic static objective function are estimated to obtain the upper bound of the second norm of the quadratic Hessian matrix in the quadratic static objective function and the upper and lower limits of the duty cycle;

[0008] Step 3: Establish a control system taking time delay into consideration according to the quadratic static objective function, and average the control system to obtain a disturbance system equivalent to the control system;

[0009] Step 4: According to the estimation results of the upper bound of the second norm of the quadratic Hessian matrix, the upper bound of the duty cycle, and the lower bound of the duty cycle, a quantitative stability analysis is performed on the disturbance system, and a quantitative relationship between the stability of the disturbance system and the controller parameters is established;

[0010] The controller parameters are selected according to the quantitative relationship between the stability of the disturbance system and the controller parameters to achieve maximum power point tracking control.

[0011] The beneficial effects of the present invention are:

[0012] The present invention takes into account the environmental parameter changes in the actual working environment of the photovoltaic power generation system, the parameter changes of the battery array itself, and the time lag in the system, and establishes a photovoltaic cell MPPT control algorithm based on bounded multivariable disturbance extreme value search, uses an averaging method based on time delay to average the system to obtain an equivalent system, uses a constant variation formula method based on ordinary differential equations to perform quantitative stability analysis on the equivalent system, establishes a quantitative relationship between system stability and controller parameters, and obtains the final controller form, thereby achieving true MPPT tracking control of photovoltaic cells, improving tracking accuracy and tracking speed, and effectively reducing power loss of photovoltaic cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is the equivalent circuit diagram of a single photovoltaic cell;

[0014] Figure 2 It is a block diagram of the bounded extremum search algorithm based on gradient estimation;

[0015] Figure 3 is a duty cycle graph;

[0016] Figure 4 is the output power curve;

[0017] Figure 5 It is the duty cycle error curve. DETAILED DESCRIPTION

[0018] Specific implementation method 1: A maximum power point tracking control method for a photovoltaic power generation system considering time lag is described in this implementation method, and the method specifically includes the following steps:

[0019] Step 1: Based on the current-voltage relationship of a single photovoltaic cell, an ideal mathematical model of a photovoltaic power generation system consisting of multiple photovoltaic cells connected in series is established;

[0020] Step 2: Taylor expansion is performed on the ideal mathematical model of the photovoltaic power generation system to obtain a quadratic static objective function, and the quadratic Hessian matrix and the duty cycle in the quadratic static objective function are estimated to obtain the upper bound of the second norm of the quadratic Hessian matrix in the quadratic static objective function and the upper and lower limits of the duty cycle;

[0021] Step 3: According to the quadratic static objective function and Figure 2 The bounded extreme value search algorithm based on gradient estimation is used to establish a control system considering time delay, and the control system is averaged using the averaging method based on time delay to obtain a disturbance system equivalent to the control system.

[0022] Step 4: According to the upper bound of the second norm of the quadratic Hessian matrix, the upper limit of the duty cycle, and the lower limit of the duty cycle, a constant variation formula method based on ordinary differential equations is used to perform a quantitative stability analysis on the disturbance system, and a quantitative relationship between the stability of the disturbance system and the controller parameters is established;

[0023] According to the quantitative relationship between the stability of the disturbance system and the controller parameters, the controller parameters are selected (i.e., given the controller parameters k i ,α i , according to the quantitative relationship to select ε * , and then we get ε∈(0,ε * ], that is, we get ω i ) to achieve maximum power point tracking control.

