Maximum power point tracking method and device based on collaborative chaos optimization algorithm

Abstract

The invention discloses a maximum power point tracking method and device based on a collaborative chaos optimization algorithm, and the method comprises the steps: initializing a chaos disturbance probability and a dynamic exploration probability, and defining a search space boundary according to the duty ratio dimension of a Boost circuit; initializing working points and calculating a fitness value according to the output power of each working point; determining global maximum output power and individual historical maximum output power; in combination with the chaotic disturbance probability and the dynamic exploration probability, a plurality of optimization units are adopted to update and iterate the global maximum output power and the individual historical maximum output power, and compliance test is performed according to the search space boundary; and outputting the maximum power point. According to the method, a plurality of optimization units are cooperatively executed in iteration to update a working point, and through cooperation of a plurality of disturbance strategies and a hierarchical update mechanism, the parameter precision, response speed and stability of an MPPT control system in a dynamic environment are improved, and more efficient and reliable global maximum power point tracking is realized.

Description

Technical Field

[0001] This invention belongs to the technical field of maximum power point tracking control in photovoltaic power generation systems, specifically relating to a maximum power point tracking method and apparatus based on a cooperative chaotic optimization algorithm. Background Technology

[0002] In new energy power generation systems, especially photovoltaic (PV) power generation systems, their output characteristics are significantly affected by natural factors such as sunlight intensity, ambient temperature, and cloud cover, exhibiting strong nonlinearity and volatility. This causes the location of the maximum power point (MPP) to change dynamically at all times. The accuracy of maximum power point tracking (MPPT) affects the energy conversion efficiency of PV power generation systems, leading to a decrease in energy conversion efficiency and hindering the improvement of new energy utilization efficiency. Currently, MPPT algorithms can be broadly categorized into traditional algorithms and intelligent optimization algorithms. Traditional perturb and observe (P&O) and incremental conductance algorithms, while simple in structure and easy to implement, are prone to misjudgment when light intensity changes rapidly due to their inherent perturbation characteristics, leading to decreased tracking accuracy and even power oscillations. Improved hill-climbing methods, while stable in static environments, suffer from dynamic response speeds that fail to meet the demands of high-penetration distributed photovoltaic (PV) grid integration. Intelligent optimization algorithms, such as particle swarm optimization (PSO) and genetic algorithms (GA), while theoretically possessing global optimization capabilities, still have significant limitations in practical applications: PSO algorithms are prone to getting trapped in local optima due to improper inertia weight settings, especially in multi-peak power curve scenarios where it struggles to escape suboptimal solutions; GA algorithms exhibit strong randomness in crossover and mutation operations, resulting in slow convergence speeds and high dependence on the initial population quality, often leading to tracking lag issues when distributed PV power output fluctuates drastically.

[0003] Patent application CN120414613A discloses a method and device for coordinated control of distributed energy storage in a photovoltaic system. The method includes: performing MPPT tracking processing on real-time power data of the modules using the incremental conductance method to obtain a target adjustment command sequence; performing fluctuation characteristic analysis processing on the target adjustment command sequence using a time-domain decomposition algorithm to obtain a power balance compensation amount; performing energy storage capacity configuration processing on the power balance compensation amount using a hierarchical control algorithm to obtain an energy storage unit distribution scheme; performing charge and discharge management processing on each energy storage unit in the energy storage unit distribution scheme using a power balancing algorithm to obtain an energy storage regulation control amount; and performing system-level joint debugging processing on the energy storage regulation control amount and the target adjustment command sequence using a collaborative optimization algorithm to obtain a system operation control strategy, thereby improving the efficiency and accuracy of coordinated control of distributed energy storage in the photovoltaic system.

[0004] The aforementioned technologies, employing the traditional incremental conductance method to achieve MPPT, are ill-suited to the demands of multi-source collaborative operation and lack robustness in complex grid topologies and scenarios with multiple superimposed disturbances. Achieving efficient maximum power point tracking while ensuring the accuracy of the maximum power point in new energy power generation systems operating in highly volatile and strongly nonlinear environments is a problem that needs to be solved. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a maximum power point tracking (MPPT) method and apparatus based on a cooperative chaotic optimization algorithm. The method includes: initializing chaotic perturbation probabilities and dynamic exploration probabilities, and defining a search space boundary based on the duty cycle dimension of the Boost circuit; initializing operating points and calculating the fitness value of each operating point based on its output power; determining the global maximum output power and the individual historical maximum output power based on the fitness values; combining the chaotic perturbation probabilities and dynamic exploration probabilities, using multiple optimization units to iteratively update the global maximum output power and the individual historical maximum output power, and performing compliance checks based on the search space boundary; if the maximum number of iterations is reached or the output power converges, the maximum power point is output, completing the MPPT tracking. This invention collaboratively executes multiple optimization units during iteration to update the operating points. Through a combination of various perturbation strategies and a hierarchical update mechanism, it significantly improves the parameter accuracy, response speed, and stability of the MPPT control system in dynamic environments, achieving more efficient and reliable global maximum power point tracking.

[0006] In a first aspect, the present invention provides a maximum power point tracking method based on a cooperative chaotic optimization algorithm, specifically including the following steps: Initialize the chaotic perturbation probability and the dynamic exploration probability, and define the search space boundary according to the duty cycle dimension of the Boost circuit; Initialize the operating points and calculate the fitness value of each operating point based on its output power; Based on the fitness value, determine the global maximum output power and the individual's historical maximum output power; Combining chaotic perturbation probability and dynamic exploration probability, multiple optimization units are used to update and iterate the global maximum output power and the individual historical maximum output power, and compliance checks are performed based on the search space boundary; If the maximum number of iterations is reached or the output power converges, the maximum power point is output, completing the maximum power point tracking.

