A variable step size maximum power point tracking method for photovoltaic power generation system

Abstract

The application discloses a variable-coefficient step length maximum power point tracking method for a photovoltaic power generation system, and belongs to the technical field of photovoltaic power generation, and comprises the following steps: obtaining a current output voltage and an output current of a photovoltaic array; calculating a current output power and a power change rate according to the output voltage and the output current; judging a position of a current working point relative to a maximum power point based on a conductance increment method; dynamically adjusting a duty cycle step length according to the power change rate and an adaptive step length factor; dynamically optimizing the adaptive step length factor by using an adaptive particle swarm optimization algorithm; and updating a duty cycle of a DC-DC converter to track the maximum power point according to the dynamically adjusted duty cycle step length and the position judgment result. The application can quickly lock the maximum power point.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation technology, and in particular relates to a maximum power point tracking method with variable coefficient step size for photovoltaic power generation systems. Background Technology

[0002] Against the backdrop of the ongoing global energy structure transformation, photovoltaic (PV) power generation, with its advantages of being clean, renewable, and easily distributed, has become a vital force driving the low-carbon development of energy. However, the output power of a PV array exhibits a strong nonlinear variation due to factors such as sunlight intensity, ambient temperature, module aging, and partial shading. Its output power-voltage characteristic curve typically reaches maximum power output only at a specific operating point, i.e., the maximum power point (MPP). This point dynamically drifts as external conditions change. If the controller fails to adjust the operating voltage or duty cycle in a timely manner, causing the system to deviate from the MPP for an extended period, it will not only lead to a significant decrease in energy conversion efficiency but may also cause instability in the DC-DC converter. Therefore, how to quickly and stably track the MPP under complex and changing operating conditions has become a core scientific and engineering problem in PV system control.

[0003] Currently, Maximum Power Point Tracking (MPPT) methods have been extensively studied, with Perturb and Observe (P&O) and Incremental Conductance (INC) methods being widely used due to their simplicity and low hardware cost. Although the INC algorithm offers good tracking accuracy and oscillation resistance, its inherent limitations become increasingly apparent in real-world conditions. First, the fixed step size introduces a trade-off between convergence speed and steady-state accuracy: a larger step size accelerates the search but is prone to oscillations near the maximum power point (MPP); conversely, a smaller step size reduces steady-state error but significantly slows down dynamic response. Second, fixed-step-size algorithms lack environmental adaptability, easily leading to delays or even misjudgments under conditions of drastic light fluctuations or sudden temperature changes. Finally, traditional methods are highly sensitive to noise, sensor errors, and model uncertainties, making it difficult to maintain stable energy output under strong perturbation environments. Summary of the Invention

[0004] This invention proposes a maximum power point tracking method with variable coefficient step size for photovoltaic power generation systems to solve the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides a maximum power point tracking method with variable coefficient step size for photovoltaic power generation systems, comprising the following steps:

[0006] Obtain the current output voltage and output current of the photovoltaic array;

[0007] Calculate the current output power and power change rate based on the output voltage and output current;

[0008] The position of the current operating point relative to the maximum power point is determined based on the incremental conductivity method.

[0009] The duty cycle step size is dynamically adjusted based on the power change rate and the adaptive step size factor.

[0010] The adaptive step size factor is dynamically optimized using an adaptive particle swarm optimization algorithm.

[0011] Based on the dynamically adjusted duty cycle step size and the position determination result, the duty cycle of the DC-DC converter is updated to track the maximum power point.

[0012] Optionally, obtaining the current output voltage and output current includes:

[0013] The acquired voltage and current signals are standardized to eliminate noise and errors.

[0014] Optionally, the calculation of the current output power and the rate of power change includes:

[0015] Calculate the power change and voltage change based on the voltage and current data of the current moment and the previous moment;

[0016] The power change rate is determined based on the power change and voltage change.

[0017] Optionally, determining the position of the current operating point relative to the maximum power point based on the incremental conductance method includes:

[0018] Compare instantaneous conductance with incremental conductance to determine whether the operating point is to the left or right of the maximum power point, or has already been located at the maximum power point.

