Photovoltaic array adaptive global maximum power point tracking method and system

Abstract

The invention discloses a photovoltaic array adaptive global maximum power point tracking method and system. The method comprises the following steps: solving global maximum power point reference voltage and global maximum power point reference power output by a photovoltaic array based on current environment data by adopting a two-stage nested multi-strategy particle swarm algorithm; judging whether a preset accurate control switching condition is met or not according to the output power of the photovoltaic array and the global maximum power point reference power; if a precise control switching condition is met, switching to a self-adaptive super-spiral sliding mode control mode, otherwise, maintaining a PI control mode; driving a power switch tube in the DC-DC converter to act by using a PI control mode or a self-adaptive super-spiral sliding mode control mode; according to the method, reference parameters are solved through the two-stage nested multi-strategy optimization particle swarm optimization algorithm, a dual-mode switching mechanism of PI control and self-adaptive super-spiral sliding mode control is matched, accurate and rapid tracking of the global maximum power point in a complex environment is achieved, and steady-state power fluctuation is effectively restrained.

Description

Technical Field

[0001] This invention belongs to the field of power control technology, specifically relating to a method and system for adaptive global maximum power point tracking of photovoltaic arrays. Background Technology

[0002] As the global energy structure transitions towards clean and low-carbon energy, the optimization of photovoltaic (PV) power generation efficiency, as a core form of renewable energy utilization, has become a key issue of concern in the industry. The output power of PV arrays is significantly affected by factors such as irradiance, ambient temperature, and local shading. Its output power-voltage characteristic curves tend to exhibit multiple extreme points, making it difficult for traditional maximum power point tracking (MPPT) technologies, such as the perturbation observation method and the incremental conductance method, to accurately locate the global maximum power point (GMPP), which severely restricts the energy utilization efficiency of PV systems.

[0003] Currently, global maximum power point tracking (GMP) methods for photovoltaic arrays under complex operating conditions are mainly divided into three categories: First, hardware tracking control methods based on array structure, which improve the anti-interference capability of complex environments by optimizing the system structure, but require additional hardware configuration and have high costs; second, improved direct tracking control methods based on sampled data, which narrow the search range by preprocessing the output characteristic curve or by assigning a new reference voltage when the algorithm gets stuck in a local extremum to guide it out of the local optimum; and third, tracking control methods based on intelligent algorithms. These methods have gained widespread attention due to their excellent global optimization capabilities, but existing solutions have single control strategies and cannot meet the dual requirements of tracking accuracy and tracking speed. For example, although PI control (proportional-integral control) has a simple structure and fast response, it has the defects of large steady-state power fluctuations and weak anti-disturbance capability.

[0004] In summary, existing MPPT technology has significant shortcomings in global optimization accuracy, steady-state fluctuation suppression, dynamic environment adaptability, and robustness against parameter disturbances. It cannot simultaneously meet the requirements of tracking speed, tracking accuracy, and disturbance robustness, and thus struggles to effectively address the energy utilization efficiency optimization problem of photovoltaic arrays in complex environments. Therefore, there is an urgent need to develop an MPPT control method that balances these three aspects to overcome existing technological bottlenecks and improve the overall performance and application value of photovoltaic systems. Summary of the Invention

[0005] This invention provides a method and system for adaptive global maximum power point tracking of photovoltaic arrays to solve the problem of optimizing the energy utilization efficiency of photovoltaic arrays under complex environments.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] The first aspect of this invention provides an adaptive global maximum power point tracking method for photovoltaic arrays, comprising:

[0008] Real-time acquisition of environmental data, as well as the output current and voltage of the photovoltaic array; the photovoltaic array is connected to an external load system via a DC-DC converter.

[0009] A two-level nested multi-strategy particle swarm optimization algorithm is used to solve for the global maximum power point reference voltage and corresponding current output by the photovoltaic array based on the current environmental data. The environmental data includes the photovoltaic module temperature and light intensity. The global maximum power point reference power is calculated from the global maximum power point reference voltage and corresponding current output by the photovoltaic array.

[0010] The output voltage of the photovoltaic array and the global maximum power point reference voltage are used as input parameters of the PI controller, and the output control command is used to drive the power switching transistors in the DC-DC converter.

[0011] The system determines whether the preset precise control switching conditions have been met based on the output power of the photovoltaic array and the global maximum power point reference power. If the precise control switching conditions are met, the system switches to the adaptive super-spiral sliding mode control mode; otherwise, the PI control mode is maintained.

[0012] When switching to the adaptive superspiral sliding mode control mode, the output current and output voltage of the photovoltaic array and the initial duty cycle are used as input parameters of the adaptive superspiral sliding mode controller, and control commands are output to drive the power switching transistors in the DC-DC converter.

[0013] Furthermore, it also includes: storing the global maximum power point reference voltage and global maximum power point reference power of the photovoltaic array into a preset storage unit;

[0014] The power deviation rate is calculated based on the real-time output power of the photovoltaic array and the currently stored global maximum power point reference power; the formula is as follows:

[0015] ;

[0016] In the formula, P represents the power deviation rate. real P represents the real-time output power of the photovoltaic array. m_cached The current stored global maximum power point reference power;

[0017] When the power deviation rate is greater than the set first power deviation threshold, or when the change in light intensity of any photovoltaic module in the photovoltaic array exceeds the preset light intensity change threshold, the global maximum power point reference voltage and global maximum power point reference power of the photovoltaic array are solved and updated again using the two-level nested multi-strategy particle swarm algorithm under the current environment.

[0018] Furthermore, a two-level nested multi-strategy particle swarm optimization algorithm is employed to solve for the global maximum power point reference voltage and corresponding current output by the photovoltaic array based on the current environmental data. Specifically, this includes:

[0019] The two-level nested multi-strategy particle swarm algorithm includes a first-level multi-strategy particle swarm algorithm and a second-level multi-strategy particle swarm algorithm.

[0020] The first-level multi-strategy particle swarm optimization algorithm generates candidate values ​​for the output voltage of the photovoltaic array. The second-level multi-strategy particle swarm optimization algorithm uses the current of each parallel branch in the photovoltaic array as the optimization variable to solve the output current of the photovoltaic array corresponding to the candidate values ​​of the output voltage of the photovoltaic array based on the current environmental data.

[0021] In the first-level multi-strategy particle swarm optimization algorithm, the first-level fitness value is calculated based on the candidate value of the photovoltaic array output voltage and the corresponding photovoltaic array output current. Based on the first-level fitness value, the output voltage of the photovoltaic array is used as the optimization variable for iterative optimization, and the global maximum power point reference voltage of the photovoltaic array under the current environment is obtained.

[0022] Furthermore, the first-level multi-strategy particle swarm optimization algorithm generates candidate values ​​for the photovoltaic array output voltage, specifically including:

[0023] The first chaotic sequence is generated using a logical self-mapping function, expressed as follows:

[0024] ;

[0025] In the formula, L n L is the nth iteration value of the first chaotic sequence; n+1 This is the (n+1)th iteration value of the first chaotic sequence;

[0026] Based on the current environmental data, the voltage variable value range is set. Within this range, the positions and velocities of N first-level particles in the first-level particle population are initialized using the first chaotic sequence, expressed as follows:

[0027] ;

[0028] ;

[0029] In the formula, X max and X min V represents the maximum and minimum values ​​of the position of the first-order particle, respectively. max and V min Let X and V be the maximum and minimum velocities of the first-stage particle, respectively, and let X and V be the position and velocity of the first-stage particle, respectively. n The position of the first-level particle corresponds to the first chaotic sequence, V nThe velocity of the first-level particle corresponds to the first chaotic sequence; wherein, the position of the first-level particle corresponds to the candidate value of the output voltage of the photovoltaic array.

