High-frequency soft switching control method and device of photovoltaic optimizer

Abstract

The invention relates to the technical field of switch control, and discloses a high-frequency soft switching control method and device for a photovoltaic optimizer. The method comprises the following steps: carrying out dead-zone time calculation processing on a system quality factor and a basic dead-zone time value to obtain an actual dead-zone time value and a voltage and current change rate of a switching device, and carrying out frequency modulation processing on the actual dead-zone time value and the voltage and current change rate of the switching device through a pulse frequency modulation algorithm, obtaining a system working frequency value and a frequency adjusting quantity; and performing maximum power point tracking processing on the system working frequency value and the frequency regulation quantity to obtain an optimized system working frequency value and a power tracking increment value, and determining a soft switching working mode of the switching device according to the optimized system working frequency value. According to the invention, the efficiency and accuracy of high-frequency soft switching control of the photovoltaic optimizer are improved.

Description

Technical Field

[0001] This application relates to the field of switch control, and particularly to a high-frequency soft-switching control method and device for a photovoltaic optimizer. Background Art

[0002] Existing photovoltaic optimizers mainly optimize the output of a photovoltaic system by improving conversion efficiency and reducing power loss. Traditional photovoltaic optimizers mostly adopt hard-switching control methods, which generate relatively large switching losses at higher frequencies, leading to heat accumulation and affecting the reliability and efficiency of the system. With the development of photovoltaic technology, how to effectively manage energy and improve system efficiency has become an important research direction. Many photovoltaic optimizers use fixed frequencies or simple algorithms in maximum power point tracking (MPPT) to achieve this, but it is often difficult to achieve optimal performance at high frequencies.

[0003] However, these traditional methods have certain deficiencies in high-frequency operations. The hard-switching method results in relatively large switching losses, low stability of the system frequency, and it is difficult to ensure the reliable operation of switching devices at higher operating frequencies. Especially when performing frequency regulation and maximum power point tracking, it is often difficult to precisely control, leading to increased power loss and reduced efficiency, which limits the overall performance improvement of the photovoltaic optimizer. Summary of the Invention

[0004] This application provides a high-frequency soft-switching control method and device for a photovoltaic optimizer, which are used to improve the efficiency and accuracy of high-frequency soft-switching control of the photovoltaic optimizer.

[0005] In a first aspect, this application provides a high-frequency soft-switching control method for a photovoltaic optimizer. The high-frequency soft-switching control method for the photovoltaic optimizer includes: collecting and processing the output voltage, output current, voltage and current at the optimizer output terminal of a photovoltaic module to obtain system initialization parameters, where the system initialization parameters include a resonance inductance coefficient, a resonance capacitance reactance coefficient, an excitation inductance coefficient, and a power tracking increment value;

[0006] Performing characteristic impedance calculation processing on the system initialization parameters to obtain the characteristic impedance value, the first resonance frequency value, the second resonance frequency value, and the voltage transformation ratio of a resonance network;

[0007] Performing working interval calculation processing on the characteristic impedance value, the first resonance frequency value, the second resonance frequency value, and the voltage transformation ratio of the resonance network to obtain the system quality factor and the basic dead time value, where the working interval includes a zero-voltage switching interval, a zero-current switching interval, and a mixed switching interval;

[0008] Perform dead-time calculation processing on the system quality factor and the basic dead-time value to obtain the actual dead-time value and the change rates of the voltage and current of the switching device. The actual dead-time value is set separately according to different switching intervals;

[0009] Perform frequency modulation processing on the actual dead-time value and the change rates of the voltage and current of the switching device through a pulse frequency modulation algorithm to obtain the system operating frequency value and the frequency adjustment amount. The system operating frequency value is within the high-frequency operating range of 90 kHz to 200 kHz;

[0010] Perform maximum power point tracking processing on the system operating frequency value and the frequency adjustment amount through a three-point comparison method to obtain the optimized system operating frequency value and the power tracking increment value, and determine the soft-switching operating mode of the switching device according to the optimized system operating frequency value.

[0011] In a second aspect, the present application provides a high-frequency soft-switching control device for a photovoltaic optimizer. The high-frequency soft-switching control device for the photovoltaic optimizer includes:

[0012] An acquisition module for performing acquisition processing on the output voltage, output current, voltage and current at the output end of the optimizer of the photovoltaic module to obtain system initialization parameters. The system initialization parameters include a resonance inductance coefficient, a resonance capacitance reactance coefficient, an excitation inductance coefficient, and a power tracking increment value;

[0013] A calculation module for performing characteristic impedance calculation processing on the system initialization parameters to obtain the characteristic impedance value, the first resonance frequency value, the second resonance frequency value, and the voltage transformation ratio of the resonance network;

[0014] A processing module for performing working interval calculation processing on the characteristic impedance value, the first resonance frequency value, the second resonance frequency value, and the voltage transformation ratio of the resonance network to obtain the system quality factor and the basic dead-time value. The working intervals include a zero-voltage switching interval, a zero-current switching interval, and a mixed switching interval;

[0015] An analysis module for performing dead-time calculation processing on the system quality factor and the basic dead-time value to obtain the actual dead-time value and the change rates of the voltage and current of the switching device. The actual dead-time value is set separately according to different switching intervals;

[0016] A modulation module for performing frequency modulation processing on the actual dead-time value and the change rates of the voltage and current of the switching device through a pulse frequency modulation algorithm to obtain the system operating frequency value and the frequency adjustment amount. The system operating frequency value is within the high-frequency operating range of 90 kHz to 200 kHz;

[0017] A tracking module, configured to perform maximum power point tracking processing on the system operating frequency value and the frequency adjustment amount through a three-point comparison method, obtain an optimized system operating frequency value and a power tracking increment value, and determine the soft-switching operating mode of the switching device according to the optimized system operating frequency value.

