CN-121979359-A - Photovoltaic array self-adaptive global maximum power point tracking method and system

CN121979359ACN 121979359 ACN121979359 ACN 121979359ACN-121979359-A

Abstract

The invention discloses a photovoltaic array self-adaptive global maximum power point tracking method and a system, wherein the method comprises the steps of adopting a two-stage nested multi-strategy particle swarm algorithm to solve global maximum power point reference voltage and global maximum power point reference power output by a photovoltaic array based on current environmental data; the method comprises the steps of judging whether a preset accurate control switching condition is achieved according to output power of a photovoltaic array and global maximum power point reference power, switching to a self-adaptive supercoiled sliding mode control mode if the accurate control switching condition is achieved, otherwise maintaining the PI control mode, driving a power switch tube in a DC-DC converter to act by using the PI control mode or the self-adaptive supercoiled sliding mode control mode, solving reference parameters through a two-stage nesting multi-strategy optimization particle swarm algorithm, and matching a dual-mode switching mechanism of PI control and self-adaptive supercoiled sliding mode control, so that accurate and rapid tracking of the global maximum power point under a complex environment is achieved, and steady-state power fluctuation is effectively restrained.

Inventors

ZHANG GUOYU
ZHAO HUIWEN
CHEN WEI
LIU XIN
CUI LI
ZHOU YUN

Assignees

南京工程学院

Dates

Publication Date: 20260505
Application Date: 20260116

Claims (10)

