Photovoltaic maximum power tracking method and system based on variable population quantum whale optimization

Abstract

The present disclosure provides a photovoltaic maximum power tracking method and system based on variable population quantum whale optimization, applied to a photovoltaic power generation system, the photovoltaic power generation system comprising a photovoltaic array, a maximum power point tracking controller and a DC-DC converter, the method comprising obtaining the output voltage and output current of the photovoltaic array, and the maximum power point tracking controller calculating the output power of the photovoltaic array based on the output voltage and output current; the maximum power point tracking controller uses a variable population quantum whale optimization algorithm to obtain the optimal duty cycle, wherein the variable population quantum whale optimization algorithm is obtained by optimizing the whale optimization algorithm using an adaptive population size and quantum coding, the fitness function is the output power, and the position of each individual in the whale population is a duty cycle candidate solution; the maximum power point tracking controller controls the DC-DC converter based on the optimal duty cycle to achieve real-time control of the maximum output power of the photovoltaic array. The method according to the present disclosure can improve the power tracking accuracy and convergence speed.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of photovoltaic maximum power tracking control, and particularly relates to a photovoltaic maximum power tracking method and system based on variable population quantum whale optimization. BACKGROUND

[0002] With the proposal of the "3060" strategic plan, China's power industry has opened a new chapter in energy structure transformation. For many years, coal-fired power generation has been the main power source in China's power generation field, causing a series of energy and environmental problems. Today, under the demand for large-scale new energy consumption in the power grid, on the basis of giving full play to the role of thermal power regulation, the development and integration of wind, light and other new energy power generation technologies will help the clean, low-carbon, safe and efficient development of the power system.

[0003] Considering the advantages of solar energy, such as easy access, easy use, low cost, and clean and pollution-free, it can be used as one of the primary choices for new energy power generation. A photovoltaic power generation system mainly includes a photovoltaic array and a maximum power tracking controller, etc., wherein the photovoltaic array is arranged by a plurality of series and parallel photovoltaic components, used for absorbing solar energy and coordinating current and voltage, and finally forming a power output. However, the output power of the photovoltaic array shows great volatility and uncertainty with changes in external conditions such as environmental temperature, sunlight irradiance and photovoltaic panel shading degree, and is often difficult to stabilize near the maximum power point. In an ideal state, the photovoltaic array is under uniform illumination, at which time its output power curve only has one maximum power point; and when the photovoltaic panel is partially shaded by dust, fallen leaves, cloud shadows, building shadows and the like, multiple local maximum power points will appear. The goal of the photovoltaic maximum power tracking controller is to keep the output of the photovoltaic array near the maximum power point by adjusting the duty cycle of the DC-DC converter (DC-to-DC converter) in real time under the condition that the above external disturbances exist.

[0004] Although the prior art has photovoltaic system maximum power tracking methods using swarm intelligence optimization algorithms, the convergence speed is slow and the tracking accuracy needs to be improved. SUMMARY

[0005] The present disclosure aims to at least partially solve one of the technical problems in the related art. To this end, the present disclosure provides a photovoltaic maximum power tracking method and system based on variable population quantum whale optimization, mainly aiming to improve the convergence speed and power tracking accuracy.

[0006] According to a first aspect of the present disclosure, a photovoltaic maximum power tracking method based on variable population quantum whale optimization is provided, applied to a photovoltaic power generation system, the photovoltaic power generation system including a photovoltaic array, a maximum power point tracking controller and a DC-DC converter, the photovoltaic maximum power tracking method comprising:

[0007] The output voltage and output current of the photovoltaic array are obtained, and the maximum power point tracking controller calculates the output power of the photovoltaic array based on the output voltage and output current.

[0008] The maximum power point tracking controller uses a variant swarm quantum whale optimization algorithm to obtain the optimal duty cycle. The variant swarm quantum whale optimization algorithm is obtained by using adaptive population size and quantum coding optimization. The fitness function is the output power, and the position of each individual in the whale swarm is a candidate solution for the duty cycle.

[0009] The maximum power point tracking controller controls the DC-DC converter based on the optimal duty cycle to achieve real-time control of the maximum output power of the photovoltaic array.

[0010] In one embodiment of this disclosure, the quantum encoding is quantum probability amplitude encoding, and the step of using adaptive population size and quantum encoding to optimize the whale optimization algorithm to obtain the variant population quantum whale optimization algorithm includes: in the initialization step of the whale optimization algorithm, using quantum probability amplitude encoding to calculate the initial position of each individual in the whale pod.

[0011] In one embodiment of this disclosure, the step of using adaptive population size and quantum coding to optimize the whale optimization algorithm to obtain the variable population quantum whale optimization algorithm further includes: at the end of each iteration, judging whether the optimization state of the algorithm is good based on the number of consecutive updates of the current best candidate solution or the number of consecutive non-updates of the current best candidate solution; if it is good, the population size is reduced; otherwise, the population size is expanded, and the next iteration is performed based on the new population size after reduction or expansion.

[0012] In one embodiment of this disclosure, determining whether the algorithm's optimization state is good based on the number of consecutive updates of the current optimal candidate solution or the number of consecutive non-updates of the current optimal candidate solution includes: determining whether the number of consecutive updates of the current optimal candidate solution is greater than or equal to a first threshold; if so, the algorithm's optimization state is good; and determining whether the number of consecutive non-updates of the current optimal candidate solution is greater than or equal to a second threshold; if so, the algorithm's optimization state is poor or trapped in a local optimum.

[0013] In one embodiment of this disclosure, before reducing the population size, it is first determined whether the population size of the current iteration is less than or equal to the minimum tolerable population size. If so, the population size reduction is stopped; otherwise, the population size reduction is performed.

[0014] In one embodiment of this disclosure, before expanding the population size, it is first determined whether the population size of the current iteration is greater than or equal to the initial population size. If so, the population size expansion is stopped; otherwise, the population size expansion is carried out.

[0015] According to a second aspect of this disclosure, a photovoltaic maximum power point tracking (MPPT) system based on a mutant population quantum whale optimization is also provided, applied to a photovoltaic power generation system. The photovoltaic power generation system includes a photovoltaic array, a maximum power point tracking controller, and a DC-DC converter. The maximum power point tracking controller includes the photovoltaic MPPT system. The photovoltaic MPPT system includes:

[0016] The acquisition module is used to acquire the output voltage and output current of the photovoltaic array, and calculate the output power of the photovoltaic array based on the output voltage and output current;

[0017] An optimization control module is used to obtain the optimal duty cycle using a variant swarm quantum whale optimization algorithm. The variant swarm quantum whale optimization algorithm is obtained by using adaptive population size and quantum coding optimization. The fitness function is the output power, and the position of each individual in the whale swarm is a candidate solution for the duty cycle. The DC-DC converter is controlled based on the optimal duty cycle to achieve real-time control of the maximum output power of the photovoltaic array.

[0018] In one embodiment of this disclosure, the quantum encoding is quantum probability amplitude encoding, and the optimization control module is specifically used to: calculate the initial position of each individual in the whale pod using quantum probability amplitude encoding in the initialization step of the whale optimization algorithm; and at the end of each iteration, determine whether the algorithm's optimization state is good based on the number of consecutive updates of the current best candidate solution or the number of consecutive non-updates of the current best candidate solution. If it is good, reduce the population size; otherwise, expand the population size, and perform the next iteration based on the new population size after reduction or expansion.

