Power adapter control method for stabilizing grid-connected inverter system and power adapter

Abstract

The invention relates to the technical field of power electronics, and discloses a power adapter control method for stabilizing a grid-connected inverter system, which comprises the following steps: S1, data acquisition and preprocessing, S2, power regulation modeling, S3, optimization control strategy solving, S4, optimal control signal generation, S5, control signal execution, and S6, real-time feedback and adjustment. The invention further provides a power adapter control system for stabilizing the grid-connected inverter system, the power adapter control system comprises a power grid state monitoring module, an optimization algorithm module, a feedback and adjustment module and an execution and adjustment module, and the power adapter comprises an input interface, a power conversion circuit, an output interface, a sensor module and a control unit. Through voltage-current-power coupling control, a self-adaptive adjustment mechanism, dynamic error compensation and intelligent parameter self-learning, high-precision power adjustment is realized, load change is quickly responded, grid-connected stability is improved, and long-term operation performance is optimized.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and specifically to a power adapter control method and a power adapter for stabilizing a grid-connected inverter system. Background Art

[0002] In modern power systems, inverters are widely used in scenarios such as photovoltaic grid connection, wind power generation, and energy storage systems. Its main function is to convert direct current into alternating current and stably deliver power to the grid. With the rapid development of new energy, the grid's requirements for the grid connection performance of inverters are constantly increasing. Especially in distributed energy systems, the grid load changes relatively frequently. If the power output of the inverter is unstable, it is easy to cause problems such as voltage fluctuations and power factor deviations, affecting the overall power quality. Therefore, how to achieve optimal control of the inverter output power so that it can quickly respond to grid demands and improve grid connection stability has become an important research direction in the current field of power electronics.

[0003] Existing inverter control technologies mainly adopt power regulation strategies based on voltage or current feedback to ensure the stability of grid-connected electric energy. Such control methods can maintain the basic stability of the grid voltage and current under specific operating conditions and provide a certain degree of power error compensation. In addition, in order to reduce the impact of power fluctuations on the system, some technologies adopt a fixed parameter adjustment mode, setting a set of optimized adjustment coefficients when the inverter leaves the factory so that it can maintain good power output characteristics under normal load conditions. In addition, in some high-end application scenarios, a simple error compensation algorithm is also introduced to reduce the impact of power deviation on grid connection quality. These technical solutions have improved the grid connection adaptability of the inverter to a certain extent, enabling it to meet the basic grid operation requirements under most working conditions.

[0004] However, there are still some deficiencies in the existing technologies. First of all, the single voltage or current feedback control cannot fully consider the dynamic coupling relationship between powers, resulting in a low power regulation accuracy. When the load changes rapidly, the single-parameter regulation method often cannot respond in time, making it difficult to effectively suppress power fluctuations. Especially in the scenario of large-scale distributed energy grid connection, it is easy to cause power quality problems. Secondly, the fixed-parameter regulation mode lacks self-adaptability and is difficult to maintain the optimal regulation state under different load conditions. Due to the dynamic change characteristics of the power grid environment and load characteristics, the fixed regulation parameters are prone to over-response under certain working conditions, resulting in power output overshoot or insufficient regulation, reducing the dynamic response ability of the inverter and the overall operation efficiency. In addition, the error compensation mechanism usually uses static threshold judgment and cannot be refined according to the real-time power grid state. When the power grid load suddenly changes, the hysteresis of error compensation is strong, which will lead to a large power deviation in a short time and affect the power grid stability. Finally, the existing technologies cannot automatically optimize the regulation strategy during the long-term operation process and lack the self-learning ability. The longer the inverter operates, the more complex the power grid operation state becomes. The control scheme with fixed parameter setting will show a decline in adaptability during the long-term application, resulting in a gradual deterioration of the system performance and making it difficult to meet the requirements of modern smart grids. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technologies, the present invention provides a power adapter control method and a power adapter for a stable grid-connected inverter system, which solve the problems of low power regulation accuracy, insufficient adaptability, error compensation hysteresis and lack of long-term optimization ability in the existing technologies.

