Distributed photovoltaic maximum power point tracking method and device and electronic equipment

Abstract

The application discloses a kind of distributed photovoltaic maximum power point tracking method, device and electronic equipment.Distributed photovoltaic field is related, and the method comprises: determining the multiple duty cycles corresponding to distributed photovoltaic;According to the output power corresponding to distributed photovoltaic under each duty cycle, determine the optimal duty cycle from multiple duty cycles;Using Kepler optimization algorithm, according to the optimal duty cycle, update multiple duty cycles, obtain multiple updated duty cycles;In the case where the iteration number does not reach the iteration number threshold, repeat the step of determining the optimal duty cycle from multiple updated duty cycles until the iteration number reaches the iteration number threshold, in the case where the latest optimal duty cycle is used to control the output power of distributed photovoltaic, and the tracking of the maximum power point of distributed photovoltaic is determined to be completed.The application solves the technical problem of low accuracy in tracking photovoltaic maximum power point in related technology.

Description

Technical Field

[0001] This invention relates to the field of distributed photovoltaics, and more specifically, to a method, apparatus, and electronic device for maximum power point tracking in distributed photovoltaics. Background Technology

[0002] With the increasing penetration rate of new energy sources, solar energy, as a clean energy source, has been widely researched and applied. However, due to the influence of local shading from buildings, clouds, and other factors, the output power-voltage characteristic curve of photovoltaic arrays exhibits multiple peaks, making it difficult for photovoltaic power generation systems to continuously operate at their optimal operating point. Therefore, research on maximum power point tracking technology has significant theoretical and practical value for improving the solar energy utilization rate of photovoltaic power generation systems.

[0003] Currently, there are relatively mature traditional maximum point power tracking methods, including the perturbation and observation (P&O) method and the incremental conductance method. However, these traditional methods are difficult to accurately and quickly track the global maximum power point, resulting in low accuracy in tracking the photovoltaic maximum power point.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for maximum power point tracking of distributed photovoltaic systems, in order to at least solve the technical problem of low accuracy in tracking the maximum power point of photovoltaic systems in related technologies.

[0006] According to one aspect of the present invention, a maximum power point tracking (MPPT) method for distributed photovoltaic (PV) power is provided, comprising: determining multiple duty cycles corresponding to the distributed PV power, wherein the duty cycle is used to control the output power of the distributed PV power; determining an optimal duty cycle from the multiple duty cycles based on the output power of the distributed PV power at each duty cycle; updating the multiple duty cycles based on the optimal duty cycle using a Kepler optimization algorithm to obtain multiple updated duty cycles; and repeatedly executing the step of determining the optimal duty cycle from the multiple updated duty cycles based on the output power of the distributed PV power at each updated duty cycle until the number of iterations reaches the iteration threshold, then controlling the output power of the distributed PV power based on the latest optimal duty cycle, and determining that the tracking of the maximum power point of the distributed PV power is complete.

[0007] Furthermore, the maximum power point tracking method for distributed photovoltaic systems also includes: using the optimal duty cycle as the sun in the Kepler optimization algorithm, and determining the value of the optimal duty cycle as the sun's position; for each duty cycle, using the duty cycle as a planet in the Kepler optimization algorithm, and determining the value of the duty cycle as the planet's position; using the Kepler optimization algorithm, determining the gravitational force on the planet based on the sun's position and the planet's position, and determining the planet's velocity based on the preset mass corresponding to the sun and the preset mass corresponding to the planet; updating the planet's position based on the planet's gravitational force and the planet's velocity, and determining the updated position of the planet as the updated duty cycle, to obtain multiple updated duty cycles.

[0008] Furthermore, the maximum power point tracking method for distributed photovoltaic systems also includes: calculating the normalized value of the Euclidean distance between the sun and the planet based on the positions of the sun and the planet; calculating the target gravitational force based on the initial value of the gravitational force and the current iteration number; and determining the gravitational force acting on the planet based on the normalized value of the Euclidean distance between the sun and the planet and the target gravitational force.

[0009] Furthermore, the maximum power point tracking method for distributed photovoltaic systems also includes: calculating the semi-major axis of the planet's orbit based on the preset mass of the sun, the preset mass of the planet, the gravitational force of the target, and the orbital period; calculating the normalized value of the distance between the planet and the sun to obtain a first value; and determining the planet's velocity based on the semi-major axis of the planet's orbit and the numerical relationship between the first value and the preset value.

[0010] Furthermore, the maximum power point tracking method for distributed photovoltaic systems also includes: randomly determining two duty cycles from multiple duty cycles to obtain two target duty cycles; if the first value is less than or equal to a preset value, determining the planet's velocity based on the two target duty cycles and the semi-major axis of the planet's orbit; if the first value is greater than the preset value, determining the planet's velocity based on one of the two target duty cycles and the semi-major axis of the planet's orbit.

[0011] Furthermore, the maximum power point tracking method for distributed photovoltaics also includes: using the perturbation observation method to track the maximum power point of distributed photovoltaics based on the latest optimal duty cycle to obtain the target optimal duty cycle; and controlling the output power of distributed photovoltaics based on the target optimal duty cycle.