[0018] In the technical solution provided by this application, through the high-frequency soft-switching control method, the conversion efficiency and overall performance of the photovoltaic optimizer are effectively improved. First, the solution obtains the initialization parameters of the system by collecting the output voltage and current of the photovoltaic module and the voltage and current data at the output end of the optimizer, including the resonance inductance coefficient, resonance capacitance reactance coefficient, excitation inductance coefficient, and power tracking increment value. These parameters lay a data foundation for subsequent control and optimization. Secondly, the solution calculates the characteristic impedance of the system initialization parameters, obtaining the characteristic impedance value, the first resonance frequency value, the second resonance frequency value, and the voltage transformation ratio of the resonance network. These characteristic parameters make the behavior of the system more controllable under different frequency conditions, enhancing the adaptability and reliability of the system. By calculating and processing the working intervals of parameters such as the characteristic impedance and resonance frequency of the resonance network, the quality factor and the basic dead time value of the system are further obtained, ensuring that the system can achieve stable zero-voltage switching (ZVS), zero-current switching (ZCS), and hybrid switching modes in different working intervals, thus greatly reducing the switching loss and device heating problems. This precisely controlled switching mode has a significant effect on improving the efficiency of the photovoltaic optimizer. The solution also optimizes the quality factor and the basic dead time value of the system. By calculating the actual dead time value and the change rate of the voltage and current of the switching device, precise dead time setting in different switching intervals is achieved, enabling the switching device to start and close at appropriate times, effectively reducing unnecessary energy losses. More importantly, through the pulse frequency modulation algorithm, frequency modulation processing is performed on the actual dead time value and the change rate of the voltage and current of the switching device, keeping the system operating frequency within the high-frequency operating range of 90 kHz to 200 kHz. This stable control within this high-frequency range significantly improves the working efficiency of the system. In addition, the three-point comparison method is used in the solution for maximum power point tracking processing, which helps to quickly and accurately obtain the optimal operating frequency value and achieve maximum power output, thus making full use of the output power of the photovoltaic module. The optimized system operating frequency value obtained through the maximum power point tracking algorithm and the soft-switching operating mode of the switching device determined according to this frequency value effectively improve the power conversion efficiency and stability of the photovoltaic system under dynamic conditions. Through a series of precise data collection, parameter calculation, working interval division, dead time adjustment, and frequency modulation processing, precise control of high-frequency soft switching is achieved. While improving the conversion efficiency, the switching loss and system temperature rise are significantly reduced, further enhancing the overall performance and service life of the photovoltaic optimizer. Description of the Drawings

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a schematic diagram of an embodiment of the high-frequency soft-switching control method of the photovoltaic optimizer in the embodiments of the present application;

[0021] Figure 2 It is a schematic diagram of an embodiment of the high-frequency soft-switching control device of the photovoltaic optimizer in the embodiments of the present application. Specific Embodiments

[0022] The embodiments of the present application provide a high-frequency soft-switching control method and device for a photovoltaic optimizer. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned accompanying drawings of the present application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 , an embodiment of the high-frequency soft-switching control method of the photovoltaic optimizer in the embodiments of the present application includes:

[0024] Step S101: Collect and process the output voltage, output current, optimizer output terminal voltage and current of the photovoltaic module to obtain system initialization parameters, where the system initialization parameters include a resonance inductance coefficient, a resonance capacitive reactance coefficient, an excitation inductance coefficient and a power tracking increment value;

[0025] Step S102: Perform characteristic impedance calculation processing on the system initialization parameters to obtain the characteristic impedance value, the first resonance frequency value, the second resonance frequency value and the voltage transformation ratio of the resonance network;

[0026] Step S103: Perform working range calculation and processing on the characteristic impedance value, first resonance frequency value, second resonance frequency value, and voltage conversion ratio of the resonant network to obtain the system quality factor and the basic dead time value. The working range includes the zero-voltage switching range, zero-current switching range, and hybrid switching range;

[0027] Step S104: Perform dead time calculation and processing on the system quality factor and the basic dead time value to obtain the actual dead time value and the voltage and current change rates of the switching device. The actual dead time value is set separately according to different switching ranges;

[0028] Step S105: Perform frequency modulation processing on the actual dead time value and the voltage and current change rates of the switching device through the pulse frequency modulation algorithm to obtain the system operating frequency value and the frequency adjustment amount. The system operating frequency value is within the high-frequency operating range of 90 kHz to 200 kHz;

[0029] Step S106: Perform maximum power point tracking processing on the system operating frequency value and the frequency adjustment amount through the three-point comparison method to obtain the optimized system operating frequency value and the power tracking increment value, and determine the soft switching operating mode of the switching device according to the optimized system operating frequency value.

[0030] It can be understood that the execution subject of this application can be the high-frequency soft-switching control device of the photovoltaic optimizer, or it can also be a terminal or a server, and specific limitations are not made here. In this embodiment of the application, the server is used as the execution subject for illustration.

[0031] Specifically, first, the output voltage and current of the photovoltaic module, as well as the voltage and current at the output end of the optimizer, are collected. After these initial parameters are collected, the system performs normalization and clipping processing on them to obtain the key resonant inductance coefficient, resonant capacitive reactance coefficient, excitation inductance coefficient, and power tracking increment value. Through processing steps such as clipping and normalization, it is ensured that the parameters are within the specified range to avoid errors caused by abnormal voltages and currents. This initialization process aims to provide accurate and stable basic parameters for the subsequent characteristic impedance calculation, so that the overall system operates in an efficient range. In the second stage of data processing, the characteristic impedance of the system initialization parameters is calculated. The characteristic impedance is an important parameter in the resonant network. By performing a square root operation on the resonant inductance coefficient and the resonant capacitive reactance coefficient, the characteristic impedance value of the resonant network is obtained. In addition, the first resonance frequency value is calculated further through the ratio of the resonant inductance coefficient to the resonant capacitive reactance coefficient. At the same time, according to the collaborative calculation of the excitation inductance coefficient, resonant inductance coefficient, and resonant capacitive reactance coefficient, the second resonance frequency value and the voltage conversion ratio are obtained. In this way, through these characteristic parameters obtained by preliminary processing and calculation processing, in the resonant network, the system can divide the working range according to the ratio of the characteristic impedance to the resonance frequency, ensuring that the system operates in the optimal frequency state.

[0032] In the working interval calculation and processing step, parameters such as characteristic impedance value, first resonance frequency, second resonance frequency, and voltage transformation ratio are input into the algorithm to calculate the zero-voltage switching interval, zero-current switching interval, and hybrid switching interval. The division of the working interval is achieved by adjusting the voltage transformation ratio based on different resonance frequencies, thereby obtaining the system quality factor and the basic dead-time value. The core purpose of this step is to determine the optimized parameters under different switching modes through precise working interval division, so as to reduce switching losses and achieve high-efficiency frequency control. Subsequently, it enters the dead-time calculation process. By combining the system quality factor and the basic dead-time value, the actual dead-time of the system is set. The actual dead-time value determines the duration of each switching interval. In this step, through dead-time calculation and processing, the specific dead-time values in different intervals such as zero-voltage switching, zero-current switching, and hybrid switching are obtained. Within each switching interval, based on the change rates of voltage and current, the actual voltage and current change rates are calculated. The calculation process of the change rates provides a reference basis for subsequent frequency modulation to ensure stable operation of each interval under different frequency states.

