A maximum power point tracking control method and system based on dynamic impedance matching

Abstract

This application provides a maximum power point tracking (MPPT) control method and system based on dynamic impedance matching, relating to the field of electronic digital data processing technology. By controlling the power converter to enter a specific operating state, the open-circuit voltage, short-circuit current, and load impedance at the output terminal of the photovoltaic (PV) module are obtained. Next, the obtained open-circuit voltage and short-circuit current are substituted into the mathematical model of the PV module to calculate its equivalent internal resistance at the maximum power point. Subsequently, based on the impedance matching principle in circuit theory, a relational equation is constructed using the calculated equivalent internal resistance and the measured load impedance. By solving this equation, which directly relates the system's internal and external impedances to the duty cycle, the target duty cycle that matches the two can be calculated. Finally, this target duty cycle is directly assigned to the power converter. This replaces the iterative search process, thus avoiding continuous disturbances in the operating point near the maximum power point and reducing the resulting steady-state power loss.

Description

Technical Field

[0001] This application relates to the field of electrical digital data processing technology, and in particular to a maximum power point tracking control method and system based on dynamic impedance matching. Background Technology

[0002] In photovoltaic (PV) power generation systems, the output power of PV modules exhibits a non-linear, single-peak function relationship with their operating voltage; the peak of this function is called the maximum power point. Due to continuous changes in environmental factors such as sunlight intensity and temperature, the location of this maximum power point also dynamically shifts.

[0003] Related technologies, such as the perturbation-observation method and the incremental conductance method, are common maximum power point tracking (MPPT) control methods based on gradient climbing iterative optimization strategies. These strategies typically work by adjusting the power converter's duty cycle in small, pre-set steps, using the system's current operating point as a reference. This adjustment causes a corresponding change in the output voltage. The controller passively observes the resulting change in system power or conductance and uses this posterior local gradient information to determine the optimization direction. Through continuous closed-loop iteration, it guides the operating point to gradually approach the peak value of the power curve.

[0004] However, the inherent mechanism of the aforementioned technologies dictates that the operating point cannot remain stationary at the maximum power point, but can only continuously move within a very small neighborhood. Each operation that actively deviates from the maximum power point in order to obtain gradient information constitutes an irreversible energy capture loss. These continuous small losses accumulate over time and will have a considerable impact on the long-term total power generation of the power plant. Summary of the Invention

[0005] This application provides a maximum power point tracking control method and system based on dynamic impedance matching, which avoids continuous disturbances in the operating point near the maximum power point and reduces the resulting steady-state power loss.

[0006] In a first aspect, this application provides a maximum power point tracking control method based on dynamic impedance matching, comprising: controlling a power converter to sequentially switch to a first preset state, a second preset state, and a third preset state, respectively acquiring the open-circuit voltage, short-circuit current, and load impedance of the photovoltaic module; substituting the open-circuit voltage and short-circuit current into the mathematical model of the photovoltaic module to calculate the equivalent internal resistance of the photovoltaic module when it is at the maximum power point; constructing an impedance matching relationship equation based on the equivalent internal resistance and the load impedance, and calculating the target duty cycle according to the impedance matching relationship equation; and updating the operating duty cycle of the power converter to the target duty cycle.

[0007] By adopting the above technical solution, the photovoltaic module is equivalent to a power source with nonlinear characteristics from a circuit perspective. It possesses an equivalent internal resistance at its maximum power point. The idea is that the photovoltaic module's output power reaches its maximum when the external load impedance matches this equivalent internal resistance. This is achieved by controlling the power converter to enter a specific operating state and obtaining the open-circuit voltage, short-circuit current, and output load impedance of the photovoltaic module. Next, the obtained open-circuit voltage and short-circuit current are substituted into the mathematical model of the photovoltaic module to calculate its equivalent internal resistance at the maximum power point. Subsequently, based on the impedance matching principle in circuit theory, a relational equation is constructed using the calculated equivalent internal resistance and the measured load impedance. By solving this equation, which directly relates the system's internal and external impedances to the duty cycle, the target duty cycle that achieves the matching can be calculated. Finally, this target duty cycle is directly assigned to the power converter. This replaces the iterative search process, thus avoiding continuous disturbances in the operating point near the maximum power point and reducing the resulting steady-state power loss.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of controlling the power converter to sequentially switch to a first preset state, a second preset state, and a third preset state to obtain the open-circuit voltage, short-circuit current, and load impedance of the photovoltaic module, respectively, specifically includes: controlling the on / off state of the power converter to be in the off state, and collecting the resting voltage of the input side of the photovoltaic module as the open-circuit voltage; controlling the on / off state of the power converter to be in the extreme short-circuit state, and collecting the limiting current of the input side of the photovoltaic module as the short-circuit current; controlling the on / off state of the power converter to be in the direct-on state, and collecting the output voltage and output current of the output terminal respectively, and dividing the output voltage by the output current to determine the load impedance.

[0009] By employing the above technical solution, different physical quantities can be measured by setting the power converter to three defined operating states: open, extreme short circuit, and shoot-through. In the open state, the input and output are isolated, allowing the acquired input voltage to represent the open-circuit voltage of the photovoltaic module. In the extreme short circuit state, the maximum output current capability of the photovoltaic module can be measured. In the shoot-through state, the converter loss is minimized, allowing the voltage-current relationship at the output terminal to better reflect the impedance characteristics of the downstream load. These three states are switched using the power converter's existing switching function, eliminating the need for additional hardware circuitry for parameter measurement.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, after updating the operating duty cycle of the power converter to the target duty cycle, the method further includes: collecting the current operating conditions of the photovoltaic module according to a preset time series and extracting the real-time calculated power; and when the fluctuation of the real-time calculated power relative to the reference maintenance power at the maximum power point exceeds a preset tolerance threshold, switching the power converter sequentially to a first preset state, a second preset state, and a third preset state to obtain the open-circuit voltage, short-circuit current, and load impedance of the photovoltaic module, respectively.

[0011] By adopting the above technical solution, after the system operates stably according to the calculated duty cycle, the mechanism continuously collects the system's real-time operating power and compares it with the theoretical maximum power benchmark. When the difference between the two fluctuates beyond a preset threshold, the algorithm determines that the external environmental conditions have changed significantly. This determination triggers a jump instruction, causing the system to re-execute the complete parameter acquisition and calculation process. This working mode allows the system to remain silent when the environment is stable, avoiding unnecessary retesting; while it can be awakened by the monitoring mechanism when the environment changes abruptly, updating the operating parameters in a timely manner. Thus, while maintaining the advantage of static, disturbance-free operation, it also possesses the ability to quickly adapt to environmental changes, ensuring power tracking accuracy under dynamic operating conditions.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the mathematical model of the photovoltaic module is as follows: In the formula, I is the output current of the photovoltaic module; V is the output voltage of the photovoltaic module; This refers to the short-circuit current of the photovoltaic module. is the open-circuit voltage of the photovoltaic module; K is the curve shape parameter.

[0013] By adopting the above technical solution, the structural feature of this expression is that it only relies on open-circuit voltage (Voc), short-circuit current (Isc), and curve shape parameter (k) as input variables. Compared to complex models that require more and more difficult-to-measure semiconductor physical parameters, this model significantly simplifies the data required for modeling. When performing calculations, the controller does not need to perform complex iterative approximations to solve implicit equations; it can describe the output characteristics of the photovoltaic module simply by performing direct algebraic operations.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the impedance matching equation is as follows: In the formula, This is the output power of the power converter; Input power to the power converter; This refers to the output voltage of the power converter. This is the input voltage for the power converter; The target duty cycle in the power converter; Output load impedance; This represents the equivalent load of the system.

