Photovoltaic maximum power tracking method applied to photovoltaic sunshade

Abstract

With the rapid development of a renewable energy source technology, a photovoltaic power generation system is widely applied to the power generation industry due to the characteristics of cleanness and no pollution. The photovoltaic sunshade combines the functions of photovoltaic power generation and building sunshade, but the efficiency of the photovoltaic sunshade is greatly influenced by the change of illumination conditions and local shadow shielding. A traditional maximum power point tracking (MPPT) algorithm is limited in performance under the conditions, and the output power of a photovoltaic array cannot be effectively improved. The invention provides an MPPT (Maximum Power Point Tracking) control method based on an improved HQPSO (Hybrid Quantum Particle Swarm Optimization). The MPPT control method is used for a photovoltaic sunshade system. According to the algorithm, by introducing the quantum behavior and the Levy flight strategy, the searching capability of the global maximum power point is enhanced, and the diversity and the global convergence of the algorithm are improved. The HQPSO algorithm has high tracking precision, high convergence speed and good steady-state performance, the power generation efficiency of the photovoltaic sunshade is remarkably improved, the problems of illumination change and shadow shielding in a photovoltaic sunshade system can be effectively solved, and the tracking precision and speed of the system to the maximum power point are improved.

Description

Technical Field

[0001] The invention relates to the technical field of photovoltaic power generation system control, in particular to an improved hybrid quantum particle swarm algorithm for maximum power point tracking (MPPT) in a photovoltaic awning power generation system. Background Art

[0002] Photovoltaic power generation systems have attracted widespread attention due to their clean and renewable characteristics. In photovoltaic systems, maximum power point tracking technology is crucial to improving power generation efficiency. Traditional MPPT algorithms may not be able to accurately track the global maximum power point when lighting conditions change or there is local shadow obstruction, affecting power generation efficiency. In order to improve the MPPT performance of photovoltaic systems in complex environments, intelligent algorithms such as particle swarm optimization (PSO) algorithms and quantum particle swarm optimization (QPSO) algorithms have been widely studied and applied. However, the existing QPSO algorithm still has the shortcomings of premature convergence and easy to fall into local optimality in practical applications.

[0003] The present invention relates to the field of solar photovoltaic power generation, and in particular to a maximum power point tracking (MPPT) control method based on an improved hybrid quantum particle swarm optimization (QPSO) algorithm, which is used to improve the efficiency of a photovoltaic power generation system under partial shadow shading conditions. Summary of the invention

[0004] In order to solve the above problems in the prior art, the present invention provides a photovoltaic array maximum power point tracking method based on an improved hybrid quantum particle swarm optimization algorithm (HQPSO). The method optimizes the contraction-expansion coefficient of the quantum particle swarm algorithm and introduces the Levy flight strategy to improve the global search capability and local search capability of the algorithm, thereby realizing fast and accurate tracking of the global maximum power point of the photovoltaic array under multi-peak conditions.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] The HQPSO algorithm of the present invention comprises the following steps:

[0007] Initialize the population size, dimension, maximum number of iterations, positions of particles in the population, individual optimal positions of particles, and global optimal positions; calculate the average optimal position of the particle swarm; update the positions of particles according to the improved quantum particle swarm optimization algorithm formula; calculate the fitness value of particles. Update the individual optimal position and global optimal position of particles; dynamically adjust the contraction-expansion coefficient according to the fitness value; determine whether the termination condition is met, and output the result if it is met, and return to recalculate the average optimal position of the particle swarm if it is not met.

