Virtual synchronous machine control method and device of photovoltaic power station

Abstract

The invention relates to the technical field of data processing, and discloses a virtual synchronous machine control method and device of a photovoltaic power station. The method comprises the steps that virtual synchronous calculation is conducted on a virtual inertia parameter and a virtual damping coefficient through a double-closed-loop controller, a PWM control signal of an inverter is obtained, an outer loop is power angle control, and an inner loop is current proportional resonance control; performing coordination control on the power grid frequency change data and the virtual inertia parameters through a dynamic adjustment algorithm to obtain a system power reserve control strategy and a seamless switching strategy; and performing performance evaluation and parameter optimization on the system operation data through a fuzzy adaptive algorithm to obtain optimized virtual synchronous machine parameters and MPPT control parameters. According to the invention, the efficiency and accuracy of virtual synchronous machine control of the photovoltaic power station are improved.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a virtual synchronous machine control method and device for a photovoltaic power station. Background Art

[0002] With the continuous increase in the penetration rate of photovoltaic power generation in the power system, it has brought severe challenges to the stable operation of the power grid. Traditional photovoltaic power generation systems usually adopt the maximum power point tracking (MPPT) control strategy, lacking inertia support capabilities and being difficult to participate in power grid frequency regulation. To solve this problem, virtual synchronous machine control technology has gradually become a research hotspot. Existing virtual synchronous machine control methods mainly simulate the mechanical characteristics of synchronous generators to enable the photovoltaic system to have certain inertia and damping characteristics. Among them, the PLL-based virtual synchronous machine control method obtains the grid angular frequency information through a phase-locked loop and realizes frequency support based on the power-frequency droop characteristic; the VSG-based virtual synchronous machine control method directly simulates the mechanical-electrical coupling characteristics of synchronous generators and provides inertia support through virtual rotor equations.

[0003] However, the existing technologies have the following deficiencies: First, traditional virtual synchronous machine control methods often control the photovoltaic system as a whole, lacking precise perception and control of the operating states of each component level, resulting in slow system response speed and low control accuracy; Second, existing methods often need to sacrifice some power generation efficiency when realizing virtual synchronous characteristics, and it is difficult to achieve the coordination of MPPT control and virtual synchronous control; Finally, due to the intermittent characteristics of photovoltaic power generation, existing control methods have great difficulties in system parameter tuning and dynamic regulation, and it is difficult to adapt to complex and changeable operating conditions. Summary of the Invention

[0004] This application provides a virtual synchronous machine control method and device for a photovoltaic power station, which is used to achieve high-precision and fast-response virtual synchronous control while ensuring the maximum power generation efficiency of the photovoltaic system, and at the same time ensure the stable operation of the system under different working conditions.

[0005] In a first aspect, this application provides a virtual synchronous machine control method for a photovoltaic power station, and the virtual synchronous machine control method for the photovoltaic power station includes:

[0006] Synchronously collect the IEEE 1588 protocol for each photovoltaic component in the photovoltaic power station to obtain the real-time voltage and current data of each component, where the acquisition time synchronization error is less than 1 millisecond;

[0007] Calculate the maximum power point through the conductance increment method for the real-time voltage and current data to obtain the optimal operating point data and optimal boost ratio data of each photovoltaic component;

[0008] Model the inertial characteristics of the real-time voltage and current data and the optimal operating point data through the virtual synchronous machine state equation to obtain the virtual inertia parameter and virtual damping coefficient of the photovoltaic system;

[0009] Perform virtual synchronous calculation on the virtual inertia parameter and virtual damping coefficient through a double closed-loop controller to obtain the PWM control signal of the inverter, where the outer loop is power angle control and the inner loop is current proportional resonance control;

[0010] Perform coordinated control on the grid frequency change data and the virtual inertia parameter through a dynamic adjustment algorithm to obtain the system power reserve control strategy and seamless switching strategy;

[0011] Perform performance evaluation and parameter optimization on the system operation data through a fuzzy adaptive algorithm to obtain the optimized virtual synchronous machine parameters and MPPT control parameters.

[0012] In a second aspect, the present application provides a virtual synchronous machine control device for a photovoltaic power station. The virtual synchronous machine control device for the photovoltaic power station includes:

[0013] An acquisition module for synchronously acquiring the real-time voltage and current data of each photovoltaic module in the photovoltaic power station through the IEEE1588 protocol, where the acquisition time synchronization error is less than 1 millisecond;

[0014] A calculation module for calculating the maximum power point of each photovoltaic module through the conductance increment method for the real-time voltage and current data to obtain the optimal operating point data and optimal boost ratio data of each photovoltaic module;

[0015] A modeling module for modeling the inertial characteristics of the real-time voltage and current data and the optimal operating point data through the virtual synchronous machine state equation to obtain the virtual inertia parameter and virtual damping coefficient of the photovoltaic system;

[0016] A calculation module for performing virtual synchronous calculation on the virtual inertia parameter and virtual damping coefficient through a double closed-loop controller to obtain the PWM control signal of the inverter, where the outer loop is power angle control and the inner loop is current proportional resonance control;

[0017] A control module for performing coordinated control on the grid frequency change data and the virtual inertia parameter through a dynamic adjustment algorithm to obtain the system power reserve control strategy and seamless switching strategy;

[0018] An optimization module for performing performance evaluation and parameter optimization on the system operation data through a fuzzy adaptive algorithm to obtain the optimized virtual synchronous machine parameters and MPPT control parameters.

[0019] In the technical solution provided by this application, the virtual synchronous machine control method of the photovoltaic system is combined, significantly improving the dynamic response ability and stability of the system in the power grid. First of all, through the trend analysis of the grid frequency change data and the calculation of the frequency change slope, the system can perceive the degree and change trend of the grid frequency fluctuation in real time. Combined with the dynamic matching of the virtual inertia parameters, the system obtains the adjusted inertia support coefficient, enabling the photovoltaic system to provide enhanced inertia support ability during severe grid fluctuations, effectively reducing the impact of frequency disturbances on the system. Compared with the traditional inertialess photovoltaic system, this inertia support mechanism can make corresponding adjustments instantly when the frequency fluctuation occurs, providing a response characteristic closer to that of a traditional synchronous generator for the power grid, thus improving the grid adaptability of the photovoltaic power station. Secondly, through the dynamic calculation of the inertia support coefficient, the solution realizes the operating point shift of the photovoltaic modules with the help of the MPPT controller. The setting of the operating point shift enables the photovoltaic system to reserve a certain amount of power, ensuring that it can respond quickly when the grid frequency changes without affecting the overall power output of the system. This feature not only ensures the adaptability of the photovoltaic system under different loads and frequency changes, but also improves the power generation efficiency of the system. Especially when the grid frequency drops rapidly, the system can make effective adjustments with the help of the reserved power, slowing down the rate of frequency drop, thus playing a positive role in grid stability. In addition, through the calculation of the response ability of the reserved power by the primary frequency regulation characteristic equation, the system can reasonably set the reserved power under various frequency change conditions, ensuring that the response ability of the output power is more adaptable and making the frequency regulation more flexible and efficient.

