Power adjustment method, apparatus, device, and storage medium

Abstract

The application discloses a power adjustment method, device and equipment and a storage medium, and the method comprises the following steps: determining an initial adjustment power according to an initial illumination and an initial temperature; updating the initial duty cycle according to a learning factor and an inertia weight, and updating the initial adjustment power according to the updated duty cycle; and obtaining a target adjustment power when the number of updates reaches a preset number. The application takes into account the illumination and temperature of a photovoltaic array, determines the initial adjustment power according to the illumination and temperature, introduces the learning factor and the inertia weight to constantly update the local and global duty cycles of the photovoltaic array, and constantly updates the initial adjustment power according to the duty cycles. The target adjustment power obtained when the number of updates reaches the preset number is the maximum adjustment power, so that more light energy can be converted into electric energy, and the accuracy of calculating the maximum adjustment power is improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and in particular to a power adjustment method, apparatus, equipment and storage medium. Background Technology

[0002] As photovoltaic (PV) power generation technology matures, the demands for power generation efficiency are increasing, with maximum power point (MPPT) being a key indicator of efficiency. However, when a PV array is subjected to uneven illumination, the system may fall into a local optimum, severely impacting its MPPT tracking efficiency. Therefore, MPPT tracking becomes crucial. However, current methods for calculating the MPPT solar controller's maximum power tracking still suffer from insufficient accuracy.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a power adjustment method, apparatus, device, and storage medium, aiming to solve the technical problem of how to improve the accuracy of MPPT solar controller in calculating maximum power.

[0005] To achieve the above objectives, the present invention provides a power adjustment method, the power adjustment method comprising the following steps:

[0006] The initial regulation power is determined based on the initial illuminance and initial temperature.

[0007] The initial duty cycle is updated based on the learning factor and inertia weight, and the initial adjustment power is updated based on the updated duty cycle.

[0008] The target adjustment power is obtained when the number of updates reaches the preset number.

[0009] Optionally, the learning factor includes: a first learning factor and a second learning factor;

[0010] The step of updating the initial duty cycle based on the learning factor and inertia weight includes:

[0011] Update the local duty cycle of each photovoltaic block and the global duty cycle of all photovoltaic blocks;

[0012] The first learning factor and the second learning factor are determined based on the number of photovoltaic blocks;

[0013] The initial duty cycle is updated based on the first learning factor, the second learning factor, the updated local duty cycle, the updated global duty cycle, and the inertia weight.

[0014] Optionally, the updated duty cycle is:

[0015]

[0016] In the formula, v i (k+1) represents the intermediate variable, w represents the inertia weight, c1 represents the updated first learning factor, c2 represents the updated second learning factor, r1 represents the first random number, r2 represents the second random number, and p i (k) represents the updated local duty cycle, p g (k) represents the updated global duty cycle, x i (k) represents the duty cycle of the i-th photovoltaic block in the k-th iteration, v i (k) represents the intermediate variable x in the k-th update. i (k+1) represents the updated duty cycle.

[0017] Optionally, the updated first learning factor and the updated second learning factor are:

[0018] c1 = c1max * sin(k / M);

[0019] c2 = c2max * arccot(k / M);

[0020] In the formula, c1max represents the initial setting value of the first learning factor, c2max represents the initial setting value of the second learning factor, k represents the number of updates, and M represents the number of photovoltaic blocks.

[0021] Optionally, the inertia weight is:

[0022] w = (V load (t)*I load (t)*E) / T;

[0023] In the formula, V load (t) represents the voltage generated at time t, I load (t) represents the current generated at time t, E represents the initial illuminance, and T represents the initial temperature.

[0024] Optionally, the step of determining the initial adjustment power based on the initial illuminance and initial temperature includes:

[0025] The initial regulation power is determined based on the initial illuminance and initial temperature input to the photovoltaic array, as well as the power weighting coefficient and initial duty cycle.

[0026] Optionally, the initial adjustment power is:

[0027] Pout(t) = qETD(t);

[0028] In the formula, q represents the power weighting coefficient, and D(t) represents the initial duty cycle.

[0029] In addition, to achieve the above objectives, the present invention also proposes a power adjustment device, which includes: a power determination module and a power adjustment module;

[0030] The power determination module is used to determine the initial adjustment power based on the initial illuminance and initial temperature.