[0033] In the pulse frequency modulation processing step, through pulse frequency modulation processing of the actual dead-time and voltage and current change rates, the system obtains the working frequency value and the frequency adjustment amount, so that the system working frequency is stabilized within the high-frequency range of 90 kHz to 200 kHz. This frequency control range ensures the high-efficiency conversion performance of the PV optimizer and maintains stable switching operation within the high-frequency range. Through real-time update of the frequency adjustment amount, the system can be automatically optimized under different frequency conditions to make the power output of the PV module reach the best state. In the final maximum power point tracking step, through the three-point comparison method, the system working frequency value and the frequency adjustment amount are subjected to maximum power point tracking processing, and then the optimized system working frequency value is obtained, and based on this frequency value, the soft switching mode of the switching device is determined. The three-point comparison method is to compare and calculate the sampling points of the system at different frequencies, and dynamically adjust the power output through the calculated frequency difference, so as to achieve the maximum power output state. The application of the three-point comparison method ensures that the PV optimizer can adjust the power output in real time in a changing environment and effectively improve the overall conversion efficiency of the system.

[0034] For example, assume that the output voltage of a photovoltaic module is 40V, the output current is 8A, the output voltage of the optimizer is 35V, and the output current is 7.5A. First, collect the above data and perform normalization processing to obtain a resonance induction coefficient of 1.5, a resonance capacitive reactance coefficient of 2.2, an excitation induction coefficient of 1.8, and a power tracking increment value of 0.05. When calculating the characteristic impedance, the characteristic impedance is obtained as 1.8Ω through square root calculation; when calculating the first and second resonance frequencies, the first resonance frequency is calculated as 100kHz and the second resonance frequency is calculated as 180kHz based on the aforementioned resonance coefficients. At the same time, the voltage ratio is 1.14. Combining these parameters, the basic dead time of the zero voltage switching interval of the system is calculated as 10μs. Through the dead time processing algorithm, the system obtains an optimized actual dead time of 12μs and adjusts the system frequency to the range of 100kHz to 200kHz according to pulse modulation. Using the three-point comparison method, maximum power point tracking is performed at sampling points of 100kHz, 120kHz, and 180kHz, and finally it is concluded that the system obtains the maximum power at 180kHz, thereby determining the soft switching mode at this frequency.

[0035] In the embodiments of the present application, the conversion efficiency and overall performance of the photovoltaic optimizer are effectively improved through the high-frequency soft-switching control method. First, the scheme obtains the initialization parameters of the system by collecting the output voltage and current of the photovoltaic module and the voltage and current data at the output end of the optimizer, including the resonance inductance coefficient, resonance capacitance reactance coefficient, excitation inductance coefficient, and power tracking increment value. These parameters lay a data foundation for subsequent control and optimization. Secondly, the scheme calculates the characteristic impedance of the system initialization parameters to obtain the characteristic impedance value, the first resonance frequency value, the second resonance frequency value, and the voltage transformation ratio of the resonance network. These characteristic parameters make the behavior of the system more controllable under different frequency conditions, enhancing the adaptability and reliability of the system. By calculating and processing the working range of parameters such as the characteristic impedance and resonance frequency of the resonance network, the quality factor and the basic dead time value of the system are further obtained, ensuring that the system can achieve stable zero-voltage switching (ZVS), zero-current switching (ZCS), and hybrid switching modes in different working ranges, thus greatly reducing the switching loss and device heating problems. This precisely controlled switching mode has a significant effect on improving the efficiency of the photovoltaic optimizer. The scheme also optimizes the quality factor and the basic dead time value of the system. By calculating the actual dead time value and the change rate of the voltage and current of the switching device, the precise dead time setting in different switching intervals is realized, enabling the switching device to start and close at the appropriate time, effectively reducing unnecessary energy loss. More importantly, through the pulse frequency modulation algorithm, the actual dead time value and the change rate of the voltage and current of the switching device are frequency-modulated, so that the system operating frequency remains in the high-frequency operating range of 90 kHz to 200 kHz. This stable control in this high-frequency range significantly improves the operating efficiency of the system. In addition, the three-point comparison method is used in the scheme for maximum power point tracking processing, which helps to quickly and accurately obtain the optimal operating frequency value and achieve maximum power output, thus making full use of the output power of the photovoltaic module. Through the optimized system operating frequency value by the maximum power point tracking algorithm and the soft-switching operating mode of the switching device determined according to this frequency value, the power conversion efficiency and stability of the photovoltaic system under dynamic conditions are effectively improved. Through a series of precise data collection, parameter calculation, working range division, dead time adjustment, and frequency modulation processing, the precise control of high-frequency soft switching is realized, while improving the conversion efficiency, significantly reducing the switching loss and system temperature rise, and further enhancing the overall performance and service life of the photovoltaic optimizer.

[0036] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0037] (1) Sample the output voltage and output current of the photovoltaic module to obtain the input power value, which is the product of the output voltage of the photovoltaic module and the output current of the photovoltaic module;

[0038] (2) Limit the voltage and current at the output end of the optimizer to obtain the limiting parameters. When the voltage and current at the output end of the optimizer exceed the preset range, set the voltage and current at the output end of the optimizer to the limiting values within the preset range;

[0039] (3) Normalize the limiting parameters to obtain the resonance induction coefficient and the resonance capacitive reactance coefficient;

[0040] (4) Compensate the resonance induction coefficient and the resonance capacitive reactance coefficient to obtain the excitation induction coefficient;

[0041] (5) Perform incremental calculation processing on the input power value to obtain the power tracking increment value, and combine the resonance induction coefficient, the resonance capacitive reactance coefficient, the excitation induction coefficient, and the power tracking increment value into the system initialization parameters.

[0042] Specifically, preliminary data collection and processing are carried out for the output of the photovoltaic module. Specifically, by sampling the output voltage and current signals of the photovoltaic module, the input power value is calculated. The input power value is obtained by multiplying the output voltage of the photovoltaic module by the output current, which represents the current output power level of the photovoltaic module and is the basis for subsequent power tracking and optimization control. Limit the voltage and current signals at the output end of the optimizer to prevent abnormal data beyond the system preset range from interfering with the normal operation of the system. The basic principle of the limiting process is: when it is detected that the voltage or current at the output end of the optimizer exceeds the preset upper or lower limit range, its value is limited to the boundary value within this range, thereby ensuring the stability and safety of the voltage and current. This limiting process generates the limiting parameters, providing safe and reliable initial data for subsequent normalization and compensation processing.