1. The photovoltaic array self-adaptive global maximum power point tracking method is characterized by comprising the following steps of: Acquiring environmental data and output current and output voltage of a photovoltaic array in real time, wherein the photovoltaic array is externally connected with a load system through a DC-DC converter; Solving global maximum power point reference voltage and corresponding current output by a photovoltaic array based on current environmental data by adopting a two-stage nested multi-strategy particle swarm algorithm, wherein the environmental data comprises photovoltaic module temperature and illumination intensity; Taking the output voltage of the photovoltaic array and the global maximum power point reference voltage as input parameters of a PI controller, and outputting a control instruction to drive a power switch tube in the DC-DC converter to act; judging whether a preset accurate control switching condition is reached according to the output power of the photovoltaic array and the global maximum power point reference power, switching to a self-adaptive supercoiled sliding mode control mode if the accurate control switching condition is reached, and otherwise maintaining a PI control mode; When the self-adaptive supercoiled sliding mode is switched to the self-adaptive supercoiled sliding mode control mode, the output current and the output voltage of the photovoltaic array and the initial duty ratio are used as input parameters of the self-adaptive supercoiled sliding mode controller, and a control command is output to drive a power switch tube in the DC-DC converter to act.
2. The method of claim 1, further comprising storing the global maximum power point reference voltage and the global maximum power point reference power of the photovoltaic array to a preset storage unit; calculating to obtain a power deviation rate according to the real-time output power of the photovoltaic array and the currently stored global maximum power point reference power, wherein the expression formula is as follows: ; in the formula (i), P real is the real-time output power of the photovoltaic array, P m_cached is the currently stored global maximum power point reference power; When the power deviation rate is larger than a set first power deviation threshold value or the illumination intensity variation of any photovoltaic module in the photovoltaic array exceeds a preset illumination intensity variation threshold value, solving and updating the global maximum power point reference voltage and the global maximum power point reference power of the photovoltaic array in the current environment by utilizing a two-stage nested multi-strategy particle swarm algorithm.
3. The photovoltaic array adaptive global maximum power point tracking method according to claim 1, wherein the method for solving the global maximum power point reference voltage and the corresponding current output by the photovoltaic array based on the current environmental data by adopting a two-stage nested multi-strategy particle swarm algorithm specifically comprises: The two-stage nested multi-strategy particle swarm algorithm comprises a first-stage multi-strategy particle swarm algorithm and a second-stage multi-strategy particle swarm algorithm; the method comprises the steps of generating a photovoltaic array output voltage candidate value by a first-stage multi-strategy particle swarm algorithm, utilizing a second-stage multi-strategy particle swarm algorithm to take current of each parallel branch in the photovoltaic array as an optimization variable, and solving the photovoltaic array output current corresponding to the photovoltaic array output voltage candidate value based on current environmental data; And in a first-stage multi-strategy particle swarm algorithm, calculating a first-stage fitness value based on the photovoltaic array output voltage candidate value and the corresponding photovoltaic array output current, performing iterative optimization by taking the output voltage of the photovoltaic array as an optimization variable according to the first-stage fitness value, and solving to obtain the global maximum power point reference voltage of the photovoltaic array in the current environment.
4. The method for tracking the photovoltaic array adaptive global maximum power point according to claim 3, wherein the generating the photovoltaic array output voltage candidate value by the first-stage multi-strategy particle swarm algorithm specifically comprises: Generating a first chaotic sequence by using a logic self-mapping function, wherein the expression formula is as follows: ; In the formula, L n is the nth generation iteration value of the first chaotic sequence, L n+1 is the (n+1) th generation iteration value of the first chaotic sequence; Setting a voltage variable value range according to current environmental data, and initializing the positions and the speeds of N first-stage particles in a first-stage particle population in the voltage variable value range through the first chaotic sequence, wherein the expression formula is as follows: ; ; In the formula, X max and X min are respectively the maximum value and the minimum value of the position of the first-stage particles, V max and V min are respectively the maximum value and the minimum value of the speed of the first-stage particles, X and V are respectively the position and the speed of the first-stage particles, X n is a first chaotic sequence corresponding to the position of the first-stage particles, V n is a first chaotic sequence corresponding to the speed of the first-stage particles, and the position of the first-stage particles corresponds to the candidate value of the output voltage of the photovoltaic array.
5. The method for tracking the photovoltaic array self-adaptive global maximum power point according to claim 4, wherein the method is characterized in that the method comprises the steps of performing iterative optimization by taking the output voltage of the photovoltaic array as an optimization variable according to the first-stage fitness value, and solving to obtain the global maximum power point reference voltage of the photovoltaic array in the current environment, and specifically comprises the following steps: Setting the initial position of each first-stage particle as an individual extremum position of the first-stage particle, and screening a global extremum position with the optimal fitness value from the individual extremum positions of all the first-stage particles, wherein the expression formula is as follows: ; in the formula (i), For the first level of fitness value, Output current values corresponding to the photovoltaic array output voltage candidates, Outputting voltage candidate values for the photovoltaic array; calculating the inertia weight of each first-stage particle by using an adaptive inertia weight formula, wherein the inertia weight of the first-stage particle is dynamically adjusted based on the current maximum fitness value and the current average fitness value of the first-stage particle population and the fitness value of each first-stage particle, and the expression formula is as follows: ; In the formula, omega