[0020] Finally, during the mode switching process, the solution adopts switching timing control to implement a seamless switching strategy. The design of seamless switching avoids the instability phenomenon caused by sudden power changes or delays during the switching process. The seamless switching strategy allows the system to maintain a stable output power when entering or exiting the power reserve mode, achieving a smooth transition of dynamic power regulation and ensuring the stability of the PV system under various operating modes. This switching strategy improves the dynamic response effect of the PV system during grid fluctuations and also keeps the output power of the PV power station stable at all times. Different from the situation where traditional PV systems are difficult to adapt to grid fluctuations, this solution provides an efficient and seamless power adjustment process, enabling the PV system to provide corresponding support according to the real-time grid demand and enhancing the role of the PV power station in grid frequency regulation. Through the implementation of dynamic response and seamless switching strategies, the PV system has acquired the inertial support and frequency regulation capabilities similar to those of traditional synchronous generators. The design of the solution not only improves the operating stability and response speed of the PV power station but also enhances its adaptability in complex grid environments, endowing the PV system with higher power regulation and frequency support functions. Compared with traditional PV systems, this solution provides an efficient power support mode for the PV power station, enabling it to effectively enhance the overall stability and reliability of the grid while providing clean energy. Brief Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0022] Figure 1 Schematic diagram of an embodiment of the virtual synchronous machine control method for a PV power station in an embodiment of the present application;

[0023] Figure 2 Schematic diagram of an embodiment of the virtual synchronous machine control device for a PV power station in an embodiment of the present application. Detailed Embodiments

[0024] The embodiments of the present application provide a method and device for controlling a virtual synchronous machine in a photovoltaic power station. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims, and the above-mentioned 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 that illustrated or described here. In addition, the term "including" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0025] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 One embodiment of the method for controlling a virtual synchronous machine in a photovoltaic power station in the embodiments of the present application includes:

[0026] Step S101: Synchronously collect the IEEE 1588 protocol for each photovoltaic module in the photovoltaic power station to obtain the real-time voltage and current data of each module, where the acquisition time synchronization error is less than 1 millisecond;

[0027] Step S102: Calculate the maximum power point of the real-time voltage and current data by the conductance increment method to obtain the optimal operating point data and the optimal boost ratio data of each photovoltaic module;

[0028] Step S103: Model the inertia characteristics of the real-time voltage and current data and the optimal operating point data through the virtual synchronous machine state equation to obtain the virtual inertia parameter and the virtual damping coefficient of the photovoltaic system;

[0029] Step S104: Perform virtual synchronous calculation on the virtual inertia parameter and the virtual damping coefficient through a double closed-loop controller to obtain the PWM control signal of the inverter, where the outer loop is power angle control and the inner loop is current proportional resonance control;

[0030] Step S105: Coordinate and control the power grid frequency change data and the virtual inertia parameter through a dynamic adjustment algorithm to obtain the system power reserve control strategy and the seamless switching strategy;

[0031] Step S106: Perform performance evaluation and parameter optimization on the system operation data through a fuzzy adaptive algorithm to obtain the optimized virtual synchronous machine parameters and MPPT control parameters.

[0032] It can be understood that the execution entity of this application can be the virtual synchronous machine control device of a photovoltaic power station, or it can also be a terminal or a server, and specific details are not limited here. In the embodiments of this application, the server is used as the execution entity for illustration.

[0033] Specifically, by synchronously collecting each photovoltaic module through the IEEE 1588 protocol, accurate real-time voltage and current data can be obtained, and the synchronization error of the collection time can be controlled within 1 millisecond. The IEEE 1588 protocol is a high-precision network time protocol that can achieve nanosecond-level time synchronization between different devices, thus ensuring that the sampling time benchmarks of all components in the photovoltaic power station are consistent. This process of synchronous collection lays a high-precision foundation for subsequent data processing, avoiding data errors caused by asynchronous sampling times, and thus providing accurate inputs for virtual synchronous machine modeling and control. After obtaining the real-time voltage and current data of each photovoltaic module, the maximum power point of these data is calculated through the conductance increment method to obtain the optimal operating point data and the optimal boost ratio data of each photovoltaic module. The conductance increment method is a commonly used MPPT (maximum power point tracking) algorithm that determines and adjusts the operating point by calculating the ratio of the conductance change amount to the power change amount, enabling the photovoltaic module to adjust its operating voltage and current in real time in a dynamic environment and thus maintain at the maximum power point. This method is particularly suitable for power optimization of photovoltaic systems under continuously changing environmental conditions (such as changes in light intensity), ensuring that the photovoltaic module always operates at the maximum efficiency. The optimal boost ratio data provides the best parameters for voltage boosting of each photovoltaic module to support subsequent synchronous calculations and controls.

[0034] After determining the optimal operating point, the system models the inertia characteristics through the virtual synchronous generator (VSG) state equation based on real-time voltage and current data and the optimal operating point data to obtain the virtual inertia parameter and virtual damping coefficient of the photovoltaic system. The virtual synchronous generator technology simulates the characteristics of photovoltaic power generation as the behavior of a traditional synchronous generator, enabling the photovoltaic power station to participate in the frequency and power regulation of the power grid. Inertia characteristic modeling is a key step. By constructing the state equation, the output characteristics of the photovoltaic system have a certain inertia response ability. The virtual inertia parameter and virtual damping coefficient respectively reflect the system's ability to respond to frequency changes and stability. This modeling transforms the connection between the photovoltaic system and the power grid into a power generation system with synchronous characteristics, providing support for the stable operation of the system. Further, through the double-loop controller, virtual synchronous calculation is performed on the virtual inertia parameter and virtual damping coefficient to generate the PWM (pulse width modulation) control signal of the inverter to control the synchronization of power output. In the double-loop control, the outer loop is responsible for power angle control to ensure that the phase of the output power is synchronized with the power grid, and the inner loop uses current proportional resonance control to refine the control of the current, improving the quality and response speed of the output current. The double-loop controller not only ensures the synchronization of the system output but also effectively reduces the harmonic content of the output current, improving the power quality of the photovoltaic power station.

[0035] To cope with the power grid frequency fluctuations, through the dynamic adjustment algorithm, the power grid frequency change data and virtual inertia parameter are coordinately controlled to generate the power reserve control strategy and seamless switching strategy. The dynamic adjustment algorithm can automatically adjust the output power of the system according to the change of the power grid frequency to support the frequency regulation of the power grid. When a mutation or anomaly occurs in the power grid, the system realizes fast decoupling or reconnecting with the power grid according to the seamless switching strategy, avoiding power shocks and power grid instability caused by frequency mismatch. The power reserve strategy enables the photovoltaic system to provide a certain inertia response support during the power grid frequency fluctuations to balance the power supply and demand of the power grid and enhance the regulation ability of the photovoltaic power station in the power grid.