[0043] After that, normalize the limited voltage and current data to facilitate the subsequent calculation of the resonance induction coefficient and the resonance capacitive reactance coefficient. Normalization processing is a data standardization operation that converts the limiting parameters into dimensionless standard values, enabling direct comparison and calculation between different data, and ensuring the accuracy and consistency of subsequent algorithms. After normalization processing, the obtained resonance induction coefficient and resonance capacitive reactance coefficient are two core parameters of the system resonance characteristics, and these two coefficients lay the foundation for subsequent frequency control and optimization. After obtaining the resonance induction coefficient and the resonance capacitive reactance coefficient, perform compensation processing to balance the resonance changes under different working conditions. Through compensation processing, the system generates the excitation induction coefficient, which reflects the resonance characteristics of the system in a dynamic environment and helps the optimizer to be stable and accurate during the power conversion process. The compensation processing is an accurate calculation based on the resonance characteristics of the system, enabling the resonance characteristics to remain consistent under different working conditions and ensuring the stable operation of the system.

[0044] Finally, an incremental calculation is performed on the input power value to obtain the power tracking increment value. The incremental calculation is based on the change of the output power of the photovoltaic module over time, aiming to dynamically track and adjust the changing trend of the power output. The power tracking increment value is one of the key parameters for real-time adjustment of the system operating state, which can help the system respond promptly to changes in environmental conditions such as external light and temperature. The result of the incremental calculation, together with the previously obtained resonance inductance coefficient, resonance capacitance reactance coefficient, and excitation inductance coefficient, constitutes the system initialization parameters. The initialization parameters provide accurate and comprehensive data support for the high-frequency soft-switching control of the entire photovoltaic optimizer, ensuring the efficient operation and stable performance of the system under high-frequency conditions.

[0045] In a specific embodiment, the process of performing step S102 may specifically include the following steps:

[0046] (1) Perform a square root operation on the resonance inductance coefficient and the resonance capacitance reactance coefficient to obtain the characteristic impedance value of the resonance network;

[0047] (2) Perform a first frequency calculation on the resonance inductance coefficient and the resonance capacitance reactance coefficient to obtain the first resonance frequency value, and the first resonance frequency value is inversely proportional to the square root of the product of the resonance inductance coefficient and the resonance capacitance reactance coefficient;

[0048] (3) Perform a second frequency calculation based on the resonance inductance coefficient, the excitation inductance coefficient, and the resonance capacitance reactance coefficient to obtain the second resonance frequency value, and the second resonance frequency value is inversely proportional to the square root of the product of the sum of the resonance inductance coefficient and the excitation inductance coefficient and the resonance capacitance reactance coefficient;

[0049] (4) Perform a ratio calculation on the voltage at the optimizer output terminal and the output voltage of the photovoltaic module to obtain the voltage change ratio.

[0050] Specifically, a square root operation is performed on the resonance inductance coefficient and the resonance capacitance reactance coefficient to obtain the characteristic impedance value of the resonance network. The characteristic impedance is an important parameter reflecting the voltage-current relationship of the system under a specific resonance state. The square root operation enables the system to intuitively understand the impedance characteristics of the resonance network. This characteristic impedance value will directly affect the energy transfer efficiency of the system under resonance conditions and is an important basis for subsequent resonance frequency adjustment. Then, a frequency calculation is performed on the resonance inductance coefficient and the resonance capacitance reactance coefficient to obtain the first resonance frequency value. The calculation of the first resonance frequency value is based on the square root of the product of these two coefficients. Since the first resonance frequency value is inversely proportional to the product of the resonance inductance coefficient and the resonance capacitance reactance coefficient, this means that under resonance conditions, if the resonance inductance coefficient or the resonance capacitance reactance coefficient changes, the first resonance frequency will be adjusted accordingly to achieve adaptability to external loads or environmental changes. This resonance frequency value provides a reference frequency for the system, ensuring that the system can operate in the optimal resonance state under different working conditions.

[0051] Next, using the relationships among the resonance induction coefficient, excitation induction coefficient, and resonance capacitive reactance coefficient, the second resonance frequency is calculated. The second resonance frequency is inversely proportional to the square root of the product of the sum of the resonance induction coefficient and the excitation induction coefficient and the resonance capacitive reactance coefficient. The value of the second resonance frequency complements the first resonance frequency and reflects the resonance response characteristics of the system under different excitation conditions. This value can help the system cope with a wider range of input or output fluctuations, enabling the optimizer to flexibly adjust the frequency under dynamic conditions. Finally, by calculating the ratio of the voltage at the output terminal of the optimizer to the output voltage of the photovoltaic module, the voltage conversion ratio is obtained. The voltage conversion ratio is a key indicator for measuring the relationship between the output of the photovoltaic module and the output of the optimizer, and it reflects the gain effect of the optimizer during the voltage conversion process. Calculating the voltage conversion ratio can provide an effective reference for the operating mode of the control system at different voltages, so that when the output voltage of the photovoltaic module changes, the optimizer can quickly adjust the voltage ratio to maintain a stable output. The calculation and adjustment of this series of parameters ensure that the resonant network can operate efficiently and provide consistent output performance under different environmental conditions.

[0052] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0053] (1) Perform quality calculation processing on the characteristic impedance value of the resonant network to obtain the system quality factor;

[0054] (2) Perform interval determination processing on the first resonance frequency value and the second resonance frequency value to obtain the zero-voltage switching interval, zero-current switching interval, and mixed switching interval;

[0055] (3) Perform data compensation processing on the zero-voltage switching interval, zero-current switching interval, and mixed switching interval according to the voltage conversion ratio to obtain the compensated working interval;

[0056] (4) Perform dead-time calculation processing on the compensated working interval to obtain the basic dead-time value;

[0057] (5) Process the system quality factor and the basic dead-time value through interpolation operation to obtain the corresponding relationship between the dead-time and the quality factor.

[0058] Specifically, the characteristic impedance value of the resonant network is subjected to quality calculation processing to obtain the quality factor of the system. The quality factor (Q value) is an important parameter for measuring the resonant characteristics of the system, representing the balance relationship between the energy storage and loss of the resonant network. The higher the quality factor, the more obvious the resonant characteristics of the system and the higher the energy transfer efficiency. In this process, the Q value can be accurately calculated using the characteristic impedance value, providing reliable data support for subsequent frequency control and switching mode selection. Next, interval determination processing is performed on the first resonant frequency value and the second resonant frequency value to determine the zero-voltage switching (ZVS) interval, zero-current switching (ZCS) interval, and hybrid switching interval. This determination processing is achieved by analyzing the response of the two resonant frequencies in different frequency bands. When the frequency is in a specific interval, the system can select different switching modes to reduce switching losses. For example, the ZVS mode can reduce the voltage stress of the switching device during conduction and turn-off, while the ZCS mode can reduce the current stress. The interval determination of these switching modes ensures that the system can automatically switch to the optimal switching mode under different operating conditions, further improving the conversion efficiency and switching life.