max 、ω min respectively represents the maximum value and the minimum value of the inertia weight omega, omega 1_i and f 1_i respectively represent the inertia weight and the fitness value of the ith first-stage particle, and f max1 、f avg1 respectively represents the current maximum fitness value and the current average fitness value of the first-stage particle population; The speed and the position of each first-stage particle are updated by combining the individual learning factors, the social learning factors and the random numbers in [0,1], and the expression formula is as follows: ; ; In the formula, c 1 and c 2 are respectively an individual learning factor and a social learning factor, r 1 and r 2 are [0,1] internal random numbers, k is the current iteration number, X i 、V i 、pbest i respectively represents the position, speed and individual extremum position of the ith first-stage particle, and gbest is the global extremum position in the first-stage particle population; performing out-of-range processing on the first-stage particle population, calculating the fitness value of each new first-stage particle, and updating the individual extremum position and the global extremum position; performing a cauchy variation operation on the individual extremum positions pbest of each first-stage particle, the expression formula being: ; ; In the formula, cau is a random number distributed in cauchy, pbest new is an individual extremum position of the first-stage particles after mutation, and pbest is a current individual extremum position of the first-stage particles; is a tangent function; to generate random numbers uniformly distributed in the [0,1] interval; Is the circumference ratio; Performing out-of-range processing on the individual extremum position of each first-stage particle after the cauchy variation, calculating an adaptability value corresponding to the new position, and updating the individual extremum position and the global extremum position; repeatedly executing a first-stage multi-strategy particle swarm algorithm to iteratively update a first-stage particle swarm, and outputting a global optimal solution through the first-stage multi-strategy particle swarm algorithm when a preset iteration termination condition is met, wherein the global optimal solution is the global maximum power point reference voltage of the photovoltaic array in the current environment.
6. The method for tracking the photovoltaic array adaptive global maximum power point according to claim 4, wherein the second-stage multi-strategy particle swarm algorithm is used for solving the photovoltaic array output current corresponding to the photovoltaic array output voltage candidate value based on the current environmental data by taking each parallel branch current in the photovoltaic array as an optimization variable, and specifically comprising the following steps: Setting a current variable value range of the parallel branch circuit according to the current environment data, initializing the position and the speed of second-stage particles in a second-stage particle population through the second chaotic sequence in the current variable value range of the parallel branch circuit, wherein the position of the second-stage particles corresponds to each parallel branch circuit candidate value in the photovoltaic array; and calculating the fitness value of each second-stage particle, wherein the expression formula is as follows: ; in the formula (i), Outputting a voltage candidate value for the photovoltaic array generated by the first-stage particle swarm algorithm; the terminal voltage of the J-th series photovoltaic module in the H parallel branch in the J X H type photovoltaic array is represented by H, wherein H is the number of parallel branches in the photovoltaic array, and J is the number of series photovoltaic modules on the parallel branches; the adaptability value of the second-stage particle swarm algorithm is obtained; when the short-circuit current of the jth series photovoltaic module in the h parallel branch under the current environment When the terminal voltage calculation formula of the j-th series photovoltaic module in the h-th parallel branch is smaller than the parallel branch current candidate value I h is as follows: ; in the formula (i), The current is the current flowing through the jth series photovoltaic module in the h parallel branch, R on is the equivalent series resistance of the anti-parallel diode, U F is the forward conduction voltage drop of the anti-parallel diode, and I h is the current candidate value of the h parallel branch; when the short-circuit current of the jth series photovoltaic module in the h parallel branch under the current environment When the current is larger than or equal to the parallel branch current candidate value I h , a coupling nonlinear equation of the parallel branch current candidate value I h and the corresponding terminal voltage U hj is established, and the expression formula is as follows: ; in the formula (i), The method is characterized in that the method is a left end function of a nonlinear equation, I ph is photo-generated current, I o1 and I o2 are diode reverse saturated current, n 1 and n 2 are diode management factors, n s is the serial number of photovoltaic cells in a photovoltaic module, R s is the serial equivalent resistance of the photovoltaic module, R p is the parallel equivalent resistance of the photovoltaic module, q is electron charge quantity, k B is Boltzmann constant, and T is the absolute temperature of the photovoltaic module; Is a natural exponential function; Solving the coupling nonlinear equation by utilizing a Newton iteration method to obtain the terminal voltage U hj of the jth series photovoltaic module in the h parallel branch corresponding to the current candidate value I h ; Setting the initial position of each second-stage particle as an individual extremum position of the second-stage particle, and screening global extremum positions with optimal fitness values from the individual extremum positions of all the second-stage particles, and marking the global extremum positions as the global extremum positions of the second-stage particle population; Calculating the inertia weight of each second-stage particle by using a self-adaptive inertia weight formula, wherein the inertia weight of the second-stage particle is dynamically adjusted based on the current minimum fitness value and the current average fitness value of the second-stage particle population and the fitness value of each second-stage particle, and the expression formula is as follows: ; In the formula, ω max 、ω min represents the maximum value and the minimum value of the inertia weight ω, ω 2_i and f 2_i represent the inertia weight and the fitness value of the ith second-stage particle, and f min2 、f avg2 represents the current minimum fitness value and the current average fitness value of the second-stage particle population; Updating the speed and the position of each second-stage particle by combining the individual learning factors, the social learning factors and the random numbers in [0,1]; performing out-of-range processing on the second-stage particle population, calculating the fitness value of each new second-stage particle, and updating the individual extremum position and the global extremum position; updating the individual extremum position of each second-stage particle by adopting the cauchy variation, and then executing out-of-range processing on the updated individual extremum position; repeatedly executing a second-stage multi-strategy particle swarm algorithm to iteratively update a second-stage particle swarm, and outputting the optimal current value of each parallel branch by the second-stage multi-strategy particle swarm algorithm when a preset iteration termination condition is met; and superposing the optimal current values of all the parallel branches to obtain the photovoltaic array output current corresponding to the photovoltaic array output voltage candidate value.