[0019] Optionally, the dynamic adjustment of the duty cycle step size includes:

[0020] The duty cycle step size is calculated using a variable step size function based on the power change rate, voltage change rate, and current.

[0021] The adaptive step size factor in the variable step size function is optimized using an adaptive particle swarm optimization algorithm.

[0022] Optionally, the expression for the variable step size function is:

[0023] ;

[0024] In the formula, Indicates the current value. This represents the change in power. It represents the amount of voltage change. As a dynamic adjustment factor, It is a constant.

[0025] Optionally, the step size factor is dynamically optimized using an adaptive particle swarm optimization algorithm, including:

[0026] Initialize the particle swarm, with each particle representing a step size factor;

[0027] The fitness function is defined based on the power prediction error, where the fitness function is: , Step size factor This represents the change in power. Indicates the amount of voltage change;

[0028] The step size factor is optimized by iteratively updating the particle's velocity and position.

[0029] Optionally, the expression for the adaptive particle swarm optimization algorithm is:

[0030] ;

[0031] In the formula, For the optimal position of an individual, To be the globally optimal position Step size factor k express k time, Let represent the random perturbation terms guided by individual experience and group experience, respectively, with values ​​ranging from [0,1]. This represents the velocity of particle i. This represents the inertia weight.

[0032] Optionally, the iterative update includes:

[0033] Adjust particle velocity based on individual optimal position and global optimal position;

[0034] The particle positions are updated based on the adjusted velocity to approximate the optimal step size factor.

[0035] Optionally, the updated duty cycle of the DC-DC converter includes:

[0036] Based on the dynamically adjusted duty cycle step size and position judgment results, increase, decrease, or maintain the current duty cycle;

[0037] The updated duty cycle is limited to ensure it remains within the preset range.

[0038] The duty cycle update expression is:

[0039] ;

[0040] In the formula, This indicates the change in step size. It represents the amount of voltage change. This represents the change in current. This indicates the output voltage of the photovoltaic array at the current sampling moment. This indicates the output current of the photovoltaic array at the current sampling moment. express k Duty cycle at time step D(k+1) represents the duty cycle updated in the next sampling period.

[0041] Compared with the prior art, the present invention has the following advantages and technical effects:

[0042] This invention compares the tracking accuracy of different step lengths based on the fixed-step-size incremental conductance method. By introducing a current-related coefficient to correct the step-size change, the step-size coefficient and control parameters are adjusted in real time to reduce the impact of current variations on the photovoltaic cell's output characteristics. Secondly, this method introduces an APSO mechanism within the variable-step-size incremental conductance framework, using the global search capability of particle swarm optimization to dynamically adjust the step-size factor, thereby maintaining a rapid response during sudden changes in illumination and temperature, and effectively suppressing oscillations in the steady-state phase. This invention can quickly lock onto the maximum power point. Attached Figure Description

[0043] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0044] Figure 1 This is a schematic diagram of the maximum power point tracking method according to an embodiment of the present invention;

[0045] Figure 2 This is a simulation model diagram of the maximum power point tracking method according to an embodiment of the present invention. Detailed Implementation

[0046] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0047] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0048] Research findings:

[0049] In recent years, to overcome the performance bottleneck caused by the fixed step size in the traditional MPPT algorithm, researchers have conducted extensive research on the Variable-Step Incremental Conductance (INC) method. Early improvements were mainly based on heuristic adjustment mechanisms of the power change rate, such as setting the step size to be proportional to the power change rate. The algorithm proportionally increases the step size when the power changes drastically, thus accelerating convergence; while gradually decreasing the step size near the steady-state region to suppress oscillations. This type of algorithm significantly improves dynamic response performance. However, since the adjustment function is mostly empirically designed, its parameter selection depends on specific operating conditions, making it difficult to maintain consistent performance under different illumination conditions. Especially under complex illumination patterns (such as partial shading), this type of algorithm may still experience misjudgments and oscillations, indicating that its adaptability and global optimization capabilities are still limited.