[0012] Furthermore, the maximum power point tracking method for distributed photovoltaics also includes: after controlling the output power of the distributed photovoltaics based on the latest optimal duty cycle, determining whether the degree of change in the illumination environment of the distributed photovoltaics is greater than a preset level; if the degree of change in the illumination environment of the distributed photovoltaics is greater than the preset level, re-determining multiple duty cycles corresponding to the distributed photovoltaics to re-determine the optimal duty cycle, and controlling the maximum output power of the distributed photovoltaics based on the re-determined optimal duty cycle.

[0013] According to another aspect of the present invention, a maximum power point tracking device for distributed photovoltaic (PV) is also provided, comprising: a first determining module, configured to determine multiple duty cycles corresponding to the distributed PV, wherein the duty cycle is used to control the output power of the distributed PV; a second determining module, configured to determine an optimal duty cycle from the multiple duty cycles based on the output power of the distributed PV at each duty cycle; an updating module, configured to update the multiple duty cycles based on the optimal duty cycle using a Kepler optimization algorithm to obtain multiple updated duty cycles; and a first processing module, configured to repeatedly execute the step of determining the optimal duty cycle from the multiple updated duty cycles based on the output power of the distributed PV at each updated duty cycle until the iteration count reaches the iteration count threshold, and then control the output power of the distributed PV based on the latest optimal duty cycle, and determine that the tracking of the maximum power point of the distributed PV is complete.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, and the computer program is configured to execute the above-described maximum power point tracking method for distributed photovoltaic systems at runtime.

[0015] According to another aspect of the present invention, an electronic device is also provided, the electronic device including one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are configured to run the programs, wherein the programs are configured to execute the above-described maximum power point tracking method for distributed photovoltaic systems.

[0016] In this embodiment of the invention, a method for maximum power point tracking (MPPT) of distributed photovoltaic (PV) power is employed using the Kepler optimization algorithm. This involves determining multiple duty cycles corresponding to the distributed PV, identifying the optimal duty cycle based on the output power of the distributed PV at each duty cycle, and then updating these duty cycles using the Kepler optimization algorithm. This process is repeated until the iteration count reaches the threshold, at which point the output power of the distributed PV is controlled based on the latest optimal duty cycle, thus completing the tracking of the MPPT. The duty cycle is used to control the output power of the distributed PV.

[0017] In the above process, because the Kepler optimization algorithm simulates the motion of planets around the sun and uses gravity to adjust the search method of candidate solutions, it can more effectively explore and utilize the search space, avoid getting trapped in local maximum power points, and improve tracking accuracy. Therefore, by first determining multiple duty cycles corresponding to distributed photovoltaics, and then using the Kepler optimization algorithm to update multiple duty cycles according to the optimal duty cycle, multiple updated duty cycles are obtained. This process is iterated until the number of iterations reaches the iteration threshold. The output power of distributed photovoltaics is controlled based on the latest optimal duty cycle, which can effectively achieve the search for the optimal duty cycle. As a result, the final output power of distributed photovoltaics is a more accurate maximum power point, which effectively improves the accuracy of tracking the maximum power point of photovoltaics.

[0018] Therefore, the solution provided in this application achieves the goal of using the Kepler optimization algorithm for maximum power point tracking of distributed photovoltaics, thereby improving the accuracy of tracking the maximum power point of photovoltaics and solving the technical problem of low accuracy in tracking the maximum power point of photovoltaics in related technologies. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0020] Figure 1 This is a flowchart of an optional maximum power point tracking method for distributed photovoltaic systems according to an embodiment of the present invention. Figure 1 ;

[0021] Figure 2 This is a schematic diagram of an optional Boost circuit according to an embodiment of the present invention;

[0022] Figure 3 This is a flowchart of an optional maximum power point tracking method for distributed photovoltaic systems according to an embodiment of the present invention. Figure 2 ;

[0023] Figure 4 This is a schematic diagram of an optional output power-voltage curve according to an embodiment of the present invention;

[0024] Figure 5 This is a schematic diagram of an optional power curve according to an embodiment of the present invention. Figure 1 ;

[0025] Figure 6 This is a schematic diagram of an optional duty cycle curve according to an embodiment of the present invention. Figure 1 ;

[0026] Figure 7 This is a schematic diagram of an optional power curve according to an embodiment of the present invention. Figure 2 ;

[0027] Figure 8 This is a schematic diagram of an optional duty cycle curve according to an embodiment of the present invention. Figure 2 ;

[0028] Figure 9 This is a schematic diagram of an optional power curve according to an embodiment of the present invention. Figure 3 ;

[0029] Figure 10 This is a schematic diagram of an optional duty cycle curve according to an embodiment of the present invention. Figure 3 ;

[0030] Figure 11 This is a schematic diagram of an optional maximum power point tracking device for distributed photovoltaic systems according to an embodiment of the present invention;

[0031] Figure 12 This is a schematic diagram of an optional electronic device according to an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0035] Example 1

[0036] According to an embodiment of the present invention, an embodiment of a maximum power point tracking method for distributed photovoltaics is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] Figure 1 This is a flowchart of an optional maximum power point tracking method for distributed photovoltaic systems according to an embodiment of the present invention. Figure 1 ,like Figure 1 As shown, the method includes the following steps:

[0038] Step S101: Determine multiple duty cycles corresponding to the distributed photovoltaic system, wherein the duty cycle is used to control the output power of the distributed photovoltaic system.

