Global optimal power point determination method and device, storage medium and electronic device

Abstract

The invention discloses a global optimal power point determination method and device, a storage medium and an electronic device, and relates to the field of computers.The global optimal power point determination method comprises the steps that particle information of a plurality of particles is initialized to obtain the particles, the particle information comprises the particle position and the particle speed, and the particle position and the particle speed are obtained; the particle position is used for determining the power value of the power point of the photovoltaic module, and the particle speed is used for indicating the change size of the next update of the particle position; iteratively updating the particle information of the plurality of particles through a target function, and determining a global maximum power point of the photovoltaic module; iteratively updating the particle position of the global maximum power point through a variable step size perturbation and observation method, and determining a global optimal power point of the photovoltaic module; by adopting the scheme, the problems of poor dynamic search activity and insufficient algorithm restarting capability of the traditional particle swarm algorithm in the related technology are solved.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular, to a method and device for determining a global optimal power point, a storage medium, and an electronic device. Background Art

[0002] Inspired by the results of artificial life research, the particle swarm algorithm is an intelligent optimization algorithm proposed by simulating bird flocks foraging. Due to its good optimization effect, the particle swarm algorithm has been widely used in the optimization problems of nonlinear systems. Each example in the particle swarm algorithm represents an individual bird. The idea is to use the individual birds to collect food information in a fixed area, share and interact with each other after obtaining the information, and modify individual action strategies until the most delicious food is found.

[0003] However, the existing traditional particle swarm algorithm has shortcomings such as poor dynamic search activity and algorithm restart capability.

[0004] In view of the problems of poor dynamic search activity and insufficient algorithm restart capability of traditional particle swarm algorithm in related technologies, no effective solution has been proposed so far.

[0005] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0006] The embodiments of the present application provide a method and device for determining a global optimal power point, a storage medium, and an electronic device to at least solve the problems in the related art that the traditional particle swarm algorithm has poor dynamic search activity and insufficient algorithm restart capability.

[0007] According to one aspect of an embodiment of the present application, a method for determining a global optimal power point is provided, comprising: initializing particle information of a plurality of particles to obtain the plurality of particles, wherein the particle information comprises: a particle position and a particle velocity, the particle position being used to determine a power value of a power point of a photovoltaic component, and the particle velocity being used to indicate a change in the particle position for the next update; iteratively updating the particle information of the plurality of particles through an objective function to determine a global maximum power point of the photovoltaic component; and iteratively updating the particle position of the global maximum power point through a perturbation observation method with a variable step size to determine a global optimal power point of the photovoltaic component.

[0008] In an exemplary embodiment, the particle information of a plurality of particles is initialized to obtain the plurality of particles, including: 2 +2n to determine the number of particles of the plurality of particles, wherein n is the maximum peak number of the photovoltaic module, and N is the number of particles; by the formula Initialize the particle positions of the plurality of particles, where Isc-array is the short-circuit current value of the photovoltaic module, X max is the upper bound of the particle position, X min is the lower bound of the particle position, X i is the particle position of the i-th particle among the multiple particles, rand is a random number (0, 1); by the formula Initialize the particle velocities of the plurality of particles, where V max is the upper bound of the particle velocity, V min is the lower bound of the particle velocity, V i is the particle velocity of the i-th particle.

[0009] In an exemplary embodiment, the particle information of the plurality of particles is iteratively updated through an objective function to determine the global maximum power point of the photovoltaic module, including: a first updating step: updating the particle position according to the particle speed of the plurality of particles through the objective function, determining the individual optimal position of each particle according to the updated particle position, and determining the global optimal position of the plurality of particles, wherein the individual optimal position is the particle position with the largest power value of each particle in multiple iterations, and the global optimal position is the particle position with the largest power value among the plurality of individual optimal positions corresponding to the plurality of particles; a second updating step: updating the particle position according to the formula Update the particle velocity, where k is the current iteration number, W is the inertia weight coefficient, C1 and C2 are convergence factors, r1 and r2 are random numbers (0, 1), is the individual optimal position of the i-th particle among the multiple particles at the k-th iteration, is the global optimal position of the ith particle at the kth iteration; a determining step: determining a current global maximum power point according to the global optimal position and the historical global maximum power point, and determining a maximum power point change value according to the global optimal position and the historical global maximum power point, wherein the power value of the current global maximum power point is a larger value between a first power value of the global optimal position and a second power value of the historical global maximum power point, and the historical global maximum power point is the current global maximum power point determined at the k-1th iteration; the first updating step, the second updating step and the determining step are cyclically executed until the maximum power point change value is less than a first preset threshold, and the current global maximum power point finally obtained is determined as the global maximum power point.