[0019] In one embodiment of this disclosure, the optimization control module is specifically used to: determine whether the number of consecutive updates of the current optimal candidate solution is greater than or equal to a first threshold; if so, the algorithm's optimization state is good; determine whether the number of consecutive non-updates of the current optimal candidate solution is greater than or equal to a second threshold; if so, the algorithm's optimization state is poor or trapped in a local optimum.

[0020] According to a third aspect of this disclosure, a photovoltaic maximum power point tracking device based on mutant swarm quantum whale optimization is also provided, comprising: 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 execute the photovoltaic maximum power point tracking method based on mutant swarm quantum whale optimization proposed in the first aspect of this disclosure.

[0021] In one or more embodiments of this disclosure, the method is applied to a photovoltaic power generation system. The photovoltaic power generation system includes a photovoltaic array, a maximum power point tracking controller, and a DC-DC converter. The photovoltaic maximum power point tracking method includes: acquiring the output voltage and output current of the photovoltaic array; the maximum power point tracking controller calculates the output power of the photovoltaic array based on the output voltage and output current; the maximum power point tracking controller uses a variant swarm quantum whale optimization algorithm to obtain the optimal duty cycle, wherein the variant swarm quantum whale optimization algorithm is obtained by using adaptive population size and quantum coding optimization, the fitness function is the output power, and the position of each individual in the swarm is a candidate solution for the duty cycle; the maximum power point tracking controller controls the DC-DC converter based on the optimal duty cycle to achieve real-time control of the maximum output power of the photovoltaic array. In this scenario, the maximum power point tracking (MPPT) controller sets the fitness function to the output power, and the position of each individual in the pod as a candidate solution for the duty cycle. A variant swarm quantum whale optimization algorithm is employed to obtain the optimal duty cycle. Specifically, the variant swarm quantum whale optimization algorithm is obtained by utilizing adaptive population size and quantum encoding. Under the optimal duty cycle, the MPPT controller controls the DC-DC converter to enable the photovoltaic array to achieve its maximum output power, thereby improving power point tracking accuracy. Furthermore, the improvement of the whale optimization algorithm using adaptive population size and quantum encoding effectively saves computational resources and increases the convergence speed of the optimization process.

[0022] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0024] Figure 1 This diagram illustrates the structure of a photovoltaic power generation system according to an embodiment of the present disclosure.

[0025] Figure 2 The diagram shows an equivalent circuit diagram of a photovoltaic array provided in an embodiment of this disclosure;

[0026] Figure 3 A flowchart illustrating the variant swarm quantum whale optimization algorithm provided in an embodiment of this disclosure is shown.

[0027] Figure 4 The diagram shows a flowchart of a photovoltaic maximum power point tracking method based on mutant population quantum whale optimization provided in an embodiment of this disclosure;

[0028] Figure 5This diagram illustrates a block diagram of a photovoltaic maximum power point tracking system based on a variant population quantum whale optimization, as provided in an embodiment of this disclosure.

[0029] Figure 6 This is a block diagram of a photovoltaic maximum power point tracking device based on a mutant swarm quantum whale optimization method used to implement the photovoltaic maximum power point tracking method based on mutant swarm quantum whale optimization in the embodiments of this disclosure. Detailed Implementation

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this disclosure as detailed in the appended claims.

[0031] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0032] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise expressly and specifically defined. It should also be understood that the term "and / or" as used in this disclosure refers to and includes any or all possible combinations of one or more associated listed items.

[0033] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0034] This disclosure provides a photovoltaic maximum power point tracking method and system based on mutant population quantum whale optimization, with the main purpose of improving convergence speed and power point tracking accuracy.

[0035] This disclosure presents a photovoltaic maximum power point tracking (MPPT) method and system based on mutant swarm quantum whale optimization, applicable to a photovoltaic power generation system. The photovoltaic power generation system includes a photovoltaic array, a maximum power point tracking controller, and a DC-DC converter. The maximum power point tracking controller is formally known as a Maximum Power Point Tracking (MPPT) solar controller. The DC-DC converter is a direct-to-direct-current voltage converter, mainly classified as boost converters, buck converters, and boost-buck converters. For convenience and high efficiency, a boost converter is selected as the DC-DC converter in this disclosure.

[0036] Figure 1 This diagram illustrates the structure of a photovoltaic power generation system provided in an embodiment of the present disclosure. Figure 2 An equivalent circuit diagram of a photovoltaic array provided in an embodiment of this disclosure is shown. Figure 1 As shown, the photovoltaic power generation system includes a photovoltaic array (PV array) and a photovoltaic capacitor C. pv MPPT controller, DC-DC boost converter, load R load It consists of auxiliary connection lines, etc. The photovoltaic array includes multiple photovoltaic modules, multiple bypass diodes D1, and multiple choke diodes D2. The input terminal of the MPPT controller is connected to the photovoltaic array, and the output terminal of the MPPT controller is connected to the pulse modulation wave generation circuit in the DC-DC boost converter. The DC-DC boost converter includes a pulse modulation wave generation circuit (PWM), a boost capacitor C, an inductor L, a diode D3, and an insulated gate bipolar transistor (IGBT). The base of the IGBT is connected to the pulse modulation wave generation circuit. The bypass diodes are used to suppress hot spot effects and reduce power waste caused by local shading, while the choke diodes effectively prevent current backflow. L Let I be the current in inductor L. load For load R load The current, V load For load R load The voltage.

[0037] like Figure 1 As shown, during the operation of the photovoltaic system, the MPPT controller calculates the output current I of the photovoltaic array. pv (Unit: A) and output voltage V pv(Unit: V) After adaptively adjusting the duty cycle d of the DC-DC boost converter, the duty cycle d is sent to the PWM to generate a pulse modulation wave to control the DC-DC boost converter, which enables the photovoltaic array to output greater output power.

[0038] The MPPT controller executes the photovoltaic maximum power point tracking method based on the mutant swarm quantum whale optimization disclosed herein. By iteratively optimizing the duty cycle of the DC-DC boost converter, the optimal duty cycle is obtained, and then the DC-DC boost converter is controlled to enable the photovoltaic array to output maximum power, thereby achieving the effect of accurately and quickly tracking GMPP (Global Maximum Power Point) under various irradiance conditions.

[0039] Before describing the photovoltaic maximum power point tracking method based on mutant swarm quantum whale optimization disclosed herein, the photovoltaic system is analyzed to clarify the relationship between the duty cycle and the maximum output power (i.e., the maximum power point) of the photovoltaic array. Specifically:

[0040] 1) Modeling the current-voltage (IV) characteristics of the photovoltaic array. Through... Figure 2 The study of the internal mechanism of photovoltaic cells involves writing voltage and current balance equations, ultimately achieving modeling of the dynamic characteristics of the entire photovoltaic array. Specifically, a photovoltaic array typically consists of P×Q photovoltaic modules connected in series and parallel, and includes bypass diodes D1 and P choke diodes D2, the same number as the photovoltaic array itself. In this embodiment, P=1 and Q=3. Figure 2 As shown, the photovoltaic cell is the smallest unit of the photovoltaic array, and each photovoltaic cell includes a parallel diode D. d Series resistor R s Parallel resistor R th The load R and the photosensitive element. The photosensitive element is used to generate a photocurrent I based on the irradiance G and the ambient temperature T. ph I d For the current flowing through the parallel diode D d I th For the current flowing through the parallel resistor R th current, I pva For the current flowing through the series resistor R s The current (i.e., the output current of each photovoltaic cell), V pva Let V represent the output voltage of the photovoltaic cell. Then, the IV characteristic of the P×Q photovoltaic array can be expressed as:

[0041]

[0042] In the formula, I pv V represents the output current of the photovoltaic array. pvI represents the output voltage of the photovoltaic array. ph Represents the photocurrent of a single photovoltaic cell; P represents the number of columns in the photovoltaic module; Q represents the number of rows in the photovoltaic module; R s and R th These represent the series resistance and parallel resistance of a single photovoltaic cell, respectively; I0 is the reverse saturation current; B is the ideality factor; V T The parallel diode D represents a single photovoltaic cell. d The diode thermal voltage; exp represents an exponential function with base e; n s This indicates the number of photovoltaic cells in each series section (i.e., each column) of the photovoltaic array.