[0039] Optionally, electronic devices, application systems, servers, and other devices can be used as the execution subject. In this embodiment, the target processing system is used as the execution subject to execute the maximum power point tracking method for distributed photovoltaics.

[0040] Optionally, in this embodiment, distributed photovoltaic power can be deployed in a Boost circuit. Figure 2 This is a schematic diagram of an optional Boost circuit according to an embodiment of the present invention, such as... Figure 2 As shown, the Boost circuit includes a photovoltaic array, which comprises multiple photovoltaic modules. This photovoltaic array is equivalent to distributed photovoltaics. The Boost circuit also includes an input capacitor C. in Inductor, switching transistor, output capacitor C out In addition to the load, the duty cycle refers to the PWM (Pulse Width Modulation) duty cycle. The target processing system can output the duty cycle to generate a corresponding PWM signal to control the conduction and cutoff of the switching transistor in the Boost circuit, thereby realizing the control of the output power of distributed photovoltaic.

[0041] Optionally, the target processing system can first initialize multiple duty cycles, and then determine these initialized duty cycles as the multiple duty cycles corresponding to distributed photovoltaic systems. For example, the duty cycles can be initialized using the following formula:

[0042]

[0043] Among them, D i D represents the i-th duty cycle; min and D max These are the lower and upper limits of the duty cycle, respectively, and N is the total number of duty cycles.

[0044] Step S102: Determine the optimal duty cycle from multiple duty cycles based on the output power of the distributed photovoltaic system at each duty cycle.

[0045] Optionally, after obtaining multiple duty cycles, for each duty cycle, the target processing system can control the distributed photovoltaic system to operate under that duty cycle, and during the operation of the distributed photovoltaic system, collect the output voltage and output current of the distributed photovoltaic system, thereby determining the output power of the distributed photovoltaic system under that duty cycle based on the output voltage and output current, and then determining the duty cycle with the highest output power as the optimal duty cycle.

[0046] For example, in an iteration, the target processing system can send different duty cycles to the distributed photovoltaic system at different times, collect the output voltage and output current of the distributed photovoltaic system at different times, calculate its output power, and then compare the output power of the distributed photovoltaic system at the previous time with the output power at the current time. If the output power at the previous time is greater than the output power at the current time, the duty cycle at the previous time is taken as the optimal duty cycle; otherwise, the duty cycle at the current time is taken as the optimal duty cycle, thus obtaining the optimal duty cycle for the current iteration.

[0047] Step S103: Using the Kepler optimization algorithm, multiple duty cycles are updated according to the optimal duty cycle to obtain multiple updated duty cycles.

[0048] Optionally, the target processing system can treat the optimal duty cycle as the sun in the Kepler Optimization Algorithm (KOA), and the duty cycles among multiple duty cycles as planets in the Kepler Optimization Algorithm. The value of the optimal duty cycle is determined as the position of the sun, and the value of the duty cycle is determined as the position of the planet. Thus, the Kepler Optimization Algorithm is used to update multiple duty cycles based on the optimal duty cycle, resulting in multiple updated duty cycles.

[0049] Step S104: If the number of iterations has not reached the iteration threshold, repeat the step of determining the optimal duty cycle from multiple updated duty cycles based on the output power of the distributed photovoltaic at each updated duty cycle until the number of iterations reaches the iteration threshold. Then, control the output power of the distributed photovoltaic based on the latest optimal duty cycle and determine that the tracking of the maximum power point of the distributed photovoltaic has been completed.

[0050] Optionally, if the number of iterations does not reach the iteration threshold, the target processing system can repeatedly execute the above steps S102-S103, and in each iteration, that is, in each execution of steps S102-S103, the duty cycle used is the duty cycle updated by the Kepler optimization algorithm in the previous iteration.

[0051] Optionally, maximum power point tracking (MPPT) is a technique used to improve the energy conversion efficiency of photovoltaic (PV) systems. It adjusts the output voltage and current of the PV system to operate at the point of maximum power generation, thereby maximizing power extraction. Therefore, the iterative process described above is equivalent to the MPPT process for distributed PV systems.

[0052] Optionally, when the number of iterations reaches the iteration threshold, the target processing system can control the output power of the distributed photovoltaic system based on the latest optimal duty cycle and determine the completion of tracking the maximum power point of the distributed photovoltaic system.

[0053] In some embodiments, the target processing system may determine the optimal duty cycle that has not been updated by the Kepler optimization algorithm in the latest iteration as the latest optimal duty cycle when the number of iterations reaches the iteration number threshold.

[0054] In some embodiments, during the process of controlling the output power of distributed photovoltaics based on the latest optimal duty cycle, the target processing system can directly control the output power of distributed photovoltaics based on the latest optimal duty cycle. Alternatively, the target processing system can combine the perturbation observation method to continue tracking the maximum power point of distributed photovoltaics based on the latest optimal duty cycle to obtain the target optimal duty cycle, thereby controlling the output power of distributed photovoltaics based on the target optimal duty cycle.