[0007] Furthermore, the optimization unit includes an exploratory optimization unit, a development optimization unit, and a perturbation optimization unit; Combining chaotic perturbation probability and dynamic exploration probability, multiple optimization units are used to update and iterate the global maximum output power and the individual historical maximum output power, specifically including: Based on the current output power of each operating point, the operating points are sorted to obtain the current operating point sorting result; By combining the exploration optimization coefficients, the current working point ranking results are divided to obtain the first working point set and the second working point set; The exploration and optimization unit optimizes and updates the working points in the first working point set according to the chaotic perturbation probability, thereby obtaining the global optimal point and the individual optimal point. The development optimization unit optimizes and updates the working points in the second working point set based on the dynamic exploration probability, thereby obtaining the global optimal point and the individual optimal point of development; The perturbation optimization unit extracts working points from the current working point sorting results based on the perturbation optimization coefficients, obtains the third working point set, and optimizes and updates the third working point set to obtain the global perturbation optimum and the individual perturbation optimum. The global optimum, the global optimum, and the global optimum are analyzed and compared, and the global maximum output power is updated and obtained. The optimal points of individuals are analyzed and compared, and the optimal points of individuals are explored and disturbed. The historical maximum output power of an individual is then updated.

[0008] Furthermore, the exploration optimization unit optimizes and updates the working points in the first set of working points based on the chaotic perturbation probability, obtaining the global optimal point and the individual optimal point, specifically including: Operating points are extracted from the first set of operating points based on the chaotic perturbation probability, forming a chaotic perturbation set and an exponentially decaying set; The decay factor is generated using the current iteration number and the maximum iteration number. Based on the generated perturbation random numbers and decay factors, the duty cycle of the operating points in the chaotic perturbation set is optimized to obtain the chaotic optimized set; By combining the current iteration count, the maximum iteration count, and the ranking of the working points in the exponentially decaying set, the duty cycle of the working points in the exponentially decaying set is optimized to obtain the exponentially optimized set. The output power of the operating points in the chaotic optimization set and the exponential optimization set is judged, and the exploration of the global optimum and the exploration of the individual optimum are given.

[0009] Furthermore, the update of the working points in the chaotic optimization set is obtained through the following steps: Determine the perturbation coefficient corresponding to the perturbation random number; The perturbation optimization term is obtained by multiplying the perturbation coefficient, duty cycle boundary difference, and attenuation factor. By integrating the perturbation optimization term and the current duty cycle, the corresponding operating point in the chaotic optimization set is updated.

[0010] Furthermore, the development optimization unit optimizes and updates the working points in the second working point set based on the dynamic exploration probability to obtain the global optimal point and the individual optimal point of development, specifically including: Based on the dynamic exploration probability, the working points in the second working point set are divided into a uniform random set and a directional movement set; Based on the generated development random numbers, combined with the duty cycle boundary difference and decay factor, the duty cycle of the working points in the uniform random set is optimized to obtain the uniform optimized set; Determine the development sign of the development coefficient corresponding to the development random number; By combining development symbols, the duty cycle of the working points in the directional movement set is optimized to obtain the directional optimization set; The output power of the operating points in the uniform optimization set and the directional optimization set is judged, and the global optimum and the individual optimum are given.

[0011] Furthermore, the duty cycle of the working points in the targeted optimization set is specifically expressed as follows:

[0012] in, To optimize the duty cycle of the i-th working point in the set after the update, D i Let r3 and r4 be the duty cycle of the individual's historical maximum output power at the i-th operating point, S be the development random number, and Sign() be the development symbol.

[0013] Furthermore, the perturbation optimization unit extracts operating points from the current operating point ranking result based on the perturbation optimization coefficients, obtains a third operating point set, and optimizes and updates the third operating point set to obtain the global perturbation optimum and the individual perturbation optimum, specifically including: Based on the perturbation optimization coefficients, extract the operating points from the current operating point sorting results to obtain the third set of operating points; Based on the median duty cycle of each working point in the third working point set, the working points in the third working point set are filtered to obtain the fourth working point set. Based on the Gaussian random number corresponding to the current working point, the set of the fourth working points is divided into a Gaussian mutation set and a historical guidance set; Based on the Gaussian coefficients corresponding to the Gaussian random numbers, and combined with the duty cycle boundary difference, the working points in the Gaussian mutation set are updated to obtain the Gaussian optimized set. Based on Gaussian random numbers, combined with the duty cycle corresponding to the global maximum output power and the duty cycle of the individual historical maximum output power, the working points in the historical bootstrap set are updated to obtain the historical optimization set. The output power of the operating points in the Gaussian optimization set and the historical optimization set is judged, and the global optimum and the individual optimum are obtained.

[0014] Furthermore, compliance checks are conducted based on the search space boundaries, specifically including: Iterate through each updated work point and perform compliance checks on each dimension of the work point; If the dimension value of the working point exceeds the upper bound of the corresponding search space boundary, the dimension value of the working point is assigned to the upper bound of the search space boundary. If the dimension value of the working point is lower than the lower bound of the corresponding search space boundary, the dimension value of the working point is assigned to the lower bound of the search space boundary. If the dimension value of the working point is within the boundary of the corresponding search space, retain the dimension value of the working point.