[0010] In an exemplary embodiment, after updating the particle positions according to the particle velocities of the plurality of particles, determining the individual optimal position of each particle according to the updated particle positions, and determining the global optimal position of the plurality of particles, the method further comprises: The inertia weight coefficient is determined, where: V max is the upper limit of the particle velocity, n is the maximum wave peak number of the photovoltaic module, N is the number of particles of the plurality of particles, V min is the lower bound of the particle speed, and rand is a random number between (0, 1).

[0011] In an exemplary embodiment, after updating the particle positions according to the particle velocities of the plurality of particles, determining the individual optimal position of each particle according to the updated particle positions, and determining the global optimal position of the plurality of particles, the method further comprises: Determine the particle search local extreme value C1, and through the formula Determine the particle search global extreme value C2, where, V max is the upper limit of the particle velocity, n is the maximum wave peak number of the photovoltaic module, N is the number of particles of the plurality of particles, V min is the lower bound of the particle speed, and rand is a random number between (0, 1).

[0012] In an exemplary embodiment, the particle position of the global maximum power point is iteratively updated by a perturbation observation method with a variable step size to determine the global optimal power point of the photovoltaic component, including: a third update step: updating the particle position of the global maximum power point according to the perturbation direction by a perturbation step size, determining the size of a first power value before the global maximum power point is updated and a second power value after the update, and determining the power change between the first power value and the second power value; a fourth update step: updating the perturbation step size according to the power change, and updating the perturbation direction according to the size of the first power value and the second power value; looping the third update step and the fourth update step until the absolute value of the power change between the first power value and the second power value is less than a second power threshold, and determining the final particle position as the global optimal power point.

[0013] According to another aspect of an embodiment of the present application, a device for determining a global optimal power point is also provided, including: an initialization module, used to initialize particle information of multiple particles to obtain the multiple particles, wherein the particle information includes: particle position, particle velocity, the particle position is used to determine the power value of the power point of the photovoltaic component, and the particle velocity is used to indicate the change size of the particle position for the next update; a first update module, used to iteratively update the particle information of the multiple particles through an objective function to determine the global maximum power point of the photovoltaic component; a second update module, used to iteratively update the particle position of the global maximum power point through a perturbation observation method with a variable step size to determine the global optimal power point of the photovoltaic component.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining the global optimal power point when running.

[0015] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the global optimal power point through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0017] Through the present application, the particle information of multiple particles is first initialized to obtain multiple particles, wherein the particle information includes particle position and particle velocity, the particle position is used to determine the power value of the power point of the photovoltaic component, and the particle velocity is used to indicate the change size of the particle position for the next update; the particle information of the multiple particles is iteratively updated through the objective function to determine the global maximum power point of the photovoltaic component; after determining the global maximum power point, the particle position of the global maximum power point is iteratively updated through a perturbation observation method with a variable step size to determine the global optimal power point of the photovoltaic component; the above scheme is adopted to improve the particle swarm algorithm, thereby improving the activity of dynamic search, and ensuring the tracking accuracy and stability by adding the improved perturbation observation method; thereby solving the problems of poor dynamic search activity and insufficient algorithm restart capability of the traditional particle swarm algorithm in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for determining a global optimal power point according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of a method for determining a global optimal power point according to an embodiment of the present application;

[0022] Figure 3 is a flow chart of an optional method for determining a global optimal power point according to an embodiment of the present application;

[0023] Figure 4 It is a structural block diagram of a device for determining a global optimal power point according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 11 is a hardware structure block diagram of a computer terminal for determining a global optimal power point according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the global optimal power point in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] A wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0029] For the particle swarm algorithm, the position update of the particle depends on the convergence factor C and the inertia weight W. The convergence factors C1 and C2 mainly affect the interaction of the particle's own experience information and the experience information between particles. The larger C1 is, the particles gather in a local range for search, and the larger C2 is, the shorter the particle search convergence time is. For the particle swarm algorithm, the selection of appropriate C1 and C2 values ​​can effectively control the particle search speed, and it is crucial to ensure that the particles maintain a balance between searching away from the original trajectory and searching along the original trajectory.

[0030] Starting from the access of inertia weight, the current V is stored and retained, and its convergence domain is globally controllable. The following two parts introduce the convergence factor, which mainly retains the local convergence ability of particles. Among them, when W = 1, the search performance of the particle swarm algorithm depends on the value of the convergence factor.