[0055] Based on the scheme defined in steps S101 to S104 above, it can be understood that in this embodiment of the invention, the method of maximum power point tracking of distributed photovoltaic (PV) is adopted using the Kepler optimization algorithm. This involves determining multiple duty cycles corresponding to the distributed PV, identifying the optimal duty cycle from among these based on the output power of the distributed PV at each duty cycle, and then using the Kepler optimization algorithm to update these multiple duty cycles based on the optimal duty cycle. This process is repeated until the iteration count reaches the threshold, at which point the output power of the distributed PV is controlled according to the latest optimal duty cycle, and the tracking of the maximum power point of the distributed PV is considered complete. The duty cycle is used to control the output power of the distributed PV.

[0056] It is noteworthy that in the above process, because the Kepler optimization algorithm simulates the motion of planets around the sun and uses gravity to adjust the search method of candidate solutions, it can more effectively explore and utilize the search space, avoid getting trapped in local maximum power points, and improve tracking accuracy. Therefore, by first determining multiple duty cycles corresponding to the distributed photovoltaic system, and then using the Kepler optimization algorithm to update multiple duty cycles based on the optimal duty cycle, multiple updated duty cycles are obtained. This process is iterated until the number of iterations reaches the iteration threshold. The output power of the distributed photovoltaic system is then controlled based on the latest optimal duty cycle. This effectively achieves the search for the optimal duty cycle, resulting in a more accurate maximum power point output by the distributed photovoltaic system, thus effectively improving the accuracy of tracking the maximum power point of the photovoltaic system.

[0057] Therefore, the solution provided in this application achieves the goal of using the Kepler optimization algorithm for maximum power point tracking of distributed photovoltaics, thereby improving the accuracy of tracking the maximum power point of photovoltaics and solving the technical problem of low accuracy in tracking the maximum power point of photovoltaics in related technologies.

[0058] In one optional embodiment, during the process of updating multiple duty cycles based on the optimal duty cycle using the Kepler optimization algorithm to obtain multiple updated duty cycles, the target processing system can use the optimal duty cycle as the sun in the Kepler optimization algorithm and determine the value of the optimal duty cycle as the position of the sun. Then, for each duty cycle, the duty cycle is used as a planet in the Kepler optimization algorithm and the value of the duty cycle is determined as the position of the planet. Next, using the Kepler optimization algorithm, the gravitational force on the planet is determined based on the position of the sun and the position of the planet, and the velocity of the planet is determined based on the preset mass corresponding to the sun and the preset mass corresponding to the planet. Thus, the position of the planet is updated based on the gravitational force on the planet and the velocity of the planet, and the updated position of the planet is determined as the updated duty cycle to obtain multiple updated duty cycles.

[0059] In some embodiments, the target processing system can determine the preset mass of the Sun and the preset mass of each planet based on the Kepler optimization algorithm.

[0060] Optionally, after determining the gravitational force acting on the planet and the planet's velocity, the target processing system can use the Kepler optimization algorithm to update the planet's position based on the gravitational force acting on the planet and the planet's velocity.

[0061] Optionally, after updating the position of each of the multiple planets and determining the updated position of the planet as the updated duty cycle, multiple updated duty cycles are obtained.

[0062] It should be noted that through the above process, various duty cycles are effectively substituted into the Kepler optimization algorithm, thereby enabling the tracking of the maximum power point through the Kepler optimization algorithm and improving the accuracy of maximum power point tracking.

[0063] In one optional embodiment, in the process of determining the gravitational force on a planet based on the position of the sun and the position of the planet, the target processing system can calculate the normalized value of the Euclidean distance between the sun and the planet based on the position of the sun and the position of the planet, and then calculate the target gravitational force based on the initial value of the gravitational force and the current iteration number, thereby determining the gravitational force on the planet based on the normalized value of the Euclidean distance between the sun and the planet and the target gravitational force.

[0064] Optionally, before the iteration begins, the target processing system can initialize the eccentricity and motion period in the Kepler optimization algorithm. The initial calculation formula for the eccentricity is as follows:

[0065] e i = rand[0,1], i = 1, 2, ..., N

[0066] Among them, e iLet be the eccentricity of the elliptical orbit of the i-th planet; rand [0,1] is a random number within the interval [0,1]; N is the number of planets, that is, the total number of multiple duty cycles.

[0067] The initial calculation formula for the motion cycle is:

[0068] T i =|r|, i = 1, 2, ..., N

[0069] Among them, T i Let be the orbital period of the i-th planet; r is a random number obtained based on the standard normal distribution.

[0070] During the iteration process, the target processing system calculates the gravitational force acting on the planet using the following formula:

[0071]

[0072] Among them, F i μ(t) represents the gravitational force exerted on the i-th planet in the t-th iteration; μ(t) represents the gravitational constant in the t-th iteration, which is also the target gravitational force. and These are the normalized values ​​for the masses of the Sun and planets, respectively. X is the normalized value of the Euclidean distance between the Sun and the planets; s (t) represents the position of the sun in the t-th iteration; X i (t) represents the position of the i-th planet in the t-th iteration; ε is a small number, a preset value; r1 is a random number within the interval [0,1]; μ0 is the initial value of gravity; γ is a constant; t and T max These are the current iteration number and the iteration threshold, respectively.

[0073] It should be noted that the above process enables accurate calculation of the gravitational force acting on a planet, thereby effectively improving the accuracy of tracking the maximum power point.