[0043] Photocurrent I of a single photovoltaic cell ph The calculation depends on the short-circuit current I of the photovoltaic array under standard test conditions. SC and open circuit voltage V OC The irradiance under standard test conditions meets G STC =1000W / m 2 The ambient temperature meets T STC At 25℃, the formula for calculating the photocurrent is as follows:

[0044]

[0045] Where, k i Represents short-circuit current I SC The temperature coefficient; G and T represent the actual irradiance and ambient temperature, respectively. STC and T STC These represent the irradiance and ambient temperature under standard test conditions, respectively.

[0046] The calculation of the reverse saturation current I0 is related to the ambient temperature, and the reverse saturation current I0 satisfies:

[0047]

[0048] In the formula, k v Represents open-circuit voltage V OC Temperature coefficient.

[0049] Diode thermal voltage V of a single photovoltaic cell in parallel diode T The expression is as follows:

[0050]

[0051] In the formula, k b This represents the Boltzmann constant, typically taken as 1.38065 × 10⁻⁶. -23 J / K; q represents the charge of one electron, typically taken as 1.60218 × 10⁻⁶. -19 C.

[0052] The irradiation conditions of photovoltaic (PV) arrays can generally be categorized into three types: normal illumination, uniform shading, and partial shading. Under normal illumination and uniform shading, the PV array receives uniform illumination, resulting in a single peak on the PV (power-voltage) curve. However, under partial shading, due to uneven illumination, the PV curve of the PV array shows multiple peaks. If the MPPT controller cannot track the Global Maximum Power Point (GMPP) quickly and accurately in real time, it may get trapped in a local optimum, significantly impacting power generation efficiency.

[0053] 2) The energy absorbed by the inductor L in the DC-DC boost converter is equal to the energy released during the IGBT's turn-on and turn-off periods. Therefore, the energy conservation relationship can be expressed as:

[0054] U in I L t on =(U out -U in )I L t off

[0055] t on-off =t on +t off (5)

[0056] In the formula, U in and U out These represent the input and output voltages of the IGBT, respectively; t on and t off These are the IGBT's turn-on and turn-off times, respectively; t on-off I represents the total turn-on and turn-off time of the IGBT. L Let L be the current in the inductor.

[0057] Equation (5) is derived into a form containing the duty cycle d. The derived formula is:

[0058]

[0059] In the formula, And d∈[0,1].

[0060] As the above analysis shows, as long as the output impedance of the photovoltaic array matches the internal resistance of the battery, the system power can reach GMPP by adjusting the duty cycle of the DC-DC boost converter. In this disclosure, the MPPT controller executes the photovoltaic maximum power point tracking method based on variant swarm quantum whale optimization according to the embodiments of this disclosure. By iteratively optimizing the duty cycle of the DC-DC boost converter, the optimal duty cycle corresponding to GMPP is obtained. Based on the optimal duty cycle, the DC-DC boost converter is controlled, thereby ensuring the speed and accuracy of the GMPP tracking process under various irradiation conditions, including partial shading.

[0061] The photovoltaic maximum power point tracking method based on mutant population quantum whale optimization disclosed herein will be described in detail below with reference to the accompanying drawings.

[0062] First, the whale optimization algorithm and the improved whale optimization algorithm of this disclosure (i.e., the variant population quantum whale optimization algorithm) are described.

[0063] To put it simply, the Whale Optimization Algorithm (WOA) was inspired by the hunting behavior of humpback whales using bubble webs. Compared to the currently mature biomimetic intelligent algorithms such as genetic algorithms, particle swarm optimization, and bee swarm optimization, WOA is simple in principle and easy to implement. Although it has certain search and development capabilities, it still cannot avoid premature convergence. If it is directly used for photovoltaic maximum power tracking, it will be difficult to achieve the ideal tracking accuracy and speed.

[0064] Therefore, this disclosure introduces quantum encoding and adaptive population size operation into the optimization process of the original WOA to improve algorithm performance. The resulting Variable Population Quantum Whale Optimization Algorithm (CQWOA) is used in the MPPT controller to optimize the duty cycle. The position of each member of the pod (i.e., each individual in the pod) is considered as a candidate solution. The solution vector is continuously optimized through individual position updates and greedy selection until the optimal solution in the search space, i.e., the optimal duty cycle (also called the ideal duty cycle), is obtained. In the pod position initialization process, quantum probability amplitude encoding is introduced to form the initial position of individuals in the pod, which is then transformed into a position vector in the general solution space. After fitness calculation, the prey position is initially determined, and then the positions of individuals in the pod are updated through a shrinking encirclement mechanism and a spiral convergence mechanism. Furthermore, the fitness of the pod after the position update is calculated, and greedy selection is used to optimize the current optimal solution. To improve search efficiency and avoid wasting computational resources, the pod size is adaptively adjusted through reasonable scaling down and expansion.

[0065] Specifically, Figure 3 A flowchart illustrating the variant swarm quantum whale optimization algorithm provided in an embodiment of this disclosure is shown. Figure 3 As shown, the optimization process of the mutant swarm quantum whale optimization algorithm is as follows:

[0066] 1) Algorithm parameters and whale pod location initialization. Parameter initialization includes setting the initial whale pod size to N, the search dimension to D, and the maximum number of iterations during the optimization search to M. In this embodiment, based on general experience, N = 40, D = 10, and M = 20.

[0067] If quantum encoding is, for example, quantum probability amplitude encoding, then the following quantum probability amplitude encoding operation is introduced to determine the position of an individual in the population that satisfies:

[0068]

[0069] In the formula, k = 0 is P i (0) represents the initial position of the i-th individual in the whale pod, P i (k) is the vector of the i-th individual during the k-th optimization iteration. rand(0,1) represents a random value between 0 and 1. P represents the phase angle of the i-th individual in dimension j during the k-th optimization iteration. i (k) represents the position of the i-th individual in the whale pod during the k-th optimization iteration, P i cos (k) is the cosine position vector of the i-th individual during the k-th optimization iteration, P i sin (k) is the sinusoidal position vector of the i-th individual during the k-th optimization iteration.

[0070] Correspondingly, the position of an individual whale in the general solution (i.e., candidate solution) space can be represented as:

[0071]

[0072] In the formula, d represents the sine and cosine position vectors of the i-th individual in the whale pod at dimension j during the k-th optimization iteration; max d min These represent the upper and lower limits of the candidate solution, respectively.