[0031] Considering that the inertia weight and convergence factor determine the algorithm search results, and the existing traditional particle swarm algorithm has poor dynamic search activity and algorithm restart ability, this application proposes a climbing method based on the particle swarm algorithm. In this embodiment, a method for determining the global optimal power point is provided. Figure 2 is a flow chart of a method for determining a global optimal power point according to an embodiment of the present application. Figure 2 As shown, the process includes the following steps S202-S206:

[0032] Step S202: Initializing particle information of a plurality of particles to obtain the plurality of particles, wherein the particle information includes: a particle position and a particle velocity, wherein the particle position is used to determine a power value of a power point of a photovoltaic component, and the particle velocity is used to indicate a change size of the particle position for the next update;

[0033] Step S204: iteratively updating the particle information of the plurality of particles through an objective function to determine a global maximum power point of the photovoltaic assembly;

[0034] Step S206: iteratively updating the particle position of the global maximum power point by a variable step-size perturbation observation method to determine the global optimal power point of the photovoltaic module.

[0035] Through the above steps, the particle information of multiple particles is first initialized to obtain multiple particles, wherein the particle information includes particle position and particle velocity, the particle position is used to determine the power value of the power point of the photovoltaic component, and the particle velocity is used to indicate the change size of the particle position for the next update; the particle information of the multiple particles is iteratively updated through the objective function to determine the global maximum power point of the photovoltaic component; after determining the global maximum power point, the particle position of the global maximum power point is iteratively updated through the perturbation observation method with a variable step size to determine the global optimal power point of the photovoltaic component; the above scheme is adopted to improve the particle swarm algorithm, thereby improving the activity of dynamic search, and ensuring the tracking accuracy and stability by adding the improved perturbation observation method; thereby solving the problems of poor dynamic search activity and insufficient algorithm restart capability of the traditional particle swarm algorithm in the related technology.

[0036] In an exemplary embodiment, the particle information of a plurality of particles is initialized to obtain the plurality of particles, including: 2 +2n to determine the number of particles of the plurality of particles, wherein n is the maximum peak number of the photovoltaic module, and N is the number of particles; by the formula Initialize the particle positions of the plurality of particles, where I sc-array is the short-circuit current value of the photovoltaic module, X max is the upper bound of the particle position, X min is the lower bound of the particle position, X i is the particle position of the i-th particle among the multiple particles, rand is a random number (0, 1); by the formula Initialize the particle velocities of the plurality of particles, where V max is the upper bound of the particle velocity, V min is the lower bound of the particle velocity, V i is the particle velocity of the i-th particle.

[0037] The particle swarm algorithm assumes that there are N particles in the H-dimensional target space. The position of the i-th particle is represented by X i The velocity of the particle is represented by V i Indicates. When there is only one peak, the particle does not exist locally, and within a sufficient time, the particle can effectively track the maximum power point. In the case of multiple peaks, the particle may fall into a local extreme value. Therefore, the fewer the peaks, the faster the particle converges, and the more the peaks, the slower the particle converges. The search range can be increased by increasing the number of particles, thereby speeding up the convergence of the algorithm. Set the relationship between the number of particles and the peaks as: N = n 2 +2n, where n is the maximum wave peak number of the photovoltaic module. The number of particles increases with the increase of the wave peak number.

[0038] The PI curve range is much smaller than the PU curve range, so the short-circuit current value (i.e. I sc-array ) as the particle search area will effectively improve the particle convergence speed, so the initial position of the particle is: Initial velocity of the particle: Among them, rand is a random number between 0 and 1, n is the number of wave peaks, and the particle speed is inversely proportional to the number of wave peaks.

[0039] Optionally, the particle information of the multiple particles is iteratively updated through the objective function to determine the global maximum power point of the photovoltaic module, including: a first updating step: updating the particle position according to the particle speed of the multiple particles through the objective function, determining the individual optimal position of each particle according to the updated particle position, and determining the global optimal position of the multiple particles, wherein the individual optimal position is the particle position with the largest power value of each particle in multiple iterations, and the global optimal position is the particle position with the largest power value among the multiple individual optimal positions corresponding to the multiple particles; a second updating step: updating the particle position according to the formula Update the particle velocity, where k is the current iteration number, W is the inertia weight coefficient, C1 and C2 are convergence factors, r1 and r2 are random numbers (0, 1), is the individual optimal position of the i-th particle among the multiple particles at the k-th iteration, is the global optimal position of the ith particle at the kth iteration; a determining step: determining a current global maximum power point according to the global optimal position and the historical global maximum power point, and determining a maximum power point change value according to the global optimal position and the historical global maximum power point, wherein the power value of the current global maximum power point is a larger value between a first power value of the global optimal position and a second power value of the historical global maximum power point, and the historical global maximum power point is the current global maximum power point determined at the k-1th iteration; the first updating step, the second updating step and the determining step are cyclically executed until the maximum power point change value is less than a first preset threshold, and the current global maximum power point finally obtained is determined as the global maximum power point.