[0008] According to an embodiment of the present invention, preferably, the positions of particles in the population are initialized, and the output voltage range is set to 0 to U oc , and initially set the individual optimal output voltage and the global optimal output voltage; detect the current light intensity and temperature, determine the objective function; calculate the output power corresponding to the output voltage; calculate the average optimal output voltage according to the average value of the global optimal position; update the CE coefficient through a variable contraction-expansion (CE) coefficient strategy. The CE coefficient β is adjusted according to the change of the particle fitness value during the optimization process, so that each particle can obtain a unique CE coefficient β; according to the conditions for judging the optimal position of the particle, if the global optimal position of the group is updated, the individual optimal output voltage and the global optimal output voltage are updated, otherwise proceed to the next step; add the Levy flight strategy to the particle position update formula, and update the output voltage according to this formula; after calculating the output power, update the individual optimal output voltage and the global optimal output voltage; determine whether the restart condition is met, if so, return to the detection environment parameters, re-detect and calculate; determine whether the termination condition is met, if so, output the result, otherwise return to the Levy flight strategy step to continue to execute the Levy flight strategy; the output result is the voltage and power value of the maximum power point.

[0009] According to an embodiment of the present invention, preferably, the output voltage range of the photovoltaic array is set to be 0 to the open circuit voltage (U oc ), determine that the initial search space of the algorithm covers the entire possible maximum power point range. Within the set output voltage range, randomly generate the initial position of each particle in the population, and the position is the output voltage value of each particle. The initialization method can prevent the initial positions of particles from being too concentrated and avoid premature convergence. Set the current position of each particle as its individual optimal output voltage (P best ), the initial individual optimal position of each particle is its own position. Select the particle with the highest fitness value in the population and set its output voltage value to the global optimal output voltage (G best ). The above setting can make the algorithm have higher search efficiency and accuracy in the initial stage.

[0010] According to an embodiment of the present invention, preferably, a high-sensitivity and fast-response light sensor is used to detect in real time the intensity of light currently received by the photovoltaic array and the instantaneous change of ambient light. A high-precision and stable temperature sensor is used to monitor in real time the temperature around the photovoltaic array. The detected light intensity and temperature data are transmitted to a controller for processing. The controller filters and corrects the data to remove noise and errors. Based on the processed light intensity and temperature data, an objective function that can reflect the output characteristics of the photovoltaic array under the current environmental conditions is dynamically adjusted.

[0011] According to an embodiment of the present invention, preferably, a high-precision voltage and current sensor is used to collect the current output voltage V of the photovoltaic array in real time. out and output current I out Data. Using the formula P out =V out ×I out Calculate the output power corresponding to the current output voltage.

[0012] The calculated output power P out As a fitness value, the fitness value is used to measure the quality of the current position of the particle, which directly affects the subsequent position update of the particle and the optimization process of the global optimal solution. According to the above environmental parameters (light intensity and temperature) and historical data, the parameters of the fitness function are dynamically adjusted to calculate the high-precision fitness value.

[0013] According to an embodiment of the present invention, preferably, the current global optimal position data G of each particle in the particle swarm is collected. best , these data are the best positions found by the particles in the optimization process, and the corresponding output voltage values. Calculate the fitness value f(G best ), the higher the fitness value, the closer the particle's current position is to the maximum power point (MPP). Using the fitness value of each particle as a weight, the weighted average of the global optimal position is calculated. The calculation formula is:

[0014] According to an embodiment of the present invention, preferably, V i is the global optimal position of the ith particle, f(G best,i ) is the corresponding fitness value, and N is the total number of particles. By weighted averaging, abnormal data can be effectively filtered out to obtain a more representative average optimal output voltage. The calculated average optimal output voltage is verified, and if abnormal data is found, it is recalibrated and adjusted.

[0015] According to an embodiment of the present invention, preferably, the fitness value f(P i ), where P i is the current position of the particle. Evaluate the change in the particle fitness value and record the current fitness value f current With the historical optimal fitness value f best According to the change of fitness value, the CE coefficient β is dynamically adjusted through the adaptive function. The adjustment formula is as follows:

[0016] According to an embodiment of the present invention, preferably, β 0 is the initial CE coefficient, f max and f minThe updated CE coefficient β is used to adjust the speed and position of the particle to enhance the flexibility and adaptability of the search. The specific formula is as follows: V i (t+1)=β i ×V i (t)+c 1 ×rand()×(P best -P i (t))+c 2 ×rand()×(G best -P i (t))

[0017] According to an embodiment of the present invention, preferably, V i (t) is the current velocity of the particle, P best is the historical optimal position of the particle, G best is the global optimal position, c 1 and c 2 is the acceleration constant, and rand() is the random function.