[0015] By adopting the above technical solution, this approach integrates the independent equations describing the characteristics of photovoltaic modules, power converters, and loads into a unified system equation by simultaneously solving the power conservation equation, the converter voltage relationship, and the load power definition. Subsequently, through algebraic simplification and variable substitution, this solution provides an explicit analytical expression with the target duty cycle as the solution result. The existence of this analytical expression means that the controller can transform the optimization process, which originally required multiple logical judgments and iterations, into a single deterministic formula calculation, reducing the computational cycle required for algorithm execution and improving the computational efficiency and response speed of MPPT control.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of substituting the open-circuit voltage and short-circuit current into the mathematical model of the photovoltaic module to calculate the equivalent internal resistance, the method further includes: during the period when the control power converter is sequentially switched to a first preset state and a second preset state, acquiring a sampling sequence of the photovoltaic module output current changing with the output voltage to determine the PV curve; determining whether the PV curve has a single-peak characteristic or a composite-peak characteristic; if the PV curve has a single-peak characteristic, performing the step of substituting the open-circuit voltage and short-circuit current into the mathematical model of the photovoltaic module to calculate the equivalent internal resistance; if the PV curve has a multi-peak characteristic, dividing the PV curve into several sub-curves according to the centroid as the dividing line; for each sub-curve, dividing the effective region with data and the invalid region without data. The process involves: identifying the invalid region of the current sub-curve; filling in the invalid region based on the valid region of the current sub-curve and the corresponding region of the adjacent sub-curves to obtain the completed sub-curve; extracting the largest virtual short-circuit current and the largest virtual open-circuit voltage from all completed sub-curves; obtaining the characteristic value of the on-state voltage drop and the current reduction factor of the bypass element inside the photovoltaic module; establishing the characteristic value of the on-state voltage drop and the current reduction factor as noise attenuation parameters; performing a fusion calculation by combining the largest virtual short-circuit current and the largest virtual open-circuit voltage with the noise attenuation parameters to generate the global equivalent open-circuit voltage and the global equivalent short-circuit current; and substituting the global equivalent open-circuit voltage and the global equivalent short-circuit current into the mathematical model of the photovoltaic module to calculate the equivalent internal resistance of the photovoltaic module at its maximum power point.

[0017] By adopting the above technical solution, the topological characteristics of the PV curve are first determined by collecting sampling sequences of transient periods, and different processing paths are selected based on whether it is single-peaked or multi-peaked. In the case of multi-peaked curves, the algorithm performs curve segmentation, invalid region completion, and extraction of virtual boundary parameters. Finally, it uses the physical characteristic parameters of bypass components to fuse and correct these virtual parameters, generating a set of globally equivalent open-circuit voltages and short-circuit currents. This mathematically transforms a multi-peaked PV curve, which becomes complex due to local shading, into a standard single-peaked curve with the same global optimum. This allows the original single-peaked mathematical model to still be used for calculations, thus solving the problem of the method easily getting trapped in local optima under multi-peak conditions without changing the core algorithm architecture, and enhancing the system's adaptability to complex working environments.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, for the invalid region of the current sub-curve, the invalid region is filled in by the corresponding regions of adjacent sub-curves corresponding to the valid region of the current sub-curve and the invalid region of the current sub-curve, to obtain the filled sub-curve. Specifically, this includes: extracting the array of first-order partial derivatives and the array of second-order partial derivatives of the current sub-curve within the valid region, calculating the curve fitting extrapolation equation, and calculating its own extended trajectory; extracting the curve shape feature set of the corresponding region of the adjacent sub-curve, comparing the actual parameter values ​​of the curve shape feature set with the predicted parameter values ​​at the corresponding positions of the curve fitting extrapolation equation, and obtaining the curvature correction coefficient or fitting error compensation amount; substituting its own extended trajectory into the compensation function containing the curvature correction coefficient or fitting error compensation amount for iterative optimization to generate the reconstructed extended trajectory; and splicing the reconstructed extended trajectory and the valid region of the current sub-curve at the boundary point between the valid region and the invalid region to fill in the invalid region and obtain the filled sub-curve.

[0019] By adopting the above technical solution, firstly, extrapolation calculations are performed using the derivative characteristics of the effective data segments of the current sub-curve itself. This ensures a smooth transition between the completed curve and the original curve at the starting point. Simultaneously, to avoid potential mathematical divergence from a single extrapolation, the algorithm also extracts characteristic parameters from adjacent sub-curves as a correction basis, and corrects the initial extension trajectory through an iteratively optimized compensation function. This completion method, combining its own trend and neighborhood characteristics for dual constraints, ensures that the reconstructed curve segment not only continues its local characteristics but also conforms to the overall physical laws of the photovoltaic module. This improves the accuracy of virtual boundary parameter calculations and provides more reliable data support for subsequent global optimum determination.

[0020] Secondly, this application provides a maximum power point tracking control system based on dynamic impedance matching. The maximum power point tracking control system based on dynamic impedance matching includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the maximum power point tracking control system based on dynamic impedance matching to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer program product containing instructions that, when the computer program product is run on a maximum power point tracking control system based on dynamic impedance matching, cause the maximum power point tracking control system based on dynamic impedance matching to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer-readable storage medium including a computer program that, when executed by a processor, implements the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. From a circuit perspective, a photovoltaic (PV) module is equivalent to a power source with nonlinear characteristics. It possesses an equivalent internal resistance at its maximum power point (MPP). The idea is that the PV module's output power reaches its maximum when the external load impedance matches this equivalent internal resistance. This approach involves controlling the power converter to enter a specific operating state and obtaining the PV module's open-circuit voltage, short-circuit current, and output load impedance. Next, the obtained open-circuit voltage and short-circuit current are substituted into the PV module's mathematical model to calculate its equivalent internal resistance at the MPP. Subsequently, based on the impedance matching principle in circuit theory, a relational equation is constructed using the calculated equivalent internal resistance and the measured load impedance. By solving this equation, which directly relates the system's internal and external impedances to the duty cycle, the target duty cycle that achieves the matching can be calculated. Finally, this target duty cycle is directly assigned to the power converter. This replaces the iterative search process, thus avoiding continuous disturbances near the MPP and reducing the resulting steady-state power loss.

[0025] 2. First, the topological characteristics of the PV curve are determined by collecting sampling sequences of transient periods, and different processing paths are selected based on whether it is single-peaked or multi-peaked. In the case of multi-peaked curves, the algorithm performs curve segmentation, invalid region completion, and extraction of virtual boundary parameters. Finally, it uses the physical characteristic parameters of bypass components to fuse and correct these virtual parameters, generating a set of globally equivalent open-circuit voltages and short-circuit currents. This mathematically transforms a complex multi-peaked PV curve due to local shading into a standard single-peaked curve with the same global optimum. This allows the original single-peaked mathematical model to still be used for calculations, thus solving the problem of the method easily getting trapped in local optima under multi-peak conditions without changing the core algorithm architecture, and enhancing the system's adaptability to complex working environments.