[0030] Furthermore, based on the first-level fitness value, iterative optimization is performed using the output voltage of the photovoltaic array as the optimization variable to obtain the global maximum power point reference voltage of the photovoltaic array under the current environment, specifically including:

[0031] The initial position of each first-level particle is set as its individual extreme position, and the global extreme position with the best fitness value is selected from all the individual extreme positions of first-level particles; the formula is as follows:

[0032]

[0033] In the formula, This is the first-level fitness value. This represents the output current value corresponding to the candidate output voltage value of the photovoltaic array. Candidate values ​​for the output voltage of the photovoltaic array;

[0034] The inertia weight of each first-level particle is calculated using an adaptive inertia weight formula. The inertia weight of the first-level particle is dynamically adjusted based on the current maximum fitness value, the current average fitness value, and the fitness value of each first-level particle in the first-level particle population. The formula is as follows:

[0035]

[0036] In the formula, ω max ω min These represent the maximum and minimum values ​​of the inertial weight ω, respectively. 1_i and f 1_i Let f represent the inertial weight and fitness value of the i-th first-order particle, respectively. max1 f avg1 These represent the current maximum fitness value and the current average fitness value of the first-level particle population, respectively.

[0037] By combining individual learning factors, social learning factors, and random numbers within [0,1], the velocity and position of each first-level particle are updated; the formula is as follows:

[0038]

[0039]

[0040] In the formula, c1 and c2 are the individual learning factor and the social learning factor, respectively; r1 and r2 are random numbers within [0,1]; k is the current iteration number; and X... i V i pbest iLet represent the position, velocity, and individual extreme position of the i-th first-level particle, respectively, and gbest is the global extreme position in the first-level particle population;

[0041] Perform out-of-bounds processing on the first-level particle population, calculate the fitness value of each new first-level particle, and update the individual extreme position and the global extreme position;

[0042] Perform a Cauchy mutation operation on the individual extreme position pbest of each first-order particle, expressed as follows:

[0043]

[0044]

[0045] In the formula, Cau is a random number distributed by Cauchy, and pbest new pbest represents the individual extreme position of the first-level particle after mutation, and pbest represents the current individual extreme position of the first-level particle. It is the tangent function; To generate a uniformly distributed random number within the interval [0,1]; Pi;

[0046] For each first-level particle after Cauchy mutation, perform out-of-bounds processing on the individual extreme position, calculate the fitness value corresponding to the new position, and update the individual extreme position and the global extreme position;

[0047] The first-level multi-strategy particle swarm optimization algorithm is repeatedly executed to iteratively update the first-level particle population. When the preset iteration termination condition is met, the first-level multi-strategy particle swarm optimization algorithm outputs the global optimal solution, which is the global maximum power point reference voltage of the photovoltaic array under the current environment.

[0048] Furthermore, out-of-bounds processing is performed on the first-level particle population, the fitness value of each new first-level particle is calculated, and the individual extreme position and global extreme position are updated, specifically including:

[0049] If the position of the first-level particle exceeds the preset upper and lower limits of the particle position, or the velocity exceeds the preset upper and lower limits of the particle velocity, the velocity or position that satisfies the boundary constraints will be regenerated through the logical self-mapping function and the replacement will be performed.

[0050] Calculate the fitness value of each first-level particle in the updated first-level particle population. If the fitness value of the updated first-level particle is better than the fitness value corresponding to the current individual extreme position of the first-level particle, then update the position of the updated first-level particle to the current individual extreme position; if the fitness value of the updated first-level particle is better than the fitness value corresponding to the current global extreme position, then update the position of the updated first-level particle to the current global extreme position.

[0051] Furthermore, using a second-level multi-strategy particle swarm optimization algorithm with the current of each parallel branch in the photovoltaic array as the optimization variable, the photovoltaic array output current corresponding to the candidate value of the photovoltaic array output voltage is solved based on the current environmental data. Specifically, this includes:

[0052] A second chaotic sequence is generated using a logical self-mapping function; the range of current variables in the parallel branches is set according to the current environmental data; within the range of current variables in the parallel branches, the position and velocity of the second-level particles in the second-level particle population are initialized using the second chaotic sequence; the position of the second-level particles corresponds to the candidate current values ​​of each parallel branch in the photovoltaic array.

[0053] The fitness value of each second-level particle is calculated using the following formula:

[0054]

[0055] In the formula, Candidate values ​​for the output voltage of the photovoltaic array generated by the first-level particle swarm optimization algorithm; Let be the terminal voltage of the j-th series photovoltaic module in the h-th parallel branch of the J×H photovoltaic array; H is the number of parallel branches in the photovoltaic array; J is the number of series photovoltaic modules in the parallel branches; This represents the fitness value of the second-level particle swarm optimization algorithm.

[0056] When the short-circuit current of the j-th series photovoltaic module in the h-th parallel branch is under the current environment Less than the candidate value I of the parallel branch current h When the terminal voltage of the j-th series photovoltaic module in the h-th parallel branch is calculated, the formula is as follows:

[0057]

[0058] In the formula, R is the current flowing through the j-th series photovoltaic module in the h-th parallel branch; on U is the equivalent series resistance of the anti-parallel diode; F I is the forward voltage drop of the anti-parallel diode; h This is a candidate value for the current of the h-th parallel branch;

[0059] When the short-circuit current of the j-th series photovoltaic module in the h-th parallel branch is under the current environment Greater than or equal to the candidate value I of the parallel branch current h At that time, establish candidate values ​​I for the parallel branch current. h With the corresponding terminal voltage U hj The coupled nonlinear equations are expressed as follows:

[0060]

[0061] In the formula, I is the left-hand side function of the nonlinear equation; ph Photocurrent; I o1 and I o2 n is the reverse saturation current of the diode; n1 and n2 are the diode ideality factors; n s R is the number of photovoltaic cells connected in series within a photovoltaic module. s R is the series equivalent resistance of the photovoltaic module; p q is the parallel equivalent resistance of the photovoltaic module; k is the electron charge; B is Boltzmann's constant; T is the absolute temperature of the photovoltaic module; It is a natural exponential function;

[0062] The coupled nonlinear equation is solved using Newton's iterative method to obtain candidate values ​​for the current I. h The terminal voltage U of the j-th series photovoltaic module in the h-th parallel branch is corresponding to hj ;

[0063] The initial position of each second-level particle is set as its individual extreme position, and the global extreme position with the best fitness value is selected from all the individual extreme positions of the second-level particles and recorded as the global extreme position of the second-level particle population.

[0064] The inertia weight of each second-level particle is calculated using an adaptive inertia weight formula. The inertia weight of the second-level particle is dynamically adjusted based on the current minimum fitness value, the current average fitness value, and the fitness value of each second-level particle in the second-level particle population. The formula is as follows:

[0065]

[0066] In the formula, ω max ω min These represent the maximum and minimum values ​​of the inertial weight ω, respectively. 2_i and f 2_i Let f represent the inertial weight and fitness value of the i-th second-order particle, respectively. min2 f avg2 These represent the current minimum fitness value and the current average fitness value of the second-level particle population, respectively.

[0067] By combining individual learning factors, social learning factors, and random numbers within [0,1], the velocity and position of each second-level particle are updated;

[0068] Perform out-of-bounds processing on the second-level particle population, calculate the fitness value of each new second-level particle, and update the individual extreme position and the global extreme position;

[0069] The individual extremum position of each second-level particle is updated using Cauchy mutation, and then out-of-bounds processing is performed on the updated individual extremum positions; the fitness value corresponding to the new position is calculated, and the individual extremum position and the global extremum position are updated.

[0070] The second-level multi-strategy particle swarm algorithm is repeatedly executed to iteratively update the second-level particle population. When the preset iteration termination condition is met, the optimal current value of each parallel branch is output through the second-level multi-strategy particle swarm algorithm.

[0071] The photovoltaic array output current corresponding to the candidate value of the photovoltaic array output voltage is obtained by superimposing the optimal current values ​​of all parallel branches.