[0024] Specific implementation method 2: This implementation method is different from the specific implementation method 1 in that the current-voltage relationship of the single photovoltaic cell is established as follows:

[0025] The photovoltaic effect of photovoltaic cells can convert solar energy into electrical energy. When the cell receives light, a voltage difference will appear in the cell, such as Figure 1 As shown, a photovoltaic cell is represented by an ideal current source, and the current source is connected in parallel with an ideal diode, and the current of the current source is recorded as I ph ;

[0026] The current source I ph for:

[0027]

[0028] in, is the standard short-circuit current, T r is the standard temperature, T is the actual temperature, S is the solar irradiance, and k′ is the dimensionless short-circuit temperature coefficient;

[0029] The current flowing through the diode in parallel with the current source is recorded as I D , the terminal voltage of the diode connected in parallel with the current source is recorded as V D ,but

[0030]

[0031] in, is the diode reference reverse saturation current, V D is the diode terminal voltage, E g is the semiconductor band gap energy, N is the semiconductor emission coefficient, V t is the thermal battery voltage, k is a constant, and the value of k is 1.38×10 -23 J / K, q is the electron charge number, and the value of q is 1.6×10 -19 C;

[0032] Using KCL and KVL laws, we can get:

[0033]

[0034] Among them, R p is the contactor resistance, I is the current of the photovoltaic cell, V is the voltage of the photovoltaic cell, R s It is the electrical loss;

[0035] According to formula (3), the current-voltage relationship of a single photovoltaic cell is obtained:

[0036]

[0037] The other steps and parameters are the same as those in the first embodiment.

[0038] Specific implementation method three: This implementation method is different from specific implementation method one or two in that, in step one, an ideal mathematical model of a photovoltaic power generation system in which multiple photovoltaic cells are connected in series is established, specifically:

[0039] Connect n photovoltaic cells in series to obtain the entire photovoltaic power generation system, and express the current-voltage relationship of the i-th photovoltaic cell as follows:

[0040]

[0041] Among them, I ph,i is the current of the current source corresponding to the i-th photovoltaic cell, I 0,i is the intermediate variable, V i is the voltage of the ith photovoltaic cell, R s,i is the electrical loss in the equivalent circuit corresponding to the i-th photovoltaic cell, I i is the current of the ith photovoltaic cell;

[0042] According to formula (5), IV curve and PV curve are plotted to prove the existence of the peak output power of photovoltaic cells and their dependence on irradiance and temperature;

[0043] The maximum power point tracking task is completed by using DC / DC power electronic devices, which can adjust the output DC voltage to a nearly constant value, and at the same time force the output voltage of the photovoltaic array to be equal to the optimal voltage value to obtain maximum power output, thereby achieving impedance matching by changing the duty cycle parameters without changing the external connected load.

[0044] Use the DC / DC converter to adjust the DC voltage output by the photovoltaic power generation system to a constant value:

[0045]

[0046] Among them, V dc The photovoltaic power generation system outputs a constant DC voltage, V oi is the output voltage of the ith photovoltaic cell after passing through the DC / DC converter, I oi is the output current of the ith photovoltaic cell after passing through the DC / DC converter, I dc is the output current of the photovoltaic power generation system;

[0047] When the DC / DC converter is in continuous current mode (CCM), the switching pulse width modulation (PWM) frequency f s Significantly higher than the bandwidth of the control loop, and at the same time by I i -V i Functional Relationship I i =f i (V i ), the voltage of each photovoltaic cell in the photovoltaic power generation system can be obtained as V = [V 1 V 2 … V n ] Τ With duty cycle d = [d 1 d 2 …d n ] Τ The relationship is:

[0048]

[0049] in, is the power efficiency of the DC / DC converter, d i is the diode switch duty cycle corresponding to the i-th photovoltaic cell;

[0050] For each set of duty cycles, there is a set of photovoltaic cell voltages. In addition, each photovoltaic cell has a bypass diode, which means that the overall output power of the photovoltaic power generation system has only one peak value.

[0051] The output power of the photovoltaic power generation system is:

[0052]

[0053] Among them, P i =V oi I oi .

[0054] The other steps and parameters are the same as those in the first or second embodiment.

[0055] When n = 2, T = T r When , the relationship between current and voltage is:

[0056]

[0057] From formula (7) and formula (8), we can get the following equations:

[0058]

[0059] In practical applications, the accuracy of the parameters in the formula is often difficult to meet the requirements and changes with the external environment. It is impossible to directly track the maximum power point through this ideal mathematical model, and it is necessary to use a model-free extreme value search algorithm.