[0073] 2) Fitness calculation and preliminary rough location of prey. Based on practical application requirements, a fitness function is constructed for the k-th optimization iteration of the optimization process, denoted as f[d(k)]. Since each individual in the population searches in different dimensions, the fitness is processed as follows for ease of calculation and comparison:

[0074]

[0075] In the formula, f[d j cos [(k)] represents the fitness cosine component in dimension j during the k-th optimization iteration, f[dj sin [(k)] represents the sinusoidal component of the fitness in dimension j during the k-th optimization iteration, f[d N,j cos [(k)] represents the cosine component of the fitness of the Nth individual in dimension j during the k-th optimization iteration, f[d N,j sin [(k)] represents the sinusoidal component of the fitness of the Nth individual in dimension j during the kth optimization iteration.

[0076] Then, the minimum fitness value is selected from the obtained fitness values ​​using equation (10) for the initial rough location of the prey:

[0077]

[0078] In the formula, f min [d(k)] represents the minimum fitness value at the k-th optimization iteration, f[d] D cos (k)]] represents the fitness cosine component in dimension D during the k-th optimization iteration, f[d D sin [(k)] represents the sinusoidal component of the fitness in dimension D during the k-th optimization iteration.

[0079] 3) Position updates of individuals within a whale pod. Whales employ two position update mechanisms during prey hunting: the shrinking encirclement update mechanism and the spiral convergence update mechanism. Both mechanisms can occur simultaneously within a population. Therefore, it is assumed that any member of the whale pod has a 50% probability of choosing either of these two search mechanisms.

[0080] Under the shrinking encirclement update mechanism, the whale's position update follows two modes, and the selection of the mode depends on the value of the update coefficient A(k) as shown in the following formula:

[0081]

[0082] In the formula, as the number of iterations k changes, the value of parameter a changes linearly from 2 to 0, and the random value r satisfies r = rand[0,1].

[0083] When A(k)≥1, randomly select individual whales from the existing population and update their positions based on their position vectors:

[0084]

[0085] In the formula, d new (k) represents the updated position after the k-th optimization iteration, d rand (k) represents the position of the randomly selected whale individual in the k-th optimization iteration, and d(k) represents the position before the update in the k-th optimization iteration.

[0086] When |A(k)|<1, the position is updated based on the current best candidate solution in the population:

[0087]

[0088] In the formula, d* represents the current optimal candidate solution.

[0089] Under the spiral convergence update mechanism, equation (14) is used to achieve the iterative update of the whale's position:

[0090] d new (k)=|d * -d(k)|·e bl ·cos(2πl)+d * (14)

[0091] In the formula, the shape of the spiral depends on the logarithmic constant b, and the random value l satisfies l=rand[[-1,1], and in this embodiment, b=0.1.

[0092] In summary, the whale pod location update process can be represented as:

[0093]

[0094] In the formula, the random value p satisfies p = rand[0,1].

[0095] 4) Fitness calculation and greedy selection after position update. In order to make the position update process of the whale pod continuously approach the location of the prey (i.e. the optimal solution), after each position update, the fitness value of the individual position in the population needs to be calculated by the fitness function and greedily selected with the current optimal solution. If there is a value in the newly calculated fitness that is better than the current optimal solution, then the current optimal solution is replaced; otherwise, the current optimal solution remains unchanged.

[0096] 5) Adaptive Population Size Adjustment for Efficient Search. Swarm intelligence algorithms typically involve large populations, and traversing every member in each iteration often leads to resource waste and increased computational complexity. Therefore, if the population size could be adaptively adjusted in real-time based on the optimization state (i.e., adjusting the population size at the end of each iteration using an adaptive population size adjustment), search speed would be significantly improved, promoting efficient use of computational resources. Changes in whale pod population size mainly fall into two modes: population reduction and population expansion.

[0097] For population reduction: If the algorithm's current optimization state is good, the number of individuals in the population is reduced as needed to achieve population reduction. The optimization state is judged by the number of consecutive updates (T1) of the current optimal candidate solution. A first threshold H1 needs to be set for T1. When T1 ≥ H1, it indicates that the algorithm's optimization state is good, and some members with poor fitness can be removed from the whale pod while ensuring the algorithm's search and development performance. Assuming the number of whales removed is N1, for ease of calculation, N1 is proportional to N, i.e., N1 = λ1N, where λ1 is the reduction ratio coefficient. In this embodiment, H1 = 3, λ1 = 0.1. When the number of remaining individuals in the population is N... r Reaching (i.e., less than or equal to) the minimum tolerable population size N min When this happens, the population reduction operation stops. To ensure population diversity during the search process, in this embodiment, N... min =0.5N.

[0098] For population expansion operations: If the algorithm's optimization state is poor or there is a possibility of getting stuck in a local optimum, population expansion will be carried out through elite replenishment operations. Let T2 be the number of times the current optimal candidate solution has not been updated consecutively. Similarly, a second threshold H2 is set for T2. When T2≥H2, it indicates that the algorithm's optimization state is poor or the search process may get stuck in a local convergence. In order to improve the algorithm's search ability, it is necessary to give full play to the effective information carried in the current optimal solution. Elite individuals with ideal fitness are constructed by formula (10) and added to the existing population. The number of whales added, N2, is still proportional to N, that is, N2=λ2N, where λ2 is the expansion ratio coefficient. In this embodiment, H2=3, λ2=0.05. When the number of individuals in the population reaches the initial population size N, the population expansion operation stops. Formula (16) satisfies:

[0099]

[0100] In the formula, d i '(k) represents the new position of the i-th individual constructed after the k-th optimization iteration, d i (k) represents the position of the original i-th individual in the k-th optimization iteration, d i (k) is the set of positions constructed after the kth optimization iteration.

[0101] 6) After each iteration, check whether the termination condition is met. If yes, terminate the iteration and output the optimal solution. Otherwise, return to the initialization step and repeat the above iteration process until the termination condition is met.

[0102] To verify the overall performance of the mutant swarm quantum whale optimization algorithm proposed in this disclosure, the optimization performance of the mutant swarm quantum whale optimization algorithm is also verified in this disclosure.

[0103] Optimization performance verification includes benchmark function testing, nonparametric statistical tests, and comparative experiments.

[0104] 1) Benchmark function testing: Benchmark function testing relies on a test function library composed of multiple complex functions. By freely selecting function categories, solution dimensions, and iteration counts, the convergence speed and search accuracy of the optimization algorithm are verified. In the testing of this embodiment, 2-3 representative test functions are selected from different categories of the benchmark function library. Then, optimization is performed using search dimensions of 30 and 100. The influence of uncertainties during the testing process is eliminated by taking average, maximum, and minimum values ​​through multiple experiments. Finally, performance indicators such as solution time and accuracy are statistically analyzed and verified.

[0105] Specifically, the CEC 2014 benchmark function library was used as the algorithm testing platform. This library contains 30 benchmark functions in 4 categories, and the members in each category exhibit similar characteristics. In this embodiment, two functions from each of the four categories of benchmark functions in CEC 2014 were selected to test the performance of the proposed optimization algorithm. Details of the final selected test functions are shown in Table 1.

[0106] Table 1. Statistics on the functions selected in the benchmark function test.

[0107]

[0108] In the table: F i * F represents the i-th reference function i (1,2,…,30) in [-100,100] Dim The optimal solution is defined within the given information; Dim is the dimension of the benchmark function. In this embodiment, Dim = 50 is set to verify the excellent performance of the proposed algorithm in handling high-dimensional complex optimization problems.