[0040] The velocity of a particle can be expressed as a vector: X i =(X1, X2, ..., X i ), i = 1, 2, ..., N, the position of the particle can be expressed as a vector: V i =(V1, V2, ..., V i )i=1,2,...,N,the optimal position searched by the particle is called the individual extreme value (that is, the above-mentioned individual optimal position), which can be recorded as: P besti =(Pbest1 , P best2 , ..., P besti ), i = 1, 2, ..., N, the optimal position searched by the particle swarm is the global extreme value, which can be recorded as: g besti =(g best1 , g best2 , ..., g besti )i=1,2,...,N. In each particle position iteration, the particle completes this iteration based on the current position and current particle velocity, and the formula is expressed as The superscript k represents the current number of iterations, and k+1 represents the number of iterations in the next stage. After completing one iteration, the state of the current particle swarm is determined, thereby determining the individual extreme value of each particle and the global extreme value of the particle swarm, and then using the formula Update the particle velocity to determine the change speed of the particle position in the next iteration, where w is the inertia weight coefficient, C1 and C2 are convergence factors, also known as learning coefficients, which are generally [0, 2], and r1 and r2 are random numbers in (0, 1).

[0041] Due to the complex external environment, the particle swarm algorithm uses the formula to determine whether the photovoltaic module has shadows, faults, etc. When the maximum power change value is greater than 0.1 (that is, the first preset threshold mentioned above, which is only used as an example here), it indicates that the external environment has changed, and the algorithm needs to be restarted to perform power tracking. Among them, g best is the maximum power point currently searched (i.e. the global optimal position mentioned above), g max is the global maximum power point (i.e., the above-mentioned historical global maximum power point), ΔP is the maximum power point change value, and if ΔP is less than the first preset threshold, it is determined that the global search phase is completed, and the current global maximum power point finally obtained is output as the global maximum power point.

[0042] Optionally, the method further comprises: using the formula Update the individual optimal position of each particle.

[0043] In the search area, each particle is a free individual. The particle can compare the calculated current fitness value with the fitness value of the next iteration search based on the initialized position, obtain the change of the particle's fitness value, and determine whether the particle's next Xi will increase or decrease. Among them, f() is the objective function.

[0044] It should be noted that in the iterative calculation, in order to prevent particles from jumping out of the search area due to speed reasons, the particle speed is limited to V i ∈[-V max , V max]. The user sets the speed of the particles. When the particle speed exceeds the maximum speed or is lower than the minimum speed, the limit value is taken. The particle swarm algorithm will output the maximum power point when the precision error of the set optimal value meets the conditions.

[0045] Optionally, after updating the particle positions according to the particle velocities of the multiple particles, determining the individual optimal position of each particle according to the updated particle positions, and determining the global optimal position of the multiple particles, the method further comprises: calculating the optimal position of each particle according to the formula The inertia weight coefficient is determined, where: V max is the upper limit of the particle velocity, n is the maximum wave peak number of the photovoltaic module, N is the number of particles of the plurality of particles, V min is the lower bound of the particle speed, and rand is a random number between (0, 1).

[0046] W is the inertia weight coefficient, which represents the inheritance of the flight speed of the particles in the previous stage. In the early stage, the particle swarm algorithm needs to maintain global search and avoid local peaks. In the later stage, the particle swarm algorithm needs to strengthen local search and improve search accuracy. The particle swarm algorithm W is set to: Where W is a decreasing function, k is the current iteration number, N is the total number of particles, n is the maximum number of peaks.

[0047] Based on the above steps, the particle positions are updated according to the particle velocities of the multiple particles, the individual optimal position of each particle is determined according to the updated particle positions, and after the global optimal position of the multiple particles is determined, the method further includes: Determine the particle search local extreme value C1, and through the formula Determine the particle search global extreme value C2, where, V max is the upper limit of the particle velocity, n is the maximum wave peak number of the photovoltaic module, N is the number of particles of the plurality of particles, V min is the lower bound of the particle speed, and rand is a random number between (0, 1).

[0048] The convergence factor C1 is the particle search for local extrema, and C2 is the particle search for global extrema. C1 is calculated by the formula To determine, C2 is determined by the formula To determine, among them,

[0049] Optionally, the particle position of the global maximum power point is iteratively updated by a perturbation observation method with a variable step size to determine the global optimal power point of the photovoltaic component, including: a third update step: updating the particle position of the global maximum power point according to the perturbation direction by using the perturbation step size to determine the size of the first power value before the global maximum power point is updated and the second power value after the update, and determining the power change between the first power value and the second power value; a fourth update step: updating the perturbation step size according to the power change, and updating the perturbation direction according to the size of the first power value and the second power value; looping the third update step and the fourth update step until the absolute value of the power change between the first power value and the second power value is less than the second power threshold, and determining the final particle position as the global optimal power point.

