Photovoltaic power generation system maximum power point tracking control method, device and equipment

Abstract

The embodiment of the invention relates to the technical field of photovoltaic power generation system maximum power point tracking control, and provides a photovoltaic power generation system maximum power point tracking control method, device and equipment, and the method comprises the steps: determining an optimal duty ratio control signal when the output power of a photovoltaic power generation system is maximum through employing an improved whale optimization algorithm; determining an optimal current corresponding to the optimal duty ratio control signal by using a perturbation and observation method; and controlling the photovoltaic power generation system according to the optimal duty ratio control signal and the corresponding optimal current, so that the output power of the photovoltaic power generation system is maximized. According to the embodiment of the invention, the maximum power point tracking control effect of the photovoltaic power generation system especially under a complex working condition is effectively improved.

Description

Technical Field

[0001] This disclosure relates to the field of maximum power point tracking control technology for photovoltaic power generation systems, and particularly to a method, apparatus, and equipment for maximum power point tracking control of photovoltaic power generation systems. Background Technology

[0002] In existing technologies, the Whale Optimization Algorithm (WOA) can be used to solve the maximum power point tracking problem (MPP) of photovoltaic (PV) power generation systems under complex conditions such as uniform illumination and partial shading. As a novel intelligent algorithm, WOA is characterized by its simple structure, few required parameters, and ease of implementation. Compared to most intelligent algorithms, WOA's unique search mechanism makes its optimization performance more outstanding, and it has been widely applied in various fields such as function optimization, path planning, and workshop scheduling. However, WOA still has the following shortcomings: In the initial stage of the algorithm, the initial positions of the whales are randomly generated, and the population distribution may be uneven, failing to guarantee the diversity of the initial population. This affects the optimization speed and accuracy of WOA to varying degrees. Furthermore, WOA is prone to getting trapped in local optima. Therefore, using existing WOA algorithms for MPP tracking in complex conditions is not ideal. Summary of the Invention

[0003] This disclosure aims to address at least one of the problems existing in the prior art, and to provide a method, apparatus, and equipment for maximum power point tracking control of a photovoltaic power generation system.

[0004] One aspect of this disclosure provides a maximum power point tracking (MPPT) control method for a photovoltaic (PV) power generation system, the method comprising:

[0005] By using an improved whale optimization algorithm, the optimal duty cycle control signal for maximizing the output power of the photovoltaic power generation system is determined.

[0006] The optimal current corresponding to the optimal duty cycle control signal is determined using the perturbation observation method.

[0007] The photovoltaic power generation system is controlled according to the optimal duty cycle control signal and its corresponding optimal current to maximize the output power of the photovoltaic power generation system.

[0008] Optionally, the step of using the improved whale optimization algorithm to determine the optimal duty cycle control signal when the output power of the photovoltaic power generation system is maximized includes:

[0009] Initialization steps: Initialize the whale optimization algorithm parameters and the whale swarm, and determine the initial position of each whale in the whale swarm; wherein, the whale is used to indicate the duty cycle of the photovoltaic power generation system, and the position of the whale is used to indicate the value of the duty cycle;

[0010] Fitness calculation steps: Calculate the fitness of each whale separately, and use the output power of the photovoltaic power generation system corresponding to each whale as the fitness value of each whale;

[0011] Calculate the instantaneous power change of the whale based on its current position.

[0012] Determine whether the restart conditions are met. If so, return to the initialization step; otherwise, update the current position of the whale with the highest fitness value to the optimal individual position. The restart conditions include: the instantaneous power change is greater than a preset power change threshold.

[0013] The current position of each whale is updated according to the optimal individual position and the preset position update function;

[0014] Calculate the output power of the photovoltaic power generation system corresponding to each whale, and take the duty cycle signal corresponding to the current position of the whale with the largest current output power as the optimal duty cycle control signal when the output power of the photovoltaic power generation system is the largest.

[0015] Determine whether the termination condition is met. If so, output the optimal duty cycle signal; otherwise, return to the fitness calculation step. The termination condition includes reaching a preset maximum number of iterations or the maximum value of the difference between the operating voltages of the photovoltaic power generation system corresponding to the current positions of each whale being less than a preset multiple of the open-circuit voltage of the photovoltaic power generation system.