[0060] Specific implementation method 4: This implementation method is different from any one of specific implementation methods 1 to 3 in that, in step 2, Taylor expansion is performed on the ideal mathematical model of the photovoltaic power generation system to obtain a quadratic static objective function; specifically:

[0061] The ideal mathematical model of the photovoltaic power generation system (i.e., formula (9)) is Taylor expanded, and the expansion terms higher than quadratic in the expansion result are ignored. The quadratic static objective function is obtained as follows:

[0062]

[0063] Among them, P * is the maximum output power of the photovoltaic power generation system; P(t) is the power output of the photovoltaic power generation system at time t; d * is the maximum output power P * The duty cycle vector composed of the diode switch duty cycles corresponding to each photovoltaic cell is: is the maximum output power P * is the diode switch duty cycle corresponding to the first, second, …, nth photovoltaic cell at the output power P(t), d(t) is the duty cycle vector composed of the diode switch duty cycles corresponding to each photovoltaic cell at the output power P(t), the superscript T represents the transpose of the matrix, and H(·) is the quadratic Hessian matrix (Hessian matrix);

[0064]

[0065] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0066] Specific implementation method 5: This implementation method is different from the specific implementation methods 1 to 4 in that the quadratic Hessian matrix satisfies in, is a known matrix, and ||ΔH||≤κ, κ is a known scalar, ||·|| is the L2 norm;

[0067] The duty cycle vector d * satisfy Among them, d i * is the maximum output power P * The duty cycle corresponding to the i-th photovoltaic cell is, Yes i * The lower limit of Yes i * The upper limit of

[0068] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0069] Specific implementation method 6: This implementation method is different from the specific implementation methods 1 to 5 in that the specific process of step 3 is as follows:

[0070] Step 3: Consider an uncertain static quadratic mapping with a small constant output time lag, and express the quadratic static objective function as:

[0071]

[0072] Where D is the time lag, and d(tD) is the duty cycle vector composed of the diode switch duty cycles corresponding to each photovoltaic cell after considering the time lag D;

[0073] In order to further design the algorithm and analyze the control system, the quadratic Hessian matrix is ​​orthogonally transformed:

[0074]

[0075] Among them, U Τ is the transpose of U, which is an orthogonal matrix, is the orthogonal transformation result of H, yes The orthogonal transformation result is: is the orthogonal transformation result of ΔH, and is an intermediate variable;

[0076] Then there is yes The first, ..., nth diagonal elements in , Equation (14) can be rewritten as:

[0077]

[0078] The error is defined as:

[0079]

[0080] in, is the duty cycle vector error, is the time lag error;

[0081] make According to formula (15), Then there is

[0082] Let the gain matrix be K = diag{k 1 ,K,k n},i=1,K,n, where k 1 ,K,k n are the controller gains of the 1st, ..., nth photovoltaic cells respectively; let the control system considering time lag be:

[0083]

[0084] in, yes The first derivative of yes The i-th element in ω i is the frequency parameter of the controller of the ith photovoltaic cell, α i is the controller parameter of the i-th photovoltaic cell;

[0085] Then there is

[0086]

[0087] make

[0088]

[0089] Among them, ε is the parameter to be designed, l i is a rational number, N + is a set of positive integers;

[0090] Integrate both sides of equation (19) from t-ε to t, and then divide by ε. When t≥2D+ε, we have

[0091]

[0092] Among them, τ is the integration variable;

[0093] The first term on the right side of formula (21) is:

[0094]

[0095] in, represent The column vector composed of

[0096] Since formula (23) holds:

[0097]

[0098] Then the first term on the right side of formula (22) is:

[0099]

[0100]

[0101] because

[0102]

[0103] Then the second term on the right side of formula (22) is:

[0104]

[0105]

[0106] Among them, h i is the i-th element in H, yes The jth element in; the second term on the right side of formula (21) is:

[0107]

[0108] Among them, sin(k i P(τ))±sin(k i P(t))=sin(k i P(τ))+sin(k i P(t))-sin(k i P(t)), due to

[0109]

[0110] Then the first term on the right side of equation (27) is:

[0111]

[0112]

[0113]

[0114] Among them, v is the integral variable; due to

[0115]

[0116] The second term on the right side of formula (27) is:

[0117]

[0118]

[0119] make

[0120]

[0121] Then there is

[0122]

[0123] When t≥2D+ε, let

[0124]

[0125] Among them, v and s are integral variables, then we have

[0126]

[0127] make

[0128]

[0129] Then equation (18) is equivalent to the perturbation system:

[0130]

[0131] in, Yes i The first derivative of (t), w i (t) is an intermediate variable.