[0072] Furthermore, based on the output power of the photovoltaic array and the global maximum power point reference power, it is determined whether the preset precise control switching conditions have been met, specifically including:

[0073] The precise control switching conditions include the photovoltaic array's output power reaching near the global maximum power point and no sudden change in light intensity or photovoltaic module temperature.

[0074] The formula for calculating the output power fluctuation ΔP of a photovoltaic array is as follows:

[0075]

[0076] In the formula, P real (t) represents the real-time output power of the photovoltaic array during the t-th sampling period; ΔP represents the output power fluctuation of the photovoltaic array; P real (t-1) represents the real-time output power of the photovoltaic array during the (t-1)th sampling period;

[0077] The power fluctuation ΔP is compared with the preset power fluctuation threshold λ. The operation logic and judgment rules of the counter are as follows: if ΔP < λ, the counter is started and the number of sampling periods is accumulated; if ΔP ≥ λ, the counter is immediately reset and the number of accumulated sampling periods is cleared to zero.

[0078] When the number of sampling periods accumulated by the counter reaches the preset counting threshold, it is determined that the output power of the photovoltaic array has been tracked to near the global maximum power point; otherwise, it is determined that the output power of the photovoltaic array has not reached near the global maximum power point.

[0079] The relative power deviation is calculated based on the real-time output power of the photovoltaic array and the global maximum power point reference power, expressed by the following formula:

[0080]

[0081] In the formula, P real P represents the real-time output power of the photovoltaic array. mThe reference power at the global maximum power point is ρ; the relative power deviation is ρ.

[0082] If the relative power deviation ρ is greater than the set second power deviation threshold, it is determined that a sudden change has occurred in the light intensity or the temperature of the photovoltaic module; otherwise, it is determined that no sudden change has occurred in the light intensity or the temperature of the photovoltaic module.

[0083] Furthermore, the output current and voltage of the photovoltaic array, as well as the initial duty cycle, are used as input parameters for the adaptive superspiral sliding mode controller, and control commands are output to drive the power switching transistors in the DC-DC converter. Specifically, this includes:

[0084] Substituting the output current and output voltage of the photovoltaic array into the sliding mode surface function, the comprehensive deviation state of the photovoltaic array's operating point is obtained, expressed by the following formula:

[0085]

[0086] In the formula, S represents the overall deviation of the photovoltaic array's operating point; This refers to the output voltage of the photovoltaic array; This refers to the output current of the photovoltaic array.

[0087] Based on the comprehensive deviation state S of the photovoltaic array's operating point, the adaptive superspiral sliding mode control law is obtained, expressed as follows:

[0088]

[0089]

[0090]

[0091] In the formula, ξ, γ, ε, and φ are preset arbitrary positive constants, τ is the deviation sensitivity coefficient, α and β are adaptively adjusted positive control gains, and u is the output control quantity of the adaptive super-spiral sliding mode controller. It is a saturation function; It is a symbolic function; The rate of change of the positive control gain α;

[0092] The output control quantity u is processed for magnitude matching to obtain the duty cycle adjustment increment ΔD; the duty cycle adjustment increment ΔD is superimposed with the initial duty cycle D0 to form a candidate duty cycle value;

[0093] The final control duty cycle D is obtained by hard-limiting the candidate duty cycle values. AST The final control duty cycle D will be... AST It is converted into a PWM drive signal to drive the power switching transistors of the DC-DC converter to turn on or off.

[0094] A second aspect of the present invention provides a photovoltaic array adaptive global maximum power point tracking system, comprising:

[0095] The data acquisition module is used to acquire environmental data and the output current and output voltage of the photovoltaic array in real time; the photovoltaic array is connected to an external load system through a DC-DC converter.

[0096] The optimization solution module is used to solve the global maximum power point reference voltage and corresponding current output by the photovoltaic array based on the current environmental data using a two-level nested multi-strategy particle swarm algorithm. The global maximum power point reference power is then calculated from the global maximum power point reference voltage and corresponding current output by the photovoltaic array.

[0097] The PI control execution module is used to take the output voltage of the photovoltaic array and the global maximum power point reference voltage as input parameters of the PI controller, and output control commands to drive the power switching transistors in the DC-DC converter.

[0098] The mode switching module is used to determine whether the preset precise control switching conditions have been met based on the output power of the photovoltaic array and the global maximum power point reference power. If the precise control switching conditions are met, the module switches to the adaptive super-spiral sliding mode control mode; otherwise, it maintains the PI control mode.

[0099] When the sliding mode control execution module switches to the adaptive super-spiral sliding mode control mode, it uses the output current and output voltage of the photovoltaic array and the initial duty cycle as input parameters of the adaptive super-spiral sliding mode controller, and outputs control commands to drive the power switching transistors in the DC-DC converter.

[0100] A third aspect of the present invention provides an electronic terminal, characterized in that it includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the photovoltaic array adaptive global maximum power point tracking method of the first aspect.

[0101] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0102] This invention employs a two-level nested multi-strategy optimization particle swarm optimization algorithm to solve for the global maximum power point reference voltage, corresponding current, and reference power. Through multi-strategy optimization, it effectively avoids the problem of the algorithm getting trapped in local optima, and can accurately locate the global maximum power point of the photovoltaic array under the current environment. This ensures that the obtained reference voltage, corresponding current, and reference power have extremely high accuracy and reliability, providing a precise tracking target for subsequent control modes and guaranteeing the effectiveness of maximum power point tracking from the source.

[0103] This invention uses the photovoltaic array output voltage and the global maximum power point (MPPT) reference voltage as inputs to a PI controller. By rapidly responding to voltage deviations and outputting control commands, it drives the power switches of the DC-DC converter, achieving rapid tracking of the global maximum power point. PI control possesses the core advantages of low computational complexity and rapid dynamic response. In the initial stage of system startup or after changes in environmental parameters such as illumination and temperature, it can quickly reduce the deviation between the actual output and the global maximum power point, laying the foundation for subsequent precise control and effectively improving the dynamic tracking performance of the photovoltaic MPPT system.

[0104] This invention determines whether the precise control switching conditions are met based on the photovoltaic array's output power and the global maximum power point reference power. It then adaptively switches between PI control mode and adaptive superspiral sliding mode control, balancing tracking speed and control accuracy. When the switching conditions are not met, the fast tracking advantage of the PI control mode is maintained; when the switching conditions are met, the system switches to a higher-precision control mode. This avoids the limitations of a single control mode in terms of speed or accuracy, allowing the system to dynamically adjust the control strategy according to the tracking status and optimize the overall control effect.

[0105] The adaptive superspiral sliding mode control mode of this invention combines high-precision tracking characteristics with strong robustness. Under complex operating conditions such as sudden changes in light intensity, temperature, and array parameter perturbations, it can accurately correct power tracking deviations and significantly suppress steady-state power fluctuations. At the same time, its adaptive mechanism greatly reduces the dependence on system modeling accuracy and engineering tuning complexity by dynamically adjusting the control gain, further improving the control accuracy and steady-state operation reliability of the photovoltaic MPPT system. Attached Figure Description

[0106] Figure 1 This is a flowchart of the photovoltaic array adaptive global maximum power point tracking method provided in Embodiment 1 of the present invention;

[0107] Figure 2 This is a flowchart of the multi-strategy particle swarm optimization algorithm provided in Embodiment 1 of the present invention;

[0108] Figure 3 This is a circuit topology diagram of the independent photovoltaic power generation system provided in Embodiment 1 of the present invention;

[0109] Figure 4 This is a diagram of the photovoltaic array structure provided in Embodiment 1 of the present invention;

[0110] Figure 5 This is the output power and voltage curve under operating condition 1 provided in Embodiment 1 of the present invention;

[0111] Figure 6 This is the output power and voltage curve under operating condition 2 provided in Embodiment 1 of the present invention;

[0112] Figure 7 This is the output power and voltage curve under operating condition 3 provided in Embodiment 1 of the present invention;

[0113] Figure 8 This is the global maximum power point tracking output curve under operating condition 1 provided in Embodiment 1 of the present invention;

[0114] Figure 9 This is the global maximum power point tracking output curve under operating condition 2 provided in Embodiment 1 of the present invention;

[0115] Figure 10 This is the global maximum power point tracking output curve under operating condition 3 provided in Embodiment 1 of the present invention;

[0116] Figure 11 This is the global maximum power point tracking output curve provided in Embodiment 1 of the present invention when the external environment changes abruptly. Detailed Implementation

[0117] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0118] Example 1

[0119] like Figure 1 As shown, this embodiment provides an adaptive global maximum power point tracking method for photovoltaic arrays, including:

[0120] The global maximum power point tracking method is applied to stand-alone photovoltaic power generation systems; such as... Figure 3 As shown, the independent photovoltaic power generation system includes a photovoltaic array and a DC-DC converter; the photovoltaic array is connected to an external load system through the DC-DC converter;

[0121] At set intervals, the current environmental data, as well as the output current and output voltage of the photovoltaic array, are acquired. In this embodiment, the environmental data includes light intensity and photovoltaic module temperature.