[0026] 3. First, extrapolation calculations are performed using the derivative characteristics of the effective data segments of the current sub-curve. This ensures a smooth transition between the completed curve and the original curve at the starting point. Simultaneously, to avoid potential mathematical divergence from a single extrapolation, the algorithm also extracts characteristic parameters from adjacent sub-curves as a correction basis. An iteratively optimized compensation function is used to correct the initial extension trajectory. This completion method, combining its own trend and neighborhood characteristics for dual constraints, ensures that the reconstructed curve segment not only continues its local characteristics but also conforms to the overall physical laws of the photovoltaic module. This improves the accuracy of virtual boundary parameter calculations and provides more reliable data support for subsequent global optimum determination. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a maximum power point tracking control method based on dynamic impedance matching in an embodiment of this application.

[0028] Figure 2 This is a schematic diagram of the output characteristic curve of the photovoltaic module in the embodiments of this application;

[0029] Figure 3 This is a schematic diagram illustrating the mapping relationship between the input equivalent impedance of the power converter and the duty cycle in the embodiments of this application;

[0030] Figure 4 This is a schematic diagram of the characteristic curve of the system output power as a function of duty cycle in the embodiments of this application;

[0031] Figure 5 This is a waveform diagram showing the dynamic response of the photovoltaic module's output voltage in response to sudden changes in the external environment according to an embodiment of this application.

[0032] Figure 6 This is a waveform diagram showing the dynamic response of the photovoltaic module's output current when responding to sudden changes in the external environment, according to an embodiment of this application.

[0033] Figure 7 This is an exemplary hardware structure diagram of a maximum power point tracking control system based on dynamic impedance matching in an embodiment of this application. Detailed Implementation

[0034] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” also include the plural expressions unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0035] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0036] Please see Figure 1 , Figure 1 This is a flowchart illustrating a maximum power point tracking control method based on dynamic impedance matching in an embodiment of this application.

[0037] A maximum power point tracking control method based on dynamic impedance matching includes:

[0038] S101, control the power converter to switch to the first preset state, the second preset state and the third preset state in sequence, and obtain the open circuit voltage, short circuit current and load impedance of the photovoltaic module respectively.

[0039] Among them, the first preset state, the second preset state, and the third preset state refer to specific electrical operating modes that the power converter enters under system commands, which are used to isolate or expose the electrical characteristics of specific parts of the system.

[0040] In some embodiments, this step is the data input stage for the entire analytical MPPT algorithm. The system first sends a command to the power converter to enter a first preset state to block the current path, facilitating the measurement of the open-circuit voltage of the photovoltaic module. Subsequently, the command switches to a second preset state, forcing the formation of a low-impedance loop to obtain the short-circuit current capability of the photovoltaic module. Finally, the command switches to a third preset state, enabling the power converter to operate in a minimum-loss mode, thereby acquiring voltage and current reflecting the actual characteristics of the back-end load and calculating the load impedance accordingly. These three parameters (open-circuit voltage, short-circuit current, and load impedance) constitute the set of fundamental physical quantities required for subsequent calculations.

[0041] In some specific embodiments, the system achieves a first preset state by setting the pulse width modulation (PWM) duty cycle of the power converter to zero, then achieves a second preset state by using a dedicated bypass switch, and finally achieves a third preset state by setting the PWM duty cycle to 100%, thereby completing the switching of the three states and parameter acquisition. This is not limited here. In other specific embodiments, the system can also achieve similar state switching by adjusting the operating frequency of the converter to extremely low or extremely high values. For example, it can use the open-circuit characteristics of the inductor at high frequency to obtain the open-circuit voltage, use the short-circuit characteristics of the inductor at low frequency to obtain the short-circuit current, and then restore the normal frequency and adjust the duty cycle to achieve the penetration measurement of the load. This is not limited here.

[0042] In actual use, when switching to the second preset state (extreme short circuit state), if a large-capacity filter capacitor is connected in parallel at the input terminal of the photovoltaic module, it will cause a violent capacitor discharge surge current, which may damage the power switching device.

[0043] Therefore, in some preferred embodiments, the system first goes through a pre-charge / discharge phase, using a current-limiting resistor to discharge the energy of the input capacitor to a safe level, and then closes the short-circuit loop to sample the current. Alternatively, the system does not perform a complete physical short circuit, but instead uses a very large duty cycle (such as 99%) to bring the system into a deep saturation region, and collects the steady-state current at this time as an approximation of the short-circuit current to avoid hardware impact risks.

[0044] In some specific embodiments, step S101 specifically includes:

[0045] S1011: Control the power converter to be in the off state, and collect the resting voltage on the input side of the photovoltaic module as the open circuit voltage.

[0046] In this context, the on / off state indicates that the main power path of the power converter is cut off, and no current flows between the input and output terminals. The resting voltage refers to the terminal voltage that the photovoltaic module naturally establishes due to the photovoltaic effect when there is no load current.

[0047] This step simulates the no-load condition of the photovoltaic module by turning off the converter in order to measure its open-circuit voltage.

[0048] S1012, control the on / off state of the power converter to the extreme short-circuit state, and collect the limiting current on the input side of the photovoltaic module as the short-circuit current.

[0049] The extreme short-circuit state indicates that the converter provides an extremely low-impedance discharge loop for the photovoltaic module. The limiting current refers to the maximum current value that the photovoltaic module can output under this low-impedance loop.

[0050] This step measures the short-circuit current by providing a near-short-circuit path for the photovoltaic module.

[0051] S1013. Control the power converter to be in the direct-on state, collect the output voltage and output current at the output terminal respectively, and divide the output voltage by the output current to determine the load impedance.

[0052] In this context, the shoot-through state refers to the power converter operating in a state of minimum transmission loss, allowing the input voltage to be transmitted to the output terminal with the highest efficiency.

[0053] This step allows the converter to operate in a low-loss manner so that the voltage and current of the actual load at the back end can be measured without interference, thereby calculating its impedance value.

[0054] As can be seen, by setting the power converter to three distinct operating states—off, extreme short circuit, and shoot-through—different physical quantities can be measured. In the off state, the input and output are isolated, allowing the acquired input voltage to represent the open-circuit voltage of the photovoltaic module. In the extreme short circuit state, the maximum output current capability of the photovoltaic module can be measured. In the shoot-through state, the converter loss is minimized, allowing the voltage-current relationship at the output terminal to better reflect the impedance characteristics of the downstream load. These three states are switched using the power converter's built-in switching function, eliminating the need for additional hardware circuitry for parameter measurement.

[0055] Please see Figure 2 , Figure 2 This is a schematic diagram of the output characteristic curve of the photovoltaic module in the embodiments of this application;

[0056] As shown in the IV curve, at low voltages, the photovoltaic module exhibits constant current source characteristics; after the voltage exceeds the inflection point, the current drops rapidly. The PV curve is the product of voltage and current at each point on the IV curve, presenting a unique power peak. Therefore, the principle of this method is to reconstruct the mathematical expression of this PV curve within the controller's computing unit, and then analyze the coordinates of the maximum power point marked by the circle in the figure through differentiation and other methods.