[0018] According to an embodiment of the present invention, preferably, the current fitness value f(P i (t)), where P i (t) is the current position of the particle. Compare the current fitness value f(P i (t)) and its historical optimal fitness value f(P best,i ). If f(P i (t)) is better than f(P best,i ), then update the individual optimal position P best .

[0019] Compare the individual optimal positions of all particles and find the particle position P with the highest fitness value Gbest , and take it as the global optimal position. If f(P Gbest ) is better than the historical global optimal fitness value, then the global optimal position G is updated best and the global optimal output voltage.

[0020] According to the updated individual optimal position and global optimal position, adjust the speed and position of each particle. The update formula is:

[0021] V i (t+1)=V i (t)+c 1 ×rand()×(P best,i -P i (t))+c 2 ×rand()×(G best -P i (t))

[0022] P i (t+1)=P i (t)+V i (t+1)

[0023] According to an embodiment of the present invention, preferably, V i (t) is the current velocity of the particle, c 1 and c 2 is the acceleration constant, and rand() is the random function.

[0024] According to an embodiment of the present invention, preferably, a random step length L(s) is generated according to a Levy flight distribution, and its probability density function is:

[0025] According to an embodiment of the present invention, preferably, 1<β≤3 is a parameter of the Levy flight distribution.

[0026] According to an embodiment of the present invention, preferably, the current fitness value f(P i (t)) and historical fitness value f(P best,i ), determine the search direction and step size. Incorporate the Levy flight strategy into the particle position update formula, specifically: i (t+1)=P i (t)+V i (t)×L(s)

[0027] According to an embodiment of the present invention, preferably, V i (t) is the current velocity of the particle, and L(s) is the random step length generated according to the Levy flight distribution. According to the updated particle position P i (t+1) Calculate the output voltage to ensure that the new position is closer to the maximum power point.

[0028] According to an embodiment of the present invention, preferably, based on the updated particle position P i (t+1), calculate the new output power P out The calculation formula is: out =V i (t+1)×I i (t+1)

[0029] According to an embodiment of the present invention, preferably, V i (t+1) is the updated output voltage, I i (t+1) is the corresponding output current. The new output power P out As the fitness value f(P i (t+1)) is used to evaluate the performance of the current particle position. Compare the current fitness value of the particle f(P i(t+1)) and its historical fitness value f(P best,i ). If f(P i (t+1)) is better than f(P best,i ), then update the individual optimal output voltage P best,i is the current output voltage V i (t+1). Compare the individual optimal output voltages of all particles, find the particle position with the highest fitness value, and use it as the global optimal position. If the fitness value is better than the historical global optimal value, update the global optimal output voltage G best The updated individual optimal and global optimal output voltages are verified. If any abnormality is found, recalibration and adjustment are required to continuously optimize the system.

[0030] According to an embodiment of the present invention, preferably, the fitness value f(P i (t)) and compare it with the historical fitness value to evaluate the change of the fitness value. Record the current iteration number t and compare it with the preset maximum iteration number t max Compare and determine whether the maximum number of iterations has been reached.

[0031] Monitor the light intensity, temperature and other environmental parameters in real time to determine if there are significant changes (such as greater than the preset threshold), and re-detect and calculate the environmental parameters. Combine the fitness value changes, the number of iterations and the environmental parameter changes to determine whether the restart conditions are met.

[0032] According to an embodiment of the present invention, preferably, the specific conditions include but are not limited to:

[0033] The fitness value change is less than the preset threshold f, and the current number of iterations reaches or exceeds the maximum number of iterations t max , the environmental parameter change is greater than the preset threshold ΔE.

[0034] According to an embodiment of the present invention, preferably, if the restart condition is met, the algorithm is triggered to restart, return to detecting light intensity and temperature, determine the objective function, and re-detect and calculate environmental parameters to ensure the continuity and adaptability of the optimization process.

[0035] According to an embodiment of the present invention, preferably, if the restart condition is not met, the current optimization iteration is continued until the optimization target is reached or the restart condition is met.