[0122] This embodiment acquires environmental data (light intensity, photovoltaic module temperature) and photovoltaic array output current and output voltage in real time, which can accurately capture the dynamic operating status of the environment and photovoltaic array. This provides real and timely basic data support for subsequent global maximum power point reference parameter solution, control mode switching and control command generation, avoiding control deviations caused by data lag.

[0123] A two-level nested multi-strategy particle swarm optimization algorithm is used to solve for the global maximum power point reference voltage and corresponding current output of the photovoltaic array based on the current environmental data. Specifically, it includes:

[0124] The two-level nested multi-strategy particle swarm optimization algorithm includes a first-level multi-strategy particle swarm optimization algorithm and a second-level multi-strategy particle swarm optimization algorithm. The process of each level of the multi-strategy particle swarm optimization algorithm is as follows: Figure 2 As shown;

[0125] This embodiment employs a two-level nested multi-strategy optimization particle swarm optimization algorithm to solve for the global maximum power point reference voltage, corresponding current, and reference power. Through multi-strategy optimization, the algorithm effectively avoids getting trapped in local optima, accurately locating the global maximum power point of the photovoltaic array under the current environment. This ensures that the obtained reference voltage, corresponding current, and reference power have extremely high accuracy and reliability, providing a precise tracking target for subsequent control modes and guaranteeing the effectiveness of maximum power point tracking from the source.

[0126] The photovoltaic array output voltage candidate values ​​are generated by the first-level multi-strategy particle swarm optimization algorithm, specifically including:

[0127] The first chaotic sequence is generated using a logical self-mapping function, expressed as follows:

[0128]

[0129] In the formula, L n L is the nth iteration value of the first chaotic sequence; n+1 This is the (n+1)th iteration value of the first chaotic sequence;

[0130] Based on the current environmental data, the voltage variable value range is set. Within this range, the positions and velocities of N first-level particles in the first-level particle population are initialized using the first chaotic sequence, expressed as follows:

[0131] ;

[0132] ;

[0133] In the formula, X max and X min V represents the maximum and minimum values ​​of the position of the first-order particle, respectively. max and V min Let X and V be the maximum and minimum velocities of the first-stage particle, respectively, and let X and V be the position and velocity of the first-stage particle, respectively. n The position of the first-level particle corresponds to the first chaotic sequence, V n The velocity of the first-level particle corresponds to the first chaotic sequence; wherein, the position of the first-level particle corresponds to the candidate value of the output voltage of the photovoltaic array.

[0134] Using a second-level multi-strategy particle swarm optimization algorithm with the current of each parallel branch in the photovoltaic array as the optimization variable, the photovoltaic array output current corresponding to the candidate value of the photovoltaic array output voltage is solved based on the current environmental data. Specifically, this includes:

[0135] A second chaotic sequence is generated using a logical self-mapping function; the range of current variables in the parallel branches is set according to the current environmental data; within the range of current variables in the parallel branches, the position and velocity of the second-level particles in the second-level particle population are initialized using the second chaotic sequence; the position of the second-level particles corresponds to the candidate current values ​​of each parallel branch in the photovoltaic array.

[0136] The fitness value of each second-level particle is calculated using the following formula:

[0137]

[0138] In the formula, Candidate values ​​for the output voltage of the photovoltaic array generated by the first-level particle swarm optimization algorithm; Let be the terminal voltage of the j-th series photovoltaic module in the h-th parallel branch of the J×H photovoltaic array; H is the number of parallel branches in the photovoltaic array; J is the number of series photovoltaic modules in the parallel branches; This represents the fitness value of the second-level particle swarm optimization algorithm.

[0139] This embodiment uses a dual-diode model to describe the electrical output characteristics (current-voltage relationship) of the photovoltaic module. Alternatively, a single-diode model can also be used to describe the electrical output characteristics (current-voltage relationship) of the photovoltaic module.

[0140] When the short-circuit current of the j-th series photovoltaic module in the h-th parallel branch is under the current environment Less than the candidate value I of the parallel branch current h When the terminal voltage of the j-th series photovoltaic module in the h-th parallel branch is calculated, the formula is as follows:

[0141]

[0142] In the formula, R is the current flowing through the j-th series photovoltaic module in the h-th parallel branch; on U is the equivalent series resistance of the anti-parallel diode; F I is the forward voltage drop of the anti-parallel diode; h This is a candidate value for the current of the h-th parallel branch;

[0143] When the short-circuit current of the j-th series photovoltaic module in the h-th parallel branch is under the current environment Greater than or equal to the candidate value I of the parallel branch current h At that time, establish candidate values ​​I for the parallel branch current.h With the corresponding terminal voltage U hj The coupled nonlinear equations are expressed as follows:

[0144]

[0145] In the formula, I is the left-hand side function of the nonlinear equation; ph Photocurrent; I o1 and I o2 n is the reverse saturation current of the diode; n1 and n2 are the diode ideality factors; n s R is the number of photovoltaic cells connected in series within a photovoltaic module. s R is the series equivalent resistance of the photovoltaic module; p q is the parallel equivalent resistance of the photovoltaic module; k is the electron charge; B is Boltzmann's constant; T is the absolute temperature of the photovoltaic module; It is a natural exponential function;

[0146] Solving for candidate values ​​I of parallel branch current using Newton's iterative method h With the corresponding terminal voltage U hj The coupled nonlinear equations are used to obtain candidate values ​​for the current I. h The terminal voltage of the j-th series photovoltaic module in the h-th parallel branch; the formula is:

[0147]

[0148] In the formula, Let be the terminal voltage in the iterth iteration. This represents the terminal voltage at the (iter+1)th iteration; This is the derivative of the function on the left-hand side of the nonlinear equation;

[0149] The initial position of each second-level particle is set as its individual extreme position, and the global extreme position with the best fitness value is selected from all the individual extreme positions of the second-level particles and recorded as the global extreme position of the second-level particle population.

[0150] The inertia weight of each second-level particle is calculated using an adaptive inertia weight formula. The inertia weight of the second-level particle is dynamically adjusted based on the current minimum fitness value, the current average fitness value, and the fitness value of each second-level particle in the second-level particle population. The formula is as follows:

[0151]

[0152] In the formula, ω max ω min These represent the maximum and minimum values ​​of the inertial weight ω, respectively. 2_i and f2_i Let f represent the inertial weight and fitness value of the i-th second-order particle, respectively. min2 f avg2 These represent the current minimum fitness value and the current average fitness value of the second-level particle population, respectively.

[0153] By combining individual learning factors, social learning factors, and random numbers within [0,1], the velocity and position of each second-level particle are updated;

[0154] For the second-level particle population, out-of-bounds handling is performed, the fitness value of each new second-level particle is calculated, and the individual extreme position and global extreme position are updated; specifically including:

[0155] If the position of the second-level particle after the update exceeds the preset upper and lower limits of the particle position, or the velocity exceeds the preset upper and lower limits of the particle velocity, the velocity or position that satisfies the boundary constraints will be regenerated through the logical self-mapping function to perform the replacement.