[0057] Please see Figure 3 and Figure 4 , Figure 3 This is a schematic diagram illustrating the mapping relationship between the input equivalent impedance of the power converter and the duty cycle in the embodiments of this application; Figure 4 This is a schematic diagram of the characteristic curve of the system output power as a function of duty cycle in the embodiments of this application;

[0058] Figure 3 In the graph, the horizontal axis represents the duty cycle of the power converter (taking the Buck type as an example). The vertical axis represents the system's equivalent input impedance as seen from the photovoltaic module side. The three colored curves represent the cases when different physical load impedances are connected to the back end (0.6Ω, 1.2Ω, 2.5Ω).

[0059] The black dashed line represents the equivalent internal resistance of a photovoltaic module at its maximum power point under specific light and temperature conditions.

[0060] As an impedance converter, the input equivalent impedance of a power converter is not a constant value, but changes continuously with the duty cycle of the control variable. This application's method identifies the intersection point of a colored curve and the black dashed line. The x-axis corresponding to this intersection point represents the optimal duty cycle for achieving impedance matching.

[0061] Figure 4 In the graph, the horizontal axis represents the duty cycle of the power converter, and the vertical axis represents the actual output power of the system. The three colored curves represent the power-duty cycle relationship under different load impedances R. The black dashed line represents the theoretical maximum output power of the photovoltaic module under this operating condition.

[0062] Figure 4 This was verified from the perspective of energy output. Figure 3 The correctness of the impedance matching concept. For a given load, the system output power does not change monotonically with the duty cycle, but rather has a unique power peak. The duty cycle corresponding to this power peak is related to... Figure 3 The duty cycles of the impedance matching points (intersections) are completely consistent. By calculating the target duty cycle, the purpose is to directly analyze the x-coordinate corresponding to this peak point, and then by updating the working duty cycle, the operating point is precisely set at the highest point of the power curve, thus avoiding the process of climbing back and forth on both sides of the peak.

[0063] S102. Substitute the open-circuit voltage and short-circuit current into the mathematical model of the photovoltaic module to calculate the equivalent internal resistance of the photovoltaic module when it is at the maximum power point.

[0064] The mathematical model of a photovoltaic (PV) module refers to an analytical expression that describes the nonlinear relationship between the module's output current, output voltage, open-circuit voltage, and short-circuit current. The equivalent internal resistance refers to the ratio of the PV module's operating voltage to its operating current when its output power is at its maximum.

[0065] In some embodiments, the system uses the open-circuit voltage and short-circuit current obtained in S101 as known constants and substitutes them into a mathematical model equation preset in memory. This equation establishes a mathematical description of an IV characteristic curve. Subsequently, based on the physical law that power equals voltage multiplied by current (P=VI), the system transforms the IV model to obtain a power-voltage (PV) functional relationship. By performing a derivative operation on this PV functional relationship and setting the derivative to zero, the theoretical maximum power point coordinates (maximum power point voltage Vmpp and maximum power point current Impp) can be calculated. Finally, dividing Vmpp by Impp, the equivalent internal resistance under this specific operating condition is calculated.

[0066] In some specific embodiments, the differentiation operation can be directly implemented by analytical methods, that is, by finding the expression of the derivative of the PV function and then solving for the voltage value when the derivative is equal to zero, which is not limited here; in other specific embodiments, numerical differentiation methods can also be used, for example, by selecting several points on the theoretical PV curve, approximating the derivative by difference operations, and searching for the extreme points where the derivative is zero by numerical optimization algorithms such as bisection method or Newton's method, which is not limited here.

[0067] In some specific embodiments, the mathematical model of the photovoltaic module is as follows:

[0068]

[0069] In the formula, I is the output current of the photovoltaic module; V is the output voltage of the photovoltaic module; This refers to the short-circuit current of the photovoltaic module. is the open-circuit voltage of the photovoltaic module; K is the curve shape parameter.

[0070] As can be seen, the structural feature of this expression is that it only relies on open-circuit voltage (Voc), short-circuit current (Isc), and curve shape parameter (k) as input variables. Compared to complex models that require more and more difficult-to-measure semiconductor physical parameters, this model significantly simplifies the data required for modeling. When performing calculations, the controller does not need to perform complex iterative approximations to solve implicit equations; it can describe the output characteristics of the photovoltaic module simply by performing direct algebraic operations.

[0071] S103. Construct an impedance matching equation based on the equivalent internal resistance and load impedance, and calculate the target duty cycle based on the impedance matching equation.

[0072] This step involves establishing a mathematical equation that combines the power supply internal resistance, load impedance, and converter control parameters, and then solving this equation to directly calculate the exact control command required to maximize power.

[0073] In some specific embodiments, the impedance matching equation is as follows:

[0074]

[0075]

[0076]

[0077]

[0078] In the formula, This is the output power of the power converter; Input power to the power converter; This refers to the output voltage of the power converter. This is the input voltage for the power converter; The target duty cycle in the power converter; Output load impedance; This represents the equivalent load of the system.

[0079] As can be seen, this scheme integrates the independent equations describing the characteristics of photovoltaic modules, power converters, and loads into a unified system equation by simultaneously solving the power conservation equation, the converter voltage relationship, and the load power definition. Subsequently, through algebraic simplification and variable substitution, the scheme provides an explicit analytical expression with the target duty cycle as the solution. The existence of this analytical expression means that the controller can transform the optimization process, which previously required multiple logical judgments and iterations, into a single deterministic formula calculation, reducing the computational cycle required for algorithm execution and improving the computational efficiency and response speed of MPPT control.

[0080] S104. Update the operating duty cycle of the power converter to the target duty cycle.

[0081] This step is the final execution stage of the control algorithm, which loads the calculated optimal duty cycle value into the PWM controller of the power converter, enabling the photovoltaic module to enter the maximum power point operating state.

[0082] As can be seen, from a circuit perspective, a photovoltaic (PV) module can be equivalently represented as a power source with nonlinear characteristics. It possesses an equivalent internal resistance at its maximum power point. The idea is that the PV module's output power reaches its maximum when the external load impedance matches this equivalent internal resistance. This approach involves controlling the power converter to enter a specific operating state, obtaining the PV module's open-circuit voltage, short-circuit current, and output load impedance. Next, the obtained open-circuit voltage and short-circuit current are substituted into the PV module's mathematical model to calculate its equivalent internal resistance at the maximum power point. Subsequently, based on the impedance matching principle in circuit theory, a relational equation is constructed using the calculated equivalent internal resistance and the measured load impedance. By solving this equation, which directly relates the system's internal and external impedances to the duty cycle, the target duty cycle that achieves the matching can be calculated. Finally, this target duty cycle is directly assigned to the power converter. This replaces the iterative search process, thus avoiding continuous disturbances near the maximum power point and reducing the resulting steady-state power loss.

[0083] In some preferred embodiments, after step S104, the method further includes:

[0084] S105. Collect the current operating conditions of the photovoltaic modules according to the preset time series and extract the real-time calculated power.

[0085] This step is used to periodically monitor the actual output power of the system after it has been running stably, as a basis for judging whether the environment has changed.

[0086] S106. If the fluctuation of the real-time calculated power relative to the reference maintenance power at the maximum power point exceeds the preset tolerance threshold, jump to S101.

[0087] The reference sustained power refers to the theoretical or actual power value recorded when the system stabilizes at its maximum power point after executing S104. The change in fluctuation refers to the difference or ratio between the current real-time calculated power and the reference sustained power. The preset tolerance threshold is a threshold used to determine whether the power change is caused by a drastic environmental change.