[0015] Secondly, the present invention also provides a maximum power point tracking device based on a cooperative chaotic optimization algorithm, employing a maximum power point tracking method based on a cooperative chaotic optimization algorithm as described above, including: The parameter initialization module is used to initialize the chaotic perturbation probability and the dynamic exploration probability, and define the search space boundary according to the duty cycle dimension of the Boost circuit; The individual initialization module is used to initialize the operating points and calculate the fitness value of each operating point based on its output power. The fitness analysis module is used to determine the global maximum output power and the individual's historical maximum output power based on the fitness value; The update and optimization module is used to combine the chaotic perturbation probability and the dynamic exploration probability, and use multiple optimization units to update and iterate the global maximum output power and the individual historical maximum output power, and perform compliance checks based on the search space boundary. The result output module is used to output the maximum power point if the maximum number of iterations is reached or the output power converges, thus completing the maximum power point tracking.

[0016] The present invention provides a maximum power point tracking method and apparatus based on a cooperative chaotic optimization algorithm, which has at least the following beneficial effects: (1) By initializing the chaotic perturbation probability and the dynamic exploration probability and defining the search space boundary, the fitness value of each working point is calculated based on the output power of each working point, and the global maximum output power and the individual historical maximum output power are determined. Combining the chaotic perturbation probability and the dynamic exploration probability, multiple optimization units are used to update and iterate the global maximum output power and the individual historical maximum output power, and compliance is checked based on the search space boundary. If the maximum number of iterations is reached or the output power converges, the maximum power point is output, and the maximum power point tracking is completed. By coordinating multiple optimization units to update the working point in the iteration, multiple perturbation strategies and hierarchical update mechanisms are combined to improve the parameter accuracy, response speed and stability of MPPT in the dynamic environment, and more efficient and reliable global maximum power point tracking is achieved.

[0017] (2) By using the exploration optimization unit to apply chaotic perturbation or exponential decay update to high-performance working point individuals, the global search capability can be enhanced. The development optimization unit prompts the remaining working point individuals to conduct random exploration or targeted development, thereby achieving a balance between exploration and utilization. The perturbation optimization unit applies Gaussian mutation or random transition to randomly selected medium and low performance working point individuals, which can effectively avoid local optima. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the maximum power point tracking method based on the cooperative chaotic optimization algorithm provided in this embodiment of the invention; Figure 2 A schematic diagram illustrating the process of updating and iterating using multiple optimization units, provided for an embodiment of the present invention; Figure 3 A schematic diagram illustrating the process of updating the exploration and optimization unit provided in this embodiment of the invention; Figure 4 A schematic diagram illustrating the process of updating the development optimization unit provided in this embodiment of the invention; Figure 5 A schematic diagram illustrating the process of updating the disturbance optimization unit provided in this embodiment of the invention; Figure 6 A schematic diagram of the compliance verification process provided for embodiments of the present invention; Figure 7 A flowchart of the maximum power point tracking method based on the cooperative chaotic optimization algorithm provided in this embodiment of the invention; Figure 8The diagram shows the structure of the maximum power point tracking device based on the cooperative chaotic optimization algorithm provided in this embodiment of the invention.

[0019] The module consists of: 201. Parameter initialization module; 202. Individual initialization module; 203. Fitness analysis module; 204. Update and optimization module; and 205. Result output module. Detailed Implementation

[0020] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0022] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0023] With the high penetration rate of distributed photovoltaic power into the distribution network, the MPPT control of a single array can no longer meet the needs of multi-source collaborative operation. Traditional algorithms are not robust enough in complex grid topologies and scenarios with multiple disturbances. How to achieve efficient tracking of the maximum power point of new energy power generation systems in highly volatile and strongly nonlinear environments, while possessing global exploration capabilities, local development accuracy, and dynamic response speed, is a problem that needs to be solved.

[0024] Based on this, the present invention provides a maximum power point tracking (MPPT) method and apparatus based on a cooperative chaotic optimization algorithm. The method includes: initializing the chaotic perturbation probability and the dynamic exploration probability, and defining the search space boundary according to the duty cycle dimension of the Boost circuit; initializing the operating points and calculating the fitness value of each operating point according to its output power; determining the global maximum output power and the individual historical maximum output power based on the fitness value; combining the chaotic perturbation probability and the dynamic exploration probability, using multiple optimization units to update and iterate the global maximum output power and the individual historical maximum output power, and performing compliance checks according to the search space boundary; if the maximum number of iterations is reached or the output power converges, the maximum power point is output, completing the MPPT tracking. The present invention collaboratively executes multiple optimization units during iteration to update the operating points. Through the combination of various perturbation strategies and a hierarchical update mechanism, it significantly improves the parameter accuracy, response speed, and stability of the MPPT control system in dynamic environments, achieving more efficient and reliable global maximum power point tracking.

[0025] like Figure 1 As shown, this embodiment of the invention provides a maximum power point tracking method based on a cooperative chaotic optimization algorithm, the specific steps of which are as follows: S101: Initialize the chaotic perturbation probability and the dynamic exploration probability, and define the search space boundary according to the duty cycle dimension of the Boost circuit.

[0026] First, we set the chaotic perturbation probability and the dynamic exploration probability, and define the upper and lower boundaries of the solution space based on the problem dimension. The chaotic perturbation probability is the probability threshold for applying a "chaotic mapping" to selected individuals during the optimization algorithm's iterations, resulting in long-range jumps. The value of the chaotic perturbation probability typically decays exponentially with iteration, ensuring early global exploration and later fine-grained mining. The dynamic exploration probability is an adaptive probability for undisturbed individuals, allowing them to perform random walks or uniform sampling near the current solution space. The magnitude of the dynamic exploration probability is adjusted in real-time by the population dispersion or iteration progress to balance local search and global escape.