[0031] The power adjustment module is used to update the initial duty cycle according to the learning factor and the inertia weight, and to update the initial adjustment power according to the updated duty cycle.

[0032] The power determination module is also used to obtain the target adjustment power when the number of updates reaches a preset number.

[0033] Furthermore, to achieve the above objectives, the present invention also proposes a power adjustment device, the power adjustment device including a memory, a processor, and a power adjustment program stored in the memory and capable of running on the processor, the power adjustment program being configured to implement the power adjustment method as described above.

[0034] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a power adjustment program, which, when executed by a processor, implements the power adjustment method as described above.

[0035] This invention discloses a power adjustment method, apparatus, device, and storage medium. The method includes: determining an initial adjustment power based on initial illuminance and initial temperature; updating the initial duty cycle based on a learning factor and inertia weight, and updating the initial adjustment power based on the updated duty cycle; and obtaining a target adjustment power when the number of updates reaches a preset number. This invention takes into account the illuminance and temperature of the photovoltaic array, determines the initial adjustment power based on illuminance and temperature, and introduces a learning factor and inertia weight to continuously update the local and global duty cycle of the photovoltaic array. The initial adjustment power is then continuously updated based on the duty cycle. The target adjustment power obtained when the number of updates reaches a preset number is the maximum adjustment power, thereby maximizing the conversion of light energy into electrical energy and improving the accuracy of calculating the maximum adjustment power. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the structure of the power adjustment device in the hardware operating environment involved in the embodiments of the present invention;

[0037] Figure 2 This is a flowchart illustrating the first embodiment of the power adjustment method of the present invention;

[0038] Figure 3This is a flowchart illustrating the second embodiment of the power adjustment method of the present invention;

[0039] Figure 4 This is a power adjustment structure diagram of an embodiment of the power adjustment method of the present invention;

[0040] Figure 5 This is a structural block diagram of the first embodiment of the power adjustment device of the present invention.

[0041] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0042] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0043] Reference Figure 1 , Figure 1 This is a schematic diagram of the power adjustment device structure in the hardware operating environment involved in the embodiments of the present invention.

[0044] like Figure 1 As shown, the power adjustment device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen, and optionally, it may also include a standard wired interface or a wireless interface. In this invention, the wired interface of the user interface 1003 may be a USB interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RAM) or a non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0045] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the power adjustment device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0046] like Figure 1 As shown, the memory 1005, which is identified as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a power adjustment program.

[0047] exist Figure 1 In the power adjustment device shown, the network interface 1004 is mainly used to connect to the backend server and communicate with the backend server; the user interface 1003 is mainly used to connect to the user equipment; the power adjustment device calls the power adjustment program stored in the memory 1005 through the processor 1001 and executes the power adjustment method provided in the embodiment of the present invention.

[0048] Based on the above hardware structure, an embodiment of the power adjustment method of the present invention is proposed.

[0049] Reference Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the power adjustment method of the present invention, which presents the first embodiment of the power adjustment method of the present invention.

[0050] Step S10: Determine the initial adjustment power based on the initial illuminance and initial temperature.

[0051] It should be noted that the executing entity in this embodiment may be a computer software service device with data processing, network communication and program running functions, such as a power adjustment device, or other electronic devices that can achieve the same or similar functions. This embodiment does not limit this.

[0052] It should be understood that when a photovoltaic array is subjected to uneven illumination, the photovoltaic system may fall into a local optimum. Therefore, current methods include ergodic methods for rapid global optimization and the application of neural network algorithms to determine maximum power, finding a near-maximum power point and a near-optimal duty cycle. However, existing MPPT solar controllers are still not precise enough in tracking maximum power, resulting in low efficiency of the photovoltaic circuit board. Traditional methods for maximum power point tracking also include perturbation observation, incremental conductance methods, and fuzzy control methods, but these are prone to causing the photovoltaic system to fall into local optima.

[0053] To overcome the above-mentioned defects, this embodiment introduces illuminance and temperature as parameters for calculating the maximum power and determines the inertia weight based on illuminance and temperature. The duty cycle is continuously updated based on the inertia weight, learning factor, and the introduced global and local duty cycles of the photovoltaic array. The maximum regulating power is calculated based on the updated duty cycle, thereby overcoming the situation where the photovoltaic system gets trapped in local optima and improving the accuracy of the MPPT solar controller in calculating the maximum regulating power.