[0074] In one optional embodiment, during the process of determining the velocity of a planet based on the preset mass corresponding to the sun and the preset mass corresponding to the planet, the target processing system can calculate the semi-major axis of the planet's orbit based on the preset mass corresponding to the sun, the preset mass corresponding to the planet, the gravitational force of the target, and the orbital period, calculate the normalized value of the distance between the planet and the sun, obtain a first value, and then determine the velocity of the planet based on the semi-major axis of the planet's orbit and the numerical relationship between the first value and the preset value.

[0075] Optionally, the target processing system can calculate the semi-major axis of the planetary orbit using the following formula:

[0076]

[0077] Among them, a i (t) is the semi-major axis of the orbit of the i-th planet in the t-th iteration, and r5 is a random number in the interval [0,1].

[0078] The target processing system can then calculate the normalized value of the distance between the planet and the sun to obtain a first value, and then determine the planet's speed based on the semi-major axis of the planet's orbit and the numerical relationship between the first value and a preset value.

[0079] For example, in the two cases where the first value is less than or equal to the preset value and the first value is greater than the preset value, different calculation methods are used to determine the planet's velocity based on the semi-major axis of the planet's orbit.

[0080] It should be noted that the above process enables accurate determination of planetary velocity, thereby effectively improving the accuracy of tracking the maximum power point.

[0081] In one optional embodiment, in the process of determining the planet's velocity based on the semi-major axis of the planet's orbit and the numerical relationship between a first value and a preset value, the target processing system can randomly determine two duty cycles from multiple duty cycles to obtain two target duty cycles. Then, if the first value is less than or equal to the preset value, the planet's velocity is determined based on the two target duty cycles and the semi-major axis of the planet's orbit. If the first value is greater than the preset value, the planet's velocity is determined based on one of the two target duty cycles and the semi-major axis of the planet's orbit.

[0082] Optionally, in determining the planet's velocity based on the semi-major axis of the planet's orbit and the numerical relationship between the first value and the preset value, the target processing system can calculate the planet's velocity using the following formula:

[0083]

[0084] Among them, V i (t) represents the velocity of the i-th planet in the t-th iteration; r2, r3, r4, r5, and r6 are all random numbers within the interval [0,1]; X a and X b Two candidate solutions, namely X, are randomly selected. a and X b For the duty cycles of the two targets, i.e., X a and X b The positions of two randomly selected planets; X i R represents the position of the i-th planet; i-norm(t) represents the normalized distance between the planet and the sun; F is a flag used to change the search direction of the Kepler optimization algorithm; U, U1, U2, L, and η are coefficients; M s and m i The masses of the Sun and planets, respectively; T i ε is the period of planetary motion; ε is a small number, a preset value, to prevent the denominator from being 0.

[0085] In one alternative embodiment, after determining the gravitational force acting on the planet and the planet's velocity, the target processing system can use the Kepler optimization algorithm to update the planet's position using the following formula:

[0086]

[0087] Among them, X i (t+1) represents the position of the i-th planet in iteration t+1, which is also the updated position of the planet.

[0088] It should be noted that the above process enables accurate determination of planetary velocity, thereby effectively improving the accuracy of tracking the maximum power point.

[0089] In an alternative embodiment, during the process of controlling the output power of distributed photovoltaics based on the latest optimal duty cycle, the target processing system can use the perturbation observation method to track the maximum power point of distributed photovoltaics based on the latest optimal duty cycle, obtain the target optimal duty cycle, and control the output power of distributed photovoltaics based on the target optimal duty cycle.

[0090] Optionally, when the number of iterations reaches the iteration threshold, the target processing system can determine the optimal duty cycle that has not been updated by the Kepler optimization algorithm in the latest iteration as the latest optimal duty cycle, and then switch to the perturbation observation method to track the global maximum power point of distributed photovoltaics.

[0091] For example, the latest optimal duty cycle is perturbed, the output power of the distributed photovoltaic (PV) system after the perturbation is measured, and compared with the output power of the PV system before the perturbation. Then, based on the observed power change, it is determined whether the perturbation direction increases the power output. If the perturbation increases the power output, the perturbation direction is determined to be close to the maximum power point, and the current duty cycle can be perturbed along this direction. If the perturbation decreases the power output, the perturbation direction is determined to be far from the maximum power point, and the current duty cycle can be perturbed along the opposite direction. This process is repeated until the current duty cycle stabilizes near the maximum power point, thus identifying the target optimal duty cycle. The output power of the distributed PV system is then controlled based on this target optimal duty cycle.

[0092] It should be noted that in this application, by first leveraging the excellent global search capability of the Kepler optimization algorithm to track to the vicinity of the global maximum power point, and then switching to the perturbation observation method to accurately locate and track the global maximum power point, the tracking speed can be effectively improved, the tracking accuracy increased, the power oscillation during the tracking process reduced, and the photovoltaic utilization rate improved.

[0093] In one optional embodiment, after controlling the output power of the distributed photovoltaic system based on the latest optimal duty cycle, the target processing system can determine whether the degree of change in the illumination environment of the distributed photovoltaic system is greater than a preset degree. If the degree of change in the illumination environment of the distributed photovoltaic system is greater than the preset degree, the system redetermines the multiple duty cycles corresponding to the distributed photovoltaic system to redetermine the optimal duty cycle, and controls the maximum output power of the distributed photovoltaic system based on the redetermined optimal duty cycle.