[0027] In a specific example, the probability of chaotic perturbation is set to 0.1, the probability of dynamic exploration is set to 0.3, the search space boundary is defined according to the problem dimension dim, and a random permutation vector is generated for each dimension j (j=1,2,…,dim).

[0028]

[0029] Where c is the basic upper bound of the search space boundary, d is the basic lower bound of the search space boundary, lb is the lower bound of the search space boundary, ub is the upper bound of the search space boundary, and ones(1, dim) is used to generate a matrix with 1 row and dim columns.

[0030] S102: Initialize the operating points and calculate the fitness value of each operating point based on its output power.

[0031] A dimensionality-separated uniform distribution sampling technique is used to initialize the population, ensuring that individuals are evenly distributed across each dimension. The population includes n operating points, each with photovoltaic current, photovoltaic voltage, output power, and duty cycle. The fitness value is calculated based on the output power of each operating point. Simultaneously, a two-layer memory system is established to record the global optimum and the historical optimum for each individual, laying the foundation for the optimization process.

[0032] In a specific example, the individual position of work point i in dimension j is specifically represented as:

[0033] Where n is the population size, i.e. the number of working points, perm(i) is the random permutation index of working point i in dimension j, r is a uniform random number and r∈[0,1], x(i,j) is the position of working point i in dimension j, lb(j) is the upper bound of dimension j in the search space boundary, and ub(j) is the lower bound of dimension j in the search space boundary.

[0034] In the example provided by this invention, the fitness function aims at maximum power point tracking and uses the negative value of photovoltaic output power. The fitness function is specifically expressed as follows:

[0035] Where f(X) is the fitness function, used to balance the performance of the photovoltaic system, P pv To determine the output power of the photovoltaic system under a given parameter X, the fitness function is set to -P. pv This indicates that the higher the output power, the lower the fitness value.

[0036] By substituting the parameters of each working point in each dimension into the fitness function, the fitness value corresponding to each working point is obtained.

[0037] S103: Determine the global maximum output power and the individual's historical maximum output power based on the fitness value.

[0038] Based on the fitness value of each operating point, determine the global maximum output power and the individual historical maximum output power. The individual historical optimal fitness value of operating point i is f. i The global optimal fitness value is f min =min(f i The globally optimal position is X. best =argmin(f iThe output power corresponding to the global optimal position is the global maximum output power, and the output power of each working point corresponding to the individual optimal position during the update process is the individual's historical maximum output power.

[0039] S104: Combining chaotic perturbation probability and dynamic exploration probability, multiple optimization units are used to update and iterate the global maximum output power and the individual historical maximum output power, and compliance checks are performed based on the search space boundary.

[0040] Specifically, the optimization units include exploratory optimization units, development optimization units, and perturbation optimization units.

[0041] Furthermore, combining the chaotic perturbation probability and the dynamic exploration probability, multiple optimization units are used to update and iterate the global maximum output power and the individual historical maximum output power, referring to... Figure 2 Specifically, it includes: Based on the current output power of each operating point, the operating points are sorted to obtain the current operating point sorting result; By combining the exploration optimization coefficients, the current working point ranking results are divided to obtain the first working point set and the second working point set; The exploration and optimization unit optimizes and updates the working points in the first working point set according to the chaotic perturbation probability, thereby obtaining the global optimal point and the individual optimal point. The development optimization unit optimizes and updates the working points in the second working point set based on the dynamic exploration probability, thereby obtaining the global optimal point and the individual optimal point of development; The perturbation optimization unit extracts working points from the current working point sorting results based on the perturbation optimization coefficients, obtains the third working point set, and optimizes and updates the third working point set to obtain the global perturbation optimum and the individual perturbation optimum. The global optimum, the global optimum, and the global optimum are analyzed and compared, and the global maximum output power is updated and obtained. The optimal points of individuals are analyzed and compared, and the optimal points of individuals are explored and disturbed. The historical maximum output power of an individual is then updated.

[0042] In one specific implementation, during the iterative loop of global maximum output power and individual historical maximum output power, three optimization units are executed, including an exploration optimization unit, a development optimization unit, and a perturbation optimization unit. In a specific example, the exploration optimization coefficient is 20%. Based on the current output power of each working point, the working points are sorted in descending order to obtain the current working point ranking result. Then, the top 20% of the working points in the current working point ranking result form the first working point set, and the remaining 80% of the working points form the second working point set. In other examples, the exploration optimization coefficient can be set according to the actual situation and is not limited thereto. The exploration optimization unit performs a dual-mode update on the first working point set (i.e., the first 20% of the working points in the ranking result). It sets a 10% probability to add nonlinear perturbation to the global optimum during the update process of the first working point set using chaotic mapping, and sets a 90% probability to perform exponential decay position shrinkage on the first working point set. The development optimization unit selects the second working point set (i.e., the last 80% of the working points in the ranking result) according to a dynamic probability selection strategy, setting a 30% probability to perform a uniform random walk near the current position and a 70% probability to construct a symbol vector to move directionally towards the current optimal solution. The perturbation optimization unit randomly selects 20% of the operating points in the current operating point ranking results as the third operating point set to implement dual-mode perturbation: with a 50% probability of performing Gaussian mutation near the global optimum and a 50% probability of randomly transitioning towards the global optimum.