[0054] It should be noted that calculating the initial regulation power requires not only the initial illuminance and initial temperature, but also the power weighting coefficient and the initial duty cycle.

[0055] It should be noted that by incorporating factors such as illuminance and temperature, the weakness of local search capability in the early stages of iteration is overcome.

[0056] Understandably, the initial duty cycle can be set to around 50%, and the illuminance and temperature can be initialized. This embodiment does not impose any restrictions on this.

[0057] Furthermore, in order to improve the accuracy of the maximum regulating power, step S10 in this embodiment may include:

[0058] The initial regulation power is determined based on the initial illuminance and initial temperature input to the photovoltaic array, as well as the power weighting coefficient and initial duty cycle.

[0059] It should be noted that the initial adjustment power is:

[0060] Pout(t) = qETD(t);

[0061] In the formula, q represents the power weighting coefficient, D(t) represents the initial duty cycle, E represents the initial illuminance, and T represents the initial temperature.

[0062] Step S20: Update the initial duty cycle according to the learning factor and inertia weight, and update the initial adjustment power according to the updated duty cycle.

[0063] It should be noted that the learning factor includes a first learning factor and a second learning factor. Updating the initial duty cycle based on the learning factor can accelerate the convergence of the algorithm and improve its accuracy. Furthermore, temperature and illuminance are incorporated into the inertial weight to prevent the final calculated maximum adjustment power from getting trapped in a local optimum.

[0064] It is understandable that the duty cycle is continuously updated by adjusting the inertia weight, the local duty cycle and the global duty cycle of the photovoltaic array, and the regulating power is updated according to the duty cycle to achieve the maximum regulating power.

[0065] It should be noted that the updated duty cycle can be:

[0066]

[0067] In the formula, v i (k+1) represents the intermediate variable, w represents the inertia weight, c1 represents the updated first learning factor, c2 represents the updated second learning factor, r1 represents the first random number, r2 represents the second random number, and p i (k) represents the updated local duty cycle, p g (k) represents the updated global duty cycle, x i (k) represents the duty cycle of the i-th photovoltaic block in the k-th iteration, v i(k) represents the intermediate variable x in the k-th update. i (k+1) represents the updated duty cycle.

[0068] Step S30: When the number of updates reaches the preset number, obtain the target adjustment power.

[0069] Understandably, updates can be stopped when the number of updated parameters reaches a preset number, or when the adjusted power reaches a preset threshold.

[0070] It should be understood that the target regulation power is the same as the maximum regulation power. The greater the regulation power, the more energy is converted from light energy into electrical energy.

[0071] For ease of understanding, please refer to Figure 4 To explain, Figure 4 This is a diagram of the power adjustment structure. The illuminance E and temperature T are input into the photovoltaic array, and the DC / DC control circuit calculates the target adjustable power Pout(t). The voltage V in the DC / DC control circuit is also considered. load (t) and current I load The voltage Vef(t) in the DC / DC control circuit is transmitted to the photovoltaic array. The duty cycle D(t) is updated by the MPPT control algorithm module and transmitted to the PWM. The PWM controls the DC / DC control circuit to calculate the target regulation power based on the duty cycle D(t).

[0072] This embodiment determines the initial regulation power based on the initial illuminance and initial temperature; updates the initial duty cycle based on the learning factor and inertia weight, and then updates the initial regulation power based on the updated duty cycle; the target regulation power is obtained when the number of updates reaches a preset number. This embodiment takes into account the illuminance and temperature of the photovoltaic array, determines the initial regulation power based on illuminance and temperature, and introduces a learning factor and inertia weight to continuously update the local and global duty cycle of the photovoltaic array. The initial regulation power is then continuously updated based on the duty cycle. The target regulation power obtained when the number of updates reaches a preset number is the maximum regulation power, thereby maximizing the conversion of light energy into electrical energy and improving the accuracy of calculating the maximum regulation power.

[0073] Reference Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the power adjustment method of the present invention, based on the above. Figure 2 The first embodiment shown is followed by a second embodiment of the power adjustment method of the present invention.

[0074] In the second embodiment, step S20 includes:

[0075] Step S201: Update the local duty cycle of each photovoltaic block and the global duty cycle of all photovoltaic blocks.