[0036] Finally, through the fuzzy adaptive algorithm, the performance of the system operation data is evaluated and the parameters are optimized to obtain the optimized virtual synchronous generator parameters and MPPT control parameters. The fuzzy adaptive algorithm is an optimization algorithm based on fuzzy logic. It can dynamically adjust the parameter configuration of the virtual synchronous generator according to the real-time feedback of the system operation state to make it more suitable for the current operation state of the power grid and photovoltaic modules. Through this optimization process, the response speed, regulation ability, and output quality of the system are further improved. The fuzzy adaptive algorithm plays a key role in evaluating voltage, current fluctuations, and the stability of power output, enabling the virtual synchronous generator of the photovoltaic power station to maintain the best operation state under dynamic power grid conditions.

[0037] For example, in practical applications, assume that the real-time voltage and current sampling results of a photovoltaic module show that the current output power deviates from the maximum power point. Through the conductance increment method, calculations are performed to adjust the operating point data so that the module output reaches the optimal power. After obtaining the optimal operating point, this data and the real-time current and voltage data are input into the virtual synchronous machine state equation to obtain the virtual inertia parameter and the virtual damping coefficient. These parameters are processed by a double closed-loop controller to generate a PWM control signal, enabling the inverter output current phase to be synchronized with the power grid. When the power grid frequency fluctuates by 0.5 Hz, the power reserve response is optimized through a dynamic adjustment algorithm to stabilize the impact of the frequency fluctuation on the system. Subsequently, the fuzzy adaptive algorithm optimally adjusts the virtual inertia parameter of the system to ensure the stable operation of the photovoltaic power station during frequency fluctuations.

[0038] In the embodiment of the present application, a collection module is configured to perform IEEE 1588 protocol synchronous collection on each photovoltaic module in a photovoltaic power station to obtain the real-time voltage and current data of each module, where the collection time synchronization error is less than 1 millisecond;

[0039] A calculation module is configured to perform maximum power point calculation on the real-time voltage and current data through the conductance increment method to obtain the optimal operating point data and the optimal boost ratio data of each photovoltaic module;

[0040] A modeling module is configured to perform inertia characteristic modeling on the real-time voltage and current data and the optimal operating point data through the virtual synchronous machine state equation to obtain the virtual inertia parameter and the virtual damping coefficient of the photovoltaic system;

[0041] A calculation module is configured to perform virtual synchronous calculation on the virtual inertia parameter and the virtual damping coefficient through a double closed-loop controller to obtain the PWM control signal of the inverter, where the outer loop is power angle control and the inner loop is current proportional resonance control;

[0042] A control module is configured to perform coordinated control on the power grid frequency change data and the virtual inertia parameter through a dynamic adjustment algorithm to obtain a system power reserve control strategy and a seamless switching strategy;

[0043] An optimization module is configured to perform performance evaluation and parameter optimization on the system operation data through a fuzzy adaptive algorithm to obtain optimized virtual synchronous machine parameters and MPPT control parameters.

[0044] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0045] (1) Configure the master-slave network of the POWERBUS fieldbus to obtain the data acquisition network topology of one master station and multiple slave stations, and set the sampling frequency of the slave stations with a 1 MHz clock signal to obtain the sampling timing;

[0046] (2) Configure the high-precision AD converter for the acquisition channels of the slave stations to obtain acquisition parameters with a voltage acquisition accuracy less than 0.5%, and configure the communication method between the master station and the slave stations with the MODBUS protocol to obtain the communication timing;

[0047] (3) Extract the time reference from the 1PPS signal of the GPS receiver to obtain the system unified time reference signal, and perform frequency stabilization processing on the time reference signal through a disciplined clock to obtain a clock signal with an accuracy of ±15 ns;

[0048] (4) Encapsulate the clock signal with the IEEE1588 v2 protocol to obtain PTP packets, and perform time synchronization processing on the PTP packets through the end-to-end transparent clock mode to obtain the corrected timestamps;

[0049] (5) Broadcast the synchronization acquisition command of the master station to obtain the acquisition trigger signal, and mark the timestamps for the voltage and current data collected by the slave stations to obtain the real-time voltage and current data with timestamps;

[0050] (6) Verify the synchronization of the real-time voltage and current data with timestamps to obtain the synchronization acquisition error data, and correct the synchronization acquisition error data through the time error compensation algorithm to obtain the real-time voltage and current data of each component.

[0051] Specifically, to ensure precise communication and data synchronization acquisition between the master station and the slave stations, high-precision real-time data acquisition needs to be achieved through refined timing configuration and synchronization algorithms. First, configure the master-slave network of the POWERBUS fieldbus to construct a data acquisition network topology structure including one master station and multiple slave stations. In this structure, the master station is responsible for sending acquisition instructions and centralized management of data, while the slave stations are responsible for specific voltage and current signal acquisition. To ensure the time accuracy of data acquisition, the sampling frequency of the slave stations is set to 1 MHz to ensure that each slave station can perform data acquisition according to this sampling timing. The setting of the 1 MHz clock signal provides a sampling accuracy of every microsecond, which is particularly crucial for the real-time monitoring of the photovoltaic system and can capture voltage and current changes more accurately. Configure the high-precision AD converter for the acquisition channels of the slave stations to improve the data acquisition accuracy, making the voltage acquisition error less than 0.5%. This high-precision AD converter can convert analog signals into digital signals and ensure the authenticity and accuracy of data during the signal conversion process. At the same time, to achieve stable data transmission between the master station and the slave stations, the system uses the MODBUS protocol for communication configuration. MODBUS is a standardized communication protocol that can ensure the consistency and stability of data during transmission and set clear communication timing for data acquisition. The establishment of this communication configuration provides a guarantee for the subsequent fast and accurate transmission of data.

[0052] To achieve time synchronization between the master station and slave stations, the system extracts the time reference using the 1PPS (one pulse per second) signal of the GPS receiver. The 1PPS signal can provide a time reference with extremely high precision at the second level, ensuring the unification of the system's clock reference. On this basis, the frequency stability of the time reference signal is processed by a disciplined oscillator to obtain a clock signal with an accuracy of ±15 ns. The disciplined oscillator is a fine-tuning process for the internal clock of the system, ensuring that the system can maintain a stable frequency for a long time, which provides support for the accuracy and stability of data acquisition in the photovoltaic system. Subsequently, the high-precision clock signal is encapsulated through the IEEE1588v2 protocol to form a PTP (Precision Time Protocol) message. The IEEE1588 v2 protocol is used for high-precision time synchronization, and the PTP message is transmitted in the entire acquisition network through the end-to-end transparent clock mode to achieve time synchronization from the master station to each slave station. The end-to-end transparent clock mode can ensure that the time information transmitted between different network nodes remains consistent, thereby reducing the synchronization error and obtaining the corrected timestamp. The corrected timestamp provides an accurate time reference for subsequent acquisition command sending and data marking.