[0050] To further improve tracking efficiency and stability, an increasing number of researchers are introducing intelligent optimization algorithms into the MPPT control field. Typical methods include Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Grey Wolf Optimizer (GWO). These algorithms simulate the intelligent behavior of natural swarms to achieve global exploration of the search space. Among them, PSO is widely used due to its few parameters, simple implementation, and strong global optimization capability. However, standard PSO, with its fixed inertia weights and learning factors, often suffers from premature convergence and local optima. Furthermore, there is a trade-off between the convergence speed and computational complexity of traditional PSO, making it difficult to meet the dual requirements of real-time control systems for response speed and computational load.

[0051] Beyond improvements to single optimization algorithms, some studies have proposed fusion or adaptive MPPT control strategies. For example, a hybrid algorithm combining PSO and INC (PSO–INC) obtains the optimal initial step size or duty cycle through particle swarm optimization, followed by fine-tuning using incremental conductance, thus balancing global search and local accuracy. Similarly, fuzzy logic (FL) and artificial neural networks (ANN) have been used for adaptive adjustment of step size or gain. These methods improve the intelligence and adaptability of the algorithms to some extent, but also face challenges such as strong model dependence, complex parameter design, and limited real-time performance. In particular, neural network methods require a large amount of training data and computational resources, making them unsuitable for resource-constrained embedded control systems. While fuzzy control has a simple structure, its rule design relies on expert experience, resulting in limited generalization.

[0052] The above research reveals that the main challenge of current MPPT control methods lies in how to suppress steady-state oscillations while ensuring rapid dynamic response and achieving real-time adaptive adjustment of the step size factor. Traditional fixed-step-size (INC) methods lack self-adjustment capabilities; heuristic-based variable-step-size methods offer some improvement but are highly dependent on operating conditions; and while intelligent optimization algorithms possess global search capabilities, a trade-off between real-time performance and stability remains. To address this issue, a class of adaptive parameter optimization algorithms has emerged in recent years, with Adaptive Particle Swarm Optimization (APSO) being the most representative. This algorithm achieves a self-balancing effect between global search and local convergence by dynamically adjusting inertia weights and learning factors, providing a new theoretical foundation and implementation path for adaptive step-size optimization in MPPT.

[0053] Example 1

[0054] like Figure 1 As shown, this embodiment provides a maximum power point tracking method with variable coefficient step size for photovoltaic power generation systems, including:

[0055] Obtain the current output voltage and output current of the photovoltaic array;

[0056] Calculate the current output power and power change rate based on the output voltage and output current;

[0057] The position of the current operating point relative to the maximum power point is determined based on the incremental conductivity method.

[0058] The duty cycle step size is dynamically adjusted based on the power change rate and the adaptive step size factor.

[0059] The adaptive step size factor is dynamically optimized using an adaptive particle swarm optimization algorithm.

[0060] Based on the dynamically adjusted duty cycle step size and the position determination result, the duty cycle of the DC-DC converter is updated to track the maximum power point.

[0061] Specifically, this method first acquires current and voltage data under different operating conditions and temperatures through an experimental platform. Then, the integrity of the data is checked, and the current and voltage are standardized using data cleaning methods. This data is then used as input for the tracking method. This preprocessed data helps the power curve more closely approximate the true shape, improves the stability of the fitness function, and effectively reduces oscillations at the maximum power point output.

[0062] The electrical characteristics of the photovoltaic array were verified through mathematical modeling and experimental platform construction. Voltage and current signals were input to the MPPT control module, and the duty cycle was adjusted in real time using a boost DC-DC converter and a PWM modulator. In this way, the photovoltaic output could stably track the optimal operating point under different irradiance and temperature conditions. At the core of the method, the traditional incremental conductance method (INC) was first adopted as the basic control strategy. Its principle lies in comparing... and The relationship between the operating point and the maximum power point is used to determine the position of the operating point, thereby determining the direction of duty cycle increase or decrease. To improve dynamic response and steady-state accuracy, a variable step size mechanism is introduced. This mechanism can adaptively adjust the step size according to the photovoltaic output characteristics, and has faster convergence performance under sudden changes in irradiance or rapid dynamics.