[0076] It should be noted that, in order to prevent the final calculated maximum regulation power from falling into a local optimum, it is necessary to continuously update the local duty cycle of each photovoltaic block in the photovoltaic array and update the global duty cycle of all photovoltaic blocks in the photovoltaic array.

[0077] It should be noted that the optimal local duty cycle is when the center of each photovoltaic block is perpendicular to the line connecting the sun, and the optimal global duty cycle is when the center of all photovoltaic blocks is perpendicular to the line connecting the sun.

[0078] Step S202: Determine the first learning factor and the second learning factor based on the number of photovoltaic blocks.

[0079] It is understandable that the number of photovoltaic blocks is a parameter of the first learning factor and the second learning factor. The first learning factor is calculated using a sine function based on the number of photovoltaic blocks, and the second learning factor is calculated using an inverse cotangent function based on the number of photovoltaic blocks.

[0080] It should be noted that the first learning factor and the second learning factor can be:

[0081] c1 = c1 max*sin(k / M);

[0082] c2 = c2max * arccot(k / M);

[0083] In the formula, c1max represents the initial setting value of the first learning factor, c2max represents the initial setting value of the second learning factor, k represents the number of updates, and M represents the number of photovoltaic blocks.

[0084] Step S203: Update the initial duty cycle based on the first learning factor, the second learning factor, the updated local duty cycle, the updated global duty cycle, and the inertia weight.

[0085] It should be noted that the inertia weight can be:

[0086] w = (V load (t)*I load (t)*E) / T;

[0087] In the formula, V load (t) represents the voltage generated at time t, I load (t) represents the current generated at time t, E represents the initial illuminance, and T represents the initial temperature.

[0088] This embodiment updates the local duty cycle of each photovoltaic (PV) block and the global duty cycle of all PV blocks; it determines the first learning factor and the second learning factor based on the number of PV blocks; and it updates the initial duty cycle based on the first learning factor, the second learning factor, the updated local duty cycle, the updated global duty cycle, and the inertia weight. This embodiment continuously updates the local and global duty cycles of the PV blocks, thereby updating the initial duty cycle based on the first learning factor, the second learning factor, the updated local duty cycle, the updated global duty cycle, and the inertia weight. This not only improves the accuracy of duty cycle calculation but also increases the working efficiency of the photovoltaic circuit board.

[0089] Furthermore, embodiments of the present invention also propose a storage medium storing a power adjustment program, which, when executed by a processor, implements the power adjustment method as described above.

[0090] In addition, refer to Figure 5 The present invention also proposes a power adjustment device, which includes: a power determination module 10 and a power adjustment module 20;

[0091] The power determination module 10 is used to determine the initial adjustment power based on the initial illuminance and the initial temperature.

[0092] The power adjustment module 20 is used to update the initial duty cycle according to the learning factor and the inertia weight, and to update the initial adjustment power according to the updated duty cycle.

[0093] The power determination module 10 is also used to obtain the target adjustment power when the number of updates reaches a preset number.

[0094] This embodiment determines the initial regulation power based on the initial illuminance and initial temperature; updates the initial duty cycle based on the learning factor and inertia weight, and then updates the initial regulation power based on the updated duty cycle; the target regulation power is obtained when the number of updates reaches a preset number. This embodiment takes into account the illuminance and temperature of the photovoltaic array, determines the initial regulation power based on illuminance and temperature, and introduces a learning factor and inertia weight to continuously update the local and global duty cycle of the photovoltaic array. The initial regulation power is then continuously updated based on the duty cycle. The target regulation power obtained when the number of updates reaches a preset number is the maximum regulation power, thereby maximizing the conversion of light energy into electrical energy and improving the accuracy of calculating the maximum regulation power.

[0095] Based on the first embodiment of the power adjustment device of the present invention described above, a second embodiment of the power adjustment device of the present invention is proposed.

[0096] In this embodiment, the power adjustment module 20 is used to update the local duty cycle of each photovoltaic block and the global duty cycle of all photovoltaic blocks.

[0097] Furthermore, the power adjustment module 20 is also used to determine the first learning factor and the second learning factor based on the number of photovoltaic blocks.

[0098] Furthermore, the power adjustment module 20 is also used to update the initial duty cycle based on the first learning factor, the second learning factor, the updated local duty cycle, the updated global duty cycle, and the inertia weight.