[0036] According to an embodiment of the present invention, preferably, the fitness value f(P i (t)) and compared with the preset optimal fitness value threshold f optCompare and determine whether the optimal fitness value is reached. Check the change of the current fitness value to determine whether the fitness value convergence condition is met, such as the fitness value change is less than the preset threshold Δf or the fitness value change is less than the threshold in several consecutive iterations.

[0037] Record the current number of iterations t and compare it with the preset maximum number of iterations t max Compare and judge whether the maximum number of iterations has been reached. Combine the fitness value, convergence and number of iterations to judge whether the termination condition is met.

[0038] According to an embodiment of the present invention, preferably, the specific conditions include but are not limited to:

[0039] The fitness value reaches or exceeds the preset threshold f opt , the fitness value converges, that is, the fitness value change is less than the preset threshold Δf, and the current number of iterations reaches or exceeds the maximum number of iterations t max .

[0040] According to an embodiment of the present invention, preferably, if the termination condition is met, the optimization calculation is terminated, and the final result is output, including the optimal fitness value, the optimal output voltage, the optimal output power, etc.

[0041] According to an embodiment of the present invention, preferably, if the termination condition is not met, the Levy flight strategy is added to the particle position update formula, the output voltage is updated according to the formula, and the Levy flight strategy is continued to be executed to further optimize the particle position and the output voltage.

[0042] According to an embodiment of the present invention, preferably, in the termination condition judgment whether the termination condition is satisfied, the particle position P with the highest fitness value is found. best , the voltage V corresponding to this position MPP and power P MPP This is the maximum power point of the photovoltaic system. best , accurately calculate the output voltage V MPP . The output voltage V MPP Substitute into the power calculation formula to calculate the corresponding output power P MPP =V MPP ×I MPP

[0043] According to an embodiment of the present invention, preferably, I MPP is the corresponding output current.

[0044] According to an embodiment of the present invention, preferably, the calculated maximum power point voltage V MPP and power P MPP Output and used to adjust the operating point of the photovoltaic system.

[0045] According to an embodiment of the present invention, preferably, the contraction-expansion coefficient β has a direct impact on the behavior of the particle. The present invention uses a variable CE coefficient strategy to enable each particle to have its own corresponding CE coefficient β, and its expression is as follows:

[0046]

[0047] According to an embodiment of the present invention, preferably, f avg 、f min 、f max They are the average fitness value, minimum fitness value and maximum fitness value of the current population respectively.

[0048] According to an embodiment of the present invention, preferably, in the process of updating the particle position, the present invention introduces the Levy flight strategy to increase the population diversity of the algorithm in the late convergence stage and avoid the algorithm falling into the local optimum. The specific formula is as follows:

[0049] X i,j (t+1)=X i,j (t)+β·|m best (t)-X i,j (t)|·Levy(λ)

[0050] According to an embodiment of the present invention, preferably, m best (t) is the average optimal position of the current population, and Levy(λ) is the Levy flight step length. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0052] Figure 1 The overall method flow chart of a photovoltaic maximum power tracking method applied to a photovoltaic awning is shown; DETAILED DESCRIPTION

[0053] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0055] like Figure 1 As shown, the present invention provides a photovoltaic maximum power tracking method applied to a photovoltaic awning, comprising the following steps:

[0056] S101: Initialize the positions of particles in the population, set the output voltage range to 0 to Uoc, and initially set the individual optimal output voltage and the global optimal output voltage.

[0057] S102: Real-time detection of environmental parameters such as light intensity and temperature to determine the working conditions and objective function of the photovoltaic system.

[0058] It should be noted that the objective function includes but is not limited to output power maximization function, output power change rate minimization function, voltage or current stability function, power loss minimization function, efficiency maximization function, etc.

[0059] S103: Calculate the corresponding output power according to the current output voltage as the fitness value for evaluating the performance of the particle.

[0060] S104: Calculate the average optimal output voltage according to the average value of the global optimal position to provide a reference for the next step.