[0156] Calculate the fitness value of each second-level particle in the updated second-level particle population. If the fitness value of the updated second-level particle is better than the fitness value corresponding to the current individual extreme position of the second-level particle, then update the position of the updated second-level particle to the current individual extreme position; if the fitness value of the updated second-level particle is better than the fitness value corresponding to the current global extreme position, then update the position of the updated second-level particle to the current global extreme position.

[0157] The individual extremum position of each second-level particle is updated using Cauchy mutation, and then out-of-bounds processing is performed on the updated individual extremum positions; the fitness value corresponding to the new position is calculated, and the individual extremum position and the global extremum position are updated.

[0158] The second-level multi-strategy particle swarm algorithm is repeatedly executed to iteratively update the second-level particle population. When the preset iteration termination condition is met, the optimal current value of each parallel branch is output through the second-level multi-strategy particle swarm algorithm.

[0159] The photovoltaic array output current corresponding to the candidate value of the photovoltaic array output voltage is obtained by superimposing the optimal current values ​​of all parallel branches.

[0160] Based on the first-level fitness value, iterative optimization is performed using the output voltage of the photovoltaic array as the optimization variable to obtain the global maximum power point reference voltage of the photovoltaic array under the current environment. Specifically, this includes:

[0161] The initial position of each first-level particle is set as its individual extreme position, and the global extreme position with the best fitness value is selected from all the individual extreme positions of first-level particles; the formula is as follows:

[0162]

[0163] In the formula, This is the first-level fitness value. These are candidate values ​​for the output voltage of the photovoltaic array. The output current value corresponding to the candidate output voltage value of the photovoltaic array;

[0164] The inertia weight of each first-level particle is calculated using an adaptive inertia weight formula. The inertia weight of the first-level particle is dynamically adjusted based on the current maximum fitness value, the current average fitness value, and the fitness value of each first-level particle in the first-level particle population. The formula is as follows:

[0165]

[0166] In the formula, ω max ω min These represent the maximum and minimum values ​​of the inertial weight ω, respectively. 1_i and f 1_i Let f represent the inertial weight and fitness value of the i-th first-order particle, respectively. max1 f avg1 These represent the current maximum fitness value and the current average fitness value of the first-level particle population, respectively.

[0167] By combining individual learning factors, social learning factors, and random numbers within [0,1], the velocity and position of each first-level particle are updated; the formula is as follows:

[0168]

[0169]

[0170] In the formula, c1 and c2 are the individual learning factor and the social learning factor, respectively; r1 and r2 are random numbers within [0,1]; k is the current iteration number; and X... i V i pbest i Let represent the position, velocity, and individual extreme position of the i-th first-level particle, respectively, and gbest is the global extreme position in the first-level particle population;

[0171] Perform out-of-bounds processing on the first-level particle population, calculate the fitness value of each new first-level particle, and update the individual extreme position and the global extreme position.

[0172] Perform a Cauchy mutation operation on the individual extreme position pbest of each first-order particle, expressed as follows:

[0173]

[0174]

[0175] In the formula, Cau is a random number distributed by Cauchy, and pbest new This represents the individual extreme position of the first-order particle after mutation. Let π be the mathematical constant pi, and pbest be the current individual extreme position of the first-order particle. It is the tangent function; To generate a uniformly distributed random number within the interval [0,1];

[0176] For each first-level particle after Cauchy mutation, perform out-of-bounds processing on the individual extreme position, calculate the fitness value corresponding to the new position, and update the individual extreme position and the global extreme position;

[0177] The first-level multi-strategy particle swarm optimization algorithm is repeatedly executed to iteratively update the first-level particle population. When the preset iteration termination condition is met, the first-level multi-strategy particle swarm optimization algorithm outputs the global optimal solution, which is the global maximum power point reference voltage of the photovoltaic array under the current environment.

[0178] The global maximum power point reference power is calculated from the global maximum power point reference voltage and the corresponding current output by the photovoltaic array; the global maximum power point reference voltage and the global maximum power point reference power of the photovoltaic array are stored in a preset storage unit;

[0179] The power deviation rate is calculated based on the real-time output power of the photovoltaic array and the currently stored global maximum power point reference power; the formula is as follows:

[0180]

[0181] In the formula, P represents the power deviation rate. real P represents the real-time output power of the photovoltaic array. m_cached The current stored global maximum power point reference power;

[0182] When the power deviation rate is greater than the set first power deviation threshold, or when the change in light intensity of any photovoltaic module in the photovoltaic array exceeds the preset light intensity change threshold, the global maximum power point reference voltage and global maximum power point reference power of the photovoltaic array are solved and updated again using the two-level nested multi-strategy particle swarm algorithm under the current environment.

[0183] The output voltage of the photovoltaic array and the global maximum power point reference voltage are used as input parameters of the PI controller, and the output control command is used to drive the power switching transistors in the DC-DC converter.

[0184] The system determines whether the preset precise control switching conditions have been met based on the output power of the photovoltaic array and the global maximum power point reference power. Specifically, this includes:

[0185] The precise control switching conditions include the photovoltaic array's output power reaching near the global maximum power point and no sudden change in light intensity or photovoltaic module temperature.

[0186] The formula for calculating the output power fluctuation ΔP of a photovoltaic array is as follows:

[0187]

[0188] In the formula, P real (t) represents the real-time output power of the photovoltaic array during the t-th sampling period; ΔP represents the output power fluctuation of the photovoltaic array; P real (t-1) represents the real-time output power of the photovoltaic array during the (t-1)th sampling period;

[0189] The power fluctuation ΔP is compared with the preset power fluctuation threshold λ. The operation logic and judgment rules of the counter are as follows: if ΔP < λ, the counter is started and the number of sampling periods is accumulated; if ΔP ≥ λ, the counter is immediately reset and the number of accumulated sampling periods is cleared to zero.

[0190] When the number of sampling periods accumulated by the counter reaches the preset counting threshold, it is determined that the output power of the photovoltaic array has been tracked to near the global maximum power point; otherwise, it is determined that the output power of the photovoltaic array has not reached near the global maximum power point.

[0191] The relative power deviation is calculated based on the real-time output power of the photovoltaic array and the global maximum power point reference power, expressed by the following formula:

[0192]

[0193] In the formula, P real P represents the real-time output power of the photovoltaic array. m The reference power at the global maximum power point is ρ; the relative power deviation is ρ.

[0194] If the relative power deviation ρ is greater than the set second power deviation threshold, it is determined that a sudden change has occurred in the light intensity or the temperature of the photovoltaic module; otherwise, it is determined that no sudden change has occurred in the light intensity or the temperature of the photovoltaic module.

[0195] This embodiment determines whether the precise control switching conditions are met based on the photovoltaic array's output power and the global maximum power point reference power. It then adaptively switches between PI control mode and adaptive superspiral sliding mode control, balancing tracking speed and control accuracy. When the switching conditions are not met, the fast tracking advantage of the PI control mode is maintained; when the switching conditions are met, it switches to a higher-precision control mode. This avoids the limitations of a single control mode in terms of speed or accuracy, allowing the system to dynamically adjust the control strategy according to the tracking status and optimize the overall control effect.

[0196] If the precise control switching conditions are met, switch to the adaptive super-helical sliding mode control mode; otherwise, maintain the PI control mode.

[0197] When switching to the adaptive superspiral sliding mode control mode, the output current and output voltage of the photovoltaic array, as well as the initial duty cycle, are used as input parameters for the adaptive superspiral sliding mode controller. Control commands are then output to drive the power switches in the DC-DC converter, specifically including:

[0198] Substituting the output current and output voltage of the photovoltaic array into the sliding mode surface function, the comprehensive deviation state of the photovoltaic array's operating point is obtained, expressed by the following formula:

[0199]

[0200] In the formula, S represents the overall deviation of the photovoltaic array's operating point; This refers to the output voltage of the photovoltaic array; This refers to the output current of the photovoltaic array.