[0043] Furthermore, the exploration and optimization unit optimizes and updates the working points in the first set of working points based on the chaotic perturbation probability, obtaining the global optimum and the individual optimum, referring to... Figure 3 Specifically, it includes: Operating points are extracted from the first set of operating points based on the chaotic perturbation probability, forming a chaotic perturbation set and an exponentially decaying set; The decay factor is generated using the current iteration number and the maximum iteration number. Based on the generated perturbation random numbers and decay factors, the duty cycle of the operating points in the chaotic perturbation set is optimized to obtain the chaotic optimized set; By combining the current iteration count, the maximum iteration count, and the ranking of the working points in the exponentially decaying set, the duty cycle of the working points in the exponentially decaying set is optimized to obtain the exponentially optimized set. The output power of the operating points in the chaotic optimization set and the exponential optimization set is judged, and the exploration of the global optimum and the exploration of the individual optimum are given.

[0044] The update of the working points in the chaotic optimization set is obtained through the following steps: Determine the perturbation coefficient corresponding to the perturbation random number; The perturbation optimization term is obtained by multiplying the perturbation coefficient, duty cycle boundary difference, and attenuation factor. By integrating the perturbation optimization term and the current duty cycle, the corresponding operating point in the chaotic optimization set is updated.

[0045] The duty cycle of the working points in the chaotic optimization set is specifically expressed as:

[0046] in, To update the duty cycle of the i-th working point in the chaotic optimization set, D best r1 is the duty cycle corresponding to the current global maximum output power, and r1 is a perturbation random number. Let be the perturbation coefficient, ΔD be the duty cycle boundary difference, o(t) be the decay factor corresponding to the current iteration number t, and T be the perturbation coefficient. max This represents the maximum number of iterations.

[0047] The duty cycle of the working points in the exponentially optimized set is specifically expressed as:

[0048] in, To optimize the duty cycle of the i-th working point in the exponentially optimized set, D i Let r2 be the duty cycle of the historical maximum output power of an individual at the i-th operating point, r2 be the perturbation random number, and o(t) be the decay factor corresponding to the current iteration number t. max This represents the maximum number of iterations.

[0049] The exploration optimization unit combines chaotic perturbation and exponential decay to optimize the top 20% of working points in the ranking, thereby enhancing the global search capability.

[0050] It's important to understand that during the update process, the duty cycle for each operating point is calculated and updated. This updated duty cycle is immediately written into the PWM comparator register (e.g., CMPA), waiting for the Boost inductor current to build up, typically for 1-2 switching cycles (50-100µs for 20kHz switching). After the Boost inductor current builds up, synchronous sampling is performed, and the analog-to-digital converter simultaneously captures the photovoltaic voltage V. pv and photovoltaic current I pv (Hardware-triggered sampling, avoiding switching edges). The photovoltaic voltage and current are multiplied to obtain the updated output power corresponding to the duty cycle, thus obtaining a new fitness value to participate in the next round of individual history and global optimum updates. That is, after each duty cycle update, the photovoltaic voltage V is remeasured. pv and photovoltaic current I pv And calculate the output power P in real time pv This serves as a feedback signal for the next generation of search.

[0051] Furthermore, the development optimization unit optimizes and updates the working points in the second working point set based on the dynamic exploration probability, obtaining the global optimal point and the individual optimal point of development, referring to... Figure 4 Specifically, it includes: Based on the dynamic exploration probability, the working points in the second working point set are divided into a uniform random set and a directional movement set; Based on the generated development random numbers, combined with the duty cycle boundary difference and decay factor, the duty cycle of the working points in the uniform random set is optimized to obtain the uniform optimized set; Determine the development sign of the development coefficient corresponding to the development random number; By combining development symbols, the duty cycle of the working points in the directional movement set is optimized to obtain the directional optimization set; The output power of the operating points in the uniform optimization set and the directional optimization set is judged, and the global optimum and the individual optimum are given.

[0052] The duty cycle of the working points in the uniform optimization set is specifically expressed as follows:

[0053] The duty cycle of the working points in the targeted optimization set is specifically expressed as follows:

[0054] in, To optimize the duty cycle of the i-th working point in a uniformly optimized set or a directional optimized set, D i Let r3 and r4 be the duty cycle of the individual's historical maximum output power at the i-th operating point, S be the development random number, and Sign() be the development symbol.

[0055] The development optimization unit integrates random walk and directional movement to optimize 80% of the working points after sorting, thereby improving the accuracy of local development.

[0056] Furthermore, the perturbation optimization unit extracts operating points from the current operating point ranking results based on the perturbation optimization coefficients, obtains a third operating point set, and optimizes and updates the third operating point set to obtain the global perturbation optimum and the individual perturbation optimum, referring to... Figure 5 Specifically, it includes: Based on the perturbation optimization coefficients, extract the operating points from the current operating point sorting results to obtain the third set of operating points; Based on the median duty cycle of each working point in the third working point set, the working points in the third working point set are filtered to obtain the fourth working point set. Based on the Gaussian random number corresponding to the current working point, the set of the fourth working points is divided into a Gaussian mutation set and a historical guidance set; Based on the Gaussian coefficients corresponding to the Gaussian random numbers, and combined with the duty cycle boundary difference, the working points in the Gaussian mutation set are updated to obtain the Gaussian optimized set. Based on Gaussian random numbers, combined with the duty cycle corresponding to the global maximum output power and the duty cycle of the individual historical maximum output power, the working points in the historical bootstrap set are updated to obtain the historical optimization set. The output power of the operating points in the Gaussian optimization set and the historical optimization set is judged, and the global optimum and the individual optimum are obtained.

[0057] In one specific implementation, the working points in the third working point set are filtered based on the median of the duty cycle of each working point in the third working point set. Working points with a duty cycle less than the median are filtered out to obtain the fourth working point set. Combined with the Gaussian random number corresponding to the current working point, the fourth working point set is divided into a Gaussian mutation set and a historical guide set according to the different values ​​of the Gaussian random number. Different methods are used to perform random perturbation on the two sets to update the working points.