[0016] Optionally, determining the initial position of each whale in the whale pod includes:

[0017] The initial position of each whale in the whale population is optimized using an improved Logistic mapping, where the optimized initial position x corresponds to the (i+1)th whale. i+1 Represented as:

[0018] x i+1 =sin(π(4hx) i (1-x i ))+(1-h)sin(πy i+1 ));

[0019] Where h represents a random number between 0 and 1, x iLet y represent the optimized initial position corresponding to the i-th whale. i x represents i The corresponding chaotic value, y i+1 x represents i+1 The corresponding chaotic values ​​are:

[0020]

[0021] Optionally, updating the current position of each whale based on the optimal individual position and a preset position update function includes:

[0022] The coefficient vector A is calculated according to the following formula: A = 2a·r1 - a; where r1 represents a random vector in the range [0,1], and a represents the nonlinear convergence factor. t represents the current iteration number, T max Represents the preset maximum number of iterations, μ, These are the first constant and the second constant, respectively.

[0023] Generate a random number p that follows a distribution in the range [0,1]. Determine if the random number p is less than 0.5.

[0024] If the random number p is less than 0.5, then determine whether |A|<1. If yes, then update the current position of each whale according to the first position update function; otherwise, update the current position of each whale according to the second position update function.

[0025] If the random number p is not less than 0.5, then update the function according to the third position.

[0026] Optionally, the first position update function is expressed as:

[0027] X1(t+1)=w(t)·X best (t)-A|CX best (t)-X(t)|;

[0028] Where X1(t+1) represents the current position of the whale after updating its current position X(t) at the current iteration number t according to the first position update function, X best w(t) represents the optimal individual position of the whale at the current iteration number t, C represents the coefficient vector and C = 2r², r² represents a random vector in the range [0,1], and w(t) represents the inertia weight factor at the current iteration number t.

[0029] The second position update function is expressed as: X2(t+1) = X rand (t)-A·D;

[0030] Where X2(t+1) represents the current position of the whale after updating its current position X(t) at the current iteration number t according to the second position update function, X rand (t) represents the current position of the whale randomly selected from the whale group at the current iteration number t, and D represents the intermediate parameter and D = |C·X rand (t)-X(t)|;

[0031] The third position update function is expressed as follows:

[0032] X3(t+1)=k·w(t)·D′·e bl ·cos(2πl)+X best (t);

[0033] Where X3(t+1) represents the current position of the whale after updating its current position X(t) at the current iteration number t according to the third position update function, k represents the spiral search factor and k = exp(b×n)·cos(2πn), b represents the spiral constant coefficient, n represents the path coefficient and n is a random number between [-1,1], and D′ represents the distance between the current position of the whale and the optimal individual position and D′ = |X best (t)-X(t)|, where l represents a random number between [-1, 1].

[0034] Optionally, determining the optimal current corresponding to the optimal duty cycle control signal using the perturbation observation method includes:

[0035] Initialize the optimal current corresponding to the optimal duty cycle control signal;

[0036] The optimal current corresponding to the optimal duty cycle control signal is disturbed in a preset direction to obtain the corresponding disturbed current as the current;

[0037] Output power calculation steps: Calculate the output power of the photovoltaic power generation system after the disturbance based on the current current;

[0038] If the output power of the photovoltaic power generation system after the disturbance is greater than the output power of the photovoltaic power generation system before the disturbance, then continue to disturb the current in the preset direction to obtain the corresponding disturbed current, and return to the output power calculation step;

[0039] If the output power of the photovoltaic power generation system after the disturbance is less than the output power of the photovoltaic power generation system before the disturbance, then the current is disturbed in the opposite direction of the preset direction to obtain the corresponding disturbed current, and then the process returns to the output power calculation step.

[0040] If the output power of the photovoltaic power generation system after the disturbance is equal to the output power of the photovoltaic power generation system before the disturbance, then the current current is taken as the optimal current corresponding to the optimal duty cycle control signal.