[0063] Finally, this embodiment introduces an adaptive particle swarm optimization method into the variable step-size (INC) framework to perform global search and dynamic adjustment of the step-size factor. Specifically, the step-size factor is modeled as a position variable in the particle swarm, with the fitness function being the minimization of the deviation between power change and voltage increment. Through the dynamic updating of adaptive inertia weights and learning factors, joint optimization of individual and swarm optima is achieved. This not only avoids the convergence speed or oscillation problems that may be caused by traditional fixed factors, but also enhances the global optimization capability under complex operating conditions. The described method for maximum power point tracking prediction of photovoltaic arrays is called VSINC-APSO.

[0064] To more intuitively illustrate the connections and differences between different methods, this embodiment lists the benchmark model for maximum power point tracking prediction of photovoltaic arrays and the key parameters of the proposed method in Table 1.

[0065] Table 1

[0066]

[0067] The proposed method sets upper and lower limits for the duty cycle in the control loop and updates historical voltage and current variables in real time to ensure the stability and feasibility of the algorithm. Notably, to maintain fair comparison and ensure reasonable results, the key parameters of the baseline model and the proposed method are on the same order of magnitude.

[0068] Furthermore, the photovoltaic system model analysis is as follows:

[0069] The simulation model of the maximum power point tracking method for photovoltaic power generation systems studied in this embodiment is as follows: Figure 2 As shown, the model can be mainly divided into three parts: a photovoltaic array, a DC-DC boost converter circuit, and MPPT control optimization. The photovoltaic array converts input light and temperature into DC power. The DC-DC boost converter regulates the operating voltage and current of the photovoltaic array, thereby indirectly controlling the output power of the photovoltaic panels. The MPPT controller is the intelligent decision-making unit of the entire system; it uses algorithms to determine the deviation between the current operating point and the maximum power point, and adjusts the duty cycle of the DC-DC converter in real time.

[0070] The photovoltaic array portion is as follows:

[0071] A photovoltaic (PV) array is the energy input and conversion unit of a photovoltaic (PV) power generation system. Its main function is to directly convert incident solar irradiance into direct current (DC) electricity. PV arrays typically consist of multiple PV cells connected in series and parallel to achieve the desired output voltage and current levels. In a maximum power point (MPPT) system, the PV array is the controlled object, and its electrical characteristics directly affect the location of the maximum power point and the system's dynamic response. The output of PV cells is significantly affected by external environmental factors, particularly solar irradiance and cell temperature. When irradiance increases, the output current rises approximately linearly, while increased temperature leads to a decrease in open-circuit voltage, thereby reducing output power. Therefore, to achieve maximum power output under different operating conditions, an accurate mathematical model of the PV array must be established to provide a basis for MPPT control.

[0072] A single photovoltaic cell can be represented by a nonlinear circuit model consisting of a current source and a diode connected in parallel, such as... Figure 2 The equivalent model of the photovoltaic cell is shown. Ignoring the effects of parasitic inductance and capacitance, its output current... With terminal voltage The relationship can be shown by formula (1).

[0073]

[0074] Here, Indicates the output current of the photovoltaic array. This indicates the output voltage of the photovoltaic array. It represents the photocurrent, which is proportional to the irradiance. This indicates the reverse saturation current of the diode. This indicates the series resistance, reflecting the internal wire and contact losses. This indicates the parallel resistance, reflecting leakage loss. This represents the electron charge constant (1.602 × 10⁻¹⁹ C). This represents the Boltzmann constant (1.381 × 10⁻²³ J / K). The ideal factor is typically 1 to 2. This refers to the battery's operating temperature.

[0075] To account for the impact of environmental changes, photocurrent With saturation current Empirical corrections are applied, as shown in equations (2) and (3).

[0076]

[0077]

[0078] in, For reference conditions, the short-circuit current is... The standard test temperature is 25 ℃. Standard irradiance (1000 W / m2), The temperature coefficient of current. This represents the semiconductor bandgap energy. Therefore, it can be seen that as irradiance increases, The increase in temperature leads to an increase in output current. An increase in voltage leads to a decrease in open-circuit voltage.