[0088] In some embodiments, the system maintains a reference power value in memory and continuously compares it with the real-time power acquired in S105. When the illumination suddenly weakens or strengthens, causing the real-time power to deviate significantly from the reference power and exceed a threshold (e.g., below 95% of the reference), the system determines that the original maximum power point has become invalid. This determination event generates an interrupt or flag, triggering the program flow to jump back to S101 and start a new round of parameter identification and duty cycle calculation. This mechanism allows the algorithm to remain silent when the environment is stable and be passively awakened when the environment changes, thus balancing steady-state efficiency and dynamic response.

[0089] In some specific embodiments, the preset tolerance threshold can be a fixed percentage, such as a 10% power drop, which is not limited here; in other specific embodiments, the threshold can also be dynamically adaptive. For example, the system will monitor the rate of change of power at the same time, and only when the amount of power change and the rate of change both exceed their respective thresholds will a jump be triggered to avoid unnecessary retesting caused by minor noise or instantaneous disturbances, which is not limited here.

[0090] In actual use, under cloudy weather conditions with rapid power fluctuations, an overly sensitive threshold may cause the system to frequently execute the parameter identification process of S101. The open circuit and short circuit operations in S101 will interrupt the power output, thus reducing the total power generation.

[0091] In some preferred embodiments, after a successful jump and recalculation, the system enters a lockout period lasting several seconds, during which the jump will not be triggered even if the power fluctuates again. Alternatively, two thresholds can be set: a higher trigger threshold and a lower recovery threshold. The jump is only executed if the power drops below the trigger threshold and does not recover to above the recovery threshold within a certain period of time, thus effectively filtering out situations such as brief cloud drifts.

[0092] As can be seen, after the system stabilizes at the calculated duty cycle, this mechanism continuously collects the system's real-time operating power and compares it with the theoretical maximum power benchmark. When the difference between the two exceeds a preset threshold, the algorithm determines that the external environmental conditions have changed significantly. This determination triggers a jump instruction, causing the system to re-execute the complete parameter acquisition and calculation process. This operating mode allows the system to remain silent when the environment is stable, avoiding unnecessary retesting; while it can be awakened by the monitoring mechanism when the environment changes abruptly, updating the operating parameters in a timely manner. Thus, while maintaining the advantage of static, disturbance-free operation, it also possesses the ability to quickly adapt to environmental changes, ensuring power tracking accuracy under dynamic operating conditions.

[0093] Please refer to Figure 5 and Figure 6 , Figure 5 This is a waveform diagram showing the dynamic response of the photovoltaic module's output voltage in response to sudden changes in the external environment according to an embodiment of this application. Figure 6 This is a waveform diagram showing the dynamic response of the photovoltaic module's output current when responding to sudden changes in the external environment, according to an embodiment of this application.

[0094] Figure 5 The waveform of the output voltage of the photovoltaic module is shown. Figure 6 The waveform of the output current of the photovoltaic module is shown.

[0095] At a specific moment, the light intensity drops abruptly (simulating cloud cover).

[0096] After the sudden change, both voltage and current dropped rapidly and stabilized to a new, lower steady-state value in a very short time. The key point is that after stabilization, there was almost no back-and-forth oscillation.

[0097] Figure 5 and Figure 6 Together, they demonstrate the superior dynamic performance of the proposed method in handling rapid changes in the external environment (such as illumination). When a sudden change in the environment causes the original maximum power point to become invalid, the system quickly detects the power change and re-triggers the complete calculation process from S101 to S104. As can be seen from the figure, after the disturbance occurs, the algorithm can calculate the new target duty cycle extremely quickly and drive the system to stabilize at the new maximum power point, directly proving its high efficiency and stability in highly dynamic environments.

[0098] In practical applications, localized shading can easily occur due to factors such as building shadows, cloud cover, or surface contamination. To address this, photovoltaic (PV) modules typically integrate bypass diodes. When localized shading occurs, the bypass diodes conduct, short-circuiting the shaded cell to protect the module and maintain current flow. However, the activation of this protection mechanism alters the overall output electrical characteristics of the module, distorting its power-voltage characteristic curve from a conventional single-peak shape to a complex multi-peak shape with multiple local power maximums and a global power maximum. This implementation fails to identify and track the true global power maximum, resulting in a decrease in the PV module's power generation efficiency.

[0099] Therefore, in some preferred embodiments, after step S101 and before step S102, the method further includes:

[0100] S201. During the cycle in which the control power converter is switched sequentially to the first preset state and the second preset state, a sampling sequence of the photovoltaic module output current changing with the output voltage is obtained to determine the PV curve.

[0101] In this context, a sampling sequence refers to a series of discrete (voltage, current) data pairs acquired and stored by a high-speed analog-to-digital converter (ADC) during a continuous voltage or current change process. A PV curve is a power-voltage characteristic curve formed by plotting the voltage values ​​in the sampling sequence on the x-axis and the product of voltage and current (power) on the y-axis, connecting the points.

[0102] This step utilizes the transient process of converter state switching to quickly and physically measure the complete PV characteristic curve of the photovoltaic module under the current operating conditions, serving as the basis for subsequent operating condition diagnosis.

[0103] S202. Determine whether the PV curve has a unimodal or multimodal characteristic.

[0104] A unimodal characteristic refers to a PV curve having only one local maximum point. A multimodal characteristic refers to a PV curve having two or more local maxima points.

[0105] In some embodiments, the system executes a peak-finding algorithm on the PV curve data sequence to count the number of power maxima. The peak-finding algorithm traverses the entire power data sequence, identifying all peaks by comparing the magnitudes of adjacent data points or calculating the derivative (difference) of the data sequence and searching for sign changes. If only one peak is identified, the PV curve is determined to have a unimodal characteristic; if the number is greater than one, it is determined to have a multimodal characteristic. This determination serves as the basis for decision-making, guiding the algorithm to different processing flows.

[0106] In some specific embodiments, the peak finding algorithm can be implemented based on first-order difference, that is, calculating the difference between adjacent power points. When the difference sequence changes from positive to negative, a peak point is identified. The number of peaks is determined by counting the number of sign changes, which is not limited here. In other specific embodiments, in order to resist noise interference, a more robust algorithm can also be used. For example, the PV curve data can be smoothed and filtered first, and then the filtered data can be differentiated and peak found. Alternatively, a peak significance threshold can be set to count only those significant peaks that have a sufficient height difference with the adjacent troughs, which is not limited here.

[0107] In practical applications, under conditions of significant noise interference, a true single-peak curve may exhibit numerous tiny spurious peaks caused by noise, leading the algorithm to misjudge it as a multi-peak feature.

[0108] In some preferred embodiments, before performing peak finding, the original PV curve data acquired in S201 is digitally filtered, for example, using moving average filtering or Gaussian filtering to smooth the curve and eliminate high-frequency noise. Furthermore, peak amplitude thresholds and peak spacing thresholds can be introduced; that is, a point identified as a valid peak must have a height exceeding a minimum threshold, and its voltage distance from other adjacent valid peaks must also exceed a minimum spacing. This effectively filters out closely spaced noise spurious peaks.

[0109] S203. If the PV curve has a single-peak characteristic, execute S102.

[0110] This step is a conditional branch. When the diagnosis confirms that the system is in good working order and the PV curve is a standard single peak, the algorithm jumps directly back to the original calculation process.

[0111] S204. When the PV curve has a multi-peak characteristic, divide the PV curve into several sub-curves according to the centroid as the dividing line.