[0061] S105: Update the contraction-expansion (CE) coefficient through a variable CE coefficient strategy. The CE coefficient β is adjusted according to the change of the particle fitness value during the optimization process, so that each particle can obtain a unique CE coefficient β.

[0062] S106: Based on the conditions for determining the optimal position of the particle, if the global optimal position of the group is updated, the individual optimal output voltage and the global optimal output voltage are updated, otherwise proceed to the next step.

[0063] S107: Add the Levy flight strategy to the particle position update formula and update the output voltage according to this formula.

[0064] S108: After calculating the output power, update the individual optimal output voltage and the global optimal output voltage.

[0065] S109: Determine whether the restart condition is met. If so, return to step S102 and re-test and recalculate.

[0066] S110: Determine whether the termination condition is met, if so, output the result, otherwise return to step S107 to continue executing the Levy flight strategy.

[0067] S111: Output the voltage and power value of the maximum power point.

[0068] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0069] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0070] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0071] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0072] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A photovoltaic maximum power tracking method applied to a photovoltaic awning, characterized in that: The following steps are involved: Initialize the positions of particles in the population and set the output voltage range to 0 to U oc , and initially set the individual optimal output voltage and the global optimal output voltage; detect the current light intensity and temperature, determine the objective function; calculate the output power corresponding to the output voltage; calculate the average optimal output voltage according to the average value of the global optimal position; update the CE coefficient through a variable contraction-expansion (CE) coefficient strategy. The CE coefficient β is adjusted according to the change of the particle fitness value during the optimization process, so that each particle can obtain a unique CE coefficient β; according to the conditions for judging the optimal position of the particle, if the global optimal position of the group is updated, the individual optimal output voltage and the global optimal output voltage are updated, otherwise proceed to the next step; add the Levy flight strategy to the particle position update formula, and update the output voltage according to this formula; after calculating the output power, update the individual optimal output voltage and the global optimal output voltage; determine whether the restart condition is met, if so, return to the detection environment parameters, re-detect and calculate; determine whether the termination condition is met, if so, output the result, otherwise return to the Levy flight strategy step to continue to execute the Levy flight strategy; the output result is the voltage and power value of the maximum power point.

2. A photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: Initialize the positions of particles in the population and set the output voltage range to 0 to U oc , and initially set the individual optimal output voltage and the global optimal output voltage, specifically including the following steps: Set the output voltage range of the photovoltaic array to 0 to open circuit voltage (U oc ), determine that the initial search space of the algorithm covers the entire possible maximum power point range. Within the set output voltage range, randomly generate the initial position of each particle in the population, and the position is the output voltage value of each particle. The initialization method can prevent the initial positions of particles from being too concentrated and avoid premature convergence. Set the current position of each particle as its individual optimal output voltage (P best ), the initial individual optimal position of each particle is its own position. Select the particle with the highest fitness value in the population and set its output voltage value to the global optimal output voltage (G best ). The above setting can make the algorithm have higher search efficiency and accuracy in the initial stage.

3. A photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: Detect the light intensity and temperature of the current environment and determine the objective function, which specifically includes the following steps: A highly sensitive and fast-responding light sensor is used to detect the intensity of light currently received by the photovoltaic array and the instantaneous change of ambient light in real time. A high-precision and stable temperature sensor is used to monitor the temperature around the photovoltaic array in real time. The detected light intensity and temperature data are transmitted to the controller for processing. The controller filters and corrects the data to remove noise and errors. Based on the processed light intensity and temperature data, the objective function that reflects the output characteristics of the photovoltaic array under the current environmental conditions is dynamically adjusted.

4. A photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: The corresponding output power is calculated according to the current output voltage as the fitness value, which is used to evaluate the performance of the particle. The specific steps include: The current output voltage V of the photovoltaic array is collected in real time through high-precision voltage and current sensors. out and output current I out Data. Using the formula P out =V out ×I out Calculate the output power corresponding to the current output voltage. The calculated output power P out As a fitness value, the fitness value is used to measure the quality of the current position of the particle, which directly affects the subsequent position update of the particle and the optimization process of the global optimal solution. According to the environmental parameters (light intensity and temperature) and historical data described in claim 3, the parameters of the fitness function are dynamically adjusted to calculate the high-precision fitness value.