[0079] When multiple photovoltaic cells are connected in series and parallel, the output voltage and current of the array are as shown in formula (4).

[0080]

[0081] in , These represent the number of cells connected in series and in parallel, respectively. Under specific temperature and irradiance conditions, the output power of the photovoltaic array is... Power initially increases and then decreases with increasing voltage, reaching its maximum at a certain point. This point is the point of maximum power.

[0082] The boost converter circuit model is as follows:

[0083] The DC-DC boost converter is located between the photovoltaic array and the load, and its main function is to convert the terminal voltage of the photovoltaic array... With subsequent voltage By duty cycle Energy exchange and coupling are performed. Simultaneously, the boost converter provides a controllable equivalent load to the photovoltaic array, allowing the photovoltaic panels to adjust under different operating conditions. It operates at the desired voltage point. In the MPPT closed-loop, the duty cycle of the Boost is... It is the direct execution quantity of the controller, which the controller changes by... By changing the operating point of the photovoltaic terminal, maximum power point tracking can be achieved.

[0084] Under ideal conditions (ignoring switching losses and resistance) and in continuous conduction mode, the steady-state average relationship between the output voltage and the duty cycle is shown in Equation (5).

[0085]

[0086] in That is, the photovoltaic terminal voltage Based on Kirchhoff's voltage law and current law, we can obtain... Figure 2 The mathematical model of the boost converter is defined as shown in equation (6).

[0087]

[0088] in, For resistors and inductors, For load resistance, This represents the inductor output current. In the design of a boost converter, this is crucial to ensure the ability to calculate the continuously conducting inductor. and capacitor The minimum value of Q in the figure represents the power switch, which is achieved using formulas (7) and (8).

[0089]

[0090]

[0091]

[0092] Here, This is the switching frequency. The output ripple voltage can be derived from the previous formula. The definition of ripple voltage is shown in formula (9). It can also be determined by the duty cycle. With capacitor Load resistance and frequency The relationship between them is derived.

[0093] The MPPT control method based on variable step size is as follows:

[0094] The method studied in this embodiment is an optimization and improvement based on the traditional incremental conductance method. The basic principle of the incremental conductance method is to dynamically adjust the operating point of the photovoltaic array by detecting the conductance changes of the photovoltaic system in real time, so as to ensure that the system always operates near the maximum power point. Due to its high tracking accuracy, this method has been widely used in various MPPT control strategies. The incremental conductance method utilizes the working principle of the photovoltaic cell having a maximum point. By comparing the instantaneous conductance at the current moment with its incremental conductance, the position of the current operating point relative to the maximum power point can be determined, thereby determining the direction of the next voltage disturbance. Since the power-voltage (PU) characteristic curve of the photovoltaic cell is continuous and differentiable, when the operating point is exactly at the maximum power point, its derivative is zero, that is, the slope of the PU curve at this point satisfies the condition shown in formula (10).

[0095]

[0096] Differentiate both sides of the formula with respect to U, as shown in formula (11).

[0097]

[0098] When the system operates at its maximum power point, the condition is satisfied as shown in equation (12).

[0099]

[0100] like If the working point is located to the left of the MPP, the duty cycle should be increased. .like If the working point is located to the right of the MPP, the duty cycle should be reduced. If the two are equal, the system is near the MPP and the duty cycle should be kept constant. In discrete form, the derivative can be expressed by adjacent sampling points as shown in formula (13).

[0101]

[0102] Based on equations (12) and (13), the judgment logic can be established as shown in formula (14).

[0103]

[0104] in, This is the step size factor, used to adjust the duty cycle speed.

[0105] In the traditional INC algorithm, Typically, a fixed value is used, which leads to a contradiction between convergence speed and steady-state oscillation when illumination changes rapidly or in the steady-state range. To address this, an adaptive variable step size mechanism based on the power change rate is introduced. The change in photovoltaic output power can be expressed as shown in equation (15).

[0106]

[0107] To make the step size sensitive to power and voltage changes, it is defined as shown in formula (16).

[0108]

[0109] Therefore, the basic structure of the step size factor can be obtained as shown in formula (17).