[0050] First, the perturbation step size is set. Every other sampling period, the current value (i.e., the particle position) is added or subtracted with a fixed perturbation step size. Then, the power before and after the perturbation is compared. If the power increases, the perturbation is performed in the same direction. Otherwise, the perturbation is performed in the opposite direction. The traditional perturbation observation method will cause the working point of the photovoltaic array to be unstable, making it difficult to maintain at the maximum power point, and the tracking time is long. In the embodiment of the present application, the step size of the perturbation observation method is updated in real time to improve the tracking speed while ensuring the tracking accuracy and stability. For example, the formula To update the perturbation step size, ΔD is the updated perturbation step size, D step is the perturbation step before updating, ΔP is the power change. Repeat the above steps. If the output global optimal power no longer increases, it ends and the global optimal power point is output.

[0051] In an optional embodiment, the present application embodiment provides an optional method for determining a global optimal power point, the process is as follows: Figure 3 As shown, the following steps are included:

[0052] Step 1: Initialize and generate multiple particles, set the size, initial position and initial velocity of the particle group;

[0053] Step 2: Calculate the objective function of each particle, find the current individual extreme value of each particle, and find the current global optimal solution of the entire particle swarm;

[0054] Step 3: Update the speed and position of each particle;

[0055] Step 4, calculate the maximum power point change value ΔP, and determine whether it is greater than the first preset threshold value 0.1; if it is greater, return to step 3, if it is less, execute step 5;

[0056] Step 5: Use the improved perturbation and observation method to perform local search;

[0057] Step 6: Determine whether the termination condition is met. If so, execute step 7. If not, return to execute step 5.

[0058] Step 7: Output the optimal solution (i.e. the above-mentioned global optimal power point).

[0059] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0060] In this embodiment, a device for determining a global optimal power point is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made are not repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0061] Figure 4 is a structural block diagram of a device for determining a global optimal power point according to an embodiment of the present application, the device comprising:

[0062] An initialization module 42 is used to initialize particle information of a plurality of particles to obtain the plurality of particles, wherein the particle information includes: a particle position and a particle velocity, wherein the particle position is used to determine a power value of a power point of a photovoltaic component, and the particle velocity is used to indicate a change size of the particle position for the next update;

[0063] A first updating module 44 is used to iteratively update the particle information of the plurality of particles through an objective function to determine a global maximum power point of the photovoltaic assembly;

[0064] The second updating module 46 is used to iteratively update the particle position of the global maximum power point by a variable step-size perturbation observation method to determine the global optimal power point of the photovoltaic module.

[0065] Through the above device, the particle information of multiple particles is first initialized to obtain multiple particles, wherein the particle information includes particle position and particle velocity, the particle position is used to determine the power value of the power point of the photovoltaic component, and the particle velocity is used to indicate the change size of the particle position for the next update; the particle information of the multiple particles is iteratively updated through the objective function to determine the global maximum power point of the photovoltaic component; after determining the global maximum power point, the particle position of the global maximum power point is iteratively updated through the perturbation observation method with a variable step size to determine the global optimal power point of the photovoltaic component; the above scheme is adopted to improve the particle swarm algorithm, thereby improving the activity of dynamic search, and ensuring the tracking accuracy and stability by adding the improved perturbation observation method; thereby solving the problems of poor dynamic search activity and insufficient algorithm restart capability of the traditional particle swarm algorithm in the related technology.

[0066] Optionally, the initialization module 42 is further configured to use the formula N=n 2 +2n to determine the number of particles of the plurality of particles, wherein n is the maximum peak number of the photovoltaic module, and N is the number of particles; by the formula Initialize the particle positions of the plurality of particles, where I sc-array is the short-circuit current value of the photovoltaic module, X max is the upper bound of the particle position, X min is the lower bound of the particle position, X i is the particle position of the i-th particle among the multiple particles, rand is a random number (0, 1); by the formula Initialize the particle velocities of the plurality of particles, where V max is the upper bound of the particle velocity, V min is the lower bound of the particle velocity, V i is the particle velocity of the i-th particle.

[0067] The particle swarm algorithm assumes that there are N particles in the H-dimensional target space. The position of the i-th particle is represented by X i The velocity of the particle is represented by V i Indicates. When there is only one peak, the particle does not exist locally, and within a sufficient time, the particle can effectively track the maximum power point. In the case of multiple peaks, the particle may fall into a local extreme value. Therefore, the fewer the peaks, the faster the particle converges, and the more the peaks, the slower the particle converges. The search range can be increased by increasing the number of particles, thereby speeding up the convergence of the algorithm. Set the relationship between the number of particles and the peaks as: N = n 2 +2n, where n is the maximum wave peak number of the photovoltaic module. The number of particles increases with the increase of the wave peak number.