[0059] Subsequently, data compensation processing is performed on the zero-voltage switching interval, zero-current switching interval, and hybrid switching interval according to the voltage transformation ratio to obtain the compensated operating interval. The voltage transformation ratio is the ratio of the output voltage of the photovoltaic module to the output voltage of the optimizer, reflecting the voltage conversion characteristics of the system. Under different voltage transformation ratios, the switching modes within the operating interval may shift, so data compensation is required to ensure the accurate boundary positions of each switching interval. The compensated operating interval can adapt to the change of the switching mode according to the voltage change, thus ensuring the stable output of the system under different input voltage conditions. After obtaining the compensated operating interval, the system performs dead-time calculation processing on it to obtain the basic dead-time value. The basic dead-time refers to the minimum time interval required for the switching device to perform zero-voltage switching or zero-current switching to ensure that the switching tube completes the turn-off or turn-on operation without load. The accurate setting of the dead-time is a key step in reducing switching losses and controlling interference. Therefore, when calculating the basic dead-time, the compensated interval range and the dynamic response characteristics of the system will be considered to ensure the stability of the switching operation.

[0060] Finally, through interpolation processing, the quality factor of the system and the basic dead time value are combined to obtain the corresponding relationship between the dead time and the quality factor. Interpolation is a method of predicting values between known data points. By interpolating the system quality factor and the basic dead time value, a table or curve of the dead time varying with the quality factor can be generated. This relationship indicates that the optimal dead time required by the system under different resonant states enables the system to automatically adjust the dead time to match the current resonant state, thereby achieving dynamic and precise switching control. For example, assume that the characteristic impedance value measured under a certain working condition of the system is 3 Ω, and the quality factor Q is calculated to be 20 through quality calculation. Based on the interval division of the first and second resonant frequencies, it is determined that the current working mode is the hybrid switching interval, and compensation processing is performed for the condition of the voltage transformation ratio of 1.2 to obtain the accurate working interval range. According to the compensated working interval, the basic dead time value is calculated to be 5 microseconds. Subsequently, the corresponding relationship between the dead time and the quality factor is established through the interpolation algorithm, enabling the system to automatically set the dead time to 5 microseconds under the condition of the quality factor Q of 20, thereby achieving the optimal switching control.

[0061] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0062] (1) Classify the system quality factor into intervals to obtain the dead time coefficients for different intervals;

[0063] (2) Multiply the basic dead time value by the dead time coefficient to obtain the actual dead time value;

[0064] (3) Compare the actual dead time value with a threshold to obtain the change rate of the voltage and current of the switching device;

[0065] (4) Correct the actual dead time value according to the change rate of the voltage and current of the switching device to obtain the corrected actual dead time value;

[0066] (5) Distribute the corrected actual dead time value to obtain the dead time configurations for the zero-voltage switching interval, zero-current switching interval, and hybrid switching interval.

[0067] Specifically, the system is classified according to its quality factor to obtain dead-time coefficients in different intervals. The quality factor (Q value) reflects the resonant performance of the system. The higher the Q value, the smaller the energy loss and the higher the efficiency of the system in the resonant state. Classifying the quality factor by intervals can determine the most suitable dead-time coefficient for different resonant states, enabling the system to perform switching control with the optimal dead time under various operating conditions. Through the classified dead-time coefficients, the system can automatically select appropriate dead-time adjustment parameters according to different quality factors. Then, the basic dead-time value is multiplied by the dead-time coefficient to obtain the actual dead-time value. The basic dead-time value is the minimum dead-time of the system under specific conditions, and the dead-time coefficient is adjusted according to the classification of the quality factor. The product of the two is the actual dead-time value, ensuring that the dead time can vary flexibly according to different resonant states to meet different requirements under zero-voltage and zero-current switching.

[0068] Subsequently, the actual dead-time value is subjected to a threshold comparison process to obtain the voltage and current change rates of the switching device. The purpose of the threshold comparison process is to confirm whether the actual dead-time value is within the allowable change range of the system. If the actual dead-time value exceeds the threshold range, problems such as increased switching losses or reduced efficiency may occur in the system. Therefore, through the threshold comparison, the rationality of the actual dead-time can be evaluated to ensure that it does not cause excessive voltage and current fluctuations. The measurement of the voltage and current change rates also provides a data basis for subsequent dead-time correction. Next, the actual dead-time value is corrected according to the measured voltage and current change rates of the switching device to obtain the corrected actual dead-time value. This correction process ensures that the dead time can accurately adapt to the current switching conditions and avoids switching losses or efficiency losses caused by voltage or current fluctuations. During the correction process, the system considers the actual situation of the voltage and current change rates and appropriately increases or decreases the actual dead time to obtain the optimal switching operation conditions.

[0069] Finally, the corrected actual dead-time value is subjected to a distribution process to obtain the specific dead-time configurations for the zero-voltage switching interval, zero-current switching interval, and mixed switching interval. The distribution process distributes the corrected dead time according to different switching intervals, enabling the system to achieve the optimal switching state within each interval. The dead-time configurations under different intervals help the system to automatically switch under various resonant conditions to reduce switching losses and optimize the energy transfer efficiency. In this way, the system can flexibly adjust the dead time under different operating states to ensure that the photovoltaic optimizer always maintains high efficiency and stable performance during high-frequency operation.

[0070] For example, assume that the quality factor of the system is 15 under a certain operating condition. Through interval classification, the corresponding dead-time coefficient is obtained as 1.2. The basic dead-time value of the system is 8 microseconds. Multiplying the dead-time coefficient 1.2, the actual dead-time value is obtained as 9.6 microseconds. When performing threshold comparison, it is found that this value meets the threshold range, but the voltage and current change rates indicate that it is slightly higher than the ideal value. Based on this change rate, the system corrects the actual dead-time to 9.3 microseconds. Finally, the corrected actual dead-time value of 9.3 microseconds is allocated. As a result, 9.3 microseconds is applied to the zero-voltage switching interval, 9.1 microseconds is applied to the zero-current switching interval, and the hybrid switching interval is configured as 9.5 microseconds. Through such a configuration, the system can achieve stable and efficient switching control in different switching intervals.

[0071] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0072] (1) Perform frequency conversion processing on the actual dead-time value to obtain a reference frequency point;

[0073] (2) Perform offset processing on the reference frequency point according to the voltage and current change rates of the switching device to obtain the system operating frequency value;

[0074] (3) Perform boundary check processing on the system operating frequency value to ensure that it is within the high-frequency operating range;

[0075] (4) Perform linear change processing on the system operating frequency value to obtain a frequency adjustment amount;

[0076] (5) Perform correction processing on the system operating frequency value according to the frequency adjustment amount to obtain the corrected system operating frequency value.