[0201] Based on the comprehensive deviation state S of the photovoltaic array's operating point, the adaptive superspiral sliding mode control law is obtained, expressed as follows:

[0202]

[0203]

[0204]

[0205] In the formula, ξ, γ, ε, and φ are preset arbitrary positive constants, τ is the deviation sensitivity coefficient, α and β are adaptively adjusted positive control gains, and u is the output control quantity of the adaptive super-spiral sliding mode controller. It is a saturation function; It is a symbolic function; The rate of change of the positive control gain α;

[0206] The duty cycle D of the switching transistor is tracked to near the global maximum power point using PI control mode. PI The initial duty cycle D0 of the adaptive superspiral sliding mode control is expressed by the following formula:

[0207] ;

[0208] In the formula, U o The voltage across the load;

[0209] The output control quantity u is processed for magnitude matching to obtain the duty cycle adjustment increment ΔD; the duty cycle adjustment increment ΔD is superimposed with the initial duty cycle D0 to form a candidate duty cycle value;

[0210] The final control duty cycle D is obtained by hard-limiting the candidate duty cycle values. AST The final control duty cycle D will be... AST It is converted into a PWM drive signal to drive the power switching transistors of the DC-DC converter to turn on or off.

[0211] The adaptive superspiral sliding mode control mode in this embodiment combines high-precision tracking characteristics with strong robustness. Under complex operating conditions such as sudden changes in light intensity, temperature, and array parameter perturbations, it can accurately correct power tracking deviations and significantly suppress steady-state power fluctuations. At the same time, its adaptive mechanism greatly reduces the dependence on system modeling accuracy and engineering tuning complexity by dynamically adjusting the control gain, further improving the control accuracy and steady-state operation reliability of the photovoltaic MPPT system.

[0212] Taking a 3*2 photovoltaic array as an example, the array structure is as follows: Figure 4 As shown. The performance parameters of the photovoltaic module are as follows: open circuit voltage is 21.1 V, short circuit current is 3.8 A, voltage at maximum power point is 17.1 V, current at maximum power point is 3.5 A, number of cells in series is 36, current temperature coefficient is 3 mA / ℃, and voltage temperature coefficient is -80 mV / ℃. Figure 3 In the topology, capacitor C1 is 50 μF, capacitor C2 is 500 μF, inductor L is 5 mH, and load R is 30 Ω.

[0213] To verify the tracking performance of the global maximum power point under different complex environments, three typical complex operating conditions were designed, with specific parameters and output characteristics as follows:

[0214] (1) Condition 1: The temperature of all photovoltaic modules is 25℃; the irradiance of modules 1B and 1C is 600W / m². 2 The irradiance of module 2C is 800 W / m 2 The irradiance of the remaining components is 1000 W / m². 2 The corresponding output power and voltage curves are as follows: Figure 5 As shown, its output characteristic curve has 3 extreme points, with the global maximum power point located on the far right of the curve, corresponding to a maximum output power value of 267.26 W.

[0215] (2) Condition 2: The temperature of all photovoltaic modules is 25℃; the irradiance of module 1B is 800 W / m². 2 The irradiance of module 1C is 200 W / m². 2 The irradiance of components 2B and 2C is 600 W / m². 2 The irradiance of the remaining components is 1000 W / m². 2 The corresponding output power and voltage curves are as follows: Figure 6As shown, its output characteristic curve has 3 extreme points, and the global maximum power point is not located on the far right of the curve, corresponding to a maximum output power value of 177.64 W.

[0216] (3) Condition 3: The temperature of modules 1A, 1B, and 1C is 25℃, and the temperature of modules 2A, 2B, and 2C is 75℃; the irradiance of module 1A is 1000 W / m 2 The irradiance of components 1B, 2B, and 2C is 400 W / m². 2 The irradiance of module 1C is 200 W / m². 2 The irradiance of module 2A is 800 W / m². 2 The corresponding output power and voltage curves are as follows: Figure 7 As shown, its output characteristic curve has 4 extreme points, and the global maximum power point is not located on the far right of the curve, corresponding to a maximum output power value of 105.27 W.

[0217] Table 1 shows the comparison data of global maximum power point tracking accuracy of PI control, adaptive super-helical sliding mode control, and the MPPT algorithm proposed in this invention under the above three complex operating conditions. As can be seen from the data in Table 1, PI control and the MPPT algorithm proposed in this invention can stably and accurately track the global maximum power point under all complex operating conditions. However, the adaptive super-helical sliding mode control has obvious limitations; it can only achieve global maximum power point tracking in operating condition 1 (where the global maximum power point is located on the far right of the power-voltage curve). In operating conditions 2 and 3 (where the global maximum power point is not located on the far right of the power-voltage curve), it cannot accurately track the global maximum power point, and the tracking accuracy drops significantly. Further analysis of the power fluctuation amplitude and average output power data after output stabilization in the table shows that, compared with PI control, the MPPT algorithm proposed in this invention has a smaller power fluctuation amplitude, and the average output power is closer to the theoretical global maximum power value under each operating condition, exhibiting superior tracking accuracy and steady-state fluctuation suppression performance.

[0218] Table 1. Comparison of global maximum power point tracking accuracy of different MPPT algorithms;

[0219]

[0220] The global maximum power point tracking output curves of the MPPT algorithm proposed in this invention under the above three complex static conditions are as follows: Figure 8 , Figure 9 , Figure 10 As shown in the figure, under different complex operating conditions, the MPPT algorithm proposed in this invention can quickly, smoothly, and accurately track the global maximum power point of the photovoltaic array.

[0221] To verify the algorithm's dynamic response performance, the environment was set to switch from condition 1 to condition 2 at 0.1s, and then further switch to condition 3 at 0.2s. The simulation results of its global maximum power point tracking are as follows: Figure 11 As shown in the figure. The results show that the MPPT algorithm proposed in this invention can quickly adapt to sudden changes in the external environment, enabling the system to operate stably near the global maximum power point.

[0222] Example 2

[0223] This embodiment provides a photovoltaic array adaptive global maximum power point tracking system. The photovoltaic array adaptive global maximum power point tracking system is used to execute the photovoltaic array adaptive global maximum power point tracking method described in Embodiment 1. The photovoltaic array adaptive global maximum power point tracking system includes:

[0224] The data acquisition module is used to acquire environmental data and the output current and output voltage of the photovoltaic array in real time; the photovoltaic array is connected to an external load system through a DC-DC converter.

[0225] The optimization solution module is used to solve the global maximum power point reference voltage and corresponding current output by the photovoltaic array based on the current environmental data using a two-level nested multi-strategy particle swarm algorithm. The global maximum power point reference power is then calculated from the global maximum power point reference voltage and corresponding current output by the photovoltaic array.

[0226] The PI control execution module is used to take the output voltage of the photovoltaic array and the global maximum power point reference voltage as input parameters of the PI controller, and output control commands to drive the power switching transistors in the DC-DC converter.

[0227] The mode switching module is used to determine whether the preset precise control switching conditions have been met based on the output power of the photovoltaic array and the global maximum power point reference power. If the precise control switching conditions are met, the module switches to the adaptive super-spiral sliding mode control mode; otherwise, it maintains the PI control mode.

[0228] When the sliding mode control execution module switches to the adaptive super-spiral sliding mode control mode, it uses the output current and output voltage of the photovoltaic array and the initial duty cycle as input parameters of the adaptive super-spiral sliding mode controller, and outputs control commands to drive the power switching transistors in the DC-DC converter.

[0229] Example 3

[0230] This embodiment provides an electronic terminal, characterized in that it includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the photovoltaic array adaptive global maximum power point tracking method described in Embodiment 1.