[0068] The PI curve range is much smaller than the PU curve range, so the short-circuit current value (i.e. I sc-array ) as the particle search area will effectively improve the particle convergence speed, so the initial position of the particle is: Initial velocity of the particle: Among them, rand is a random number between 0 and 1, n is the number of wave peaks, and the particle speed is inversely proportional to the number of wave peaks.

[0069] Optionally, the first updating module 44 is further used to cyclically execute the first updating step, the second updating step and the determining step until the maximum power point change value is less than the first preset threshold value, and the current global maximum power point finally obtained is determined as the global maximum power point, wherein the first updating step includes: updating the particle position according to the particle speed of the multiple particles through the objective function, determining the individual optimal position of each particle according to the updated particle position, and determining the global optimal position of the multiple particles, wherein the individual optimal position is the particle position with the largest power value of each particle in multiple iterations, and the global optimal position is the particle position with the largest power value among the multiple individual optimal positions corresponding to the multiple particles; the second updating step includes: determining the particle position with the largest power value among the multiple individual optimal positions corresponding to the multiple particles through the formula Update the particle velocity, where k is the current iteration number, W is the inertia weight coefficient, C1 and C2 are convergence factors, r1 and r2 are random numbers (0, 1), is the individual optimal position of the i-th particle among the multiple particles at the k-th iteration, is the global optimal position of the ith particle at the kth iteration; the determining step comprises: determining a current global maximum power point according to the global optimal position and the historical global maximum power point, and determining a maximum power point change value according to the global optimal position and the historical global maximum power point, wherein the power value of the current global maximum power point is a larger value between a first power value of the global optimal position and a second power value of the historical global maximum power point, and the historical global maximum power point is the current global maximum power point determined at the k-1th iteration.

[0070] The velocity of a particle can be expressed as a vector: X i =(X1, X2, ...X i )i=1,2,...,N,the position of the particle can be expressed as a vector: V i =(V1, V2, ..., V i ), i = 1, 2, ..., N, the optimal position searched by the particle is called the individual extreme value (that is, the above-mentioned individual optimal position), which can be recorded as: P besti =(P best1 , P best2 , ..., P besti)i=1,2,...,N,the optimal position searched by the particle swarm is the global extreme value, which can be recorded as: g besti =(g best1 , g best2 , ..., g besti )i=1,2,...,N. In each particle position iteration, the particle completes this iteration based on the current position and current particle velocity, and the formula is expressed as The superscript k represents the current number of iterations, and k+1 represents the number of iterations in the next stage. After completing one iteration, the state of the current particle swarm is determined, thereby determining the individual extreme value of each particle and the global extreme value of the particle swarm, and then using the formula Update the particle velocity to determine the change speed of the particle position in the next iteration, where w is the inertia weight coefficient, C1 and C2 are convergence factors, also known as learning coefficients, which are generally [0, 2], and r1 and r2 are random numbers in (0, 1).

[0071] Due to the complex external environment, the particle swarm algorithm uses the formula to determine whether the photovoltaic module has shadows, faults, etc. When the maximum power change value is greater than 0.1 (that is, the first preset threshold mentioned above, which is only used as an example here), it indicates that the external environment has changed, and the algorithm needs to be restarted to perform power tracking. Among them, g best is the maximum power point currently searched (i.e. the global optimal position mentioned above), g max is the global maximum power point (i.e., the above-mentioned historical global maximum power point), ΔP is the maximum power point change value, and if ΔP is less than the first preset threshold, it is determined that the global search phase is completed, and the current global maximum power point finally obtained is output as the global maximum power point.

[0072] Optionally, the above device is also used to pass the formula Update the individual optimal position of each particle.

[0073] In the search area, each particle is a free individual. The particle can compare the calculated current fitness value with the fitness value of the next iteration search based on the initialized position, obtain the fitness value change of the particle, and determine the next X of the particle. i Is it increasing or decreasing. Among them, f() is the objective function.

[0074] It should be noted that in the iterative calculation, in order to prevent particles from jumping out of the search area due to speed reasons, the particle speed is limited to V i ∈[-V max , V max]. The user sets the speed of the particles. When the particle speed exceeds the maximum speed or is lower than the minimum speed, the limit value is taken. The particle swarm algorithm will output the maximum power point when the precision error of the set optimal value meets the conditions.