[0109] To ensure that the test results are general, each test was repeated 20 times, and the time T taken for the optimization solution was recorded. o The following performance metrics are defined to quantitatively represent the effectiveness of the proposed algorithm in optimizing complex functions. The performance metrics include the mean (Mean) and standard deviation (SD), which satisfy the following conditions:

[0110]

[0111]

[0112] In the formula, N re In this embodiment, N represents the number of times each test is repeated. re =20; F i j This represents the optimization result of the i-th benchmark function after the j-th test.

[0113] 2) Nonparametric statistical tests: Nonparametric statistics can test whether the distribution of a variable or parameter has the same characteristics in different groups when the overall population distribution is unknown. Therefore, this principle can be used to test the values ​​of various performance indicators under different comparative algorithms, highlighting the superiority of the designed variant swarm quantum whale optimization algorithm in terms of optimization accuracy and speed. For example, considering that the sample size in this embodiment is ≥2, the Friedman test, which is suitable for multi-sample homogeneity analysis, is used to complete the performance test of the proposed algorithm in the comparative experiment (see the comparative experiment for specific results). The Friedman test can highlight the performance differences between various algorithms by recording the ranking of performance indicator values, statistics, and probability p-values.

[0114] 3) For comparative experiments: Considering the existence of various mature and well-performing intelligent optimization algorithms, such as Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), comparing the performance of any newly proposed swarm intelligence optimization algorithm with existing algorithms is crucial for confirming its effectiveness and advantages. Therefore, it is necessary to select classic examples from classical optimization algorithms for performance comparison experiments to highlight the feasibility and effectiveness of the proposed variant swarm quantum whale optimization algorithm.

[0115] This embodiment uses Differential Evolution (DE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and the classic WOA algorithm for comparative testing. Benchmark function tests and nonparametric statistical verifications were performed on each of the above algorithms, and the following performance index statistics were obtained.

[0116] Table 2. Statistical results of performance indicators of each comparison algorithm in the benchmark function test.

[0117]

[0118]

[0119] Table 2 lists the mean (Mean), variance (SD), and optimization time (T) of different comparison algorithms in every 20 repeated experiments in the benchmark function test.o Therefore, the proposed variant swarm quantum whale optimization algorithm (CQWOA) achieves near-optimal performance across almost all selected benchmark functions, demonstrating superior search speed and accuracy. This verifies the feasibility and effectiveness of the proposed optimization algorithm in solving complex high-dimensional functions.

[0120] Accordingly, the results of the Friedman test are shown in Table 3:

[0121] Table 3. Results of Friedman's Test

[0122]

[0123] The ranking results in Table 3 show that, in the Friedman test, the CQWOA proposed in this disclosure ranks first in all performance indicators, standing out among the comparative algorithms. Furthermore, all p-values ​​in the table are much smaller than the confidence level α = 0.01, indicating that the performance of CQWOA is not consistent or similar to the other comparative algorithms. Therefore, the performance of CQWOA in this disclosure is significantly better than DE, GA, PSO, GWO, and WOA.

[0124] To ensure that the mutant swarm quantum whale optimization algorithm truly serves the photovoltaic maximum power point tracking (MPPT) process, the photovoltaic MPPT method based on mutant swarm quantum whale optimization disclosed herein applies the aforementioned mutant swarm quantum whale optimization algorithm to the MPPT controller to achieve real-time, fast, and accurate GMPP tracking.

[0125] Regarding the aforementioned variant swarm quantum whale optimization algorithm, the specific details of the photovoltaic maximum power point tracking method based on variant swarm quantum whale optimization disclosed herein are as follows:

[0126] In the first embodiment, Figure 4 This diagram illustrates a flowchart of a photovoltaic maximum power point tracking method based on mutant population quantum whale optimization, as provided in an embodiment of this disclosure. Figure 4 As shown, this photovoltaic maximum power point tracking method based on mutant swarm quantum whale optimization includes:

[0127] Step S11: Obtain the output voltage and output current of the photovoltaic array. The maximum power point tracking controller calculates the output power of the photovoltaic array based on the output voltage and output current.

[0128] In step S11, the output voltage V of the photovoltaic array is acquired in real time using the maximum power point tracking controller. pv and output current I pv The output power P of the photovoltaic array is calculated based on the output voltage and output current. pv .

[0129] In step S12, the maximum power point tracking controller uses a variant swarm quantum whale optimization algorithm to obtain the optimal duty cycle. The variant swarm quantum whale optimization algorithm is obtained by using adaptive population size and quantum coding optimization. The fitness function is the output power, and the position of each individual in the whale swarm is a candidate solution for the duty cycle.

[0130] In step S12, the MPPT controller uses the mutant swarm quantum whale optimization algorithm described above to optimize the duty cycle d of the DC-DC boost converter to obtain the optimal duty cycle, so that the current and voltage of the photovoltaic power generation system reach the most matched values, thereby realizing real-time, fast and accurate tracking of GMPP.

[0131] Specifically, the MPPT controller optimizes the duty cycle d using the optimization process of the mutant swarm quantum whale optimization algorithm described above, where the position of each individual whale in the swarm is considered a candidate solution for the duty cycle. The location of the prey is the optimal solution d for the duty cycle. * Since the duty cycle ranges from 0 to 1, the upper and lower bounds of the population position vector are d and d, respectively. min =0, d max =1.

[0132] Furthermore, since the goal of the MPPT controller is to find and track the maximum power point of the photovoltaic system, the fitness function f[d(k)] of the variant swarm quantum whale optimization algorithm is set to the output power P of the photovoltaic array. pv As can be seen from step S11, the calculation of output power relies on the output current and voltage of the photovoltaic array, therefore let f[d(k)] = P pv =I pv ×V pv Then, through continuous iterative optimization, the optimal solution d for the duty cycle is obtained. * The optimal solution for the duty cycle at this time is d. * The corresponding fitness value is the maximum output power.

[0133] In step S12, the quantum encoding is quantum probability amplitude encoding. A variant population quantum whale optimization algorithm is obtained by using adaptive population size and quantum encoding to optimize the whale optimization algorithm. This includes: in the initialization step of the whale optimization algorithm, calculating the initial positions of each individual in the whale pod using quantum probability amplitude encoding. For details, please refer to the relevant description of the variant population quantum whale optimization algorithm above; it will not be repeated here.

[0134] In step S12, the variable-population quantum whale optimization algorithm is obtained by using adaptive population size and quantum coding optimization. This further includes: at the end of each iteration, judging whether the algorithm's optimization state is good based on the number of consecutive updates to the current best candidate solution or the number of consecutive non-updates to the current best candidate solution. If good, the population size is reduced; otherwise, the population size is expanded, and the next iteration is performed based on the new population size after reduction or expansion. Specifically, judging whether the algorithm's optimization state is good based on the number of consecutive updates to the current best candidate solution or the number of consecutive non-updates to the current best candidate solution includes: judging whether the number of consecutive updates to the current best candidate solution is greater than or equal to a first threshold; if so, the algorithm's optimization state is good; judging whether the number of consecutive non-updates to the current best candidate solution is greater than or equal to a second threshold; if so, the algorithm's optimization state is poor or trapped in a local optimum. Furthermore, before reducing the population size, it is first judged whether the population size of the current iteration is less than or equal to the minimum tolerable population size; if so, the population size reduction is stopped; otherwise, the population size reduction is performed. Before expanding the population size, it is first determined whether the current population size is greater than or equal to the initial population size. If so, population expansion is stopped; otherwise, population expansion proceeds. For details, please refer to the description of the variable population quantum whale optimization algorithm above, which will not be repeated here.