[0053] After having a unified time reference, the master station sends synchronous acquisition commands to each slave station and sends an acquisition trigger signal through broadcasting. After receiving this signal, each slave station starts to synchronously acquire voltage and current data and marks timestamps for the acquired signals to form real-time voltage and current data with timestamps. The timestamp marking ensures the timing consistency of the acquired data, enabling subsequent analysis to be accurate to each acquisition moment. To ensure data synchronization, the synchronization of the real-time voltage and current data with timestamps is verified to obtain synchronous acquisition error data. The synchronization verification determines whether there is a deviation in the data by comparing the timestamp differences of different slave stations. If a synchronous acquisition error is found, the system further uses a time error compensation algorithm to correct the error data. The time error compensation algorithm aligns the timing of the acquired data of each slave station by adjusting the deviation amount of the timestamp, thereby obtaining the final high-precision synchronous acquisition data.

[0054] For example, in actual operation, the master station issues an acquisition command through a 1MHz clock signal, and the slave stations acquire voltage and current data at this acquisition frequency and mark timestamps. The 1PPS signal provided by the GPS controls the time synchronization error within ±15 ns, and the IEEE1588 v2 protocol further makes the timestamps of the master and slave stations consistent. If the data of a certain slave station deviates by 0.2 microseconds at the 10 microsecond moment, it is corrected through the time error compensation algorithm, aligning the data accuracy of all slave stations within 1 microsecond, thereby ensuring the data consistency of each photovoltaic module and achieving precise real-time monitoring of the photovoltaic system.

[0055] In a specific embodiment, the process of performing step S102 may specifically include the following steps:

[0056] (1) Perform power differentiation calculation with respect to voltage on the real-time voltage and current data to obtain the power slope K value of the current operating point, and determine the positive or negative of the K value to obtain the search direction of the maximum power point.

[0057] (2) Set a variable step size for the search direction to obtain a large step size value when far from the maximum power point and a small step size value when close to the maximum power point, and adjust the switching frequency through the GaN device of the H-bridge circuit for the step size value to obtain a soft-switching control signal.

[0058] (3) Perform power conversion on the soft-switching control signal through the LLC resonant circuit to obtain the boost ratio data of each component, and perform string power analysis on the boost ratio data to obtain the optimal operating point data and optimal boost ratio data of each photovoltaic module.

[0059] Specifically, by performing differential calculation of power and voltage on the real-time collected voltage and current data, the system can obtain the power slope K value of the current operating point, thereby determining whether the module is operating near the maximum power point. Specifically, the power slope K value represents the rate of change of power with respect to voltage, that is, it reflects the increasing or decreasing trend of power as voltage increases or decreases. By determining the positive or negative of the K value, the search direction of the maximum power point can be determined. When the K value is positive, it means that the power is still increasing and the module has not reached the maximum power point, and the system should continue to increase or decrease the voltage; when the K value is negative, it indicates that the power begins to decrease, indicating that the operating point has exceeded the maximum power point, and the reverse search direction needs to be adjusted. In this way, the system can quickly and accurately lock the maximum power point of the photovoltaic module, thereby optimizing the power output under different environmental conditions. After obtaining the search direction, in order to accelerate the tracking process of the maximum power point, the search direction is set in a variable step size manner. Specifically, when the operating point of the photovoltaic module is far from the maximum power point, the system sets a larger step size value to accelerate the adjustment process; when approaching the maximum power point, the step size value is reduced to improve the tracking accuracy and avoid power fluctuations caused by too large a step size. The setting of the step size value is adjusted by the controller, and the change of the switching frequency is realized through the GaN (gallium nitride) device in the H-bridge circuit to obtain the corresponding soft-switching control signal. The GaN device, with its high switching speed and high-frequency characteristics, can more efficiently control the step size change process and reduce the switching loss, ensuring the stability of the maximum power point tracking (MPPT).

[0060] Subsequently, the soft-switching control signal completes the power conversion through the LLC resonant circuit to achieve the required step-up ratio. The LLC resonant circuit is a commonly used power conversion circuit that realizes efficient power transmission and boosts the output voltage to the required voltage level through the combination of inductors, capacitors, and transformers. During this process, the resonant frequency of the LLC resonant circuit is controlled by the switching frequency of the GaN device to ensure the stability of the output voltage. The system calculates and adjusts the step-up ratio data of each photovoltaic module based on the output power of each component and the required voltage boost value. These step-up ratio data can effectively increase the voltage of each component, enabling the entire string to reach the optimal power output state. After obtaining the step-up ratio data of each component, the power data of the entire photovoltaic string is analyzed to determine the optimal operating point and step-up ratio of each photovoltaic module. The string power analysis can determine the power output of each component under the coordinated action of multiple components and ensure that each component operates at the maximum power point. Through this optimization, the system can effectively avoid the total power reduction caused by the power deviation of a certain component in the string and improve the power generation efficiency of the entire photovoltaic system.

[0061] For example, assume that the real-time power and voltage data of a certain photovoltaic module indicate that the power slope at the current operating point is positive. The system recognizes that it is necessary to increase the voltage to approach the maximum power point and makes adjustments according to a larger step value. When the power slope begins to decrease and approaches zero, the system automatically reduces the step size and transmits it to the LLC resonant circuit through a precisely controlled soft-switching signal, ultimately achieving stable power output. At the same time, after the real-time step-up ratio adjustment of the component, it matches the power analysis result of the string, thereby maintaining the optimal power output state of the entire photovoltaic string. This series of processes realizes the dynamic optimal operating state of the photovoltaic module and improves the overall power generation efficiency of the photovoltaic power station.

[0062] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0063] (1) Calculate the power angle equation for the real-time voltage and current data and the optimal operating point data to obtain the initial value of the system power angle, and perform dynamic characteristic analysis on the initial value of the power angle through the rotor motion equation to obtain the angular velocity change relationship;

[0064] (2) Perform inertia response calculation on the angular velocity change relationship through the electromagnetic torque equation to obtain the system inertia time constant, and perform per-unit conversion on the inertia time constant according to the system rated capacity to obtain the virtual inertia parameter;

[0065] (3) Perform stability analysis on the virtual inertia parameter through the damping characteristic equation to obtain the system damping ratio requirement, and perform parameter matching on the damping ratio requirement to obtain the virtual damping coefficient.