[0112] Here, the centroid refers to the voltage coordinate corresponding to the lowest power point (trough) between two adjacent peaks in the PV curve. Sub-curves refer to the individual curve segments obtained after the original PV curve is divided by the dividing line.

[0113] This step decomposes a complex PV curve into several relatively simple, approximately unimodal sub-curve segments based on the identified multimodal structure, in order to facilitate subsequent independent analysis.

[0114] S205. For the sub-curve, divide it into valid regions with data and invalid regions without data.

[0115] The valid region refers to the sub-curve portion in S201 that is actually collected and has real physical measurement values. The invalid region refers to the region where the original curve's data is assigned to an adjacent sub-curve due to the segmentation action in S204, resulting in a data gap in the original sub-curve.

[0116] This step involves analyzing the internal structure of each sub-curve after segmentation to distinguish which parts are based on measured data and which parts require subsequent reconstruction due to physical truncation.

[0117] S206. For the invalid region of the current sub-curve, fill in the invalid region based on the valid region of the current sub-curve and the corresponding region of the adjacent sub-curve to obtain the completed sub-curve.

[0118] Among them, the complete sub-curve refers to the sub-curve that is theoretically complete and untrunculated after the invalid region is filled in through mathematical or physical model deduction.

[0119] In some embodiments, local shading causes some sub-curves to be physically incomplete and cannot be directly used for model fitting. Therefore, the algorithm needs to use known information to infer the unknown parts. Specifically, it uses the data trend of the untrunculated "healthy" part (effective region) of the sub-curve itself, and refers to the variation patterns of other adjacent sub-curves that may be more complete in shape within the corresponding voltage range. Through a comprehensive extension and correction algorithm, it calculates a series of virtual data points to fill in the invalid region, ultimately generating a mathematically complete sub-curve that can represent the ideal characteristics under that local operating condition.

[0120] In some specific embodiments, the completion algorithm can be based on polynomial fitting extrapolation, that is, using the data of the effective region to fit a polynomial function, then using the function to calculate the value of the invalid region, and using the curvature of the adjacent sub-curves for constraint correction, which is not limited here; in other specific embodiments, a physical model-based completion method can also be adopted, that is, substituting the data of the effective region into the single diode model of the photovoltaic module, solving the equivalent physical parameters of the region (such as series resistance, photocurrent, etc.), and then using this set of parameters to regenerate the entire curve, and comparing and fusing it with the features of the adjacent sub-curves, which is not limited here.

[0121] In practical use, if the effective region of a sub-curve is too short (for example, a peak is truncated as soon as it appears), the error of extrapolating based solely on its own data will be very large, resulting in severe distortion of the completed curve.

[0122] In some preferred embodiments, an adaptive weight fusion mechanism is introduced. The algorithm evaluates the "confidence" of each sub-curve based on the number of data points or voltage span in the effective region. For sub-curves with high confidence (long effective regions), the completion process relies more on extrapolation of their own data; for sub-curves with low confidence (short effective regions), the mapping and filling rely more on the morphological features of adjacent sub-curves. By dynamically adjusting the weights of their own data and reference data, the most reasonable completion result can be obtained under various truncation conditions.

[0123] In some specific embodiments, step S206 specifically includes:

[0124] S2061. Extract the array of first-order partial derivatives and the array of second-order partial derivatives of the current sub-curve within the effective region, calculate the curve fitting extrapolation equation, and calculate its own extended trajectory.

[0125] The first-order partial derivative array and the second-order partial derivative array refer to the data sets representing the trends of curve slope and curvature changes obtained by performing difference operations on discrete data points in the effective region. The curve fitting extrapolation equation is a mathematical function established based on these derivative characteristics, capable of predicting the subsequent trend of the curve.

[0126] In some embodiments, the system first processes the valid region data sequence of the current sub-curve, approximating the first derivative (slope) by calculating the difference between adjacent data points, and then approximating the second derivative (curvature) by performing a difference operation on the slope sequence. Specifically, it focuses on extracting derivative features near the boundary points of the invalid region (i.e., cut-off points). Subsequently, the system uses these derivative values ​​at the cut-off points to construct a mathematical function that satisfies the continuity of slope and curvature at that point, serving as the curve fitting extrapolation equation. For example, a commonly used curve fitting extrapolation equation is a Taylor series expansion. Finally, a series of predicted data points within the voltage range of the invalid region are calculated using this equation; these points collectively constitute its extended trajectory.

[0127] In some specific embodiments, the system uses at least three data points at the end of the effective region to determine the coefficients of the polynomial by solving a system of linear equations, thereby establishing the equation; this is not limited here. In other specific embodiments, a function based on a physical model can also be used as the curve fitting extrapolation equation. For example, the effective region data can be fitted to a simplified single diode model to determine the key parameters of the model, and then this "locally calibrated" model can be used to generate the extension trajectory of the invalid region; this is not limited here.

[0128] S2062. Extract the curve shape feature set of the corresponding region of the adjacent sub-curves, compare the actual parameter value of the curve shape feature set with the predicted parameter value of the corresponding position of the curve fitting extrapolation equation, and obtain the curvature correction coefficient or fitting error compensation amount.

[0129] The curve morphology feature set refers to a set of key coefficients that represent the inherent shape of the photovoltaic curve, such as the curve fill factor or the curvature value at a specific voltage point. The curvature correction coefficient or fitting error compensation amount is a correction amount used to quantify the difference between the initial prediction and the reference model.

[0130] In some embodiments, this step aims to find a correction basis from "neighbors." The system analyzes the more complete sub-curves adjacent to the current sub-curve. It extracts characteristic parameters reflecting the inherent curvature characteristics of these neighboring sub-curves to form a reference standard. For example, a key characteristic parameter is the curvature near the maximum power point. Simultaneously, the system uses the curve fitting extrapolation equation calculated in S2061 to predict the theoretical curvature of the current sub-curve at the corresponding voltage point. By comparing this theoretically predicted curvature with the actual reference curvature extracted from neighboring sub-curves, a curvature correction coefficient for scaling or translation can be calculated. If the difference is significant, it indicates a deviation between the curvature of the initial extended trajectory and the reference standard, requiring correction.

[0131] In some specific embodiments, the curve shape feature set can be the fill factor of adjacent sub-curves. The system will use the curve fitting extrapolation equation to calculate a predicted fill factor and compare it with the reference fill factor to obtain a correction coefficient. This is not limited here. In other specific embodiments, the curve shape feature set can also be the slope ratio at specific voltage offsets on both sides of the peak point of adjacent sub-curves. The system will calculate the slope ratio of its own extended trajectory at the same position and obtain a fitting error compensation amount for adjusting asymmetry by comparing the difference between the two. This is not limited here.

[0132] S2063. Substitute the extended trajectory into the compensation function containing curvature correction coefficients or fitting error compensation for iterative optimization to generate a reconstructed extended trajectory.

[0133] The compensation function is a mathematical operator that smoothly adjusts the original trajectory based on the input correction amount. Iterative optimization refers to gradually reducing the error between the predicted trajectory and the ideal shape through iterative calculations until convergence.