[0110]

[0111] in, This allows for a larger step size when the current is small, thereby improving the tracking speed in low light conditions. It reflects the rate of change of power. This is a dynamic adjustment factor, ranging from 0 to 1. This formula shows that the step size adaptively and dynamically adjusts with power changes, accelerating tracking when power changes are large and automatically decreasing near the steady state to suppress oscillations.

[0112] In the above step size update formula, the key parameter is the adaptive adjustment factor. Its physical meaning is "step size adjustment sensitivity coefficient". This is for dynamic optimization. This embodiment introduces an adaptive particle swarm optimization (PSO) algorithm. The inertia weight in the PSO algorithm plays a role in adjusting the particle search capability. A larger inertia weight is beneficial for global search, while a smaller inertia weight is beneficial for local search. In the adaptive PSO algorithm, the inertia weight automatically changes with the particle's objective function value. When the particle's objective function value is close to a local optimum, the inertia weight increases; when the particle's objective function value is more dispersed, the inertia weight decreases.

[0113] exist The position and velocity of each particle are shown in formula (18).

[0114]

[0115] The particles are randomly initialized within the interval [0.2, 0.8]. To make the step size selection more consistent with the power change trend, the objective function is defined as shown in formula (19).

[0116]

[0117] The above formula allows for the step size factor in particle search. This minimizes the power prediction error, where ΔU represents the change in photovoltaic array voltage between adjacent sampling times. To enhance the algorithm's convergence and ability to escape local optima, an adaptive inertia weight and learning factor are used, as shown in formula (20).

[0118]

[0119] in, This indicates the iteration progress. This design allows the algorithm to maintain global search capability in the early stages and enhance local convergence in the later stages. The particle swarm optimization strategy is shown in Equation (21).

[0120] (twenty one)

[0121] In the formula, For the optimal position of an individual, To be the globally optimal position Step size factor k express k time, Let represent the random perturbation terms guided by individual experience and group experience, respectively, with values ​​ranging from [0,1]. This represents the velocity of particle i. Let r1 and r2 represent inertial weights, which are random numbers in the interval [0,1]. They represent the individual experience and group experience-guided components, respectively, giving the particle search process randomness and preventing it from getting trapped in local optima. This can be further explained as follows: r1 is a random perturbation term associated with the individual optimality (pbest), allowing the particle to retain the variability of its own historical experience; r2 is a random perturbation term associated with the global optimality (gbest), guiding the particle towards the overall group optimal direction. To prevent... Exceeding the reasonable range, as shown in formula (22).

[0122]

[0123] After multiple local iterations, the optimal particle is obtained as shown in formula (23).

[0124]

[0125] The optimal particle serves as the final adaptive variable step size factor for updating equation (17). Combined with the conductivity increment judgment equation (14), the iterative duty cycle update can be obtained as shown in equation (24).

[0126]

[0127] Where ΔD is the step size factor, representing the step size change; ΔU and ΔI represent the voltage and current changes of the photovoltaic array in two adjacent sampling periods; U and I represent the output voltage and current of the photovoltaic array at the current sampling time; D(k) represents the duty cycle output by the MPPT controller in the current sampling period, which is the duty cycle at time k; D(k+1) represents the updated duty cycle in the next sampling period.

[0128] To ensure system security and hardware settings, the duty cycle constraint is set as shown in formula (25).

[0129]

[0130] This limiting operation prevents over-adjustment from causing the DC-DC converter to enter the nonlinear or unstable region. A dynamic adjustment mechanism using an adaptive particle swarm optimization algorithm is introduced, adjusting the step size factor... It can automatically optimize according to power changes, enabling the algorithm to quickly approach MPP when illumination changes abruptly, while automatically reducing the step size to reduce oscillations when the system is stable. Compared with the traditional fixed-step incremental conductance method, this algorithm achieves a good balance between convergence speed, steady-state error, and robustness.