[0058] The update of the working point in the Gaussian optimization set is specifically represented as:

[0059] The update of working points in the historical optimization set is specifically represented as follows:

[0060] Where N is a standard normally distributed random vector, i.e. a Gaussian random number.

[0061] The perturbation optimization unit uses Gaussian mutation and history guidance to update the fourth operating point set, performing only "individual history" updates and avoiding the global optimum to prevent inferior solutions from skewing the true peak value. The perturbation optimization unit only performs update operations on operating points whose duty cycles are greater than the median.

[0062] Furthermore, the exploration, development, and perturbation of the global optimum are analyzed and compared to update the global maximum output power, specifically including: Find the minimum fitness value among the fitness values ​​in the exploration of the global optimum, the development of the global optimum, and the perturbation of the global optimum; The minimum fitness value is compared with the optimal fitness value. If the minimum fitness value is less than the optimal fitness value, the optimal fitness value is updated to the minimum fitness value, and the maximum output power corresponding to the minimum fitness value is simultaneously updated to the global maximum output power. Specifically:

[0063] in, Let f* be the historical best fitness value of individual at work point i, and f* be the global best fitness value. best D represents the duty cycle corresponding to the current global maximum output power. i The duty cycle is the historical maximum output power of an individual at the i-th operating point.

[0064] It is understandable that when the duty cycle of each operating point is updated, the new photovoltaic voltage and photovoltaic current will be determined based on the updated duty cycle, and thus the updated output power will be obtained.

[0065] Furthermore, the methods of developing, exploring, and perturbing the individual's optimal point are analyzed and compared to update and obtain the individual's historical maximum output power. Find the minimum fitness value among the fitness values ​​of developing an individual's optimal point, exploring an individual's optimal point, and perturbing an individual's optimal point; The minimum fitness value is compared with the optimal fitness value. If the minimum fitness value is less than the individual's historical optimal fitness value, the individual's historical optimal fitness value is updated to the minimum fitness value. Simultaneously, the maximum output power corresponding to the minimum fitness value is updated to the individual's historical maximum output power. Specifically, this is expressed as follows:

[0066] in, Let i be the optimal fitness value after updating the working point i. Let be the historical best fitness value of the individual at work point i.

[0067] Furthermore, compliance checks are conducted based on the search space boundaries, referring to... Figure 6 Specifically, it includes: Iterate through each updated work point and perform compliance checks on each dimension of the work point; If the dimension value of the working point exceeds the upper bound of the corresponding search space boundary, the dimension value of the working point is assigned to the upper bound of the search space boundary. If the dimension value of the working point is lower than the lower bound of the corresponding search space boundary, the dimension value of the working point is assigned to the lower bound of the search space boundary. If the dimension value of the working point is within the boundary of the corresponding search space, retain the dimension value of the working point.

[0068] Compliance verification is specifically expressed as follows:

[0069] Where, x ij Let lb be the value of working point i in dimension j. j Let ub be the upper bound of dimension j in the search space. j Let be the lower bound of dimension j in the search space boundary.

[0070] By performing compliance checks on the dimensions of all newly generated work points (i.e., the updated global maximum output power and the individual historical maximum output power), we ensure that each work point is within the effective search space.

[0071] It is worth noting that, after the exploration and optimization unit optimizes and updates the working points in the first set of working points according to the chaotic perturbation probability to obtain the global optimum, the present invention further includes: Calculate the relative increase between the current power at the global optimum and the current global maximum output power, and if the relative increase exceeds a preset threshold, perform the following steps: The exploration optimization unit generates enhancement signals and stores them in the event register; In response to the enhancement signal in the event register, the perturbation optimization unit increases the intensity of the Gaussian mutation and updates the working points in the Gaussian mutation set in conjunction with the intensity decay mechanism.

[0072] Specifically, after each exploration optimization unit update, the relative increase in power between the newly discovered global optimal point and the current global maximum output power is calculated, and is expressed as follows:

[0073] In the formula, ΔP is the relative lift, and P explore To explore the global optimal power, P global_best This represents the current global maximum output power.

[0074] Next, the relative boost is compared with a preset dynamic threshold to determine the enhancement signal trigger condition: ΔP > θ. If the trigger condition is met, an enhancement signal is generated and stored in the event register. Here, θ is the dynamic threshold, which defaults to 1.5% and is dynamically adjusted according to changes in illumination. The event register is a 1-bit status register; it is set to 1 when the trigger condition is met, and this state is maintained until it is automatically cleared after being read by the perturbation optimization unit; otherwise, it is set to 0.

[0075] Before performing optimization, the perturbation optimization unit reads the event register status. If the status is 1, it activates the enhancement mode to increase the Gaussian mutation intensity, specifically as follows:

[0076] In the formula, Let D be the duty cycle after Gaussian mutation of the current operating point i. i is the duty cycle of the current working point i; N(0,1) represents a standard normal distribution random number, ub is the upper bound of the search space boundary, lb is the lower bound of the search space boundary, and ub-lb is the search space of the duty cycle. To enhance the Gaussian mutation intensity, the Gaussian mutation operation is used to perturb the optimization unit. ΔP is the relative boost amount; k1 is the intensity upper limit coefficient, which can be 0.5, used to limit the maximum intensity of Gaussian mutation and prevent excessive perturbation; k2 is the boost factor, which can be 10, to control the degree of influence of the relative boost amount on the intensity of Gaussian mutation. The basic Gaussian mutation intensity is set empirically or adaptively, and can be set to 0.1. Targeted collaboration between optimization units is achieved through event-driven mechanisms. When an exploration optimization unit discovers a breakthrough power increase, the influence of ΔP is amplified by k2, enhancing the Gaussian mutation intensity and enabling the optimization to quickly move towards the new potential peak. k1 limits the maximum mutation intensity to prevent excessive mutation from compromising optimization stability.