[0006] To achieve the above purposes, the present invention is realized through the following technical solutions: A power adapter control method and a power adapter for a stable grid-connected inverter system, including the following steps: S1. Data acquisition and preprocessing: Collect grid voltage, current, frequency and load power information, and preprocess the collected data; S2. Power regulation modeling: Based on the grid parameters provided by data acquisition and preprocessing, use the variational optimization algorithm to construct a power regulation objective function, set power error constraints and regulation rate constraints, and generate a power regulation mathematical model; S3. Optimization control strategy solution: Based on the generated power regulation mathematical model, use the Pontryagin maximum principle to calculate the optimal control variable, and use the state space transformation method to map the optimal control variable to the actual control signal domain; S4. Optimal control signal generation: Use the rolling optimization algorithm to calculate the time-series optimal control signal according to the result of the optimization control strategy solution step, and dynamically adjust the control signal in combination with the real-time data of the grid state; S5. Control signal execution: Parse the dynamically adjusted control signals using a finite state machine to drive the power adapter to adjust the power output, and control the inverter output power by adjusting the DC-side power conversion parameters; S6. Real-time feedback and adjustment: Monitor the output power status of the power adapter, collect real-time grid change data, calculate the power error using the mean square error method, adjust the parameters of the optimal control strategy according to the error, and feedback the adjusted data to the data acquisition and preprocessing step to form a closed-loop regulation mechanism.

[0007] Preferably, the data acquisition and preprocessing include: Collect grid voltage, current, frequency, and load power information through voltage sensors, current sensors, phase-locked loop circuits, and power meters; Use the Kalman filter algorithm to filter noise and preprocess the collected data; Calculate the grid state change rate using the finite difference method and generate data analysis results.

[0008] Preferably, the power regulation modeling includes: Construct a power regulation objective function using the variational optimization algorithm; Limit the control variables through the Lagrangian functional to constrain the power error and regulation rate; Set power output constraints to ensure that the output of the power adapter meets the grid requirements.

[0009] Preferably, the solution of the optimal control strategy includes: Calculate the optimal control variables using the Pontryagin maximum principle; Determine the extreme conditions of the Hamiltonian function; Calculate the optimal solution of the control signal by combining the state variables and co-state variables.

[0010] Preferably, the generation of the optimal control signal includes: Calculate the optimal control signal for each time step using the rolling optimization algorithm; Adjust the control strategy in real time according to the dynamic changes of the grid state; Use the finite-time convergence control algorithm to iteratively optimize and adjust the control signal to ensure fast response.

[0011] Preferably, the control signal execution includes: Parse the optimal control signal calculated by the optimization algorithm module; Adjust the DC-side power conversion parameters; Regulate the grid-connected power through the inverter output terminal to achieve stable power supply.

[0012] The present invention also provides a power adapter control system for a stable grid-connected inverter system, including: A power grid status monitoring module for obtaining power grid voltage, current, frequency, and load information; An optimization algorithm module for solving the optimal control strategy based on variational optimization theory and Pontryagin's maximum principle; A feedback and adjustment module for adjusting the power output of the power adapter based on a rolling optimization algorithm; An execution and regulation module for adjusting the power output according to a control signal to enable the power adapter to operate stably in parallel with the grid.

[0013] Preferably, the optimization algorithm module includes: A variational optimization solving unit; A Pontryagin maximum optimization unit; A rolling optimization control unit.

[0014] The present invention also provides a power adapter, comprising: An input interface for connecting to a DC power supply; A power conversion circuit adopting a DC-DC conversion topology for converting direct current into inverted alternating current; An output interface for connecting to an inverter and providing stable power; A sensor module for collecting power grid status data; A control unit for executing the optimal control signal generated by the optimization algorithm module and adjusting the power output.

[0015] Preferably, the control unit includes: A data processing unit for receiving and analyzing power grid status data; An optimal control unit for executing the optimization algorithm and generating a control signal; An adjustment execution unit for adjusting the power output to enable the power adapter to operate stably.

[0016] The present invention provides a power adapter control method and a power adapter for a stable grid-connected inverter system. The following beneficial effects are achieved: 1. Through voltage-current-power coupling control, the present invention calculates the influence of each parameter on the power output in real time to achieve refined adjustment. The technical effects of reducing power fluctuations and optimizing the power factor are achieved. Compared with the single-parameter control method in the prior art, this solution can synchronously optimize multiple power factors, avoid the problems of excessive or insufficient local adjustment, and improve the overall power quality of the system.

[0017] 2. The present invention adopts an adaptive adjustment mechanism to monitor grid parameters in real time and dynamically adjust the adjustment coefficient, enabling the inverter power output to adapt to different load conditions. Through this solution, more flexible power control is achieved. Compared with the traditional fixed-parameter control method, the problem of lagging adjustment and poor adaptability during load mutation is avoided, enabling the system to operate efficiently under different working conditions.