5. The photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: The average optimal output voltage is calculated based on the average value of the global optimal position to provide a reference for the next step, which specifically includes the following steps: Collect the current global optimal position data G of each particle in the particle swarm best , these data are the best positions found by the particles in the optimization process, and the corresponding output voltage values. Calculate the fitness value f(G best ), the higher the fitness value, the closer the particle's current position is to the maximum power point (MPP). Using the fitness value of each particle as a weight, the weighted average of the global optimal position is calculated. The calculation formula is: Among them, V i is the global optimal position of the ith particle, f(G best,i ) is the corresponding fitness value, and N is the total number of particles. By weighted averaging, abnormal data can be effectively filtered out to obtain a more representative average optimal output voltage. The calculated average optimal output voltage is verified, and if abnormal data is found, it is recalibrated and adjusted.

6. A photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: Through a dynamically adjusted contraction-expansion coefficient (CE coefficient) strategy, the CE coefficient β is updated according to the change of the particle's fitness value. This strategy enables each particle to have its own corresponding CE coefficient β, enhancing the flexibility and adaptability of the algorithm. Specifically, it includes the following steps: Calculate the fitness value f(P i ), where P i is the current position of the particle. Evaluate the change in the particle fitness value and record the current fitness value f current and the historical optimal fitness value f best According to the change of fitness value, the CE coefficient β is dynamically adjusted through the adaptive function. The adjustment formula is as follows: Among them, β0 is the initial CE coefficient, f max and f min are the maximum and minimum values ​​of the fitness value, respectively. Use the updated CE coefficient β to adjust the speed and position of the particle to enhance the flexibility and adaptability of the search. The specific formula is as follows: V i (t+1)=β i ×V i (t)+c1×rand()×(P best -P i (t))+c2×rand( )×(G best -P i (t)) Among them, V i (t) is the current velocity of the particle, P best is the historical optimal position of the particle, G best is the global optimal position, c1 and c2 are acceleration constants, and rand() is a random function.

7. A photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: According to the conditions for determining the optimal position of the particle, if the global optimal position of the group is updated, the individual optimal output voltage and the global optimal output voltage are updated, otherwise proceed to the next step. Specifically, the following steps are included: Calculate the current fitness value f(P i (t)), where P i (t) is the current position of the particle. Compare the current fitness value f(P i (t)) and its historical optimal fitness value f(P best,i ). If f(P i (t)) is better than f(P best,i ), then update the individual optimal position P best . Compare the individual optimal positions of all particles and find the particle position P with the highest fitness value Gbest , and take it as the global optimal position. If f(P Gbest ) is better than the historical global optimal fitness value, then the global optimal position G is updated best and the global optimal output voltage. According to the updated individual optimal position and global optimal position, adjust the speed and position of each particle. The update formula is: V i (t+1)=V i (t)+c1×rand()×(P best,i -P i (t))+c2×rand()×(G best -P i (t)) P i (t+1)=P i (t)+V i (t+1) Among them, V i (t) is the current velocity of the particle, c1 and c2 are acceleration constants, and rand() is a random function.

8. The photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: Add the Levy flight strategy to the particle position update formula and update the output voltage according to this formula. The specific steps include: Generate a random step length L(s) according to the Levy flight distribution, and its probability density function is: Among them, 1<β≤3 is the parameter of the Levy flight distribution. Evaluate the current fitness value of the particle f(P i (t)) and historical fitness value f(P best,i ), determine the search direction and step size. Incorporate the Levy flight strategy into the particle position update formula, specifically: i (t+1)=P i (t)+V i (t)×L(s) Among them, V i (t) is the current velocity of the particle, and L(s) is the random step length generated according to the Levy flight distribution. According to the updated particle position P i (t+1) Calculate the output voltage to ensure that the new position is closer to the maximum power point.