[0099] Furthermore, the power determination module 10 is also used to determine the initial adjustment power based on the initial illuminance and initial temperature input to the photovoltaic array, as well as the power weighting coefficient and initial duty cycle.

[0100] Other embodiments or specific implementations of the power adjustment device described in this invention can be found in the above-described method embodiments, and will not be repeated here.

[0101] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0102] 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.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory image (ROM) / random access memory (RAM), magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0104] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A power adjustment method, characterized by, The power adjustment method comprises the following steps: determining an initial adjustment power according to an initial light intensity and an initial temperature; updating an initial duty cycle according to a learning factor and an inertia weight, and updating the initial adjustment power according to the updated duty cycle; obtaining a target adjustment power when the number of updates reaches a preset number; the learning factor comprises a first learning factor and a second learning factor; the step of updating the initial duty cycle according to the learning factor and the inertia weight comprises: updating a local duty cycle of each photovoltaic block and a global duty cycle of all photovoltaic blocks; determining the first learning factor and the second learning factor according to the number of photovoltaic blocks; updating the initial duty cycle according to the first learning factor, the second learning factor, the updated local duty cycle, the updated global duty cycle and the inertia weight; the inertia weight is: w = (V load (t)*I load (t)*E) / T; where V load (t) represents the voltage generated at time t, I load (t) represents the current generated at time t, E represents the initial light intensity, and T represents the initial temperature. the updated first learning factor and the updated second learning factor are: c1 = c1max * sin (k / M); c2 = c2max * arccot (k / M); wherein c1max represents an initial setting value of the first learning factor, c2max represents an initial setting value of the second learning factor, k represents the number of updates, and M represents the number of photovoltaic blocks.

2. The power adjustment method of claim 1, wherein, the updated duty cycle is: where v i (k+1) represents an intermediate variable, w represents an inertia weight, c1 represents the first updated learning factor, c2 represents the second updated learning factor, r1 represents a first random number, r2 represents a second random number, p i (k) represents the updated local duty cycle, p g (k) represents the updated global duty cycle, x i (k) represents the duty cycle of the i-th photovoltaic block at the k-th iteration, v i (k) represents the intermediate variable of the k-th update, x i (k+1) represents the updated duty cycle.

3. The power adjustment method of claim 1 or 2, wherein, the step of determining the initial adjustment power according to the initial light intensity and the initial temperature comprises: determining the initial adjustment power according to the initial light intensity and the initial temperature input to the photovoltaic array, and a power weight coefficient and an initial duty cycle.

4. The power adjustment method of claim 3, wherein, the initial adjustment power is: Pout(t) = qETD(t); wherein q represents the power weight coefficient, and D(t) represents the initial duty cycle.

5. A power adjustment device, characterized by The power adjustment device comprises a power determination module and a power adjustment module. The power determination module is configured to determine an initial adjustment power according to an initial light intensity and an initial temperature. The power adjustment module is configured to update an initial duty cycle according to a learning factor and an inertia weight, and update the initial adjustment power according to the updated duty cycle. The power determination module is further configured to obtain a target adjustment power when the number of updates reaches a preset number. the learning factor comprises a first learning factor and a second learning factor; The power adjustment module is further configured to update a local duty cycle of each photovoltaic block and a global duty cycle of all photovoltaic blocks; determine the first learning factor and the second learning factor according to the number of photovoltaic blocks; and update the initial duty cycle according to the first learning factor, the second learning factor, the updated local duty cycle, the updated global duty cycle and the inertia weight. the inertia weight is: w = (V load (t)*I load (t)*E) / T; where V load (t) represents the voltage generated at time t, I load (t) represents the current generated at time t, E represents the initial light intensity, and T represents the initial temperature. the updated first learning factor and the updated second learning factor are: c1 = c1max * sin (k / M); c2 = c2max * arccot (k / M); wherein c1max represents an initial setting value of the first learning factor, c2max represents an initial setting value of the second learning factor, k represents the number of updates, and M represents the number of photovoltaic blocks.

6. A power adjustment device, characterized by The power adjustment device comprises a memory, a processor, and a power adjustment program stored in the memory and executable on the processor, and the power adjustment program, when executed by the processor, implements the steps of the power adjustment method according to any one of claims 1 to 4.

7. A storage medium, characterized by The storage medium stores a power adjustment program, and the power adjustment program, when executed by the processor, implements the steps of the power adjustment method according to any one of claims 1 to 4.

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