[0094] Optionally, the target processing system can monitor the output power of the distributed photovoltaic system in real time and calculate a target value based on the difference between the current output power and the previous output power. If the target value is greater than or equal to a preset threshold, the system determines that the change in the lighting environment exceeds a preset threshold; if the target value is less than the preset threshold, the system determines that the change in the lighting environment does not exceed the preset threshold. The target value and the difference between the current and previous output power of the distributed photovoltaic system are positively correlated.

[0095] For example, the following formula can be used to determine whether the degree of change in the solar environment for distributed photovoltaic systems exceeds a preset level:

[0096]

[0097] Where P(k) is the output power at time k; P(k-1) is the output power at time k-1. The left side of the formula is equivalent to the target value mentioned above, and the right side of the formula is equivalent to the preset threshold mentioned above. If the formula is satisfied, it indicates that the illumination has changed and the algorithm needs to be restarted, that is, the steps S101-S104 mentioned above need to be re-executed; otherwise, there is no change and the algorithm ends.

[0098] Figure 3 This is a flowchart of an optional maximum power point tracking method for distributed photovoltaic systems according to an embodiment of the present invention. Figure 2 ,according to Figure 3 An optional application process of this embodiment will be described. For example... Figure 3 As shown, the target processing system can perform the following steps:

[0099] Step 1: Initialize the Kepler optimization algorithm, allocate the initial duty cycle of the distributed photovoltaic system, and distribute it to the distributed photovoltaic system for execution in sequence;

[0100] Step 2: Collect the output voltage and output current of the distributed photovoltaic system at different times, and calculate its output power;

[0101] Step 3: Compare the calculated output power. Compare the output power of the distributed photovoltaic system at the previous moment with the output power at the current moment to obtain the optimal duty cycle at the current moment.

[0102] Step 4: For the optimal duty cycle at the current moment, iteratively update the duty cycle using the Kepler optimization algorithm;

[0103] Step 5: Determine if the iteration count t has reached the iteration count threshold T. max When the number of iterations t reaches the iteration threshold T max In this case, switch to the perturbation-observation method to track the global maximum power point of distributed photovoltaic power, provided that the number of iterations has not reached the iteration threshold T. max In this case, repeat steps 2-4.

[0104] Step 6: If the lighting changes, repeat steps 1 to 5 until the lighting no longer changes.

[0105] In some embodiments, three operating conditions are designed to illustrate the application effect of the method provided in this application. Optionally, the parameters of the boost circuit in which the distributed photovoltaic system is located can be: C in =100μF, L=10mH, C out =10μF, R=40Ω, f=20kHz.

[0106] Optionally, the irradiance under different operating conditions is shown in Table 1, and the output power-voltage curve of the photovoltaic array is shown in Table 1. Figure 4 As shown.

[0107] Table 1 - Irradiance and maximum power under three operating conditions

[0108]

[0109] Optionally, simulations are performed according to the method provided in this application under three operating conditions: uniform illumination in condition 1, non-uniform illumination in condition 2, and sudden illumination change in condition 3. The simulation results for condition 1 are as follows: Figure 5 , Figure 6 As shown, Figure 5 This is a schematic diagram of an optional power curve according to an embodiment of the present invention. Figure 1 , Figure 6 This is a schematic diagram of an optional duty cycle curve according to an embodiment of the present invention. Figure 1 , Figure 5 The output power in Figure 6The duty cycle change in the simulation; the simulation results under condition 2 are as follows: Figure 7 , Figure 8 As shown, Figure 7 This is a schematic diagram of an optional power curve according to an embodiment of the present invention. Figure 2 , Figure 8 This is a schematic diagram of an optional duty cycle curve according to an embodiment of the present invention. Figure 2 , Figure 7 The output power in Figure 8 The duty cycle change in the simulation; the simulation results under condition 3 are as follows: Figure 9 , Figure 10 As shown, Figure 9 This is a schematic diagram of an optional power curve according to an embodiment of the present invention. Figure 3 , Figure 10 This is a schematic diagram of an optional duty cycle curve according to an embodiment of the present invention. Figure 3 , Figure 9 The output power in Figure 10 The duty cycle changes within the data. Therefore, it is evident that the method proposed in this application can adapt to various situations and can perform maximum power point tracking faster and with higher accuracy.

[0110] Therefore, the solution provided in this application achieves the goal of using the Kepler optimization algorithm for maximum power point tracking of distributed photovoltaics, thereby improving the accuracy of tracking the maximum power point of photovoltaics and solving the technical problem of low accuracy in tracking the maximum power point of photovoltaics in related technologies.

[0111] Example 2

[0112] According to an embodiment of the present invention, an embodiment of a maximum power point tracking device for distributed photovoltaic systems is provided, wherein, Figure 11 This is a schematic diagram of an optional maximum power point tracking device for distributed photovoltaic systems according to an embodiment of the present invention, as shown below. Figure 11 As shown, the device includes:

[0113] The first determining module 1101 is used to determine multiple duty cycles corresponding to distributed photovoltaic power, wherein the duty cycle is used to control the output power of distributed photovoltaic power.