[0077] Specifically, perform frequency conversion processing on the actual dead-time value to obtain a reference frequency point. The actual dead-time value reflects the time interval required for the switching device to switch in the zero-voltage or zero-current state. By frequency conversion, this time parameter is converted into a reference frequency. The reference frequency is the initial reference point of the system control frequency and is used for subsequent offset and adjustment operations. Perform offset processing on the reference frequency point according to the voltage and current change rates of the switching device to obtain the system operating frequency value. The voltage and current change rates reflect the dynamic load response characteristics of the switching device during operation. Based on this change rate, appropriate offset processing is performed on the reference frequency, enabling the system to adapt to the frequency requirements of the switching device under different load conditions, thereby achieving more flexible frequency regulation. The offset operating frequency value helps to optimize the stability of the switching device and enables the system to maintain high efficiency during load fluctuations.

[0078] Then, perform boundary check processing on the system operating frequency value to ensure it is within the high-frequency operating range. The high-frequency operating range is typically between 90 kHz and 200 kHz, which is the optimal operating range designed for the system. The purpose of the boundary check is to prevent excessive frequency deviation that may cause the frequency to exceed the ideal range, thereby affecting the operating efficiency of the PV optimizer and the stability of the switching device. Once the boundary check detects that the frequency is out of range, it automatically adjusts to bring the frequency back within the valid range. Perform linear variation processing on the system operating frequency value within the high-frequency range to obtain the frequency adjustment amount. Linear variation processing is a smooth frequency adjustment method that gradually adjusts the current frequency to mitigate the sudden change effect caused by frequency fluctuations. The frequency adjustment amount reflects the adjustment requirement of the current frequency compared to the ideal frequency. Through this adjustment process, the system can gradually approach the optimal frequency while maintaining stability.

[0079] Perform correction processing on the system operating frequency value according to the frequency adjustment amount to obtain the corrected system operating frequency value. The correction processing adjusts the frequency adjustment amount in real time according to the amplitude of the current frequency deviation from the optimal frequency to ensure that the final operating frequency meets the high-efficiency operation standard of the system. The corrected operating frequency is the optimal frequency of the system under the current conditions, enabling the switching device to operate at an efficient frequency, reducing losses, and improving conversion efficiency.

[0080] For example, assume the actual dead time value is 8 microseconds, and the system obtains a reference frequency point of 125 kHz through frequency conversion processing. Based on the voltage and current change rates of the switching device, the obtained operating frequency value after offset is 123 kHz. Subsequently, a boundary check is performed, and it is found that 123 kHz is within the high-frequency range, so no further adjustment is required. The linear variation processing yields a frequency adjustment amount of 2 kHz, making the corrected system operating frequency value 125 kHz. In this way, the system operates under optimized high-frequency conditions, achieving maximum efficiency and stability.

[0081] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0082] (1) Perform sampling processing on the system operating frequency value to obtain three sampling frequency points;

[0083] (2) Perform power calculation processing on the three sampling frequency points according to the frequency adjustment amount to obtain the corresponding power values;

[0084] (3) Perform comparison processing on the corresponding power values to obtain the power tracking increment value;

[0085] (4) Perform adjustment processing on the system operating frequency value according to the power tracking increment value to obtain the optimized system operating frequency value;

[0086] (5) Perform mode determination processing on the optimized system operating frequency value to obtain the soft-switching operating mode of the switching device.

[0087] Specifically, perform sampling processing on the system operating frequency value and select three different sampling frequency points. The purpose of sampling is to obtain multiple frequency points near the system operating frequency, facilitating subsequent power comparison. These sampling frequency points can reflect the performance of the system under different frequency conditions and provide more reference data for the tracking of the maximum power point. Then, calculate the power at the three sampling frequency points according to the frequency adjustment amount to obtain the power value corresponding to each frequency point. The frequency adjustment amount reflects the adjustment requirements of the system at different frequencies. By calculating the power at the three sampling frequency points, the output power performance at these frequencies can be understood. The specific power calculation is based on the voltage and current parameters of the system, and the calculation result is the instantaneous power output of the system at different frequencies. Obtaining these power values helps to determine which frequency point corresponds to the highest output power, thereby guiding subsequent frequency adjustment.

[0088] Then, perform comparison processing on the power values of each sampling frequency point to obtain the power tracking increment value. The comparison processing is to compare the power values corresponding to the three sampling frequency points, identify the frequency point with the highest power, and calculate the power difference between it and other frequency points. The power tracking increment value reflects the deviation between the current operating frequency and the ideal frequency, providing data support for subsequent adjustment and enabling the system to be closer to achieving the maximum power output. After obtaining the power tracking increment value, the system adjusts the operating frequency based on this increment value to obtain the optimized operating frequency value. The frequency adjustment is carried out by increasing or decreasing the current frequency by a certain amplitude, making the adjusted frequency closer to the frequency value for obtaining the maximum power. Through this optimization process, the operating frequency of the system gradually approaches the ideal state, thereby achieving the best power output efficiency.

[0089] Finally, perform mode determination on the optimized operating frequency value to determine the soft-switching operating mode of the switching device. The soft-switching mode refers to achieving the switching action in the zero-voltage or zero-current state to reduce the switching loss. The system analyzes the optimized frequency to determine whether the current frequency is suitable for zero-voltage switching, zero-current switching, or a hybrid mode, thereby selecting the optimal switching method. This mode determination ensures that the switching device operates in the appropriate soft-switching mode, reducing energy loss and improving system stability.

[0090] For example, assume that the current operating frequency of the system is 120 kHz, and three sampling frequency points are obtained through sampling as 118 kHz, 120 kHz, and 122 kHz. Power calculations are performed on these three frequency points according to the frequency adjustment amount, and the obtained power values are 98 W, 102 W, and 99 W respectively. After comparison and processing, it is found that the power value at 120 kHz is the highest. Therefore, the power tracking increment value is calculated as 2 W. Based on this increment value, the system slightly adjusts the current frequency to 120 kHz to make the operating frequency closer to the maximum power point. Subsequently, the mode determination result shows that the zero-voltage switching mode is suitable at 120 kHz, thereby determining the optimal soft-switching mode for the switching device and enabling the system to operate efficiently with the best efficiency under the condition of 120 kHz.