[0135] In step S13, the maximum power point tracking controller controls the DC-DC converter based on the optimal duty cycle to achieve real-time control of the maximum output power of the photovoltaic array.

[0136] In step S13, the maximum power point tracking controller will determine the optimal solution d for the duty cycle. * The power is fed into a DC-DC converter to control the converter, enabling real-time control of the photovoltaic array's maximum output power. Thus, the maximum power point tracking controller (MPPT) possesses real-time, precise tracking capabilities.

[0137] In the embodiments of this disclosure, in order to evaluate the feasibility, effectiveness and excellent performance of the photovoltaic maximum power point tracking method based on mutant population quantum whale optimization under various irradiance conditions of photovoltaic systems, the method of this disclosure is also verified based on numerical simulation and statistical analysis.

[0138] 1) First, determine the parameters of the photovoltaic system, namely the photovoltaic array and the DC-DC boost converter. The specific values ​​are shown in Table 4.

[0139] Table 4 Component Parameters of Photovoltaic Systems

[0140]

[0141] In the table: P max V represents the maximum power of a single photovoltaic cell; mp and Imp These are the maximum power P max The corresponding voltage and current; C pv C and L represent the photovoltaic capacitor and boost capacitor of the boost converter, respectively; L is the inductor; R load It is a load; f s The frequency represents the number of photovoltaic cells. Additionally, the number of photovoltaic cells connected in series and parallel, P and Q, needs to be determined. To enrich the experimental design and make the results more convincing, in this embodiment, P = 1 and Q = 3.

[0142] Considering the common irradiance conditions of photovoltaic arrays, this embodiment divides the simulation experiment into two types: the first is uniform irradiance, which includes both standard conditions and uniform shading settings; the second is partial shading. Since ambient temperature has a relatively small impact on the power output of the photovoltaic array, to ensure the rigor of the experimental design and the fairness of the simulation results, the ambient temperature of the photovoltaic array is kept constant at 25℃ throughout the entire simulation process. The irradiance conditions are shown in the table below:

[0143] Table 5 shows the photovoltaic system under three different irradiance conditions during simulation.

[0144]

[0145]

[0146] Since the photovoltaic array used in this embodiment consists of 1×3 modules, the three modules are labeled as photovoltaic module 1, photovoltaic module 2 and photovoltaic module 3 in Table 5, and the irradiance is distinguished by the difference in irradiance of different photovoltaic modules.

[0147] 2) To highlight the significant advantages of the MPPT controller disclosed herein compared with the MPPT controller that incorporates classical optimization algorithms, methods optimized by PSO, GWO, and WOA algorithms are introduced for comparison.

[0148] 3) Under the three different types of solar irradiance in this embodiment, the duty cycle d of the photovoltaic system boost converter is optimized by the above comparison method and the method of this disclosure, and the simulation results are plotted to intuitively judge and analyze the duty cycle change trend and photovoltaic system output power change under different algorithm optimization.

[0149] 4) To clearly represent the performance of the algorithms from the numerical statistical results, the convergence time t of each algorithm during the GMPP tracking process was recorded. c And the corresponding tracking accuracy η is calculated using the following formula:

[0150]

[0151] In the formula, P GMPPThis represents the theoretical maximum output power of the photovoltaic array under a selected irradiance, i.e., GMPP.

[0152] Table 6 below lists the convergence time and tracking accuracy of each method in the comparative experiment. The superior performance of the proposed method in real-time, fast and accurate tracking of GMPP is verified by numerical statistical comparison.

[0153] Table 6. Statistical results of GMPP tracking performance indicators of various algorithms under different irradiance conditions.

[0154]

[0155] As shown in Table 6, the CQWOA-based method disclosed in this paper has significant advantages in both tracking accuracy and speed, and the tracking effect is ideal.

[0156] In the photovoltaic maximum power point tracking (MPPT) method based on variant swarm quantum whale optimization in this disclosure, it is applied to a photovoltaic power generation system. The photovoltaic power generation system includes a photovoltaic array, a maximum power point tracking controller, and a DC-DC converter. The photovoltaic MPPT includes: acquiring the output voltage and output current of the photovoltaic array; the maximum power point tracking controller calculates the output power of the photovoltaic array based on the output voltage and output current; the maximum power point tracking controller uses a variant swarm quantum whale optimization algorithm to obtain the optimal duty cycle, wherein the variant swarm quantum whale optimization algorithm is obtained by using adaptive population size and quantum coding optimization, the fitness function is the output power, and the position of each individual in the swarm is a candidate solution for the duty cycle; the maximum power point tracking controller controls the DC-DC converter based on the optimal duty cycle to achieve real-time control of the maximum output power of the photovoltaic array. In this scenario, the maximum power point tracking (MPPT) controller sets the fitness function to the output power, and the position of each individual in the pod as a candidate solution for the duty cycle. A variant swarm quantum whale optimization algorithm is employed to obtain the optimal duty cycle. Specifically, the variant swarm quantum whale optimization algorithm is obtained by utilizing adaptive population size and quantum encoding. Under the optimal duty cycle, the MPPT controller controls the DC-DC converter, enabling the photovoltaic array to reach its maximum output power, thereby improving power point tracking accuracy. Furthermore, the improvement of the whale optimization algorithm using adaptive population size and quantum encoding effectively saves computational resources and increases the convergence speed of the optimization process.

[0157] This disclosure aims to reduce the adverse effects of changes in external environmental factors such as solar irradiance and object / shade area on the control optimization of photovoltaic power generation systems, and to improve the power generation efficiency and safe and stable operation of photovoltaic systems under different conditions such as uniform and non-uniform illumination. Therefore, it provides a photovoltaic maximum power point tracking (MPPT) method integrating high-performance intelligent optimization algorithms. This method is based on the MPPT requirements of photovoltaic systems under the background of my country's energy structure transformation. Leveraging the superiority of swarm intelligence optimization algorithms in handling complex optimization problems, it constructs a photovoltaic system MPPT control strategy that includes the design and performance verification of a variant swarm quantum whale optimization algorithm, and the design and effectiveness verification of a photovoltaic MPPT controller based on variant swarm quantum whale optimization. This provides an effective measure to actively respond to my country's "3060" strategic plan. The variant swarm quantum whale optimization algorithm proposed in this disclosure can effectively save computational resources and improve the parameter convergence speed of the optimization process compared to existing optimization algorithms. This algorithm not only has significant effectiveness in complex function optimization but also shows great potential in practical engineering applications such as photovoltaic MPPT, and has certain theoretical and practical significance for improving power point tracking accuracy and speed. In addition, this disclosure covers the complete process of the conceptual derivation, performance verification, and application testing of the variant population quantum whale optimization algorithm in photovoltaic MPPT design. This process has reference value for the proposal and application exploration of any new intelligent optimization algorithm and shows good portability.