[0231] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A photovoltaic array adaptive global maximum power point tracking method, characterized in that, include: Real-time acquisition of environmental data, as well as the output current and voltage of the photovoltaic array; the photovoltaic array is connected to an external load system via a DC-DC converter. A two-level nested multi-strategy particle swarm optimization algorithm is used to solve for the global maximum power point reference voltage and corresponding current output by the photovoltaic array based on the current environmental data. The environmental data includes the photovoltaic module temperature and light intensity. The global maximum power point reference power is calculated from the global maximum power point reference voltage and corresponding current output by the photovoltaic array. The output voltage of the photovoltaic array and the global maximum power point reference voltage are used as input parameters of the PI controller, and the output control command is used to drive the power switching transistors in the DC-DC converter. The system determines whether the preset precise control switching conditions have been met based on the output power of the photovoltaic array and the global maximum power point reference power. If the precise control switching conditions are met, the system switches to the adaptive super-spiral sliding mode control mode; otherwise, the PI control mode is maintained. When switching to the adaptive superspiral sliding mode control mode, the output current and output voltage of the photovoltaic array and the initial duty cycle are used as input parameters of the adaptive superspiral sliding mode controller, and control commands are output to drive the power switching transistors in the DC-DC converter.

2. The photovoltaic array adaptive global maximum power point tracking method according to claim 1, characterized in that, Also includes: Store the global maximum power point reference voltage and global maximum power point reference power of the photovoltaic array into a preset storage unit; The power deviation rate is calculated based on the real-time output power of the photovoltaic array and the currently stored global maximum power point reference power; the formula is as follows: ； In the formula, P represents the power deviation rate. real P represents the real-time output power of the photovoltaic array. m_cached The current stored global maximum power point reference power; When the power deviation rate is greater than the set first power deviation threshold, or when the change in light intensity of any photovoltaic module in the photovoltaic array exceeds the preset light intensity change threshold, the global maximum power point reference voltage and global maximum power point reference power of the photovoltaic array are solved and updated again using the two-level nested multi-strategy particle swarm algorithm under the current environment.

3. The photovoltaic array adaptive global maximum power point tracking method according to claim 1, characterized in that, A two-level nested multi-strategy particle swarm optimization algorithm is used to solve for the global maximum power point reference voltage and corresponding current output of the photovoltaic array based on the current environmental data. Specifically, it includes: The two-level nested multi-strategy particle swarm algorithm includes a first-level multi-strategy particle swarm algorithm and a second-level multi-strategy particle swarm algorithm. The first-level multi-strategy particle swarm optimization algorithm generates candidate values ​​for the output voltage of the photovoltaic array. The second-level multi-strategy particle swarm optimization algorithm uses the current of each parallel branch in the photovoltaic array as the optimization variable to solve the output current of the photovoltaic array corresponding to the candidate values ​​of the output voltage of the photovoltaic array based on the current environmental data. In the first-level multi-strategy particle swarm optimization algorithm, the first-level fitness value is calculated based on the candidate value of the photovoltaic array output voltage and the corresponding photovoltaic array output current. Based on the first-level fitness value, the output voltage of the photovoltaic array is used as the optimization variable for iterative optimization, and the global maximum power point reference voltage of the photovoltaic array under the current environment is obtained.

4. The photovoltaic array adaptive global maximum power point tracking method according to claim 3, characterized in that, The photovoltaic array output voltage candidate values ​​are generated by the first-level multi-strategy particle swarm optimization algorithm, specifically including: The first chaotic sequence is generated using a logical self-mapping function, expressed as follows: ； In the formula, L n L is the nth iteration value of the first chaotic sequence; n+1 This is the (n+1)th iteration value of the first chaotic sequence; Based on the current environmental data, the voltage variable value range is set. Within this range, the positions and velocities of N first-level particles in the first-level particle population are initialized using the first chaotic sequence, expressed as follows: ； ； In the formula, X max and X min V represents the maximum and minimum values ​​of the position of the first-order particle, respectively. max and V min Let X and V be the maximum and minimum velocities of the first-stage particle, respectively, and let X and V be the position and velocity of the first-stage particle, respectively. n The position of the first-level particle corresponds to the first chaotic sequence, V n The velocity of the first-level particle corresponds to the first chaotic sequence; wherein, the position of the first-level particle corresponds to the candidate value of the output voltage of the photovoltaic array.

5. The photovoltaic array adaptive global maximum power point tracking method according to claim 4, characterized in that, Based on the first-level fitness value, iterative optimization is performed using the output voltage of the photovoltaic array as the optimization variable to obtain the global maximum power point reference voltage of the photovoltaic array under the current environment. Specifically, this includes: The initial position of each first-level particle is set as its individual extreme position, and the global extreme position with the best fitness value is selected from all the individual extreme positions of first-level particles; the formula is as follows: ； In the formula, This is the first-level fitness value. This represents the output current value corresponding to the candidate output voltage value of the photovoltaic array. Candidate values ​​for the output voltage of the photovoltaic array; The inertia weight of each first-level particle is calculated using an adaptive inertia weight formula. The inertia weight of the first-level particle is dynamically adjusted based on the current maximum fitness value, the current average fitness value, and the fitness value of each first-level particle in the first-level particle population. The formula is as follows: ； In the formula, ω max ω min These represent the maximum and minimum values ​​of the inertial weight ω, respectively. 1_i and f 1_i Let f represent the inertial weight and fitness value of the i-th first-order particle, respectively. max1 f avg1 These represent the current maximum fitness value and the current average fitness value of the first-level particle population, respectively. By combining individual learning factors, social learning factors, and random numbers within [0,1], the velocity and position of each first-level particle are updated; the formula is as follows: ； ； In the formula, c1 and c2 are the individual learning factor and the social learning factor, respectively; r1 and r2 are random numbers within [0,1]; k is the current iteration number; and X... i V i pbest i Let represent the position, velocity, and individual extreme position of the i-th first-level particle, respectively, and gbest is the global extreme position in the first-level particle population; Perform out-of-bounds processing on the first-level particle population, calculate the fitness value of each new first-level particle, and update the individual extreme position and the global extreme position; Perform a Cauchy mutation operation on the individual extreme position pbest of each first-order particle, expressed as follows: ； ； In the formula, Cau is a random number distributed by Cauchy, and pbest new pbest represents the individual extreme position of the first-level particle after mutation, and pbest represents the current individual extreme position of the first-level particle. It is the tangent function; To generate a uniformly distributed random number within the interval [0,1]; Pi; For each first-level particle after Cauchy mutation, perform out-of-bounds processing on the individual extreme position, calculate the fitness value corresponding to the new position, and update the individual extreme position and the global extreme position; The first-level multi-strategy particle swarm optimization algorithm is repeatedly executed to iteratively update the first-level particle population. When the preset iteration termination condition is met, the first-level multi-strategy particle swarm optimization algorithm outputs the global optimal solution, which is the global maximum power point reference voltage of the photovoltaic array under the current environment.