[0041] Another aspect of this disclosure provides a maximum power point tracking (MPPT) control device for a photovoltaic power generation system, the MPPT control device comprising:

[0042] The first optimization module is used to determine the optimal duty cycle control signal when the output power of the photovoltaic power generation system is maximized by using an improved whale optimization algorithm.

[0043] The second optimization module is used to determine the optimal current corresponding to the optimal duty cycle control signal using the perturbation observation method.

[0044] The control module is used to control the photovoltaic power generation system according to the optimal duty cycle control signal and its corresponding optimal current, so as to maximize the output power of the photovoltaic power generation system.

[0045] Another aspect of this disclosure provides an electronic device comprising:

[0046] At least one processor; and,

[0047] A memory that is communicatively connected to at least one processor; wherein,

[0048] The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to perform the maximum power point tracking control method for the photovoltaic power generation system described above.

[0049] Another aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the maximum power point tracking control method for a photovoltaic power generation system described above.

[0050] Another aspect of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the maximum power point tracking control method for a photovoltaic power generation system described above.

[0051] Compared with the prior art, this disclosure improves the existing whale optimization algorithm by using the improved whale optimization algorithm to determine the optimal duty cycle control signal when the output power of the photovoltaic power generation system is maximized. Based on this, the perturbation and observation method is used to determine the optimal current corresponding to the optimal duty cycle control signal. The photovoltaic power generation system is controlled according to the optimal duty cycle control signal and its corresponding optimal current, so as to maximize the output power of the photovoltaic power generation system and effectively improve the maximum power point tracking control effect of the photovoltaic power generation system, especially under complex operating conditions. Attached Figure Description

[0052] One or more embodiments are illustrated by way of example with the corresponding pictures in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0053] Figure 1 A flowchart of a maximum power point tracking control method for a photovoltaic power generation system provided in one embodiment of this disclosure;

[0054] Figure 2 This is a flowchart illustrating a maximum power point tracking control method for a photovoltaic power generation system, provided as another embodiment of this disclosure. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the various embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this disclosure to facilitate a better understanding of the disclosure. However, the technical solutions claimed in this disclosure can be implemented even without these technical details and with various variations and modifications based on the following embodiments. The division of the various embodiments below is for ease of description and should not constitute any limitation on the specific implementation of this disclosure. The various embodiments can be combined with and referenced by each other without contradiction.

[0056] One embodiment of this disclosure relates to a maximum power point tracking control method for a photovoltaic power generation system, the process of which is as follows: Figure 1 As shown, it includes steps S110 to S130. The following is in conjunction with... Figure 2 Steps S110 to S130 will be explained in detail.

[0057] Step S110: Using the improved whale optimization algorithm, determine the optimal duty cycle control signal when the output power of the photovoltaic power generation system is maximized.

[0058] For example, step S110 includes steps (1) to (7).

[0059] (1) Initialization steps: Initialize the parameters of the whale optimization algorithm and the whale group, and determine the initial position of each whale in the whale group; whereby the whale is used to indicate the duty cycle of the photovoltaic power generation system, and the position of the whale is used to indicate the value of the duty cycle.

[0060] Specifically, the initialization step can set the number of whales in the whale population to N, and set a preset maximum number of iterations T. max The initial value of the whale. The whale's position can include coordinates in one or more dimensions. For example, when the photovoltaic power generation system is a single photovoltaic string system, the whale's position can include only one dimension of coordinates to indicate a duty cycle value. When the photovoltaic power generation system is a multi-photovoltaic string or a complex system, the whale's position can include coordinates in multiple dimensions, each dimension indicating the duty cycle of a photovoltaic string, and each coordinate indicating the duty cycle value of the corresponding photovoltaic string.

[0061] For example, to make the location distribution of a whale population more uniform, an improved Logistic mapping can be used to optimize the initial position of each whale in the population. The original Logistic mapping has uniform distribution characteristics and good correlation, but it suffers from poor randomness. The improved Logistic mapping has higher ergodicity and randomness, which can effectively improve the global optimization efficiency and significantly enhance the algorithm's adaptability to complex lighting conditions in maximum power point tracking control of photovoltaic power generation systems. In this case, the initialization step, determining the initial position of each whale in the whale population, includes: optimizing the initial position of each whale in the whale population using the improved Logistic mapping, where the optimized initial position x of the (i+1)th whale is... i+1 Represented as:

[0062] x i+1 =sin(π(4hx) i (1-x i ))+(1-h)sin(πy i+1 )).