[0158] Furthermore, this disclosure not only introduces the concepts of adaptive population size and quantum coding into the whale optimization algorithm to form a variant population quantum whale optimization algorithm, but also verifies the performance of the designed optimization algorithm, including search speed and optimization accuracy, through benchmark function testing and nonparametric verification. Additionally, it analyzes the maximum power point tracking (MPPT) control principle of photovoltaic systems and applies the proposed variant population quantum whale algorithm to optimize the duty cycle of the DC-DC converter in the MPPT controller. Finally, it builds a photovoltaic system based on a simulation platform and verifies the feasibility and effectiveness of the proposed method in maximum power point tracking through comparative experiments and simulation tests. Driven by energy structure transformation, the method proposed in this disclosure enables the maximum power point tracking process of photovoltaic systems to adapt to changes in the external environment and the impact of uncertain disturbances, effectively promoting the high efficiency, energy conservation, and economic development of photovoltaic power generation.

[0159] The following are system embodiments of this disclosure, which can be used to execute the method embodiments of this disclosure. For details not disclosed in the system embodiments of this disclosure, please refer to the method embodiments of this disclosure.

[0160] Figure 5This diagram illustrates a block diagram of a photovoltaic maximum power point tracking (MPPT) system based on a variant swarm quantum whale optimization, as provided in an embodiment of this disclosure. This variant swarm quantum whale-optimized photovoltaic MPPT system can be implemented as all or part of a system through software, hardware, or a combination of both. The variant swarm quantum whale-optimized photovoltaic MPPT system is applied to a photovoltaic power generation system, which includes a photovoltaic array, a maximum power point tracking controller, and a DC-DC converter. The maximum power point tracking controller includes the photovoltaic MPPT system.

[0161] like Figure 5 As shown, the photovoltaic maximum power point tracking system 10 based on mutant swarm quantum whale optimization includes an acquisition module 11 and an optimization control module 12, wherein:

[0162] The acquisition module 11 is used to acquire the output voltage and output current of the photovoltaic array, and calculate the output power of the photovoltaic array based on the output voltage and output current.

[0163] The optimization control module 12 is used to obtain the optimal duty cycle using a variant swarm quantum whale optimization algorithm. The variant swarm quantum whale optimization algorithm is obtained by using adaptive swarm size and quantum coding optimization. The fitness function is the output power, and the position of each individual in the swarm is a candidate solution for the duty cycle. The DC-DC converter is controlled based on the optimal duty cycle to achieve real-time control of the maximum output power of the photovoltaic array.

[0164] Optionally, the quantum encoding is a quantum probability amplitude encoding.

[0165] Optionally, the optimization control module 12 is specifically used for: calculating the initial position of each individual in the whale pod using quantum probability amplitude encoding in the initialization step of the whale optimization algorithm; and judging whether the optimization state of the algorithm is good based on the number of consecutive updates of the current best candidate solution or the number of consecutive non-updates of the current best candidate solution at the end of each iteration. If it is good, the population size is reduced; otherwise, the population size is expanded, and the next iteration is performed based on the new population size after reduction or expansion.

[0166] Optionally, the optimization control module 12 is specifically used to: determine whether the number of consecutive updates of the current optimal candidate solution is greater than or equal to the first threshold; if so, the algorithm's optimization state is good; determine whether the number of consecutive non-updates of the current optimal candidate solution is greater than or equal to the second threshold; if so, the algorithm's optimization state is bad or it is trapped in a local optimum.

[0167] Optionally, the optimization control module 12 is specifically used to: before reducing the population size, determine whether the population size of the current iteration is less than or equal to the minimum tolerable population size; if so, stop reducing the population size, otherwise reduce the population size; before expanding the population size, determine whether the population size of the current iteration is greater than or equal to the initial population size; if so, stop expanding the population size, otherwise expand the population size.

[0168] It should be noted that the photovoltaic maximum power point tracking system based on mutant swarm quantum whale optimization provided in the above embodiments is only illustrated by the division of the functional modules described above when executing the photovoltaic maximum power point tracking method based on mutant swarm quantum whale optimization. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the photovoltaic maximum power point tracking device based on mutant swarm quantum whale optimization can be divided into different functional modules to complete all or part of the functions described above. In addition, the photovoltaic maximum power point tracking system based on mutant swarm quantum whale optimization provided in the above embodiments and the photovoltaic maximum power point tracking method embodiments based on mutant swarm quantum whale optimization belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0169] The sequence numbers of the embodiments disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0170] In the photovoltaic maximum power point tracking system based on variant swarm quantum whale optimization in this embodiment, the acquisition module acquires the output voltage and output current of the photovoltaic array, and calculates the output power of the photovoltaic array based on the output voltage and output current. The optimization control module uses a variant swarm quantum whale optimization algorithm to obtain the optimal duty cycle, wherein the variant swarm quantum whale optimization algorithm is obtained by using adaptive population size and quantum coding optimization, the fitness function is the output power, and the position of each individual in the swarm is a candidate solution for the duty cycle. The DC-DC converter is controlled based on the optimal duty cycle to achieve real-time control of the maximum output power of the photovoltaic array. In this case, the fitness function is set to the output power, the position of each individual in the swarm is a candidate solution for the duty cycle, and the variant swarm quantum whale optimization algorithm is used to obtain the optimal duty cycle. The maximum power point tracking controller controls the DC-DC converter under the optimal duty cycle, enabling the photovoltaic array to reach the maximum output power, thereby improving the power point tracking accuracy. Furthermore, by utilizing adaptive population size and quantum coding to improve the whale optimization algorithm, computational resources are effectively saved and the convergence speed of the optimization process is increased.

[0171] According to embodiments of this disclosure, this disclosure also provides a photovoltaic maximum power tracking device optimized based on a mutant population quantum whale, a readable storage medium, and a computer program product.

[0172] Figure 6 This is a block diagram of a photovoltaic maximum power point tracking (MPPT) device based on a variant swarm quantum whale optimization method for implementing embodiments of the present disclosure. The variant swarm quantum whale optimization MPPT device is intended to represent various forms of digital computers, such as laptops, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The variant swarm quantum whale optimization MPPT device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable electronic devices, and other similar computing devices. The components, connections and relationships between components, and functions shown in this disclosure are merely illustrative and are not intended to limit the implementation of the present disclosure as described and / or claimed herein.

[0173] like Figure 6 As shown, the photovoltaic maximum power point tracking device 20 based on mutant swarm quantum whale optimization includes a computing unit 21, which can perform various appropriate actions and processes according to a computer program stored in read-only memory (ROM) 22 or a computer program loaded from storage unit 28 into random access memory (RAM) 23. The RAM 23 may also store various programs and data required for the operation of the photovoltaic maximum power point tracking device 20 based on mutant swarm quantum whale optimization. The computing unit 21, ROM 22, and RAM 23 are interconnected via bus 24. An input / output (I / O) interface 25 is also connected to bus 24.

[0174] The photovoltaic maximum power point tracking device 20, optimized based on a mutant swarm quantum whale algorithm, has multiple components connected to an I / O interface 25, including: an input unit 26, such as a keyboard or mouse; an output unit 27, such as various types of displays or speakers; a storage unit 28, such as a disk or optical disk, which is communicatively connected to a computing unit 21; and a communication unit 29, such as a network interface card (NIC), modem, or wireless transceiver. The communication unit 29 allows the photovoltaic maximum power point tracking device 20 optimized based on a mutant swarm quantum whale algorithm to exchange information / data with other photovoltaic maximum power point tracking devices optimized based on a mutant swarm quantum whale algorithm via computer networks such as the Internet and / or various telecommunications networks.