[0114] The second determining module 1102 is used to determine the optimal duty cycle from multiple duty cycles based on the output power of the distributed photovoltaic system at each duty cycle.

[0115] Update module 1103 is used to update multiple duty cycles based on the optimal duty cycle using the Kepler optimization algorithm to obtain multiple updated duty cycles.

[0116] The first processing module 1104 is used to repeatedly execute the step of determining the optimal duty cycle from multiple updated duty cycles based on the output power corresponding to each updated duty cycle of the distributed photovoltaic system when the number of iterations has not reached the iteration threshold, until the number of iterations reaches the iteration threshold, and then control the output power of the distributed photovoltaic system based on the latest optimal duty cycle, and determine that the tracking of the maximum power point of the distributed photovoltaic system is completed.

[0117] It should be noted that the first determining module 1101, the second determining module 1102, the updating module 1103 and the first processing module 1104 mentioned above correspond to steps S101 to S104 in the above embodiments. The four modules and the corresponding steps implement the same examples and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0118] Optionally, the update module further includes: a first determination submodule, used to use the optimal duty cycle as the sun in the Kepler optimization algorithm and determine the value of the optimal duty cycle as the position of the sun; a second determination submodule, used to use the duty cycle as a planet in the Kepler optimization algorithm for each duty cycle and determine the value of the duty cycle as the position of the planet; a third determination submodule, used to use the Kepler optimization algorithm to determine the gravitational force on the planet based on the position of the sun and the position of the planet, and to determine the velocity of the planet based on the preset mass corresponding to the sun and the preset mass corresponding to the planet; and a fourth determination submodule, used to update the position of the planet based on the gravitational force on the planet and the velocity of the planet, and determine the updated position of the planet as the updated duty cycle, so as to obtain multiple updated duty cycles.

[0119] Optionally, the third determining submodule further includes: a first calculation unit, used to calculate the normalized value of the Euclidean distance between the sun and the planet based on the position of the sun and the position of the planet; a second calculation unit, used to calculate the target gravitational force based on the initial value of the gravitational force and the current iteration number; and a first determining unit, used to determine the gravitational force on the planet based on the normalized value of the Euclidean distance between the sun and the planet and the target gravitational force.

[0120] Optionally, the third determining submodule further includes: a third calculation unit, used to calculate the semi-major axis of the planet's orbit based on the preset mass corresponding to the sun, the preset mass corresponding to the planet, the target gravitational force, and the motion period; a fourth calculation unit, used to calculate the normalized value of the distance between the planet and the sun to obtain a first value; and a second determining unit, used to determine the planet's velocity based on the semi-major axis of the planet's orbit and the numerical relationship between the first value and the preset value.

[0121] Optionally, the second determining unit further includes: a first determining subunit, used to randomly determine two duty cycles from multiple duty cycles to obtain two target duty cycles; a second determining subunit, used to determine the velocity of the planet based on the two target duty cycles and the semi-major axis of the planet's orbit when the first value is less than or equal to a preset value; and a third determining subunit, used to determine the velocity of the planet based on one of the two target duty cycles and the semi-major axis of the planet's orbit when the first value is greater than the preset value.

[0122] Optionally, the first processing module further includes: a first processing submodule, used to track the maximum power point of the distributed photovoltaic system based on the latest optimal duty cycle using the perturbation observation method to obtain the target optimal duty cycle; and a second processing submodule, used to control the output power of the distributed photovoltaic system based on the target optimal duty cycle.

[0123] Optionally, the maximum power point tracking device for distributed photovoltaics also includes: a judgment module, used to judge whether the degree of change in the illumination environment of the distributed photovoltaics is greater than a preset level; and a second processing module, used to redetermine multiple duty cycles corresponding to the distributed photovoltaics when the degree of change in the illumination environment of the distributed photovoltaics is greater than the preset level, so as to redetermine the optimal duty cycle, and control the output maximum power of the distributed photovoltaics based on the redetermined optimal duty cycle.

[0124] Example 3

[0125] According to another aspect of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program is configured to execute the above-described maximum power point tracking method for distributed photovoltaic systems at runtime.

[0126] Example 4

[0127] According to another aspect of the present invention, an electronic device is also provided, wherein, Figure 12 This is a schematic diagram of an optional electronic device according to an embodiment of the present invention, such as... Figure 12 As shown, the electronic device includes one or more processors; and a memory for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to run the programs, wherein the programs are configured to execute the aforementioned maximum power point tracking method for distributed photovoltaic systems.