[0132] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0133] Specific implementation method 7: This implementation method is different from the specific implementation methods 1 to 6 in that the specific process of step 4 is as follows:

[0134] Satisfy initial conditions yes The i-th element in , given the controller parameter k i ,α i , given Let ε * >0Satisfaction:

[0135]

[0136] Among them, W i (ε * ) is an intermediate variable, δ i is the convergence speed of the ith photovoltaic cell;

[0137] Then ε∈(0,ε * ], the solution of formula (18) satisfies:

[0138]

[0139] Where e is the base of natural logarithms;

[0140] When ε∈(0,ε * ] and there are initial conditions When , the solution of equation (18) converges at a speed of δ i The exponential converges to the sphere of equation (40):

[0141]

[0142] Among them, formula (40) represents and Converge to

[0143] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0144] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that the intermediate variable W i (ε * )for:

[0145]

[0146] Among them, Δ 1 , Δ 2 , Δ 3 , Δ 4 , Δ 5 and Δ 6 are all intermediate variables, H M is the upper bound of the second norm of the matrix H.

[0147] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0148] Specific implementation method 9: This implementation method is different from the specific implementation methods 1 to 8 in that the intermediate variable δ i for:

[0149]

[0150] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0151] Specific implementation method 10: This implementation method is different from the specific implementation methods 1 to 9 in that the intermediate variable Δ 1 , Δ 2 , Δ 3 , Δ 4 , Δ 5 and Δ 6 They are:

[0152]

[0153] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.

[0154] Simulation verification:

[0155] Build a virtual photovoltaic cell system and select photovoltaic cell parameters as shown in Table 1 and Table 2;

[0156] Table 1 Simulation parameters of photovoltaic cell 1

[0157]

[0158]

[0159] Table 2 Simulation parameters of photovoltaic cell 2

[0160]

[0161] According to the model of formula (11) and the environment given in Tables 1 and 2 above, the maximum power point of a theoretical single photovoltaic cell can be calculated, as shown in Table 3.

[0162] Table 3 Theoretical maximum power point of simulated photovoltaic array

[0163]

[0164] The quadratic parameters and parameter selection are shown in Table 4.

[0165] Table 4 Simulation parameter selection of bounded multivariable extreme value search algorithm

[0166]

[0167]

[0168] A virtual single photovoltaic cell maximum power tracking control system was built in Matlab for simulation. The simulation results are as follows: Figures 3 to 5 shown.

[0169] Figure 3 The red curve in the figure represents the optimal duty cycle d of the first photovoltaic cell. 1 (t), the blue curve represents the optimal duty cycle d of the second photovoltaic cell 2 (t), Figure 4 The green curve in the figure is the simulated output power P(t) of two photovoltaic cells. Figure 5 The red curve in the figure shows the error between the optimal duty cycle of the first photovoltaic cell and the theoretical optimal duty cycle. The blue curve shows the error between the optimal duty cycle of the second photovoltaic cell and the theoretical optimal duty cycle.

[0170] according to Figure 3 , Figure 4 and Figure 5 As shown in the figure, under the simulation environment, the algorithm basically completes the search in 2 seconds, and the estimation error does not exceed 4%, thus achieving the tracking of the maximum power point of the photovoltaic battery system.