[0134] In some embodiments, this step involves finely reshaping the initial extended trajectory generated in S2061 based on physical constraints. The system does not directly use the initial extended trajectory because it may not conform to the "standard shape" extracted from adjacent curves. Therefore, the algorithm takes the extended trajectory from S2061 as input and the curvature correction coefficient or fitting error compensation amount obtained in S2062 as the target constraint, initiating an iterative optimization loop. In each loop, the compensation function makes small adjustments to each data point on the extended trajectory based on the difference (i.e., residual) between the current trajectory and the target constraint. For example, if the "curvature" of the initial extended trajectory is less than the reference standard, the compensation function will moderately "bend" it slightly. This process is repeated until the error between the characteristic parameters (such as curvature) of the adjusted extended trajectory and the reference standard is less than a preset convergence threshold. At this point, the loop stops, and the final reconstructed extended trajectory with corrected shape is output.

[0135] In some specific embodiments, the compensation function can be a gradient-based optimizer, such as gradient descent. The system constructs an objective function to quantify the error between the current extended trajectory and the S2062 reference feature. In each iteration, the gradient of the error function with respect to the trajectory data points is calculated, and the trajectory is updated slightly along the negative gradient direction until the error is minimized; this is not limited here. In other specific embodiments, the compensation function can also be a global optimizer based on particle swarm optimization or genetic algorithms. The system uses the initial extended trajectory as the initial population and the matching reference feature as the fitness function. Through multiple generations of evolution or particle optimization, it finally finds a reconstructed extended trajectory whose shape best matches the reference standard. This method has better global convergence when dealing with highly nonlinear problems; this is not limited here.

[0136] S2064. The reconstructed extended trajectory and the effective region of the current sub-curve are spliced ​​together at the boundary between the effective and invalid regions to complete the invalid region and obtain the completed sub-curve.

[0137] This step involves connecting the optimized and corrected extended trajectory data with the original valid data segments to form a complete, corrected sub-curve.

[0138] As can be seen, firstly, extrapolation calculations are performed using the derivative characteristics of the effective data segments of the current sub-curve, ensuring a smooth transition between the completed curve and the original curve at the starting point. Simultaneously, to avoid potential mathematical divergence from a single extrapolation, the algorithm also extracts characteristic parameters from adjacent sub-curves as a correction basis, using an iteratively optimized compensation function to correct the initial extension trajectory. This completion method, combining its own trend and neighborhood characteristics for dual constraints, ensures that the reconstructed curve segment both retains its local characteristics and conforms to the overall physical laws of the photovoltaic module, thereby improving the accuracy of virtual boundary parameter calculations and providing more reliable data support for subsequent global optimum determination.

[0139] S207. Extract the virtual short-circuit current and the virtual open-circuit voltage with the largest value from all the completed sub-curves.

[0140] Among them, virtual short-circuit current and virtual open-circuit voltage refer to the theoretical limiting current and limiting voltage of each local sub-curve under the condition of no truncation, which are mathematically reconstructed by the completion algorithm of S206.

[0141] In some embodiments, this step involves boundary extreme value filtering of all repaired local operating condition datasets. The system iterates through all completed sub-curves generated in S206. For each completed sub-curve, it reads its current value at zero voltage as the virtual short-circuit current of that curve and its voltage value at zero current as the virtual open-circuit voltage. For example, the system may obtain two sets of data: {virtual Isc1, virtual Voc1} and {virtual Isc2, virtual Voc2}. Subsequently, the system compares all extracted virtual short-circuit current values ​​and filters out the maximum value (e.g., max(Isc1, Isc2)) as the final global virtual short-circuit current. Similarly, it compares all virtual open-circuit voltage values ​​and filters out the maximum value as the global virtual open-circuit voltage. These two filtered maximum values ​​theoretically represent the maximum current potential that the unshaded portion of the photovoltaic module can contribute and the maximum voltage potential that all series-connected cells can contribute under the current mixed illumination conditions.

[0142] In some specific embodiments, maximum value extraction can be achieved through a simple iterative comparison algorithm, that is, initializing a maximum value variable to zero, and then comparing and updating the boundary values ​​of each completed sub-curve one by one with it, which is not limited here; in other specific embodiments, considering data noise, a more robust statistical method can also be used, for example, extracting the average current of each sub-curve in the near-zero voltage range as its virtual short-circuit current, and the average voltage in the near-zero current range as its virtual open-circuit voltage, and then comparing and filtering these average values, which is not limited here.

[0143] S208. Obtain the characteristic value of the on-state voltage drop and the current reduction factor of the bypass element inside the photovoltaic module.

[0144] The forward voltage drop characteristic value typically refers to the voltage drop of a silicon-based diode in the on-state, which is approximately a fixed voltage value. The current reduction factor is a dimensionless parameter used to quantify the proportion of the total output current reduction caused by partial battery bypass.

[0145] In some embodiments, this step aims to quantify the physical distortion caused by local shading in the multi-peak PV curve into specific electrical parameters. These parameters are not acquired arbitrarily, but are calculated through in-depth geometric analysis of the raw PV curve data acquired in S201. Since the conduction of the bypass diode creates distinct "troughs" or "steps" on the PV curve, the location (voltage coordinates) and depth (current drop amplitude) of these steps directly contain the physical information of the bypass element.

[0146] In some specific embodiments, the characteristic value of the on-state voltage drop is obtained through preset parameters. That is, according to the technical specifications of the solar cells used in the photovoltaic module, a typical on-state voltage drop value of a bypass diode is pre-stored in the non-volatile memory of the controller, and the value is directly read when needed. The current reduction factor is obtained by analyzing the current step of the PV curve. Specifically, the system identifies the height of the current plateau between two adjacent peaks in the PV curve, and determines the current reduction factor by calculating the relative difference between the currents of the two plateaus. This is not limited here.

[0147] In some other specific embodiments, the system first identifies the voltage coordinates of all local maxima (peaks) in the PV curve. Since different peaks correspond to different numbers of battery strings in operation, the difference between these voltage coordinates should theoretically be approximately equal to an integer multiple of the voltage of a single battery string. Based on this, the system deduces the number of bypassed battery strings. Then, according to the factory parameters of the photovoltaic module, it calculates the theoretically lost open-circuit voltage (i.e., the conduction voltage drop characteristic value) due to the bypassing of these battery strings, and simultaneously estimates the corresponding current reduction factor, which is not limited here.

[0148] S209. The characteristic value of the on-state voltage drop and the current reduction factor are established as noise attenuation parameters.

[0149] Among them, the noise attenuation parameter refers to one or a set of correction factors used in the mathematical model to offset or compensate for calculation biases caused by physical distortions (such as local shading).

[0150] S210. Combine the virtual short-circuit current with the largest value and the virtual open-circuit voltage with the noise attenuation parameter to perform a fusion calculation to generate the global equivalent open-circuit voltage and the global equivalent short-circuit current.

[0151] In some specific embodiments, the system reads the maximum virtual open-circuit voltage obtained in S207 and extracts the voltage compensation amount (i.e., the conduction voltage drop characteristic value) from the noise attenuation parameter in S209. The global equivalent open-circuit voltage is obtained by performing a subtraction operation (global equivalent open-circuit voltage = maximum virtual open-circuit voltage - voltage compensation amount). At the same time, the system reads the maximum virtual short-circuit current and extracts the current multiplier (i.e., 1 - current reduction coefficient) from the noise attenuation parameter. The global equivalent short-circuit current is obtained by performing a multiplication operation (global equivalent short-circuit current = maximum virtual short-circuit current * current multiplier). This is not limited here.