[0075] Optionally, the first updating module 44 is further configured to update the The inertia weight coefficient is determined, where: V max is the upper limit of the particle velocity, n is the maximum wave peak number of the photovoltaic module, N is the number of particles of the plurality of particles, V min is the lower bound of the particle speed, and rand is a random number between (0, 1).

[0076] W is the inertia weight coefficient, which represents the inheritance of the flight speed of the particles in the previous stage. In the early stage, the particle swarm algorithm needs to maintain global search and avoid local peaks. In the later stage, the particle swarm algorithm needs to strengthen local search and improve search accuracy. The particle swarm algorithm W is set to: Where W is a decreasing function, k is the current iteration number, N is the total number of particles, n is the maximum number of peaks.

[0077] Optionally, the first updating module 44 is further configured to update the Determine the particle search local extreme value C1, and through the formula Determine the particle search global extreme value C2, where, V max is the upper limit of the particle velocity, n is the maximum wave peak number of the photovoltaic module, N is the number of particles of the plurality of particles, V min is the lower bound of the particle speed, and rand is a random number between (0, 1).

[0078] The convergence factor C1 is the particle search for local extrema, and C2 is the particle search for global extrema. C1 is calculated by the formula To determine, C2 is determined by the formula To determine, among them,

[0079]

[0080] Optionally, the second update module 46 is further used to cyclically execute the third update step and the fourth update step until the absolute value of the power change between the first power value and the second power value is less than the second power threshold, and the final particle position is determined as the global optimal power point, wherein the third update step includes: updating the particle position of the global maximum power point according to the disturbance direction by the disturbance step, determining the size of the first power value before the global maximum power point is updated and the second power value after the update, and determining the power change between the first power value and the second power value; the fourth update step includes: updating the disturbance step according to the power change, and updating the disturbance direction according to the size of the first power value and the second power value.

[0081] First, the perturbation step size is set. Every other sampling period, the current value (i.e., the particle position) is added or subtracted with a fixed perturbation step size. Then, the power before and after the perturbation is compared. If the power increases, the perturbation is performed in the same direction. Otherwise, the perturbation is performed in the opposite direction. The traditional perturbation observation method will cause the working point of the photovoltaic array to be unstable, making it difficult to maintain at the maximum power point, and the tracking time is long. In the embodiment of the present application, the step size of the perturbation observation method is updated in real time to improve the tracking speed while ensuring the tracking accuracy and stability. For example, the formula To update the perturbation step size, ΔD is the updated perturbation step size, D step is the perturbation step before updating, ΔP is the power change. Repeat the above steps. If the output global optimal power no longer increases, it ends and the global optimal power point is output.

[0082] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0083] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0084] S1, initializing particle information of a plurality of particles to obtain the plurality of particles, wherein the particle information includes: a particle position and a particle velocity, wherein the particle position is used to determine a power value of a power point of a photovoltaic component, and the particle velocity is used to indicate a change size of the next update of the particle position;

[0085] S2, iteratively updating the particle information of the plurality of particles through an objective function to determine a global maximum power point of the photovoltaic module;

[0086] S3, iteratively updating the particle position of the global maximum power point by a variable step-size perturbation observation method to determine the global optimal power point of the photovoltaic module.

[0087] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0088] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0089] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0090] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0091] S1, initializing particle information of a plurality of particles to obtain the plurality of particles, wherein the particle information includes: a particle position and a particle velocity, wherein the particle position is used to determine a power value of a power point of a photovoltaic component, and the particle velocity is used to indicate a change size of the next update of the particle position;

[0092] S2, iteratively updating the particle information of the plurality of particles through an objective function to determine a global maximum power point of the photovoltaic module;

[0093] S3, iteratively updating the particle position of the global maximum power point by a variable step-size perturbation observation method to determine the global optimal power point of the photovoltaic module.

[0094] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0095] An embodiment of the present application further provides a computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program product, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0096] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0097] S1, initializing particle information of a plurality of particles to obtain the plurality of particles, wherein the particle information includes: a particle position and a particle velocity, wherein the particle position is used to determine a power value of a power point of a photovoltaic component, and the particle velocity is used to indicate a change size of the next update of the particle position;

[0098] S2, iteratively updating the particle information of the plurality of particles through an objective function to determine a global maximum power point of the photovoltaic module;

[0099] S3, iteratively updating the particle position of the global maximum power point by a variable step-size perturbation observation method to determine the global optimal power point of the photovoltaic module.

[0100] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0101] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0102] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining a global optimal power point, characterized in that: include: Initializing particle information of a plurality of particles to obtain the plurality of particles, wherein the particle information includes: a particle position and a particle speed, wherein the particle position is used to determine a power value of a power point of a photovoltaic component, and the particle speed is used to indicate a change size of the particle position for a next update; Iteratively updating the particle information of the plurality of particles through an objective function to determine a global maximum power point of the photovoltaic assembly; The particle position of the global maximum power point is iteratively updated by a variable step-size perturbation observation method to determine the global optimal power point of the photovoltaic module.