7. The method for tracking the photovoltaic array adaptive global maximum power point according to claim 1, wherein determining whether a preset accurate control switching condition is reached according to the output power of the photovoltaic array and the global maximum power point reference power specifically comprises: The accurate control switching conditions comprise that the output power of the photovoltaic array reaches the vicinity of a global maximum power point and the illumination intensity or the temperature of the photovoltaic module is not suddenly changed; Calculating the fluctuation quantity delta P of the output power of the photovoltaic array, wherein the expression formula is as follows: ; In the formula, P real (t) is the real-time output power of the photovoltaic array in the t sampling period, delta P is the fluctuation amount of the output power of the photovoltaic array, and P real (t-1) is the real-time output power of the photovoltaic array in the t-1 sampling period; Comparing the power fluctuation quantity delta P with a preset power fluctuation threshold lambda, wherein the operation logic and the judgment rule of the counter are as follows, if delta P is smaller than lambda, the counter is started and the sampling cycle number is accumulated; When the accumulated sampling cycle number of the counter reaches a preset counting threshold value, judging that the output power of the photovoltaic array is tracked to be near the global maximum power point, otherwise, judging that the output power of the photovoltaic array is not near the global maximum power point; calculating the power relative deviation according to the real-time output power of the photovoltaic array and the global maximum power point reference power, wherein the expression formula is as follows: ; In the formula, P real is the real-time output power of the photovoltaic array, P m is the global maximum power point reference power, and ρ is the relative power deviation; And when the power relative deviation rho is larger than a set second power deviation threshold value, judging that the illumination intensity or the temperature of the photovoltaic module is suddenly changed, otherwise, judging that the illumination intensity or the temperature of the photovoltaic module is not suddenly changed.
8. The method for tracking the photovoltaic array adaptive global maximum power point according to claim 1, wherein the output current, the output voltage and the initial duty ratio of the photovoltaic array are used as input parameters of the adaptive supercoiled sliding mode controller, and the output control command drives a power switch tube in the DC-DC converter to act, and the method specifically comprises the following steps: Substituting output current and output voltage of the photovoltaic array into a sliding mode surface function to obtain a comprehensive deviation state of a working point of the photovoltaic array, wherein the expression formula is as follows: ; In the formula, S is the comprehensive deviation state of the working point of the photovoltaic array; the output voltage of the photovoltaic array; The output current is the output current of the photovoltaic array; based on the comprehensive deviation state S of the photovoltaic array working points, the self-adaptive supercoiled sliding mode control law is obtained, and the expression formula is as follows: ; ; ; In the formula, xi, gamma, epsilon and phi are preset arbitrary positive constants, tau is a deviation sensitivity coefficient, alpha and beta are positive control gains which are adaptively adjusted, and u is the output control quantity of the adaptive supercoiled sliding mode controller; Is a saturation function; Is a sign function; Is the rate of change of the positive control gain alpha; Carrying out magnitude matching treatment on the output control quantity u to obtain a duty ratio adjustment increment delta D, and superposing the duty ratio adjustment increment delta D and an initial duty ratio D 0 to form a duty ratio candidate value; And converting the final control duty ratio D AST into a PWM driving signal to drive the on or off of a power switch tube of the DC-DC converter.
9. A photovoltaic array adaptive global maximum power point tracking system, comprising: The system comprises a data acquisition module, a photovoltaic array, a DC-DC converter, a load system and a power supply module, wherein the data acquisition module is used for acquiring environmental data and output current and output voltage of the photovoltaic array in real time; The optimization solving module is used for solving the global maximum power point reference voltage and the corresponding current output by the photovoltaic array based on the current environmental data by adopting a two-stage nested multi-strategy particle swarm algorithm, and calculating the global maximum power point reference voltage and the corresponding current output by the photovoltaic array to obtain global maximum power point reference power; The PI control execution module is used for taking the output voltage of the photovoltaic array and the global maximum power point reference voltage as input parameters of the PI controller and outputting a control instruction to drive a power switch tube in the DC-DC converter to act; The mode switching module is used for judging whether a preset accurate control switching condition is reached according to the output power of the photovoltaic array and the global maximum power point reference power, switching to a self-adaptive supercoiled sliding mode control mode if the accurate control switching condition is reached, and maintaining a PI control mode otherwise; And the sliding mode control execution module takes the output current, the output voltage and the initial duty ratio of the photovoltaic array as input parameters of the self-adaptive supercoiled sliding mode controller when switching to the self-adaptive supercoiled sliding mode control mode, and outputs a control instruction to drive a power switch tube in the DC-DC converter to act.
10. An electronic terminal comprising a processor and a storage medium, the storage medium storing instructions, the processor being operable according to the instructions to perform the steps of the photovoltaic array adaptive global maximum power point tracking method of any of claims 1 to 8.