[0063] Where h represents a random number between 0 and 1. When h approaches 1, x... i+1 The expression approximates the original Logistic mapping. When h approaches 0, x... i+1 The expression approximates sin(πy) i+1 This is a strongly random mapping dominated by traversal. Therefore, by adjusting the value of h, a balance can be achieved between uniformity and randomness in the traversal, preventing the initial positions of whales in the whale population from clustering in local areas. i Let y represent the optimized initial position corresponding to the i-th whale. i x represents iThe corresponding chaotic value, y i+1 x represents i+1 The corresponding chaotic values ​​are:

[0064] (2) Fitness calculation steps: Calculate the fitness of each whale separately, and use the output power of the photovoltaic power generation system corresponding to each whale as the fitness value of each whale.

[0065] Specifically, in this embodiment, the output power of the photovoltaic power generation system currently corresponding to the whale is used as its fitness value, and maximizing the fitness value is used as the objective function. The output power of the photovoltaic power generation system currently corresponding to the whale can be calculated based on the voltage and current of the operating point corresponding to the duty cycle value of the whale's current location.

[0066] (3) Calculate the instantaneous power change of the whale based on its current location.

[0067] Specifically, regarding the whale's current location, assuming the output power of the photovoltaic power generation system at the current moment is P2 and the output power of the photovoltaic power generation system at the previous moment is P1, the instantaneous power change ΔP corresponding to the whale's current location can be expressed as:

[0068] (4) Determine whether the restart conditions are met. If yes, return to the initialization step; otherwise, update the current position of the whale with the highest fitness value to the optimal individual position. The restart conditions include: the instantaneous power change is greater than the preset power change threshold.

[0069] Specifically, the saturation area of ​​the photovoltaic array in a photovoltaic power generation system can be affected by external factors such as foliage and building obstruction, leading to localized shading. To avoid the impact of localized shading, this implementation method sets a restart condition. When the restart condition is met, the algorithm returns to the initialization step to restart. The preset power change threshold can be set according to actual needs. For example, the preset power change threshold can be set to 5%. In this case, if the instantaneous power change ΔP > 5%, the algorithm returns to the initialization step; otherwise, it updates the optimal whale individual by updating the position of the whale with the highest fitness value to the optimal individual position.

[0070] (5) Update the current position of each whale according to the optimal individual position and the preset position update function.

[0071] Specifically, in response to the shortcomings of the original whale optimization algorithm in terms of convergence speed, local optimization, and global search, this implementation introduces a nonlinear convergence factor a and an inertia weight factor w(t) to improve the algorithm's tendency to get trapped in local extrema and oscillate near the maximum power point, thereby improving the accuracy and speed of the algorithm's optimization.

[0072] The nonlinear convergence factor α is expressed as: Where t represents the current iteration number, T max Indicates the preset maximum number of iterations, μ, These are the first constant and the second constant, for example, μ, The values ​​can be 0.5 and 0 respectively.

[0073] When the inertia weight factor is large, the algorithm has a larger search range, which is beneficial for global search. Conversely, when the inertia weight factor is small, a high-precision search can be performed within a smaller range. Therefore, to improve the accuracy and speed of optimization and prevent getting trapped in local optima, the inertia weight factor w(t) is set to:

[0074]

[0075] Where w(t) represents the inertia weight factor at the current iteration number t.

[0076] After introducing a nonlinear convergence factor 'a' and an inertia weight factor 'w(t), the current position of each whale is updated according to the optimal individual position and a preset position update function, including:

[0077] The coefficient vector A is calculated according to the following formula: A = 2a·r1-a; where r1 represents a random vector in the range [0,1].

[0078] Generate a random number p that follows a distribution [0,1], i.e., randomly generate a parameter p such that p∈[0,1]. Determine if the random number p is less than 0.5.