6. The photovoltaic array adaptive global maximum power point tracking method according to claim 4, characterized in that, Using a second-level multi-strategy particle swarm optimization algorithm with the current of each parallel branch in the photovoltaic array as the optimization variable, the photovoltaic array output current corresponding to the candidate value of the photovoltaic array output voltage is solved based on the current environmental data. Specifically, this includes: A second chaotic sequence is generated using a logical self-mapping function; the range of current variables in the parallel branches is set according to the current environmental data; within the range of current variables in the parallel branches, the position and velocity of the second-level particles in the second-level particle population are initialized using the second chaotic sequence; the position of the second-level particles corresponds to the candidate current values ​​of each parallel branch in the photovoltaic array. The fitness value of each second-level particle is calculated using the following formula: ； In the formula, Candidate values ​​for the output voltage of the photovoltaic array generated by the first-level particle swarm optimization algorithm; Let be the terminal voltage of the j-th series photovoltaic module in the h-th parallel branch of the J×H photovoltaic array; H is the number of parallel branches in the photovoltaic array; J is the number of series photovoltaic modules in the parallel branches; This represents the fitness value of the second-level particle swarm optimization algorithm. When the short-circuit current of the j-th series photovoltaic module in the h-th parallel branch is under the current environment Less than the candidate value I of the parallel branch current h When the terminal voltage of the j-th series photovoltaic module in the h-th parallel branch is calculated, the formula is as follows: ； In the formula, R is the current flowing through the j-th series photovoltaic module in the h-th parallel branch; on U is the equivalent series resistance of the anti-parallel diode; F I is the forward voltage drop of the anti-parallel diode; h This is a candidate value for the current of the h-th parallel branch; When the short-circuit current of the j-th series photovoltaic module in the h-th parallel branch is under the current environment Greater than or equal to the candidate value I of the parallel branch current h At that time, establish candidate values ​​I for the parallel branch current. h With the corresponding terminal voltage U hj The coupled nonlinear equations are expressed as follows: ； In the formula, I is the left-hand side function of the nonlinear equation; ph Photocurrent; I o1 and I o2 n is the reverse saturation current of the diode; n1 and n2 are the diode ideality factors; n s R is the number of photovoltaic cells connected in series within a photovoltaic module. s R is the series equivalent resistance of the photovoltaic module; p q is the parallel equivalent resistance of the photovoltaic module; k is the electron charge; B is Boltzmann's constant; T is the absolute temperature of the photovoltaic module; It is a natural exponential function; The coupled nonlinear equation is solved using Newton's iterative method to obtain candidate values ​​for the current I. h The terminal voltage U of the j-th series photovoltaic module in the h-th parallel branch is corresponding to hj ; The initial position of each second-level particle is set as its individual extreme position, and the global extreme position with the best fitness value is selected from all the individual extreme positions of the second-level particles and recorded as the global extreme position of the second-level particle population. The inertia weight of each second-level particle is calculated using an adaptive inertia weight formula. The inertia weight of the second-level particle is dynamically adjusted based on the current minimum fitness value, the current average fitness value, and the fitness value of each second-level particle in the second-level particle population. The formula is as follows: ； In the formula, ω max ω min These represent the maximum and minimum values ​​of the inertial weight ω, respectively. 2_i and f 2_i Let f represent the inertial weight and fitness value of the i-th second-order particle, respectively. min2 f avg2 These represent the current minimum fitness value and the current average fitness value of the second-level particle population, respectively. By combining individual learning factors, social learning factors, and random numbers within [0,1], the velocity and position of each second-level particle are updated; Perform out-of-bounds processing on the second-level particle population, calculate the fitness value of each new second-level particle, and update the individual extreme position and the global extreme position; The individual extremum position of each second-level particle is updated using Cauchy mutation, and then out-of-bounds processing is performed on the updated individual extremum positions; the fitness value corresponding to the new position is calculated, and the individual extremum position and the global extremum position are updated. The second-level multi-strategy particle swarm algorithm is repeatedly executed to iteratively update the second-level particle population. When the preset iteration termination condition is met, the optimal current value of each parallel branch is output through the second-level multi-strategy particle swarm algorithm. The photovoltaic array output current corresponding to the candidate value of the photovoltaic array output voltage is obtained by superimposing the optimal current values ​​of all parallel branches.

7. The photovoltaic array adaptive global maximum power point tracking method according to claim 1, characterized in that, The system determines whether the preset precise control switching conditions have been met based on the output power of the photovoltaic array and the global maximum power point reference power. Specifically, this includes: The precise control switching conditions include the photovoltaic array's output power reaching near the global maximum power point and no sudden change in light intensity or photovoltaic module temperature. The formula for calculating the output power fluctuation ΔP of a photovoltaic array is as follows: ； In the formula, P real (t) represents the real-time output power of the photovoltaic array during the t-th sampling period; ΔP represents the output power fluctuation of the photovoltaic array; P real (t-1) represents the real-time output power of the photovoltaic array during the (t-1)th sampling period; The power fluctuation ΔP is compared with the preset power fluctuation threshold λ. The operation logic and judgment rules of the counter are as follows: if ΔP < λ, the counter is started and the number of sampling periods is accumulated; if ΔP ≥ λ, the counter is immediately reset and the number of accumulated sampling periods is cleared to zero. When the number of sampling periods accumulated by the counter reaches the preset counting threshold, it is determined that the output power of the photovoltaic array has been tracked to near the global maximum power point; otherwise, it is determined that the output power of the photovoltaic array has not reached near the global maximum power point. The relative power deviation is calculated based on the real-time output power of the photovoltaic array and the global maximum power point reference power, expressed by the following formula: ； In the formula, P real P represents the real-time output power of the photovoltaic array. m The reference power at the global maximum power point is ρ; the relative power deviation is ρ. If the relative power deviation ρ is greater than the set second power deviation threshold, it is determined that a sudden change has occurred in the light intensity or the temperature of the photovoltaic module; otherwise, it is determined that no sudden change has occurred in the light intensity or the temperature of the photovoltaic module.

8. The photovoltaic array adaptive global maximum power point tracking method according to claim 1, characterized in that, The output current and voltage of the photovoltaic array, as well as the initial duty cycle, are used as input parameters for the adaptive superspiral sliding mode controller. The controller then outputs control commands to drive the power switches in the DC-DC converter. Specifically, this includes: Substituting the output current and output voltage of the photovoltaic array into the sliding mode surface function, the comprehensive deviation state of the photovoltaic array's operating point is obtained, expressed by the following formula: ； In the formula, S represents the overall deviation of the photovoltaic array's operating point; This refers to the output voltage of the photovoltaic array; This refers to the output current of the photovoltaic array. Based on the comprehensive deviation state S of the photovoltaic array's operating point, the adaptive superspiral sliding mode control law is obtained, expressed as follows: ； ； ； In the formula, ξ, γ, ε, and φ are preset arbitrary positive constants, τ is the deviation sensitivity coefficient, α and β are adaptively adjusted positive control gains, and u is the output control quantity of the adaptive super-spiral sliding mode controller. It is a saturation function; It is a symbolic function; The rate of change of the positive control gain α; The output control quantity u is processed for magnitude matching to obtain the duty cycle adjustment increment ΔD; the duty cycle adjustment increment ΔD is superimposed with the initial duty cycle D0 to form a candidate duty cycle value; The final control duty cycle D is obtained by hard-limiting the candidate duty cycle values. AST The final control duty cycle D will be... AST It is converted into a PWM drive signal to drive the power switching transistors of the DC-DC converter to turn on or off.

9. A photovoltaic array adaptive global maximum power point tracking system, characterized in that, include: The data acquisition module is used to acquire environmental data and the output current and output voltage of the photovoltaic array in real time; the photovoltaic array is connected to an external load system through a DC-DC converter. The optimization solution module is used to solve the global maximum power point reference voltage and corresponding current output by the photovoltaic array based on the current environmental data using a two-level nested multi-strategy particle swarm algorithm. The global maximum power point reference power is then calculated from the global maximum power point reference voltage and corresponding current output by the photovoltaic array. The PI control execution module is used to take the output voltage of the photovoltaic array and the global maximum power point reference voltage as input parameters of the PI controller, and output control commands to drive the power switching transistors in the DC-DC converter. The mode switching module is used to determine whether the preset precise control switching conditions have been met based on the output power of the photovoltaic array and the global maximum power point reference power. If the precise control switching conditions are met, the module switches to the adaptive super-spiral sliding mode control mode; otherwise, it maintains the PI control mode. When the sliding mode control execution module switches to the adaptive super-spiral sliding mode control mode, it uses the output current and output voltage of the photovoltaic array and the initial duty cycle as input parameters of the adaptive super-spiral sliding mode controller, and outputs control commands to drive the power switching transistors in the DC-DC converter.

10. An electronic terminal, characterized in that, It includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the photovoltaic array adaptive global maximum power point tracking method according to any one of claims 1 to 8.

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