[0175] The computing unit 21 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 21 performs the various methods and processes described above, such as performing a photovoltaic maximum power point tracking method based on mutant swarm quantum whale optimization. For example, in some embodiments, the photovoltaic maximum power point tracking method based on mutant swarm quantum whale optimization can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 28. In some embodiments, part or all of the computer program can be loaded and / or installed on the photovoltaic maximum power point tracking device 20 based on mutant swarm quantum whale optimization via ROM 22 and / or communication unit 29. When the computer program is loaded into RAM 23 and executed by the computing unit 21, one or more steps of the photovoltaic maximum power point tracking method based on mutant swarm quantum whale optimization described above can be performed. Alternatively, in other embodiments, computing unit 21 may be configured by any other suitable means (e.g., by means of firmware) to perform a photovoltaic maximum power point tracking method based on a variant population quantum whale optimization.

[0176] Various embodiments of the systems and techniques described above in this disclosure can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic electronic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0177] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0178] In this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or a variant-based quantum whale-optimized photovoltaic maximum power point tracking device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or electronic devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage electronics, magnetic storage electronics, or any suitable combination of the foregoing.

[0179] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0180] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0181] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0182] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this disclosure does not impose any limitations herein.

[0183] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A photovoltaic maximum power point tracking method based on mutant population quantum whale optimization, characterized in that, The photovoltaic power generation system includes a photovoltaic array, a maximum power point tracking controller, and a DC-DC converter. The photovoltaic maximum power point tracking method includes: The output voltage and output current of the photovoltaic array are obtained, and the maximum power point tracking controller calculates the output power of the photovoltaic array based on the output voltage and output current. The maximum power point tracking controller uses a variant swarm quantum whale optimization algorithm to obtain the optimal duty cycle. The variant swarm quantum whale optimization algorithm is obtained by using adaptive population size and quantum coding optimization. The fitness function is the output power, and the position of each individual in the whale swarm is a candidate solution for the duty cycle. The maximum power point tracking controller controls the DC-DC converter based on the optimal duty cycle to achieve real-time control of the maximum output power of the photovoltaic array; The optimization process of the mutant population quantum whale optimization algorithm includes: Initialize parameters and whale pod positions, including: initializing the initial population size, search dimension, and maximum number of iterations during the optimization search process, and using quantum probability amplitude encoding to calculate the initial position of each individual whale in the pod, and mapping the individual positions to the general solution space; Based on the actual application requirements, construct the fitness function for the kth optimization iteration of the optimization process, and select the smallest fitness value from the obtained fitness values ​​for the initial coarse location of the prey. The position of individuals in the whale pod is updated using a shrinking encirclement update mechanism and a spiral convergence update mechanism. After each position update, the fitness value of an individual's position in the population is calculated using the fitness function and greedily selected from the current best solution. If a newly calculated fitness value is better than the current best solution, it is used to replace the current best solution; otherwise, the current best solution remains unchanged. At the end of each iteration, the algorithm's optimization state is judged based on the number of consecutive updates of the current best candidate solution or the number of consecutive non-updates of the current best candidate solution. If it is good, the population size is reduced; otherwise, the population size is expanded, and the next iteration is carried out based on the new population size after reduction or expansion. Before reducing the population size, first determine whether the population size of the current iteration is less than or equal to the minimum tolerable population size. If so, stop reducing the population size; otherwise, reduce the population size. Before expanding the population size, first determine whether the population size of the current iteration is greater than or equal to the initial population size. If so, stop expanding the population size; otherwise, expand the population size. After each iteration, check if the termination condition is met. If yes, terminate the iteration and output the optimal solution. If no, return to the initialization process and continue the iteration process until the termination condition is met. The method of determining whether the algorithm's optimization state is good based on the number of consecutive updates to the current best candidate solution or the number of consecutive times the current best candidate solution has not been updated includes: Determine whether the number of consecutive updates of the current optimal candidate solution is greater than or equal to the first threshold. If so, the algorithm is in a good optimization state. Determine whether the number of consecutive times the current optimal candidate solution has not been updated is greater than or equal to the second threshold. If so, the algorithm is in a poor optimization state or is trapped in a local optimum.

2. A photovoltaic maximum power point tracking system based on mutant swarm quantum whale optimization, characterized in that, This is applied to a photovoltaic power generation system, which includes a photovoltaic array, a maximum power point tracking controller, and a DC-DC converter. The maximum power point tracking controller includes the photovoltaic maximum power tracking system, which includes: The acquisition module is used to acquire the output voltage and output current of the photovoltaic array, and calculate the output power of the photovoltaic array based on the output voltage and output current; An optimization control module is used to obtain the optimal duty cycle using a variant swarm quantum whale optimization algorithm. The variant swarm quantum whale optimization algorithm is obtained by using adaptive population size and quantum coding optimization. The fitness function is the output power, and the position of each individual in the whale swarm is a candidate solution for the duty cycle. The DC-DC converter is controlled based on the optimal duty cycle to achieve real-time control of the maximum output power of the photovoltaic array. The optimization process of the mutant population quantum whale optimization algorithm includes: Initialize parameters and whale pod positions, including: initializing the initial population size, search dimension, and maximum number of iterations during the optimization search process, and using quantum probability amplitude encoding to calculate the initial position of each individual whale in the pod, and mapping the individual positions to the general solution space; Based on the actual application requirements, construct the fitness function for the kth optimization iteration of the optimization process, and select the smallest fitness value from the obtained fitness values ​​for the initial coarse location of the prey. The position of individuals in the whale pod is updated using a shrinking encirclement update mechanism and a spiral convergence update mechanism. After each position update, the fitness value of an individual's position in the population is calculated using the fitness function and greedily selected from the current best solution. If a newly calculated fitness value is better than the current best solution, it is used to replace the current best solution; otherwise, the current best solution remains unchanged. At the end of each iteration, the algorithm's optimization state is judged based on the number of consecutive updates of the current best candidate solution or the number of consecutive non-updates of the current best candidate solution. If it is good, the population size is reduced; otherwise, the population size is expanded, and the next iteration is carried out based on the new population size after reduction or expansion. Before reducing the population size, first determine whether the population size of the current iteration is less than or equal to the minimum tolerable population size. If so, stop reducing the population size; otherwise, reduce the population size. Before expanding the population size, first determine whether the population size of the current iteration is greater than or equal to the initial population size. If so, stop expanding the population size; otherwise, expand the population size. After each iteration, check if the termination condition is met. If yes, terminate the iteration and output the optimal solution. If no, return to the initialization process and continue the iteration process until the termination condition is met. The method of determining whether the algorithm's optimization state is good based on the number of consecutive updates to the current best candidate solution or the number of consecutive times the current best candidate solution has not been updated includes: Determine whether the number of consecutive updates of the current optimal candidate solution is greater than or equal to the first threshold. If so, the algorithm is in a good optimization state. Determine whether the number of consecutive times the current optimal candidate solution has not been updated is greater than or equal to the second threshold. If so, the algorithm is in a poor optimization state or is trapped in a local optimum.

3. A photovoltaic maximum power point tracking device optimized based on a mutant swarm quantum whale, characterized in that, include: 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 photovoltaic maximum power tracking method based on mutant population quantum whale optimization as described in claim 1.

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