[0152] S211. Substitute the global equivalent open-circuit voltage and the global equivalent short-circuit current into the mathematical model of the photovoltaic module to calculate the equivalent internal resistance of the photovoltaic module when it is at the maximum power point.

[0153] As can be seen, the topological characteristics of the PV curve are first determined by collecting sampling sequences of transient periods, and different processing paths are selected based on whether it is unimodal or multimodal. In the case of multimodality, the algorithm performs curve segmentation, invalid region completion, and extraction of virtual boundary parameters. Finally, it uses the physical characteristic parameters of bypass components to fuse and correct these virtual parameters, generating a set of globally equivalent open-circuit voltages and short-circuit currents. This mathematically transforms a complex multimodal PV curve due to local shading into a standard unimodal curve with the same global optimum. This allows the original unimodal mathematical model to still be used for calculations, thus solving the problem of the method easily getting trapped in local optima under multimodal conditions without changing the core algorithm architecture, and enhancing the system's adaptability to complex working environments.

[0154] The following describes an exemplary maximum power point tracking control system 700 based on dynamic impedance matching provided in an embodiment of this application. Figure 7 This is an exemplary hardware structure diagram of the maximum power point tracking control system 700 based on dynamic impedance matching provided in this application embodiment.

[0155] In some embodiments, the maximum power point tracking control system 700 based on dynamic impedance matching is a computer device or includes a computer device in the system. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0156] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0157] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0158] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0159] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0160] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory, magnetic disks, or optical disks.

Claims

1. A maximum power point tracking control method based on dynamic impedance matching, characterized in that, include: The control power converter is switched sequentially to the first preset state, the second preset state, and the third preset state to obtain the open-circuit voltage, short-circuit current, and load impedance of the photovoltaic module, respectively. Substitute the open-circuit voltage and the short-circuit current into the mathematical model of the photovoltaic module to calculate the equivalent internal resistance of the photovoltaic module when it is at its maximum power point. An impedance matching equation is constructed based on the equivalent internal resistance and the load impedance, and the target duty cycle is calculated based on the impedance matching equation. Update the operating duty cycle of the power converter to the target duty cycle.

2. The maximum power point tracking control method based on dynamic impedance matching according to claim 1, characterized in that, The steps of controlling the power converter to sequentially switch to the first preset state, the second preset state, and the third preset state to obtain the open-circuit voltage, short-circuit current, and load impedance of the photovoltaic module, respectively, specifically include: The on / off state of the power converter is controlled to be off, and the resting voltage on the input side of the photovoltaic module is collected as the open circuit voltage; The switching state of the power converter is controlled to be an extreme short-circuit state, and the limiting current on the input side of the photovoltaic module is collected as the short-circuit current; The power converter is controlled to be in a direct-on state. The output voltage and output current at the output terminal are collected respectively, and the load impedance is determined by dividing the output voltage by the output current.

3. The maximum power point tracking control method based on dynamic impedance matching according to claim 2, characterized in that, After the step of updating the operating duty cycle of the power converter to the target duty cycle, the method further includes: The current operating conditions of the photovoltaic modules are collected according to a preset time series, and the real-time calculated power is extracted. If the fluctuation of the real-time calculated power relative to the reference sustained power at the maximum power point exceeds a preset tolerance threshold, the process jumps to the control power converter, which sequentially switches to the first preset state, the second preset state, and the third preset state to obtain the open-circuit voltage, short-circuit current, and load impedance of the photovoltaic module, respectively.

4. The maximum power point tracking control method based on dynamic impedance matching according to claim 1, characterized in that, The mathematical model of the photovoltaic module is as follows: In the formula, I is the output current of the photovoltaic module; V is the output voltage of the photovoltaic module; This refers to the short-circuit current of the photovoltaic module. is the open-circuit voltage of the photovoltaic module; K is the curve shape parameter.

5. The maximum power point tracking control method based on dynamic impedance matching according to claim 1, characterized in that, The impedance matching equation is as follows: In the formula, This is the output power of the power converter; Input power to the power converter; This refers to the output voltage of the power converter. This is the input voltage for the power converter; The target duty cycle in the power converter; Output load impedance; This represents the equivalent load of the system.

6. The maximum power point tracking control method based on dynamic impedance matching according to claim 2, characterized in that, Before the step of substituting the open-circuit voltage and the short-circuit current into the mathematical model of the photovoltaic module to calculate the equivalent internal resistance, the method further includes: During the period when the control power converter switches sequentially to the first preset state and the second preset state, the sampling sequence of the output current of the photovoltaic module changing with the output voltage is obtained to determine the PV curve; Determine whether the PV curve exhibits a single-peak characteristic or a composite-peak characteristic; If the PV curve has a single-peak characteristic, perform the step of substituting the open-circuit voltage and the short-circuit current into the mathematical model of the photovoltaic module to calculate the equivalent internal resistance; When the PV curve has a multi-peak characteristic, the PV curve is divided into several sub-curves according to the centroid as the dividing line; For the sub-curve, divide it into valid regions with data and invalid regions without data; For the invalid region of the current sub-curve, the invalid region is filled in according to the valid region of the current sub-curve and the corresponding region of the adjacent sub-curve to obtain the filled sub-curve; Extract the virtual short-circuit current and the virtual open-circuit voltage with the largest value from all the completed sub-curves; Obtain the characteristic value of the on-state voltage drop and the current reduction factor of the bypass element inside the photovoltaic module; The characteristic value of the on-state voltage drop and the current reduction coefficient are established as noise attenuation parameters; The virtual short-circuit current with the largest value and the virtual open-circuit voltage with the largest value are combined with the noise attenuation parameter to perform a fusion calculation to generate the global equivalent open-circuit voltage and the global equivalent short-circuit current. Substitute the global equivalent open-circuit voltage and the global equivalent short-circuit current into the mathematical model of the photovoltaic module to calculate the equivalent internal resistance of the photovoltaic module when it is at its maximum power point.

7. The maximum power point tracking control method based on dynamic impedance matching according to claim 6, characterized in that, The step of filling in the invalid region of the current sub-curve by matching the valid region of the current sub-curve with the corresponding region of the adjacent sub-curve, and thus completing the sub-curve, specifically includes: Extract the array of first-order partial derivatives and the array of second-order partial derivatives of the current sub-curve within the effective region, calculate the curve fitting extrapolation equation, and calculate its own extended trajectory; Extract the curve shape feature set of the region corresponding to the adjacent sub-curves, compare the actual parameter value of the curve shape feature set with the predicted parameter value of the corresponding position of the curve fitting extrapolation equation, and obtain the curvature correction coefficient or fitting error compensation amount. The extended trajectory itself is substituted into a compensation function containing the curvature correction coefficient or the fitting error compensation amount for iterative optimization to generate a reconstructed extended trajectory. The reconstructed extended trajectory and the effective region of the current sub-curve are spliced ​​together at the boundary point between the effective region and the invalid region to complete the invalid region and obtain the completed sub-curve.

8. A maximum power point tracking control system based on dynamic impedance matching, characterized in that, The maximum power point tracking control system based on dynamic impedance matching includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the maximum power point tracking control system based on dynamic impedance matching to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on a maximum power point tracking control system based on dynamic impedance matching, the maximum power point tracking control system based on dynamic impedance matching performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising a computer program, characterized in that, The method as described in any one of claims 1-7 is implemented when the computer program instructions are executed by the processor.

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