2. The method according to claim 1, characterized in that Initializing particle information of a plurality of particles to obtain the plurality of particles includes: By the formula N = n 2 +2n determines the number of particles of the plurality of particles, wherein n is the maximum peak number of the photovoltaic component, and N is the number of particles; By formula Initialize the particle positions of the plurality of particles, where I sc-array is the short-circuit current value of the photovoltaic module, X max is the upper bound of the particle position, X min is the lower bound of the particle position, X i is the particle position of the i-th particle among the multiple particles, and rand is a random number (0, 1); By formula Initialize the particle velocities of the plurality of particles, where V max is the upper bound of the particle velocity, V min is the lower bound of the particle velocity, V i is the particle velocity of the i-th particle.

3. The method according to claim 1, characterized in that Iteratively updating the particle information of the plurality of particles through an objective function to determine a global maximum power point of the photovoltaic assembly includes: The first updating step: updating the particle positions according to the particle velocities of the multiple particles through the objective function, determining the individual optimal position of each particle according to the updated particle positions, and determining the global optimal position of the multiple particles, wherein the individual optimal position is the particle position with the largest power value of each particle in multiple iterations, and the global optimal position is the particle position with the largest power value among the multiple individual optimal positions corresponding to the multiple particles; The second update step: by formula Update the particle velocity, where k is the current iteration number, W is the inertia weight coefficient, C1 and C2 are convergence factors, r1 and r2 are random numbers (0, 1), is the individual optimal position of the i-th particle among the multiple particles at the k-th iteration, is the global optimal position of the i-th particle at the k-th iteration; Determining step: determining a current global maximum power point according to the global optimal position and the historical global maximum power point, and determining a maximum power point change value according to the global optimal position and the historical global maximum power point, wherein the power value of the current global maximum power point is a larger value of a first power value of the global optimal position and a second power value of the historical global maximum power point, and the historical global maximum power point is the current global maximum power point determined at the k-1th iteration; The first updating step, the second updating step and the determining step are executed in a loop until the maximum power point change value is less than a first preset threshold, and the current global maximum power point finally obtained is determined as the global maximum power point.

4. The method according to claim 3, characterized in that After updating the particle positions according to the particle velocities of the multiple particles, determining the individual optimal position of each particle according to the updated particle positions, and determining the global optimal position of the multiple particles, the method further includes: By formula The inertia weight coefficient is determined, where: V max is the upper limit of the particle velocity, n is the maximum wave peak number of the photovoltaic module, N is the number of particles of the plurality of particles, V min is the lower bound of the particle speed, and rand is a random number between (0, 1).

5. The method according to claim 3, characterized in that: After updating the particle positions according to the particle velocities of the multiple particles, determining the individual optimal position of each particle according to the updated particle positions, and determining the global optimal position of the multiple particles, the method further includes: By formula Determine the particle search local extreme value C1, and through the formula Determine the particle search global extreme value C2, where, V max is the upper limit of the particle velocity, n is the maximum wave peak number of the photovoltaic module, N is the number of particles of the plurality of particles, V min is the lower bound of the particle speed, and rand is a random number between (0, 1).

6. The method according to claim 1, characterized in that Iteratively updating the particle position of the global maximum power point by a variable step-size perturbation observation method to determine the global optimal power point of the photovoltaic module includes: The third updating step: updating the particle position of the global maximum power point according to the disturbance direction by the disturbance step length, determining the magnitude of the first power value before the global maximum power point is updated and the second power value after the update, and determining the power change amount between the first power value and the second power value; Fourth updating step: updating the disturbance step size according to the power variation, and updating the disturbance direction according to the magnitude of the first power value and the second power value; The third updating step and the fourth updating step are executed cyclically until the absolute value of the power change between the first power value and the second power value is less than a second power threshold, and the final particle position is determined as the global optimal power point.

7. A device for determining a global optimal power point, characterized in that: include: An initialization module, used to initialize particle information of a plurality of particles to obtain the plurality of particles, wherein the particle information includes: a particle position and a particle velocity, wherein the particle position is used to determine a power value of a power point of a photovoltaic component, and the particle velocity is used to indicate a change size of the particle position for the next update; A first updating module is used to iteratively update the particle information of the plurality of particles through an objective function to determine a global maximum power point of the photovoltaic assembly; The second updating module is used to iteratively update the particle position of the global maximum power point by a variable step-size perturbation observation method to determine the global optimal power point of the photovoltaic module.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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