[0131] The present invention achieves the following effects:

[0132] This invention compares the tracking accuracy of different step lengths based on the fixed-step incremental conductance method. By introducing a current-related coefficient to correct the step length change, the step length coefficient and control parameters are adjusted in real time to reduce the impact of current changes on the output characteristics of the photovoltaic cell. Secondly, this method introduces an APSO mechanism within the variable-step incremental conductance framework. This mechanism uses the global search capability of the particle swarm optimization to dynamically adjust the step length factor, thus maintaining a fast response during sudden changes in illumination and temperature, and effectively suppressing oscillations in the steady-state phase. Specifically, the algorithm first uses the incremental conductance method to establish a dynamic relationship between voltage, current, and power change rate to identify the deviation direction between the photovoltaic array's operating point and its maximum power point; then, it dynamically adjusts the step length factor based on an adaptive particle swarm optimization strategy. Unlike standard PSO, APSO introduces a self-adjusting inertia weight and learning factor mechanism: when the particle swarm as a whole tends to converge, the algorithm automatically reduces the inertia weight to enhance local search capability; when the swarm distribution is too concentrated or trapped in a local optimum, the inertia weight is increased to enhance global exploration. This dynamic balancing mechanism effectively avoids premature convergence and improves the algorithm's global optimization capability under multi-peak power curves.

[0133] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A maximum power point tracking method with variable coefficient step size for photovoltaic power generation systems, characterized in that, Includes the following steps: Obtain the current output voltage and output current of the photovoltaic array; Calculate the current output power and power change rate based on the output voltage and output current; The calculation of the current output power and the rate of power change includes: Calculate the power change and voltage change based on the voltage and current data of the current moment and the previous moment; The power change rate is determined based on the power change and voltage change. The position of the current operating point relative to the maximum power point is determined based on the incremental conductivity method. The method of determining the position of the current operating point relative to the maximum power point based on the incremental conductance method includes: Compare instantaneous conductance and incremental conductance to determine whether the operating point is to the left or right of the maximum power point or has already been located at the maximum power point; The duty cycle step size is dynamically adjusted based on the power change rate and the adaptive step size factor. The dynamic adjustment of duty cycle step size includes: The duty cycle step size is calculated using a variable step size function based on the power change rate, voltage change rate, and current. The expression for the variable step size function is: ； In the formula, Indicates the current value. This represents the change in power. It represents the amount of voltage change. As a dynamic adjustment factor, It is a constant; The adaptive step size factor in the variable step size function is optimized using an adaptive particle swarm optimization algorithm. The adaptive step size factor is dynamically optimized using an adaptive particle swarm optimization algorithm. The method of dynamically optimizing the adaptive step size factor using the adaptive particle swarm optimization algorithm includes: Initialize the particle swarm, with each particle representing a step size factor; The fitness function is defined based on the power prediction error, where the fitness function is: , Step size factor This represents the change in power. Indicates the amount of voltage change; The step size factor is optimized by iteratively updating the particle's velocity and position. Based on the dynamically adjusted duty cycle step size and position judgment result, the duty cycle of the DC-DC converter is updated to track the maximum power point; The duty cycle of the updated DC-DC converter includes: Based on the dynamically adjusted duty cycle step size and position judgment results, increase, decrease, or maintain the current duty cycle; The updated duty cycle is limited to ensure it remains within the preset range. The duty cycle update expression is: ； In the formula, This indicates the change in step size. It represents the amount of voltage change. This represents the change in current. This indicates the output voltage of the photovoltaic array at the current sampling moment. This indicates the output current of the photovoltaic array at the current sampling moment. Let D(k+1) represent the duty cycle at time k, and let D(k+1) represent the duty cycle after the next sampling period. The expression for the adaptive particle swarm optimization algorithm is: ； In the formula, For the optimal position of an individual, To be the globally optimal position Let k be the step size factor, and k represent time k. Let represent the random perturbation terms guided by individual experience and group experience, respectively, with values ​​ranging from [0,1]. This represents the velocity of particle i. Indicates inertia weight; The iterative update includes: Adjust particle velocity based on individual optimal position and global optimal position; The particle positions are updated based on the adjusted velocity to approximate the optimal step size factor.

2. The method according to claim 1, characterized in that, Obtaining the current output voltage and output current includes: The acquired voltage and current signals are standardized to eliminate noise and errors.

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