[0077] The intensity attenuation mechanism is specifically represented as follows:

[0078] In the formula, Let be the Gaussian mutation intensity in the t-th iteration. The intensity of the enhanced Gaussian mutation is denoted by k; k3 is the decay coefficient, which can be set to 1; t represents the iteration number, and Tmax is the maximum iteration number. As the iteration progresses, the decay mechanism ensures that the perturbation intensity automatically regresses, achieving adaptive adjustment between early strong mutations exploring new regions and late weak mutations performing fine-grained searches, thus avoiding excessive perturbation in late iterations that could destroy the already found optimal solution.

[0079] By triggering dynamic collaboration between optimization units through key events, the algorithm's response speed in scenarios such as sudden changes in illumination can be significantly improved. Furthermore, it only involves scalar comparisons and coefficient adjustments, without complex calculations, making it suitable for real-time operation in embedded systems and improving optimization efficiency.

[0080] S105: If the maximum number of iterations is reached or the output power converges, output the maximum power point to complete maximum power point tracking.

[0081] When the maximum number of iterations or the output power converges, the iteration is complete, and the final optimal fitness value, the corresponding output power (i.e., the maximum power point) and the convergence trajectory curve recording the optimal value of each generation are output.

[0082] Reference Figure 7The maximum power point tracking method based on the cooperative chaotic optimization algorithm provided by this invention first initializes the parameters and operating points, and defines a fitness function to give the fitness value of each operating point. Based on the fitness value, the global maximum output power and the individual historical maximum output power of the current operating point can be determined. During the update iteration process based on the records, the operating points are first sorted and divided into two sets. Exploratory optimization units and development optimization units are used to perform targeted global maximum output power optimization on different sets, improving the accuracy of maximum power point tracking while ensuring the update effect. Simultaneously, the sorting result is input into the perturbation optimization unit, which updates the individual historical maximum output power by filtering the operating points without affecting the update results of the exploration and development optimization units. Finally, the updated operating points are verified for compliance, completing one iteration process. This process continues until the maximum number of iterations is reached or the output power converges, thus obtaining the final maximum power point and completing the tracking of the maximum power point.

[0083] Reference Figure 8 This invention provides a maximum power point tracking device based on a cooperative chaotic optimization algorithm, comprising: The parameter initialization module 201 is used to initialize the chaotic perturbation probability and the dynamic exploration probability and define the search space boundary according to the duty cycle dimension of the Boost circuit. Individual initialization module 202 is used to initialize the operating points and calculate the fitness value of each operating point based on the output power of each operating point; The fitness analysis module 203 is used to determine the global maximum output power and the individual historical maximum output power based on the fitness value; The update and optimization module 204 is used to combine the chaotic perturbation probability and the dynamic exploration probability, and use multiple optimization units to update and iterate the global maximum output power and the individual historical maximum output power, and perform compliance checks based on the search space boundary. The result output module 205 is used to output the maximum power point if the maximum number of iterations is reached or the output power converges, thus completing the maximum power point tracking.

[0084] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0085] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and variations of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A maximum power point tracking method based on a cooperative chaos optimization algorithm, characterized in that, The method comprises the following steps: initializing a chaotic disturbance probability and a dynamic exploration probability, and defining a search space boundary according to a duty cycle dimension of a Boost circuit; initializing working points and calculating fitness values of the working points according to output powers of the working points; determining a global maximum output power and an individual historical maximum output power according to the fitness values; updating and iterating the global maximum output power and the individual historical maximum output power by using multiple optimization units in combination with the chaotic disturbance probability and the dynamic exploration probability, and performing compliance testing according to the search space boundary; outputting a maximum power point and completing maximum power point tracking if a maximum iteration number is reached or the output power converges.

2. The maximum power point tracking method based on a cooperative chaos optimization algorithm as claimed in claim 1, wherein, The optimization unit comprises an exploration optimization unit, a development optimization unit and a disturbance optimization unit; The updating and iteration of the global maximum output power and the individual historical maximum output power by using multiple optimization units in combination with the chaotic disturbance probability and the dynamic exploration probability specifically comprises the following steps: sorting the working points based on the output powers of the working points to obtain a current working point sorting result; dividing the current working point sorting result in combination with an exploration optimization coefficient to obtain a first working point set and a second working point set; optimizing and updating the working points in the first working point set by the exploration optimization unit according to the chaotic disturbance probability to obtain an exploration global optimal point and an exploration individual optimal point; optimizing and updating the working points in the second working point set by the development optimization unit according to the dynamic exploration probability to obtain a development global optimal point and a development individual optimal point; extracting working points from the current working point sorting result by the disturbance optimization unit according to a disturbance optimization coefficient to obtain a third working point set and optimizing and updating the third working point set to obtain a disturbance global optimal point and a disturbance individual optimal point; analyzing and comparing the exploration global optimal point, the development global optimal point and the disturbance global optimal point to update the global maximum output power; analyzing and comparing the development individual optimal point, the exploration individual optimal point and the disturbance individual optimal point to update the individual historical maximum output power.