[0091] The high-frequency soft-switching control method of the photovoltaic optimizer in the embodiment of the present application is described above. Next, the high-frequency soft-switching control device of the photovoltaic optimizer in the embodiment of the present application will be described. Please refer to Figure 2 , an embodiment of the high-frequency soft-switching control device of the photovoltaic optimizer in the embodiment of the present application includes:

[0092] An acquisition module 201, configured to collect and process the output voltage, output current, voltage and current at the output end of the optimizer of the photovoltaic module to obtain system initialization parameters, where the system initialization parameters include a resonance inductance coefficient, a resonance capacitance reactance coefficient, an excitation inductance coefficient, and a power tracking increment value;

[0093] A calculation module 202, configured to perform characteristic impedance calculation processing on the system initialization parameters to obtain the characteristic impedance value, the first resonance frequency value, the second resonance frequency value, and the voltage transformation ratio of the resonance network;

[0094] A processing module 203, configured to perform working interval calculation processing on the characteristic impedance value, the first resonance frequency value, the second resonance frequency value, and the voltage transformation ratio of the resonance network to obtain the system quality factor and the basic dead time value, where the working interval includes a zero-voltage switching interval, a zero-current switching interval, and a hybrid switching interval;

[0095] An analysis module 204, configured to perform dead time calculation processing on the system quality factor and the basic dead time value to obtain the actual dead time value and the voltage and current change rates of the switching device, where the actual dead time value is set separately according to different switching intervals;

[0096] A modulation module 205, configured to perform frequency modulation processing on the actual dead time value and the voltage and current change rates of the switching device through a pulse frequency modulation algorithm to obtain the system operating frequency value and the frequency adjustment amount, where the system operating frequency value is within the high-frequency operating range of 90 kHz to 200 kHz;

[0097] The tracking module 206 is used to perform maximum power point tracking processing on the system operating frequency value and the frequency adjustment amount through the three-point comparison method, obtain the optimized system operating frequency value and the power tracking increment value, and determine the soft-switching operating mode of the switching device according to the optimized system operating frequency value.

[0098] Through the collaborative cooperation of the above-mentioned various components, the conversion efficiency and overall performance of the photovoltaic optimizer are effectively improved by the high-frequency soft-switching control method. First, the scheme obtains the initialization parameters of the system by collecting the output voltage and current of the photovoltaic module and the voltage and current data at the output end of the optimizer, including the resonant inductance coefficient, the resonant capacitive reactance coefficient, the excitation inductance coefficient, and the power tracking increment value. These parameters lay a data foundation for subsequent control and optimization. Secondly, the scheme calculates the characteristic impedance of the system initialization parameters, and obtains the characteristic impedance value, the first resonant frequency value, the second resonant frequency value, and the voltage transformation ratio of the resonant network. These characteristic parameters make the behavior of the system more controllable under different frequency conditions, enhancing the adaptability and reliability of the system. By calculating and processing the working intervals of parameters such as the characteristic impedance and resonant frequency of the resonant network, the quality factor and the basic dead time value of the system are further obtained, ensuring that the system can achieve stable zero-voltage switching (ZVS), zero-current switching (ZCS), and hybrid switching modes in different working intervals, thus greatly reducing the switching loss and device heating problems. This precisely controlled switching mode has a significant effect on improving the efficiency of the photovoltaic optimizer. The scheme also optimizes the quality factor and the basic dead time value of the system. By calculating the actual dead time value and the change rate of the voltage and current of the switching device, the precise dead time setting in different switching intervals is realized, enabling the switching device to start and close at the appropriate time, effectively reducing unnecessary energy loss. More importantly, through the pulse frequency modulation algorithm, the actual dead time value and the change rate of the voltage and current of the switching device are frequency modulated, so that the system operating frequency is maintained in the high-frequency operating range of 90 kHz to 200 kHz. This stable control in this high-frequency range significantly improves the working efficiency of the system. In addition, the three-point comparison method is used in the scheme for maximum power point tracking processing, which helps to quickly and accurately obtain the optimal operating frequency value and achieve maximum power output, so that the output power of the photovoltaic module is fully utilized. The optimized system operating frequency value through the maximum power point tracking algorithm, and the soft-switching operating mode of the switching device determined according to this frequency value, effectively improve the power conversion efficiency and stability of the photovoltaic system under dynamic conditions. Through a series of precise data collection, parameter calculation, working interval division, dead time adjustment, and frequency modulation processing, the precise control of high-frequency soft-switching is realized. While improving the conversion efficiency, the switching loss and system temperature rise are significantly reduced, further enhancing the overall performance and service life of the photovoltaic optimizer.

[0099] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A high-frequency soft switching control method for a photovoltaic optimizer, characterized in that: The high-frequency soft switching control method of the photovoltaic optimizer includes: The output voltage and output current of the photovoltaic module, the voltage and current at the output end of the optimizer are collected and processed to obtain system initialization parameters, wherein the system initialization parameters include a resonance inductance coefficient, a resonance capacitive reactance coefficient, an excitation inductance coefficient, and a power tracking increment value; Performing characteristic impedance calculation processing on the system initialization parameters to obtain a characteristic impedance value, a first resonant frequency value, a second resonant frequency value and a voltage transformation ratio value of the resonant network; Performing working interval calculation processing on the characteristic impedance value, the first resonant frequency value, the second resonant frequency value and the voltage transformation ratio value of the resonant network to obtain a system quality factor and a basic dead time value, wherein the working interval includes a zero voltage switching interval, a zero current switching interval and a mixed switching interval; Performing dead time calculation processing on the system quality factor and the basic dead time value to obtain an actual dead time value and a voltage and current change rate of the switching device, wherein the actual dead time value is set according to different switching intervals; Performing frequency modulation processing on the actual dead time value and the voltage and current change rate of the switching device through a pulse frequency modulation algorithm to obtain a system operating frequency value and a frequency adjustment amount, wherein the system operating frequency value is within a high frequency operating range of 90 kHz to 200 kHz; The system operating frequency value and the frequency adjustment amount are subjected to maximum power point tracking processing by a three-point comparison method to obtain an optimized system operating frequency value and a power tracking increment value, and the soft switching operating mode of the switching device is determined according to the optimized system operating frequency value.

2. The high-frequency soft switch control method of a photovoltaic optimizer according to claim 1, characterized in that: The output voltage, output current, optimizer output terminal voltage and current of the photovoltaic module are collected and processed to obtain system initialization parameters, which include resonance inductance coefficient, resonance capacitance coefficient, excitation inductance coefficient and power tracking increment value, including: Sampling the output voltage and output current of the photovoltaic module to obtain an input power value, where the input power value is the product of the output voltage of the photovoltaic module and the output current of the photovoltaic module; Performing amplitude limiting processing on the voltage and current at the output end of the optimizer to obtain amplitude limiting parameters, and when the voltage and current at the output end of the optimizer exceed a preset range, setting the voltage and current at the output end of the optimizer to limited values ​​within the preset range; Normalizing the limiting parameters to obtain the resonant inductance coefficient and the resonant capacitive reactance coefficient; Performing compensation processing on the resonant inductance coefficient and the resonant capacitive reactance coefficient to obtain the excitation inductance coefficient; The input power value is incrementally calculated to obtain the power tracking incremental value, and the resonant inductance coefficient, resonant capacitive reactance coefficient, excitation inductance coefficient and power tracking incremental value are combined into the system initialization parameter.