[0171] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A maximum power point tracking control method for a photovoltaic power generation system considering time lag, characterized in that: The method specifically comprises the following steps: Step 1: Based on the current-voltage relationship of a single photovoltaic cell, an ideal mathematical model of a photovoltaic power generation system consisting of multiple photovoltaic cells connected in series is established; Step 2: Taylor expansion is performed on the ideal mathematical model of the photovoltaic power generation system to obtain a quadratic static objective function, and the quadratic Hessian matrix and the duty cycle in the quadratic static objective function are estimated to obtain the upper bound of the second norm of the quadratic Hessian matrix in the quadratic static objective function and the upper and lower limits of the duty cycle; Step 3: Establish a control system taking time delay into consideration according to the quadratic static objective function, and average the control system to obtain a disturbance system equivalent to the control system; Step 4: According to the estimation results of the upper bound of the second norm of the quadratic Hessian matrix, the upper bound of the duty cycle, and the lower bound of the duty cycle, a quantitative stability analysis is performed on the disturbance system, and a quantitative relationship between the stability of the disturbance system and the controller parameters is established; The controller parameters are selected according to the quantitative relationship between the stability of the disturbance system and the controller parameters to achieve maximum power point tracking control.

2. A photovoltaic power generation system maximum power point tracking control method considering time lag according to claim 1, characterized in that: The current-voltage relationship of the single photovoltaic cell is established as follows: A photovoltaic cell is represented by a current source, and the current source is connected in parallel with the diode, and the current of the current source is recorded as I ph ; The current source I ph for: in, is the standard short-circuit current, T r is the standard temperature, T is the actual temperature, S is the solar irradiance, and k′ is the dimensionless short-circuit temperature coefficient; The current flowing through the diode in parallel with the current source is recorded as I D , the terminal voltage of the diode connected in parallel with the current source is recorded as V D ,but in, is the diode reference reverse saturation current, V D is the diode terminal voltage, E g is the semiconductor band gap energy, N is the semiconductor emission coefficient, V t is the thermocell voltage, k is a constant, and q is the electron charge number; Using KCL and KVL laws, we can get: Among them, R p is the contactor resistance, I is the current of the photovoltaic cell, V is the voltage of the photovoltaic cell, R s It is the electrical loss; According to formula (3), the current-voltage relationship of a single photovoltaic cell is obtained:

3. A photovoltaic power generation system maximum power point tracking control method considering time lag according to claim 2, characterized in that: In the step 1, an ideal mathematical model of a photovoltaic power generation system in which multiple photovoltaic cells are connected in series to generate electricity is established, specifically: Connect n photovoltaic cells in series to obtain the entire photovoltaic power generation system, and express the current-voltage relationship of the i-th photovoltaic cell as follows: Among them, I ph,i is the current of the current source corresponding to the i-th photovoltaic cell, I 0,i is the intermediate variable, V i is the voltage of the ith photovoltaic cell, R s,i is the electrical loss in the equivalent circuit corresponding to the i-th photovoltaic cell, I i is the current of the ith photovoltaic cell; Use the DC / DC converter to adjust the DC voltage output by the photovoltaic power generation system to a constant value: Among them, V dc The photovoltaic power generation system outputs a constant DC voltage, V oi is the output voltage of the ith photovoltaic cell after passing through the DC / DC converter, I oi is the output current of the ith photovoltaic cell after passing through the DC / DC converter, I dc is the output current of the photovoltaic power generation system; The voltage of each photovoltaic cell in the photovoltaic power generation system is V = [V1 V2…V n ] Τ With duty cycle d = [d1 d2…d n ] Τ The relationship is: in, is the power efficiency of the DC / DC converter, d i is the diode switch duty cycle corresponding to the i-th photovoltaic cell; The output power of the photovoltaic power generation system is: Among them, P i =V oi I oi .