[0128] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0129] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0131] The units described as separate components may or may not be physically separate. Similarly, the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0132] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0133] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0134] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A maximum power point tracking method for distributed photovoltaic systems, characterized in that, include: Multiple duty cycles are determined for distributed photovoltaic (PV) systems, wherein the duty cycles are used to control the output power of the distributed PV systems. Based on the output power of the distributed photovoltaic system at each duty cycle, the optimal duty cycle is determined from the plurality of duty cycles; Using the Kepler optimization algorithm, the multiple duty cycles are updated according to the optimal duty cycle to obtain multiple updated duty cycles; If the number of iterations has not reached the iteration threshold, the step of determining the optimal duty cycle from the multiple updated duty cycles based on the output power corresponding to each updated duty cycle of the distributed photovoltaic system is repeated until the number of iterations reaches the iteration threshold. Then, the output power of the distributed photovoltaic system is controlled according to the latest optimal duty cycle, and the tracking of the maximum power point of the distributed photovoltaic system is considered complete. Using the Kepler optimization algorithm, multiple duty cycles are updated based on the optimal duty cycle to obtain multiple updated duty cycles. This includes: using the optimal duty cycle as the sun in the Kepler optimization algorithm and determining the value of the optimal duty cycle as the sun's position; for each duty cycle, using the duty cycle as a planet in the Kepler optimization algorithm and determining the value of the duty cycle as the planet's position; using the Kepler optimization algorithm, determining the gravitational force on the planet based on the sun's position and the planet's position, and determining the planet's velocity based on a preset mass corresponding to the sun and the planet's preset mass; updating the planet's position based on the gravitational force and the planet's velocity, and determining the updated position of the planet as the updated duty cycle, thus obtaining the multiple updated duty cycles.

2. The method according to claim 1, characterized in that, Determining the gravitational force acting on a planet based on the position of the sun and the position of the planet includes: Based on the positions of the sun and the planets, the normalized value of the Euclidean distance between the sun and the planets is calculated; The target gravitational force is calculated based on the initial value of gravitational force and the current iteration number. The gravitational force acting on the planet is determined based on the normalized value of the Euclidean distance between the sun and the planet, and the target gravitational force.

3. The method according to claim 1, characterized in that, Determining the velocity of the planet based on the preset mass corresponding to the sun and the preset mass corresponding to the planet includes: Based on the preset mass corresponding to the sun, the preset mass corresponding to the planet, the target gravitational force, and the motion period, the semi-major axis of the planet's orbit is calculated. Calculate the normalized value of the distance between the planet and the sun to obtain the first value; The velocity of the planet is determined based on the semi-major axis of the planet's orbit and the numerical relationship between the first value and the preset value.

4. The method according to claim 3, characterized in that, Determining the planet's velocity based on the semi-major axis of its orbit and the numerical relationship between the first value and a preset value includes: Two duty cycles are randomly selected from the plurality of duty cycles to obtain two target duty cycles; If the first value is less than or equal to the preset value, the velocity of the planet is determined based on the duty cycles of the two targets and the semi-major axis of the planet's orbit. If the first value is greater than the preset value, the velocity of the planet is determined based on one of the two target duty cycles and the semi-major axis of the planet's orbit.

5. The method according to claim 1, characterized in that, Controlling the output power of the distributed photovoltaic system based on the latest optimal duty cycle includes: Using the perturbation observation method, the maximum power point of the distributed photovoltaic system is tracked based on the latest optimal duty cycle to obtain the target optimal duty cycle; The output power of the distributed photovoltaic system is controlled based on the target optimal duty cycle.

6. The method according to claim 1, characterized in that, After controlling the distributed photovoltaic output power based on the latest optimal duty cycle, the method further includes: Determine whether the degree of change in the light environment of the distributed photovoltaic system is greater than a preset level; If the degree of change in the illumination environment of the distributed photovoltaic system is greater than the preset degree, the multiple duty cycles corresponding to the distributed photovoltaic system are re-determined to redetermine the optimal duty cycle, and the maximum output power of the distributed photovoltaic system is controlled based on the redetermined optimal duty cycle.

7. A maximum power point tracking device for distributed photovoltaic systems, characterized in that, include: The first determining module is used to determine multiple duty cycles corresponding to the distributed photovoltaic system, wherein the duty cycle is used to control the output power of the distributed photovoltaic system. The second determining module is used to determine the optimal duty cycle from the plurality of duty cycles based on the output power of the distributed photovoltaic at each duty cycle. The update module is used to update the multiple duty cycles according to the optimal duty cycle using the Kepler optimization algorithm, so as to obtain multiple updated duty cycles. The first processing module is used to repeatedly execute the step of determining the optimal duty cycle from the multiple updated duty cycles based on the output power corresponding to each updated duty cycle of the distributed photovoltaic system, until the iteration count reaches the iteration count threshold. Then, it controls the output power of the distributed photovoltaic system based on the latest optimal duty cycle and determines that the tracking of the maximum power point of the distributed photovoltaic system is complete. The update module includes: a first determining submodule, used to use the optimal duty cycle as the sun in the Kepler optimization algorithm, and to determine the value of the optimal duty cycle as the position of the sun; a second determining submodule, used to use each duty cycle as a planet in the Kepler optimization algorithm, and to determine the value of the duty cycle as the position of the planet; a third determining submodule, used to use the Kepler optimization algorithm to determine the gravitational force on the planet based on the position of the sun and the position of the planet, and to determine the velocity of the planet based on the preset mass corresponding to the sun and the preset mass corresponding to the planet; and a fourth determining submodule, used to update the position of the planet based on the gravitational force on the planet and the velocity of the planet, and to determine the updated position of the planet as the updated duty cycle, so as to obtain the plurality of updated duty cycles.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the maximum power point tracking method for distributed photovoltaic systems as described in any one of claims 1 to 6 when it is run.

9. An electronic device, characterized in that, The electronic device includes one or more processors; A memory for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to perform the program, wherein the program is configured to execute the maximum power point tracking method for distributed photovoltaic systems as described in any one of claims 1 to 6.

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