[0079] If the random number p is less than 0.5, then check if |A|<1. If yes, update the current position of each whale according to the first position update function; otherwise, update the current position of each whale according to the second position update function.

[0080] If the random number p is not less than 0.5, then update the function according to the third position.

[0081] For example, the first position update function is represented as:

[0082] X1(t+1)=w(t)·X best (t)-A|CX b3st (t)-X(t)|.

[0083] Where X1(t+1) represents the whale's current position after updating its current position X(t) at the current iteration number t according to the first position update function, X best(t) represents the optimal individual position of the whale at the current iteration number t, C represents the coefficient vector and C = 2r2, r2 represents a random vector in the range [0,1].

[0084] The second position update function is expressed as: X2(t+1)=X 4and (t)-A·D.

[0085] Where X2(t+1) represents the whale's current position after updating its current position X(t) at the current iteration number t according to the second position update function, X rand (t) represents the current position of a whale randomly selected from the whale pod at the current iteration number t, and D represents an intermediate parameter and D = |C·X rand (t)-X(t)|.

[0086] The third position update function is represented as:

[0087] X3(t+1)=k·w(t)·D′·e bl ·cos(2πl)+X best (t).

[0088] Where X3(t+1) represents the current position of the whale after updating its current position X(t) at the current iteration number t according to the third position update function, k represents the spiral search factor and k = exp(b×n)·cos(2πn), b represents the spiral constant coefficient, n represents the path coefficient and n is a random number between [-1,1], and D′ represents the distance between the whale's current position and the optimal individual position and D′ = |X best (t)-X(t)|, where l represents a random number between [-1, 1].

[0089] Specifically, when p < 0.5 and |A| < 1, the first position update function is used to shrink the surrounding target prey.

[0090] When p < 0.5 and |A| ≥ 1, a random search for target prey is performed. The whale will continuously adjust its own position to search for prey randomly, thereby expanding the search range and improving the whale's optimization performance to a certain extent. Therefore, the second position update function is used to update the whale's current position at this time.

[0091] When p ≥ 0.5, a spiral update is performed. In the spiral update position stage, the whales in the original whale optimization algorithm move closer to the optimal position using a logarithmic spiral update method. However, if the spiral step size exceeds the search area, the whale optimization algorithm cannot traverse the entire search space, thus affecting its optimization performance. Therefore, this implementation improves the spiral update exploration method by introducing a spiral search factor k. This allows the whale to update its position more effectively using multiple different search paths according to the third position update function. By introducing the spiral search factor in the spiral update position stage, the whale's search ability in unknown areas is enhanced, thereby improving the overall search capability.

[0092] (6) Calculate the output power of the photovoltaic power generation system corresponding to each whale, and take the duty cycle signal corresponding to the current position of the whale with the largest output power as the optimal duty cycle control signal when the output power of the photovoltaic power generation system is the largest.

[0093] Specifically, the optimal duty cycle control signal here can be recorded as the optimal target value.

[0094] (7) Determine whether the termination condition is met. If yes, output the optimal duty cycle signal; otherwise, return to the fitness calculation step. The termination condition includes reaching the preset maximum number of iterations or the maximum value of the difference between the working voltages of the photovoltaic power generation system corresponding to the current position of each whale being less than a preset multiple of the open circuit voltage of the photovoltaic power generation system.

[0095] Specifically, the preset multiple of the open-circuit voltage Uoc of the photovoltaic power generation system can be set according to actual needs. For example, the preset multiple can be set to 0.005. In this case, the termination condition includes the current iteration number t reaching the preset maximum iteration number T. max Alternatively, the maximum value of the voltage difference between the photovoltaic power generation systems at the current locations of each whale under the duty cycle is less than 0.005Uoc.

[0096] When the termination condition is met, the duty cycle value indicated by the optimal individual position of the whale is the optimal duty cycle control signal when the output power of the photovoltaic power generation system is at its maximum.

[0097] Step S120: Using the perturbation observation method, determine the optimal current corresponding to the optimal duty cycle control signal.

[0098] Specifically, when the termination condition is met, step S120 can use a perturbation algorithm, i.e., the perturbation-observation method, to perform a local search based on the optimal duty cycle control signal in order to obtain better results.