9. A photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: After calculating the output power, the individual optimal output voltage and the global optimal output voltage are updated, which specifically includes the following steps: Recalculate the output power based on the updated particle position P i (t+1), calculate the new output power P out The calculation formula is: out =V i (t+1)×I i (t+1) Among them, V i (t+1) is the updated output voltage, I i (t+1) is the corresponding output current. The new output power P out As the fitness value f(P i (t+1)) is used to evaluate the performance of the current particle position. Compare the current fitness value of the particle f(P i (t+1)) and its historical fitness value f(P best,i ). If f(P i (t+1)) is better than f(P best,i ), then update the individual optimal output voltage P best,i is the current output voltage V i (t+1). Compare the individual optimal output voltages of all particles, find the particle position with the highest fitness value, and use it as the global optimal position. If the fitness value is better than the historical global optimal value, update the global optimal output voltage G best The updated individual optimal and global optimal output voltages are verified. If any abnormality is found, recalibration and adjustment are required to continuously optimize the system.

10. A photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: Determine whether the restart condition is met. If so, return to detect the light intensity and temperature of the current environment, and re-detect and calculate. Specifically, the following steps are included: Real-time monitoring of the fitness value f(P i (t)) and compare it with the historical fitness value to evaluate the change of the fitness value. Record the current iteration number t and compare it with the preset maximum iteration number t max Compare and determine whether the maximum number of iterations has been reached. Monitor the light intensity, temperature and other environmental parameters in real time to determine if there are significant changes (such as greater than the preset threshold), and if so, re-detect and recalculate the environmental parameters. Consider the fitness value changes, the number of iterations and the environmental parameter changes to determine if the restart conditions are met. Specific conditions include but are not limited to: The fitness value change is less than the preset threshold Δf The current iteration number reaches or exceeds the maximum iteration number t max The environmental parameter change is greater than the preset threshold ΔE If the restart conditions are met, the algorithm restarts and returns to detecting light intensity and temperature, determining the objective function, and re-detecting and calculating environmental parameters to ensure the continuity and adaptability of the optimization process. If the restart condition is not met, the current optimization iteration continues until the optimization target is reached or the restart condition is met.

11. A photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: Determine whether the termination condition is met. If so, output the result. Otherwise, return and add the Levy flight strategy to the particle position update formula. Update the output voltage according to this formula and continue to execute the Levy flight strategy. Specifically, it includes the following steps: Real-time monitoring of the fitness value f(P i (t)) and compared with the preset optimal fitness value threshold f opt Compare and determine whether the optimal fitness value is reached. Check the change of the current fitness value to determine whether the fitness value convergence condition is met, such as the fitness value change is less than the preset threshold Δf or the fitness value change is less than the threshold in several consecutive iterations. Record the current number of iterations t and compare it with the preset maximum number of iterations t max Compare and determine whether the maximum number of iterations has been reached. Combine the fitness value, convergence and number of iterations to determine whether the termination conditions are met. Specific conditions include but are not limited to: The fitness value reaches or exceeds the preset threshold f opt The fitness value converges, that is, the fitness value change is less than the preset threshold Δf The current iteration number reaches or exceeds the maximum iteration number t max If the termination condition is met, the optimization calculation is terminated and the final results are output, including the optimal fitness value, optimal output voltage, and optimal output power. If the termination condition is not met, return to add the Levy flight strategy to the particle position update formula, update the output voltage according to this formula, continue to execute the Levy flight strategy, and further optimize the particle position and output voltage.

12. A photovoltaic maximum power tracking method applied to a photovoltaic awning according to claim 1, characterized in that: Output the voltage and power value of the maximum power point. Specifically include the following steps: In the termination condition judgment whether the termination condition is met, find the particle position P with the highest fitness value best , the voltage V corresponding to this position MPP and power V MPP This is the maximum power point of the photovoltaic system. best , accurately calculate the output voltage V MPP . The output voltage V MPP Substitute into the power calculation formula to calculate the corresponding output power P MPP =V MPP ×I MPP Among them, I MPP is the corresponding output current. The calculated maximum power point voltage V MPP and power P MPP Output and used to adjust the operating point of the photovoltaic system.

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