[0066] Specifically, through in-depth analysis of real-time voltage and current data and optimal operating point data, the inertial characteristic modeling and stability analysis of the photovoltaic system are realized. First, by calculating the power angle equation for the real-time voltage and current data and the optimal operating point data, the system can obtain the initial value of the power angle of the system. The power angle is a key parameter reflecting the phase difference between voltage and current, describing the synchronization of the power output of the photovoltaic module relative to the power grid. After calculating the initial value of the power angle, it is substituted into the rotor motion equation for dynamic characteristic analysis to obtain the variation relationship of the angular velocity. The variation of the angular velocity reveals the response speed and inertial characteristics of the photovoltaic system during grid frequency fluctuations, reflecting the dynamic behavior of the system in the face of external disturbances. After obtaining the variation relationship of the angular velocity, the system further calculates the inertial response of the angular velocity change through the electromagnetic torque equation to determine the inertial time constant of the system. The inertial time constant represents the response delay degree of the system under frequency disturbance, and its magnitude directly affects the response speed of the photovoltaic to frequency fluctuations. The inertial response calculation quantifies the influence of the angular velocity change on the inertial characteristics of the system through the torque equation, enabling the system to have a certain inertial effect, similar to the mechanical inertia of a traditional synchronous generator. To standardize this time constant and facilitate control, it is normalized according to the rated capacity of the photovoltaic system to obtain the virtual inertia parameter. Normalization is a common normalization method that can uniformly compare the inertias of photovoltaic systems with different capacities, ensuring the adjustability of the inertia parameter and the adaptability of the system.

[0067] After obtaining the virtual inertia parameter, the system conducts further stability analysis based on the damping characteristic equation to calculate the damping ratio requirement of the system. The damping ratio is a stability parameter measuring the response to frequency or power fluctuations, and the oscillation characteristics of the system under frequency disturbance can be judged through the damping characteristic equation. The system matches the virtual inertia parameter with the damping characteristics of the system according to the damping ratio requirement, and finally obtains the virtual damping coefficient. The virtual damping coefficient can effectively reflect the ability of the photovoltaic to suppress frequency fluctuations, enabling the system to have better stability when the frequency fluctuates. Through this combined control of inertia and damping, the photovoltaic system can simulate the response behavior of a synchronous generator in a dynamic grid environment, providing inertial and damping support similar to that of a traditional generator for the power grid.

[0068] For example, in practical applications, assume that the real-time voltage and current data of a photovoltaic module indicate that the initial value of the power angle of the system is a certain specific angle. Substitute this initial value into the rotor motion equation to obtain the change trend of the angular velocity. The angular velocity change relationship is used for the calculation of the electromagnetic torque equation to obtain the inertia time constant, and a standardized virtual inertia parameter is obtained through per-unit processing. Then, the system further analyzes the damping ratio requirement, matches the virtual inertia parameter with the damping characteristics to obtain the virtual damping coefficient. The final virtual inertia and damping coefficient can provide the corresponding inertia response ability for the photovoltaic system, enabling it to stably output in a frequency fluctuation environment and support the frequency regulation and power stability of the power grid. This dynamic control of virtual inertia and damping ensures that the photovoltaic power station can achieve the stable characteristics of a synchronous machine in a complex power grid environment.

[0069] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0070] (1) Design an outer-loop controller for the virtual inertia parameter and the virtual damping coefficient to obtain the power angle deviation PI controller parameters, and calculate the electromagnetic torque command for the power angle deviation PI controller to obtain the torque command value;

[0071] (2) Perform proportional-resonant control on the torque command value through an inner-loop current controller to obtain the current tracking command, and perform feed-forward compensation on the current tracking command to obtain the compensated current command;

[0072] (3) Perform carrier modulation on the compensated current command through a PWM modulator to obtain the PWM control signal of the inverter.

[0073] Specifically, the system is designed with a two - layer controller structure: the outer loop is responsible for power angle control, and the inner loop conducts current proportional - resonant control, thereby achieving precise control of the inverter output and improving stability. First, the system designs the outer - loop controller using virtual inertia parameters and virtual damping coefficients, and corrects the power - angle deviation of the system through a power - angle deviation PI controller. The power - angle deviation refers to the difference between the current power angle and the set power angle, which is an important indicator of the synchronization between the system and the grid frequency. The PI controller adjusts the proportional (P) and integral (I) parameters to minimize the power - angle deviation as much as possible to ensure the synchronization of the system. Through this outer - loop control, the system calculates the corresponding electromagnetic torque command, which reflects the electromagnetic power that the system needs to provide to maintain the frequency stability of the grid. Then, the torque command value is transmitted to the current controller of the inner loop. The inner - loop controller is based on proportional - resonant control to accurately track the current command. Proportional - resonant control is a control method that achieves zero - steady - state error tracking at a specific frequency, which can maintain the current tracking accuracy even when the grid frequency changes, avoiding power fluctuations caused by frequency deviation. By real - time correcting the current - tracking command, the inner - loop controller ensures the precise matching of the current output with the torque command, making the inertial support for the grid more stable. To further improve the response effect, feed - forward compensation is added to the current - tracking command. Feed - forward compensation is to feed back the expected current change to the controller in advance, enabling the system to respond to the change of the grid frequency in advance and reducing the control error caused by delay. The compensated current command more accurately reflects the real - time demand, providing a high - precision control reference for the subsequent PWM modulation.

[0074] Finally, the compensated current command is carrier - modulated by a PWM (pulse - width modulation) modulator to generate a PWM signal for controlling the inverter. The PWM modulator converts it into a series of high - frequency switching signals according to the amplitude and phase of the current command to control the output of the inverter. Carrier modulation is to encode the amplitude and frequency of the command signal through a high - frequency signal to ensure that the waveform of the output current conforms to the setting. The PWM control signal of the inverter directly affects the output current of the inverter, ensuring that the photovoltaic system can provide stable power support for the grid according to the precise current waveform. For example, during actual operation, when the power - angle deviation between the photovoltaic system and the grid is detected as a certain value, the outer - loop PI controller adjusts the deviation to a suitable torque command according to the virtual inertia and virtual damping parameters. The torque command is received by the inner - loop current controller and conducts proportional - resonant control, and at the same time, feed - forward compensation is introduced to enable the current command to quickly follow the change of the grid frequency. Finally, the PWM modulator generates a high - precision PWM signal according to the compensated current command to control the inverter output, so that the output current of the photovoltaic system is synchronized with the grid frequency, providing stable inertial response and power support for the grid. The implementation of this two - layer control architecture enables the photovoltaic system to have the response characteristics of a synchronous - generator - like, providing effective support for the stable operation of the grid.

[0075] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0076] (1) Perform a trend analysis on the grid frequency change data to obtain the frequency change slope, and dynamically match the frequency change slope and the virtual inertia parameter to obtain an adjusted inertia support coefficient;

[0077] (2) Offset the operating point of the adjusted inertia support coefficient through an MPPT controller to obtain the power reserve, and calculate the response ability of the power reserve through the primary frequency regulation characteristic equation to obtain the system power reserve control strategy;

[0078] (3) Perform a mode conversion on the system power reserve control strategy through switching timing control to obtain a seamless switching strategy.