[0099] For example, step S120 may include:

[0100] Initialize the optimal current corresponding to the optimal duty cycle control signal;

[0101] The optimal current corresponding to the optimal duty cycle control signal is disturbed in a preset direction, and the disturbed current is used as the current.

[0102] Output power calculation steps: Calculate the output power of the photovoltaic power generation system after the disturbance based on the current current;

[0103] If the output power of the photovoltaic power generation system after the disturbance is greater than the output power of the photovoltaic power generation system before the disturbance, then continue to disturb the current in the preset direction to obtain the corresponding disturbed current, and return to the output power calculation step;

[0104] If the output power of the photovoltaic power generation system after the disturbance is less than the output power of the photovoltaic power generation system before the disturbance, then the current is disturbed in the opposite direction of the preset direction to obtain the corresponding disturbed current, and then the output power calculation step is returned.

[0105] If the output power of the photovoltaic power generation system after the disturbance is equal to the output power of the photovoltaic power generation system before the disturbance, then the current current will be taken as the optimal current corresponding to the optimal duty cycle control signal.

[0106] Specifically, the preset direction can be either the direction of current increase or the direction of current decrease. When perturbing the optimal current corresponding to the optimal duty cycle control signal according to the preset direction, the current value can be increased or decreased based on the optimal current to obtain the perturbed current. For example, when the preset direction is the direction of current increase, if the output power of the photovoltaic power generation system corresponding to the perturbed current is higher than the output power of the photovoltaic power generation system corresponding to the current before the perturbed current, it is considered that the termination condition is not met, and the current continues to be perturbed in the direction of current increase. If the output power of the photovoltaic power generation system corresponding to the perturbed current is lower than the output power of the photovoltaic power generation system corresponding to the current before the perturbed current, it is considered that the termination condition is not met, and the current is perturbed in the opposite direction of current increase, i.e., the direction of current decrease. If the output power of the photovoltaic power generation system corresponding to the perturbed current is equal to the output power of the photovoltaic power generation system corresponding to the current before the perturbed current, it is considered that the termination condition is met, and the current is output as the optimal target value. This current is the optimal current ultimately corresponding to the optimal duty cycle control signal.

[0107] Step S130: Control the photovoltaic power generation system according to the optimal duty cycle control signal and its corresponding optimal current to maximize the output power of the photovoltaic power generation system.

[0108] Specifically, by controlling the photovoltaic power generation system according to the optimal duty cycle control signal and its corresponding optimal current, the operating point voltage / current of the photovoltaic power generation system can be adjusted, thereby maximizing the output power of the photovoltaic power generation system.

[0109] The maximum power point tracking control method for photovoltaic power generation systems provided in this disclosure improves upon existing technologies by modifying the whale optimization algorithm. The improved whale optimization algorithm determines the optimal duty cycle control signal when the output power of the photovoltaic power generation system is maximized. Based on this, the perturbation and observation method is used to determine the optimal current corresponding to the optimal duty cycle control signal. The photovoltaic power generation system is then controlled according to the optimal duty cycle control signal and its corresponding optimal current, thereby maximizing the output power of the photovoltaic power generation system and effectively improving the maximum power point tracking control performance, especially under complex operating conditions.

[0110] Another embodiment of this disclosure relates to a maximum power point tracking control device for a photovoltaic power generation system, comprising a first optimization module, a second optimization module, and a control module.

[0111] The first optimization module is used to determine the optimal duty cycle control signal when the output power of the photovoltaic power generation system is maximized by using an improved whale optimization algorithm.

[0112] The second optimization module is used to determine the optimal current corresponding to the optimal duty cycle control signal using the perturbation observation method.

[0113] The control module is used to control the photovoltaic power generation system based on the optimal duty cycle control signal and its corresponding optimal current, so as to maximize the output power of the photovoltaic power generation system.

[0114] The specific implementation method of the maximum power point tracking control device for photovoltaic power generation system provided in this disclosure can be found in the description of the maximum power point tracking control method for photovoltaic power generation system provided in this disclosure, and will not be repeated here.