3. The high-frequency soft switch control method of a photovoltaic optimizer according to claim 2, characterized in that: The characteristic impedance calculation process is performed on the system initialization parameters to obtain the characteristic impedance value, the first resonant frequency value, the second resonant frequency value and the voltage transformation ratio value of the resonant network, including: Performing square root operation on the resonant inductance coefficient and the resonant capacitive reactance coefficient to obtain a characteristic impedance value of the resonant network; Performing a first frequency calculation process on the resonant inductance coefficient and the resonant capacitive reactance coefficient to obtain the first resonant frequency value, wherein the first resonant frequency value is inversely proportional to the square root of the product of the resonant inductance coefficient and the resonant capacitive reactance coefficient; Performing a second frequency calculation process according to the resonant inductance, the excitation inductance and the resonant capacitive reactance coefficient to obtain the second resonant frequency value, wherein the second resonant frequency value is inversely proportional to the square root of the product of the sum of the resonant inductance and the excitation inductance and the resonant capacitive reactance coefficient; The voltage transformation ratio is obtained by performing a ratio calculation process on the output voltage of the optimizer and the output voltage of the photovoltaic module.

4. The high-frequency soft switch control method of a photovoltaic optimizer according to claim 1, characterized in that: The characteristic impedance value, the first resonant frequency value, the second resonant frequency value and the voltage transformation ratio value of the resonant network are subjected to working interval calculation processing to obtain a system quality factor and a basic dead time value, wherein the working interval includes a zero voltage switching interval, a zero current switching interval and a mixed switching interval, including: Performing quality calculation processing on the characteristic impedance value of the resonant network to obtain the system quality factor; Performing interval determination processing on the first resonant frequency value and the second resonant frequency value to obtain the zero voltage switching interval, the zero current switching interval and the mixed switching interval; Performing data compensation processing on the zero voltage switching interval, the zero current switching interval and the mixed switching interval according to the voltage transformation ratio to obtain a compensated working interval; Performing dead time calculation processing on the compensated working interval to obtain the basic dead time value; The system quality factor and the basic dead time value are processed by interpolation operation to obtain a corresponding relationship between the dead time and the quality factor.

5. The high-frequency soft switch control method of a photovoltaic optimizer according to claim 1, characterized in that: The dead time calculation process is performed on the system quality factor and the basic dead time value to obtain the actual dead time value and the voltage and current change rate of the switching device, wherein the actual dead time value is set according to different switching intervals, including: Performing interval classification processing on the quality factor of the system to obtain dead time coefficients of different intervals; Multiplying the basic dead time value and the dead time coefficient to obtain the actual dead time value; Performing threshold comparison processing on the actual dead time value to obtain the voltage and current change rate of the switching device; Correcting the actual dead time value according to the voltage and current change rate of the switching device to obtain a corrected actual dead time value; The corrected actual dead time value is allocated to obtain the dead time configurations of the zero voltage switching interval, the zero current switching interval, and the hybrid switching interval.

6. The high-frequency soft switch control method of a photovoltaic optimizer according to claim 1, characterized in that: The actual dead time value and the voltage and current change rate of the switching device are subjected to frequency modulation processing by a pulse frequency modulation algorithm to obtain a system operating frequency value and a frequency adjustment amount, wherein the system operating frequency value is within a high frequency operating range of 90 kHz to 200 kHz, including: Performing frequency conversion processing on the actual dead time value to obtain a reference frequency point; Performing an offset process on the reference frequency point according to the voltage and current change rate of the switching device to obtain the system operating frequency value; Performing boundary checking on the system operating frequency value to ensure that it is within the high frequency operating range; Performing linear change processing on the operating frequency value of the system to obtain the frequency adjustment amount; The system operating frequency value is corrected according to the frequency adjustment amount to obtain a corrected system operating frequency value.

7. The high-frequency soft switch control method of a photovoltaic optimizer according to claim 1, characterized in that: The method of performing maximum power point tracking processing on the system operating frequency value and the frequency adjustment amount by a three-point comparison method to obtain an optimized system operating frequency value and a power tracking increment value, and determining a soft switching operating mode of a switching device according to the optimized system operating frequency value includes: Sampling the system operating frequency value to obtain three sampling frequency points; Performing power calculation processing on the three sampling frequency points according to the frequency adjustment amount to obtain corresponding power values; Comparing the corresponding power values ​​to obtain the power tracking increment value; Adjusting the system operating frequency value according to the power tracking increment value to obtain the optimized system operating frequency value; A mode determination process is performed on the optimized system operating frequency value to obtain a soft switching operating mode of the switching device.

8. A high-frequency soft switch control device for a photovoltaic optimizer, used to implement the high-frequency soft switch control method for a photovoltaic optimizer according to any one of claims 1 to 7, characterized in that: The high-frequency soft switch control device of the photovoltaic optimizer includes: The acquisition module is used to collect and process the output voltage and output current of the photovoltaic module, the voltage and current of the optimizer output terminal, and obtain system initialization parameters, wherein the system initialization parameters include the resonance inductance coefficient, the resonance capacitance coefficient, the excitation inductance coefficient, and the power tracking increment value; A calculation module, used for performing characteristic impedance calculation processing on the system initialization parameters to obtain a characteristic impedance value, a first resonant frequency value, a second resonant frequency value and a voltage transformation ratio value of the resonant network; a processing module, configured to perform working interval calculation processing on the characteristic impedance value, the first resonant frequency value, the second resonant frequency value and the voltage transformation ratio value of the resonant network to obtain a system quality factor and a basic dead time value, wherein the working interval includes a zero voltage switching interval, a zero current switching interval and a mixed switching interval; An analysis module, used to perform dead time calculation processing on the system quality factor and the basic dead time value to obtain an actual dead time value and a voltage and current change rate of the switching device, wherein the actual dead time value is set according to different switching intervals; A modulation module, used for performing frequency modulation processing on the actual dead time value and the voltage and current change rate of the switching device through a pulse frequency modulation algorithm to obtain a system operating frequency value and a frequency adjustment amount, wherein the system operating frequency value is within a high frequency operating range of 90 kHz to 200 kHz; The tracking module is used to perform maximum power point tracking processing on the system operating frequency value and the frequency adjustment amount through a three-point comparison method to obtain an optimized system operating frequency value and a power tracking increment value, and determine the soft switching working mode of the switching device according to the optimized system operating frequency value.

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