[0079] Specifically, first perform a trend analysis on the grid frequency change data to calculate the slope of the frequency change. The frequency change slope is a measure of the acceleration of the grid frequency fluctuation and reflects the severity of the frequency change. By observing the trend of the grid frequency change, the system can predict the rapid change of the grid frequency and adjust the response strategy accordingly. When the frequency change slope is large, it indicates that the grid frequency fluctuates significantly and the system needs to provide stronger inertia support. Therefore, the slope is dynamically matched with the virtual inertia parameter to generate an adjusted inertia support coefficient. The inertia support coefficient is a parameter used to adjust the inertia response ability of the photovoltaic system, enabling the system to enhance or weaken the support effect on the grid in a timely manner according to the speed of the frequency change.

[0080] Next, the system utilizes the adjusted inertia support coefficient to achieve a working point shift through the MPPT controller, thereby generating the power reserve of the photovoltaic system. The working point shift means that the system makes a slight adjustment based on the original maximum power point and reserves part of the power as standby power for frequency regulation. During this process, the MPPT controller will slightly reduce the output power of the photovoltaic modules, causing it to deviate from the maximum power point, but ensuring that the system has sufficient power reserve when the frequency fluctuates to respond to the changes in the grid frequency. This power reserve is calculated for the response ability through the primary frequency regulation characteristic equation to evaluate the regulation ability of the system under frequency fluctuations. The primary frequency regulation characteristic equation is a commonly used frequency regulation response model in traditional power systems, through which the power output requirements of the photovoltaic system at different frequency change rates can be calculated. Through this calculation, the system can determine its power reserve control strategy, that is, the amount of power reserve that should be provided under different frequency change conditions, so as to ensure fast and stable frequency regulation when the grid fluctuates. After generating the system power reserve control strategy, the system further performs mode conversion through switching timing control to achieve a seamless switching strategy. The switching timing control refers to the process arrangement of the system's switching between different operating modes, ensuring that the stability of the system is not affected during power reserve or frequency regulation. The goal of the seamless switching strategy is to enable the photovoltaic system to achieve smooth mode conversion during frequency fluctuations, thereby avoiding power mutations or delays during the switching process and ensuring the stability of the system's output power. Specifically, when the grid frequency returns to stability, the system will gradually adjust the working point of the photovoltaic modules back to the maximum power point, and automatically enter the power reserve mode when the frequency fluctuates to quickly respond to the grid demand. This seamless switching strategy not only improves the dynamic response ability of the photovoltaic system but also ensures that the system always maintains a stable output during frequency regulation, providing continuous inertia support.

[0081] For example, in actual operation, it is detected that the grid frequency decline rate is -0.1 Hz / s, indicating a relatively fast frequency decline. The system calculates that the frequency change slope is negative and large, so it increases the virtual inertia to generate a higher inertia support coefficient. Subsequently, based on this inertia support coefficient, the MPPT controller slightly shifts the working point of the photovoltaic modules from the maximum power point, reducing the output power by 5%, thereby reserving sufficient power reserve. Through the primary frequency regulation characteristic equation, the system calculates the power response required to be provided under the current frequency fluctuation condition and determines the specific power reserve control strategy. When the grid frequency stabilizes, it gradually returns to the maximum power output mode through switching timing control to ensure a smooth conversion of the output power, ultimately achieving the seamless switching strategy. This process enables the photovoltaic system to quickly provide support when the grid frequency fluctuates and return to the high-efficiency output state when the frequency stabilizes, achieving an efficient and stable power support effect.

[0082] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0083] (1) Calculate the power tracking accuracy of the system operation data to obtain a tracking error index, and evaluate the dynamic characteristics of the tracking error index through frequency response analysis to obtain a system response index;

[0084] (2) Map the system response index through a fuzzy rule base to obtain an adjustment amount of the virtual synchronous machine parameters, and update the parameters of the adjustment amount through an adaptation law to obtain the target virtual synchronous machine parameters;

[0085] (3) Match the target virtual synchronous machine parameters with the MPPT controller parameters to obtain the MPPT control parameters.

[0086] Specifically, in the virtual synchronous machine control of a photovoltaic system, to ensure that the system can adapt to a dynamic grid environment and provide accurate power output, real-time tracking and optimization of its operation data are carried out to achieve the best control effect. First, by calculating the power tracking accuracy of the system operation data, the system obtains the current tracking error index. The tracking error refers to the difference between the actual power output of the system and the desired power, reflecting the ability to follow the power set value. After obtaining this error index, the system further evaluates its dynamic characteristics through frequency response analysis to generate a system response index. Frequency response analysis is a technique for measuring the response speed and stability of a system under different frequency disturbances, and can reflect the adaptability to grid frequency fluctuations. The system response index includes key factors such as stability, response speed, and oscillation characteristics, providing a reference basis for subsequent parameter optimization. After obtaining the system response index, these indexes are input into the fuzzy rule base, and parameter mapping is realized through fuzzy logic to generate an adjustment amount of the virtual synchronous machine parameters. The fuzzy rule base contains a large number of empirical rules designed based on the actual grid operation conditions, and can match appropriate parameter adjustment values according to different states of the system. Fuzzy logic is an algorithm for processing uncertainty and fuzzy information. In the case of large fluctuations in grid frequency and power, the system parameters can be quickly and flexibly adjusted through the fuzzy rule base to ensure that the synchronous response ability of the photovoltaic system is optimized. The generated adjustment amount reflects the necessary corrections to parameters such as the inertia and damping of the virtual synchronous machine to adapt to the current grid conditions.

[0087] After obtaining the parameter adjustment amount, the virtual synchronous machine parameters are updated through an adaptation law to generate the target virtual synchronous machine parameters. The adaptation law is a feedback-based control algorithm that can dynamically update the control parameters according to the real-time operation data of the system to ensure the stable operation of the system in a changing environment. The adaptation law enables the system to continuously optimize the virtual synchronous machine parameters based on the current tracking error and response characteristics, improving the response effect to frequency fluctuations. Through this process, the system gradually obtains key parameters such as inertia and damping that better meet the actual requirements, enabling the photovoltaic system to better support the stability of the power grid. Finally, after obtaining the target virtual synchronous machine parameters, they are matched with the parameters of the MPPT (maximum power point tracking) controller to generate the final MPPT control parameters. The MPPT control parameters can guide the photovoltaic modules to always operate at the optimal power output point, improving the power generation efficiency of the entire system. The MPPT controller adjusts the voltage and current in real time to keep the output power of the photovoltaic modules at the maximum value, ensuring the output stability and efficiency of the system. By matching the virtual synchronous machine parameters with the MPPT controller parameters, the system can maintain the optimal power output of the photovoltaic system while meeting the grid inertia response requirements.