[0115] The maximum power point tracking control device for photovoltaic power generation systems provided in this disclosure improves upon existing technologies by modifying the whale optimization algorithm. The improved whale optimization algorithm determines the optimal duty cycle control signal when the output power of the photovoltaic power generation system is maximized. Based on this, the perturbation and observation method is used to determine the optimal current corresponding to the optimal duty cycle control signal. The photovoltaic power generation system is controlled according to the optimal duty cycle control signal and its corresponding optimal current, thereby maximizing the output power of the photovoltaic power generation system and effectively improving the maximum power point tracking control effect of the photovoltaic power generation system, especially under complex operating conditions.

[0116] Another embodiment of this disclosure relates to an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the maximum power point tracking control method for a photovoltaic power generation system described in the above embodiments.

[0117] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0118] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0119] Another embodiment of this disclosure relates to a computer-readable storage medium storing a computer program that, when executed by a processor, implements the maximum power point tracking control method for a photovoltaic power generation system described in the above embodiments.

[0120] That is, those skilled in the art will understand that all or part of the steps in the methods described in the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0121] Another embodiment of this disclosure relates to a computer program product, including a computer program that, when executed by a processor, implements the maximum power point tracking control method for a photovoltaic power generation system described in the above embodiments.

[0122] Those skilled in the art will understand that the above embodiments are specific implementations of this disclosure, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of this disclosure.

Claims

1. A maximum power point tracking control method for a photovoltaic power generation system, characterized in that, The maximum power point tracking control method for the photovoltaic power generation system includes: By using an improved whale optimization algorithm, the optimal duty cycle control signal for maximizing the output power of the photovoltaic power generation system is determined. The optimal current corresponding to the optimal duty cycle control signal is determined using the perturbation observation method. The photovoltaic power generation system is controlled according to the optimal duty cycle control signal and its corresponding optimal current to maximize the output power of the photovoltaic power generation system.

2. The maximum power point tracking control method for a photovoltaic power generation system according to claim 1, characterized in that, The method of using an improved whale optimization algorithm to determine the optimal duty cycle control signal when the output power of the photovoltaic power generation system is maximized includes: Initialization steps: Initialize the whale optimization algorithm parameters and the whale swarm, and determine the initial position of each whale in the whale swarm; wherein, the whale is used to indicate the duty cycle of the photovoltaic power generation system, and the position of the whale is used to indicate the value of the duty cycle; Fitness calculation steps: Calculate the fitness of each whale separately, and use the output power of the photovoltaic power generation system corresponding to each whale as the fitness value of each whale; Calculate the instantaneous power change of the whale based on its current position. Determine whether the restart conditions are met. If so, return to the initialization step; otherwise, update the current position of the whale with the highest fitness value to the optimal individual position. The restart conditions include: the instantaneous power change is greater than a preset power change threshold. The current position of each whale is updated according to the optimal individual position and the preset position update function; Calculate the output power of the photovoltaic power generation system corresponding to each whale, and take the duty cycle signal corresponding to the current position of the whale with the largest current output power as the optimal duty cycle control signal when the output power of the photovoltaic power generation system is the largest. Determine whether the termination condition is met. If so, output the optimal duty cycle signal; otherwise, return to the fitness calculation step. The termination condition includes reaching a preset maximum number of iterations or the maximum value of the difference between the operating voltages of the photovoltaic power generation system corresponding to the current positions of each whale being less than a preset multiple of the open-circuit voltage of the photovoltaic power generation system.

3. The maximum power point tracking control method for a photovoltaic power generation system according to claim 2, characterized in that, Determining the initial position of each whale in the whale pod includes: The initial position of each whale in the whale population is optimized using an improved Logistic mapping, where the optimized initial position x corresponds to the (i+1)th whale. i+1 Represented as: x i+1 =sin(π(4hx i (1-x i ))+(1-h)sin(πy i+1 )); Where h represents a random number between 0 and 1, x i Let y represent the optimized initial position corresponding to the i-th whale. i x represents i The corresponding chaotic value, y i+1 x represents i+1 The corresponding chaos values ​​are:

4. The maximum power point tracking control method for a photovoltaic power generation system according to claim 2, characterized in that, The step of updating the current position of each whale based on the optimal individual position and a preset position update function includes: The coefficient vector A is calculated according to the following formula: A = 2a·r1 - a; where r1 represents a random vector in the range [0,1], and a represents the nonlinear convergence factor. t represents the current iteration number, T max Represents the preset maximum number of iterations, μ, These are the first constant and the second constant, respectively. Generate a random number p that follows a distribution in the range [0,1]. Determine if the random number p is less than 0.