4. A maximum power point tracking control method for a photovoltaic power generation system considering time lag according to claim 3, characterized in that: In the step 2, Taylor expansion is performed on the ideal mathematical model of the photovoltaic power generation system to obtain a quadratic static objective function; specifically: The ideal mathematical model of the photovoltaic power generation system is Taylor expanded, and the expansion terms higher than the second order in the expansion result are ignored. The quadratic static objective function is obtained as follows: Among them, P * is the maximum output power of the photovoltaic power generation system; P(t) is the power output of the photovoltaic power generation system at time t; d* is the maximum output power P * The duty cycle vector composed of the diode switch duty cycles corresponding to each photovoltaic cell is: is the maximum output power P * is the diode switch duty cycle corresponding to the first, second, …, nth photovoltaic cell when t(t) is output, d(t) is the duty cycle vector composed of the diode switch duty cycles corresponding to each photovoltaic cell when the output power is P(t), the superscript T represents the transpose of the matrix, and H(·) is the quadratic Hessian matrix; 5. A photovoltaic power generation system maximum power point tracking control method considering time lag according to claim 4, characterized in that: The quadratic Hessian matrix satisfies in, is a known matrix, and ||ΔH||≤κ, κ is a known scalar, ||·|| is the L2 norm; The duty cycle vector d * satisfy i=1,2,…,n,where d i * is the maximum output power P * The duty cycle corresponding to the i-th photovoltaic cell is, Yes i * The lower limit of Yes i * The upper limit of 6. A photovoltaic power generation system maximum power point tracking control method considering time lag according to claim 5, characterized in that: The specific process of step three is: Step 3: Consider an uncertain static quadratic mapping with constant output time lag, and express the quadratic static objective function as: Where D is the time lag, and d(tD) is the duty cycle vector composed of the diode switch duty cycles corresponding to each photovoltaic cell after considering the time lag D; Perform an orthogonal transformation on the quadratic Hessian matrix: Among them, U Τ is the transpose of U, which is an orthogonal matrix, is the orthogonal transformation result of H, yes The orthogonal transformation result is: is the orthogonal transformation result of ΔH, and is an intermediate variable; Then there is The first, ..., nth diagonal elements in , Equation (14) can be rewritten as: The error is defined as: in, is the duty cycle vector error, is the time lag error; make According to formula (15), Then there is Let the gain matrix be K = diag{k1,K,k n },i=1,K,n, where k1,K,k n are the controller gains of the 1st, ..., nth photovoltaic cells respectively; let the control system considering time lag be: in, yes The first derivative of yes The i-th element in ω i is the frequency parameter of the controller of the ith photovoltaic cell, α i is the controller parameter of the i-th photovoltaic cell; Then there is make Among them, ε is the parameter to be designed, l i is a rational number, N + is a set of positive integers; Integrate both sides of equation (19) from t-ε to t, and then divide by ε. When t≥2D+ε, we have Among them, τ is the integration variable; make Then there is When t≥2D+ε, let Among them, v and s are integral variables, then we have make Then equation (18) is equivalent to the perturbation system: in, Yes i The first derivative of (t), w i (t) is an intermediate variable.

7. A photovoltaic power generation system maximum power point tracking control method considering time lag according to claim 6, characterized in that: The specific process of step 4 is as follows: Satisfy initial conditions is the i-th element in σ0, given the controller parameter k i ,α i , given Let ε * >0Satisfaction: Among them, W i (ε * ) is an intermediate variable, δ i is the convergence speed of the ith photovoltaic cell; Then ε∈(0,ε * ], the solution of formula (18) satisfies: Where e is the base of natural logarithms; When ε∈(0,ε * ] and there are initial conditions When , the solution of equation (18) converges at a speed of δ i The exponential converges to the sphere of equation (40):

8. A photovoltaic power generation system maximum power point tracking control method considering time lag according to claim 7, characterized in that: The intermediate variable W i (ε * )for: Among them, Δ1, Δ2, Δ3, Δ4, Δ5 and Δ6 are all intermediate variables, H M is the upper bound of the second norm of the matrix H.

9. A photovoltaic power generation system maximum power point tracking control method considering time lag according to claim 8, characterized in that: The intermediate variable δ i for:

10. A photovoltaic power generation system maximum power point tracking control method considering time lag according to claim 9, characterized in that: The intermediate variables Δ1, Δ2, Δ3, Δ4, Δ5 and Δ6 are respectively:

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