3. The maximum power point tracking method based on a cooperative chaos optimization algorithm as claimed in claim 2, wherein, The optimization and updating of the working points in the first working point set by the exploration optimization unit according to the chaotic disturbance probability specifically comprises the following steps: extracting the working points from the first working point set according to the chaotic disturbance probability to form a chaotic micro-disturbance set and an exponential decay set; generating a decay factor through a current iteration number and a maximum iteration number; optimizing the duty cycles of the working points in the chaotic micro-disturbance set according to the generated disturbance random number and the decay factor to obtain a chaotic optimization set; optimizing the duty cycles of the working points in the exponential decay set according to the current iteration number, the maximum iteration number and the ranking of the working points in the exponential decay set to obtain an exponential optimization set; judging the output powers of the working points in the chaotic optimization set and the exponential optimization set to give the exploration global optimal point and the exploration individual optimal point.

4. The maximum power point tracking method based on a cooperative chaos optimization algorithm as claimed in claim 3, wherein, The updating of the working points in the chaotic optimization set is achieved through the following steps: determining a disturbance coefficient corresponding to the disturbance random number; calculating a product of the disturbance coefficient, a duty cycle boundary difference value and the decay factor to obtain a disturbance optimization term; The corresponding working point in the chaos optimization set is updated by fusing the disturbance optimization item and the current duty ratio.

5. The maximum power point tracking method based on a cooperative chaos optimization algorithm as claimed in claim 2, wherein, The development optimization unit optimizes and updates the working points in the second working point set according to the dynamic exploration probability, to obtain a development global optimal point and a development individual optimal point, and the specific operations include: The working points in the second working point set are divided into a uniform random set and a directional movement set according to the dynamic exploration probability; The duty ratio of the working points in the uniform random set is optimized according to the generated development random number, in combination with the duty ratio boundary difference value and the attenuation factor, to obtain a uniform optimization set; The development symbol of the development coefficient corresponding to the development random number is determined; The duty ratio of the working points in the directional movement set is optimized in combination with the development symbol, to obtain a directional optimization set; The output power of the working points in the uniform optimization set and the directional optimization set is judged, to give the development global optimal point and the development individual optimal point.

6. The maximum power point tracking method based on a cooperative chaos optimization algorithm as claimed in claim 5, wherein, The duty ratio of the working point in the directional optimization set is specifically represented as: ； wherein, D is the updated duty cycle for the i-th operating point in the set of targeted optimizations, i D is the updated duty cycle for the i-th operating point in the set of targeted optimizations, D is the updated duty cycle for the i-th operating point in the set of targeted optimizations, 7. The maximum power point tracking method based on a cooperative chaos optimization algorithm as claimed in claim 2, wherein, The disturbance optimization unit extracts the working points from the current working point sorting result according to the disturbance optimization coefficient, to obtain a third working point set and optimizes and updates the third working point set, to obtain a disturbance global optimal point and a disturbance individual optimal point, and the specific operations include: The working points are extracted from the current working point sorting result according to the disturbance optimization coefficient, to obtain a third working point set; The working points in the third working point set are screened based on the median of the duty ratio of each working point in the third working point set, to obtain a fourth working point set; The fourth working point set is divided into a Gaussian variation set and a historical guidance set in combination with the Gaussian random number corresponding to the current working point; The working points in the Gaussian variation set are updated according to the Gaussian coefficient corresponding to the Gaussian random number, in combination with the duty ratio boundary difference value, to obtain a Gaussian optimization set; The working points in the historical guidance set are updated according to the Gaussian random number, in combination with the duty ratio corresponding to the global maximum output power and the duty ratio of the individual historical maximum output power, to obtain a historical optimization set; The output power of the working points in the Gaussian optimization set and the historical optimization set is judged, to give the development global optimal point and the development individual optimal point.

8. The maximum power point tracking method based on a cooperative chaos optimization algorithm as claimed in claim 1, wherein, The compliance is tested according to the search space boundary, and the specific operations include: The dimensions of each working point are traversed and updated, and the compliance of the working point is tested; If the dimension value of the working point exceeds the upper limit of the corresponding search space boundary, the dimension value of the working point is assigned as the upper limit of the search space boundary; If the dimension value of the working point is lower than the lower limit of the corresponding search space boundary, the dimension value of the working point is assigned as the lower limit of the search space boundary; If the dimension value of the working point is within the corresponding search space boundary, the dimension value of the working point is kept.

9. The maximum power point tracking method based on a cooperative chaos optimization algorithm as claimed in claim 7, wherein, After the exploration optimization unit optimizes and updates the working points in the first working point set according to the chaos disturbance probability, to obtain an exploration global optimal point, the specific operations further include: The relative improvement amount of the current exploration global optimal point power and the current global maximum output power is calculated, and when the relative improvement amount exceeds a preset threshold, the following steps are performed: The exploration optimization unit generates a reinforcement signal and stores it in an event register; In response to the reinforcement signal in the event register, the perturbation optimization unit raises the Gaussian variation intensity and updates the working points in the Gaussian variation set in combination with the intensity decay mechanism.

10. A maximum power point tracking device based on a cooperative chaos optimization algorithm, characterized by, The maximum power point tracking method based on the cooperative chaos optimization algorithm comprises the following steps: A parameter initialization module is configured to initialize the chaos perturbation probability and the dynamic exploration probability, and define the search space boundary according to the duty cycle dimension of the Boost circuit; An individual initialization module is configured to initialize the working points, and calculate the fitness values of the working points according to the output powers of the working points; A fitness analysis module is configured to determine the global maximum output power and the individual historical maximum output power according to the fitness values; An update optimization module is configured to combine the chaos perturbation probability and the dynamic exploration probability, update and iterate the global maximum output power and the individual historical maximum output power by using multiple optimization units, and perform compliance verification according to the search space boundary; A result output module is configured to output the maximum power point and complete the maximum power point tracking if the maximum iteration number is reached or the output power converges.

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