[0088] For example, in actual operation, the system first calculates that the tracking error is 2%, that is, the actual power output is 2% lower than the target power. Subsequently, through frequency response analysis, it is found that the system has a slow response speed under high-frequency disturbances, generating response indicators. The fuzzy rule base matches the increase adjustment amounts of the inertia and damping parameters based on these indicators, and through the adaptation law, the virtual inertia is adjusted from 0.8 to 0.85, and the damping coefficient is adjusted from 0.3 to 0.32 to obtain the target virtual synchronous machine parameters. Finally, these target parameters are matched with the gain parameters of the MPPT controller to generate new MPPT control parameters, enabling the photovoltaic system to maintain efficient and stable power output in the dynamic grid environment. This process ensures the adaptive ability and power generation efficiency of the photovoltaic system, achieving efficient and stable power support under complex grid conditions.

[0089] The virtual synchronous machine control method for a photovoltaic power station in the embodiments of the present application has been described above. Next, the virtual synchronous machine control device for a photovoltaic power station in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the virtual synchronous machine control device for a photovoltaic power station in the embodiments of the present application includes:

[0090] An acquisition module 201, configured to perform IEEE 1588 protocol synchronous acquisition on each photovoltaic module in the photovoltaic power station to obtain the real-time voltage and current data of each module, where the acquisition time synchronization error is less than 1 millisecond;

[0091] The calculation module 202 is used to calculate the maximum power point of the real-time voltage and current data by the conductance increment method, and obtain the optimal operating point data and the optimal boost ratio data of each photovoltaic module;

[0092] The modeling module 203 is used to model the inertia characteristics of the real-time voltage and current data and the optimal operating point data through the virtual synchronous machine state equation, and obtain the virtual inertia parameter and the virtual damping coefficient of the photovoltaic system;

[0093] The calculation module 204 is used to perform virtual synchronous calculation on the virtual inertia parameter and the virtual damping coefficient through a double closed-loop controller to obtain the PWM control signal of the inverter, where the outer loop is the power angle control and the inner loop is the current proportional resonance control;

[0094] The control module 205 is used to perform coordinated control on the grid frequency change data and the virtual inertia parameter through a dynamic adjustment algorithm to obtain the system power reserve control strategy and the seamless switching strategy;

[0095] The optimization module 206 is used to perform performance evaluation and parameter optimization on the system operation data through a fuzzy adaptive algorithm to obtain the optimized virtual synchronous machine parameters and MPPT control parameters.

[0096] Through the collaborative cooperation of the above-mentioned components, the virtual synchronous machine control method of the photovoltaic system is combined, significantly improving the dynamic response ability and stability of the system in the power grid. First of all, the scheme enables the system to perceive the degree and change trend of the power grid frequency fluctuation in real time by analyzing the trend of the power grid frequency change data and calculating the frequency change slope. Combining with the dynamic matching of the virtual inertia parameter, the system obtains the adjusted inertia support coefficient, enabling the photovoltaic system to provide enhanced inertia support ability when the power grid fluctuates violently, effectively reducing the impact of frequency disturbance on the system. Compared with the traditional inertialess photovoltaic system, this inertial support mechanism can make corresponding adjustments instantly when the frequency fluctuation occurs, providing a response characteristic closer to that of a traditional synchronous generator for the power grid, thus improving the power grid adaptability of the photovoltaic power station. Secondly, through the dynamic calculation of the inertia support coefficient, the scheme realizes the working point offset of the photovoltaic module by means of the MPPT controller. The setting of the working point offset enables the photovoltaic system to reserve a certain amount of power reserve, ensuring that it can respond quickly when the power grid frequency changes without affecting the overall power output of the system. This feature not only ensures the adaptability of the photovoltaic system under different loads and frequency changes, but also improves the power generation efficiency of the system. Especially in the case of a rapid decrease in the power grid frequency, the system can make effective adjustments by means of the power reserve, slowing down the rate of frequency decrease, thus playing a positive role in the power grid stability. In addition, the power reserve calculates the response ability through the primary frequency modulation characteristic equation, and the system can reasonably set the reserve under various frequency change conditions, ensuring that the response ability of the output power is more adaptable, making the frequency regulation more flexible and efficient.

[0097] Finally, during the mode switching process, the solution adopts switching timing control to implement a seamless switching strategy. The seamless switching design avoids the instability phenomenon caused by sudden power changes or delays during the switching process. The seamless switching strategy allows the system to maintain a stable output power when entering or exiting the power reserve mode, achieving a smooth transition of dynamic power regulation and ensuring the stability of the photovoltaic system under various operating modes. This switching strategy improves the dynamic response effect of the photovoltaic system during grid fluctuations and also keeps the output power of the photovoltaic power station stable at all times. Different from the situation where traditional photovoltaic systems are difficult to adapt to grid fluctuations, this solution provides an efficient and seamless power adjustment process, enabling the photovoltaic system to provide corresponding support according to the real-time grid demand and enhancing the role of the photovoltaic power station in grid frequency regulation. Through the realization of the dynamic response and seamless switching strategy, the photovoltaic system has the inertial support and frequency regulation capabilities similar to those of traditional synchronous generators. The design of the solution not only improves the operating stability and response speed of the photovoltaic power station but also enhances its adaptability in a complex grid environment, endowing the photovoltaic system with higher power regulation and frequency support functions. Compared with traditional photovoltaic systems, this solution provides an efficient power support mode for photovoltaic power stations, enabling them to effectively enhance the overall stability and reliability of the grid while providing clean energy.

[0098] The above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended 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 recorded in the foregoing embodiments or perform equivalent replacements for 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 various embodiments of the present application.

Claims

1. A virtual synchronous machine control method for a photovoltaic power station, characterized in that, The virtual synchronous machine control method of the photovoltaic power station includes: Performing IEEE 1588 protocol synchronous acquisition on each photovoltaic module in the photovoltaic power station to obtain the real-time voltage and current data of each module, where the acquisition time synchronization error is less than 1 millisecond; Calculating the maximum power point by the conductance increment method for the real-time voltage and current data to obtain the optimal operating point data and the optimal boost ratio data of each photovoltaic module; Performing inertial characteristic modeling on the real-time voltage and current data and the optimal operating point data through the virtual synchronous machine state equation to obtain the virtual inertia parameter and the virtual damping coefficient of the photovoltaic system; Performing virtual synchronous calculation on the virtual inertia parameter and the virtual damping coefficient through a double closed-loop controller to obtain the PWM control signal of the inverter, where the outer loop is power angle control and the inner loop is current proportional resonance control; Performing coordinated control on the grid frequency change data and the virtual inertia parameter through a dynamic adjustment algorithm to obtain the system power reserve control strategy and the seamless switching strategy; Performing performance evaluation and parameter optimization on the system operation data through a fuzzy adaptive algorithm to obtain the optimized virtual synchronous machine parameters and MPPT control parameters.