5. If the random number p is less than 0.5, then determine whether |A|<1. If yes, then update the current position of each whale according to the first position update function; otherwise, update the current position of each whale according to the second position update function. If the random number p is not less than 0.5, then update the function according to the third position.

5. The maximum power point tracking control method for a photovoltaic power generation system according to claim 4, characterized in that, The first position update function is expressed as: X1(t+1)=w(t)·X best (t)-A|CX best (t)-X(t)|; Where X1(t+1) represents the current position of the whale after updating its current position X(t) at the current iteration number t according to the first position update function, X best w(t) represents the optimal individual position of the whale at the current iteration number t, C represents the coefficient vector and C = 2r², r² represents a random vector in the range [0,1], and w(t) represents the inertia weight factor at the current iteration number t. The second position update function is expressed as: X2(t+1) = X rand (t)-A·D; Where X2(t+1) represents the current position of the whale after updating its current position X(t) at the current iteration number t according to the second position update function, X rand (t) represents the current position of the whale randomly selected from the whale group at the current iteration number t, and D represents the intermediate parameter and D = |C·X rand (t)-X(t)|; The third position update function is expressed as follows: X3(t+1)=k·w(t)·D′·e bl ·cos(2πl)+X best (t); Where X3(t+1) represents the current position of the whale after updating its current position X(t) at the current iteration number t according to the third position update function, k represents the spiral search factor and k = exp(b×n)·cos(2πn), b represents the spiral constant coefficient, n represents the path coefficient and n is a random number between [-1,1], and D′ represents the distance between the current position of the whale and the optimal individual position and D′ = |X best (t)-X(t)|, where l represents a random number between [-1, 1].

6. The maximum power point tracking control method for a photovoltaic power generation system according to claim 1, characterized in that, The method of determining the optimal current corresponding to the optimal duty cycle control signal using the perturbation observation method includes: Initialize the optimal current corresponding to the optimal duty cycle control signal; The optimal current corresponding to the optimal duty cycle control signal is disturbed in a preset direction to obtain the corresponding disturbed current as the current; Output power calculation steps: Calculate the output power of the photovoltaic power generation system after the disturbance based on the current current; If the output power of the photovoltaic power generation system after the disturbance is greater than the output power of the photovoltaic power generation system before the disturbance, then continue to disturb the current in the preset direction to obtain the corresponding disturbed current, and return to the output power calculation step; If the output power of the photovoltaic power generation system after the disturbance is less than the output power of the photovoltaic power generation system before the disturbance, then the current is disturbed in the opposite direction of the preset direction to obtain the corresponding disturbed current, and then the process returns to the output power calculation step. If the output power of the photovoltaic power generation system after the disturbance is equal to the output power of the photovoltaic power generation system before the disturbance, then the current current is taken as the optimal current corresponding to the optimal duty cycle control signal.

7. A maximum power point tracking control device for a photovoltaic power generation system, characterized in that, The maximum power point tracking control device for the photovoltaic power generation system includes: The first optimization module is used to determine the optimal duty cycle control signal when the output power of the photovoltaic power generation system is maximized by using an improved whale optimization algorithm. The second optimization module is used to determine the optimal current corresponding to the optimal duty cycle control signal using the perturbation observation method. The control module is used to control the photovoltaic power generation system according to the optimal duty cycle control signal and its corresponding optimal current, so as to maximize the output power of the photovoltaic power generation system.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the maximum power point tracking control method for a photovoltaic power generation system as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the maximum power point tracking control method for a photovoltaic power generation system as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the maximum power point tracking control method for a photovoltaic power generation system as described in any one of claims 1 to 6.

Images