Description

Photovoltaic array self-adaptive global maximum power point tracking method and system Technical Field The invention belongs to the technical field of power control, and particularly relates to a photovoltaic array self-adaptive global maximum power point tracking method and system. Background With the transition of the global energy structure to clean low carbon, photovoltaic power generation is used as a core utilization form of renewable energy, and the optimization of power generation efficiency has become a key subject of industry attention. The output power of the photovoltaic array is obviously influenced by factors such as illumination intensity, ambient temperature, local shielding and the like, the output power and voltage characteristic curve of the photovoltaic array easily shows multi-extreme point distribution, the traditional Maximum Power Point Tracking (MPPT) technology such as a disturbance observation method, a conductance increment method and the like is difficult to accurately position a Global Maximum Power Point (GMPP), and the energy utilization efficiency of a photovoltaic system is severely restricted. At present, the photovoltaic array global maximum power point tracking method under the working condition of a complex environment is mainly divided into three types, namely a hardware tracking control method based on an array structure, the anti-interference capability to the complex environment is improved through optimizing a system structure, additional hardware is needed, the cost is high, an improved direct tracking control method based on sampling data, the searching range is reduced through preprocessing an output characteristic curve, or new reference voltage is given when an algorithm falls into a local extremum, the algorithm is led to jump out of the local optimum, and a tracking control method based on an intelligent algorithm, the method obtains wide attention by virtue of the excellent global optimizing capability, but the control strategy of the existing scheme is single, the dual requirements of tracking precision and tracking speed are difficult to be considered, for example, the PI control (proportional integral control) is simple in structure and quick in response, but the defects of large steady-state power fluctuation and weak anti-interference capability exist. In summary, the existing MPPT technology has obvious short plates in the aspects of global optimizing precision, steady-state fluctuation suppression, dynamic environment suitability, parameter disturbance resistance robustness and the like, can not meet the requirements of tracking speed, tracking precision and disturbance resistance robustness at the same time, and is difficult to effectively solve the problem of optimizing the energy utilization efficiency of the photovoltaic array in a complex environment. Therefore, it is needed to develop an MPPT control method that combines the above three methods to break through the bottleneck of the prior art and improve the comprehensive performance and application value of the photovoltaic system. Disclosure of Invention The invention provides a photovoltaic array self-adaptive global maximum power point tracking method and system, which are used for solving the problem of optimizing the energy utilization efficiency of a photovoltaic array in a complex environment. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: the invention provides a photovoltaic array self-adaptive global maximum power point tracking method, which comprises the following steps: Acquiring environmental data and output current and output voltage of a photovoltaic array in real time, wherein the photovoltaic array is externally connected with a load system through a DC-DC converter; Solving global maximum power point reference voltage and corresponding current output by a photovoltaic array based on current environmental data by adopting a two-stage nested multi-strategy particle swarm algorithm, wherein the environmental data comprises photovoltaic module temperature and illumination intensity; Taking the output voltage of the photovoltaic array and the global maximum power point reference voltage as input parameters of a PI controller, and outputting a control instruction to drive a power switch tube in the DC-DC converter to act; judging whether a preset accurate control switching condition is reached according to the output power of the photovoltaic array and the global maximum power point reference power, switching to a self-adaptive supercoiled sliding mode control mode if the accurate control switching condition is reached, and otherwise maintaining a PI control mode; When the self-adaptive supercoiled sliding mode is switched to the self-adaptive supercoiled sliding mode control mode, the output current and the output voltage of the photovoltaic array and the initial duty ratio are used as input parameters of the self-adaptive supe