2. The virtual synchronous machine control method for a photovoltaic power station according to claim 1, wherein The performing IEEE 1588 protocol synchronous acquisition on each photovoltaic module in the photovoltaic power station to obtain the real-time voltage and current data of each module, where the acquisition time synchronization error is less than 1 millisecond, includes: Performing master-slave network configuration on the POWERBUS field bus to obtain the data acquisition network topology of one master station and multiple slave stations, and setting the sampling frequency of the slave stations with a 1 MHz clock signal to obtain the sampling timing; Configuring high-precision AD converters for the acquisition channels of the slave stations to obtain acquisition parameters with a voltage acquisition accuracy less than 0.5%, and configuring the communication method between the master station and the slave stations with the MODBUS protocol to obtain the communication timing; Extracting the time reference of the 1PPS signal of the GPS receiver to obtain the system unified time reference signal, and performing frequency stabilization processing on the time reference signal through a disciplined clock to obtain a clock signal with an accuracy of ±15 ns; Encapsulating the clock signal with the IEEE 1588 v2 protocol to obtain a PTP message, and performing time synchronization processing on the PTP message through the end-to-end transparent clock mode to obtain the corrected timestamp; Broadcasting the synchronous acquisition command of the master station to obtain the acquisition trigger signal, and marking the time stamps of the voltage and current data collected by the slave stations to obtain the real-time voltage and current data with time stamps; Verifying the synchronization of the real-time voltage and current data with time stamps to obtain the synchronous acquisition error data, and correcting the synchronous acquisition error data through a time error compensation algorithm to obtain the real-time voltage and current data of each module.

3. The virtual synchronous machine control method of the photovoltaic power station according to claim 1, characterized in that The calculating the maximum power point by the conductance increment method for the real-time voltage and current data to obtain the optimal operating point data and the optimal boost ratio data of each photovoltaic module, includes: Perform power differentiation with respect to voltage on the real-time voltage and current data to obtain the power slope K value at the current operating point, and determine the positive or negative of the K value to obtain the search direction of the maximum power point; Set a variable step size for the search direction to obtain a large step size value when far from the maximum power point and a small step size value when close to the maximum power point, and adjust the switching frequency of the GaN device in the H-bridge circuit through the step size value to obtain a soft-switching control signal; Perform power conversion on the soft-switching control signal through an LLC resonant circuit to obtain the boost ratio data of each component, and perform string power analysis on the boost ratio data to obtain the optimal operating point data and optimal boost ratio data of each photovoltaic module.

4. The virtual synchronous machine control method for a photovoltaic power station according to claim 1, wherein Model the inertia characteristics of the photovoltaic system through the virtual synchronous machine state equation using the real-time voltage and current data and the optimal operating point data, including: Calculate the power angle equation for the real-time voltage and current data and the optimal operating point data to obtain the initial value of the system power angle, and perform dynamic characteristic analysis on the initial value of the power angle through the rotor motion equation to obtain the relationship of angular velocity change; Perform inertia response calculation on the angular velocity change relationship through the electromagnetic torque equation to obtain the system inertia time constant, and perform per-unit conversion on the inertia time constant according to the system rated capacity to obtain the virtual inertia parameter; Perform stability analysis on the virtual inertia parameter through the damping characteristic equation to obtain the system damping ratio requirement, and perform parameter matching on the damping ratio requirement to obtain the virtual damping coefficient.

5. The virtual synchronous machine control method for a photovoltaic power station according to claim 1, characterized in that Perform virtual synchronous calculation on the virtual inertia parameter and the virtual damping coefficient through a double-loop controller to obtain the PWM control signal of the inverter, where the outer loop is power angle control and the inner loop is current proportional-resonant control, including: Design the outer-loop controller for the virtual inertia parameter and the virtual damping coefficient to obtain the PI controller parameters for power angle deviation, and calculate the electromagnetic torque command through the PI controller for power angle deviation to obtain the torque command value; Perform proportional-resonant control on the torque command value through the inner-loop current controller to obtain the current tracking command, and perform feed-forward compensation on the current tracking command to obtain the compensated current command; Perform carrier modulation on the compensated current command through a PWM modulator to obtain the PWM control signal of the inverter.

6. The virtual synchronous machine control method for a photovoltaic power station according to claim 1, characterized in that Perform coordinated control on the grid frequency change data and the virtual inertia parameter through a dynamic adjustment algorithm to obtain the system power reserve control strategy and seamless switching strategy, including: Perform trend analysis on the grid frequency change data to obtain the frequency change slope, and perform dynamic matching on the frequency change slope and the virtual inertia parameter to obtain the adjusted inertia support coefficient; Offset the operating point of the adjusted inertia support coefficient through an MPPT controller to obtain the power reserve amount, and perform response ability calculation on the power reserve amount through the primary frequency regulation characteristic equation to obtain the system power reserve control strategy; The mode conversion of the system power reserve control strategy is carried out through switching timing control to obtain the seamless switching strategy.

7. The virtual synchronous machine control method of the photovoltaic power station according to claim 1, characterized in that The performance evaluation and parameter optimization of the system operation data are carried out through a fuzzy adaptive algorithm to obtain the optimized virtual synchronous machine parameters and MPPT control parameters, including: Calculate the power tracking accuracy of the system operation data to obtain the tracking error index, and evaluate the dynamic characteristics of the tracking error index through frequency response analysis to obtain the system response index; Map the parameters of the system response index through a fuzzy rule base to obtain the adjustment amount of the virtual synchronous machine parameters, and update the parameters of the adjustment amount through an adaptive law to obtain the target virtual synchronous machine parameters; Match the MPPT controller parameters with the target virtual synchronous machine parameters to obtain the MPPT control parameters.

8. A virtual synchronous machine control device for a photovoltaic power station, which is used to implement the virtual synchronous machine control method for the photovoltaic power station as described in any one of claims 1-7, and is characterized in that, The virtual synchronous machine control device of the photovoltaic power station includes: An acquisition module for synchronously acquiring the real-time voltage and current data of each photovoltaic component in the photovoltaic power station through the IEEE1588 protocol, where the acquisition time synchronization error is less than 1 millisecond; A calculation module for calculating the maximum power point of the real-time voltage and current data through the conductance increment method to obtain the optimal operating point data and the optimal boost ratio data of each photovoltaic component; A modeling module for modeling the inertial characteristics of the real-time voltage and current data and the optimal operating point data through the virtual synchronous machine state equation to obtain the virtual inertia parameter and the virtual damping coefficient of the photovoltaic system; A calculation module for performing virtual synchronous calculation on the virtual inertia parameter and the virtual damping coefficient through a double closed-loop controller to obtain the PWM control signal of the inverter, where the outer loop is power angle control and the inner loop is current proportional resonance control; A control module for coordinately controlling the power grid frequency change data and the virtual inertia parameter through a dynamic adjustment algorithm to obtain the system power reserve control strategy and the seamless switching strategy; An optimization module for performing performance evaluation and parameter optimization on the system operation data through a fuzzy adaptive algorithm to obtain the optimized virtual synchronous machine parameters and MPPT control parameters.

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