[0018] 3. The present invention adopts a dynamic error compensation algorithm to quickly adjust according to the real-time power deviation, ensuring that the inverter output matches the grid demand. Through this optimization strategy, the unstable factors caused by grid fluctuations are reduced. Compared with the existing solutions that rely on fixed error tolerances, the present invention can eliminate deviations more accurately, improve the grid connection quality of the inverter, and enable it to adapt to a more complex grid environment.

[0019] 4. The present invention introduces an intelligent parameter self-learning mechanism. During long-term operation, the system automatically analyzes the historical grid data to optimize the power adjustment parameters. The longer the system operates, the higher the adjustment accuracy. Compared with the existing fixed-parameter solutions, this mechanism can continuously optimize itself, reduce manual intervention, enable the inverter to always maintain the best working state during long-term operation, and improve the overall efficiency and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is the method flowchart of the present invention; Figure 2 is the system structure diagram of the present invention; Figure 3 is the module architecture diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to the attached Figure 1 , the embodiments of the present invention provide a power adapter control method and a power adapter for a stable grid-connected inverter system, including the following steps: S1. Data acquisition and preprocessing, collecting grid voltage, current, frequency, and load power information, and preprocessing the collected data; The main task of S1 is to obtain the key parameters of the power grid operation and perform necessary processing to ensure that subsequent modules such as power regulation modeling and optimal control strategy solving can perform calculations and optimizations based on reliable data. Generally, the acquisition of power grid parameters involves multiple measurement devices, and their data is usually affected by external interference. Therefore, a series of algorithms are required for filtering, correction, and analysis to ensure the accuracy, real-time performance, and stability of the data. In addition, to adapt to the dynamic changes of the power grid, the data processing part also needs to have the ability of trend analysis to predict the changes in the power grid state in the short term and ensure that the power regulation module can take optimization measures in advance.

[0023] In this embodiment, the data acquisition and preprocessing mainly include: the acquisition of power grid parameters, data filtering, data correction, change rate calculation, and short-term trend analysis, etc., to ensure the integrity and availability of the data.

[0024] In a possible implementation manner, the acquisition of power grid parameters is completed by a voltage sensor, a current sensor, a phase-locked loop circuit (PLL), and a power meter, and the specific description is as follows: The voltage sensor is used to measure the phase voltage or line voltage of the power grid and convert it into a digital signal for subsequent calculations.

[0025] The current sensor (such as a Hall current sensor or a shunt resistor) is used to detect the current change situation on the load side or grid-connected side and provide current transient data.

[0026] The phase-locked loop circuit (PLL) is used to lock the fundamental frequency of the power grid and detect the power grid frequency offset situation to ensure that the system can accurately track the power grid synchronization state.

[0027] The power meter is used to calculate the instantaneous active power, reactive power, and power factor of the power grid and provide a reference for subsequent power regulation.

[0028] In a possible implementation manner, the acquisition of data is usually affected by external interferences such as sensor noise and electromagnetic interference. Therefore, in this embodiment, the Kalman filtering algorithm is used to filter the acquired data to enhance the anti-interference ability of the system and improve the reliability of the data.

[0029] Specifically, let the measured power grid parameter be , and the true power grid state variable be , then the power grid state can be expressed as: ; Where: represents the power grid state at the current moment, such as voltage, current, or frequency; is the power grid state variable at the previous moment ; is the state transition matrix, describing the dynamic evolution of the power grid state; is the control input matrix, reflecting the influence of input variables (such as load disturbances) on the state; is the input signal, such as external power disturbances; is the process noise.

[0030] The observation equation is expressed as: ; where: is the measurement matrix, representing the relationship between sensor measurements and the actual state; is the measurement value at the current time, such as voltage or current data detected by the sensor; is the measurement noise, satisfying the normal distribution , where is the measurement noise covariance matrix.

[0031] Calculate the Kalman gain: ; where: is the optimal gain matrix at the current time; is the estimation error covariance matrix, representing the magnitude of the system estimation error; is the estimation error covariance matrix at the previous time, representing the uncertainty of the state estimation; is the transpose of the measurement matrix; is the inverse matrix of the uncertainty weighted by the measurement noise.

[0032] Update the state estimation: ; where: the current time the optimal state estimation after correction; is the state estimation at the previous time k−1k-1k−1; is the residual between the measurement value and the predicted state, that is, the observation error.

[0033] This formula is used to correct the current power grid state according to the measurement value to make it as close as possible to the true value.

[0034] In some embodiments, in order to further improve the reliability of the data, this embodiment also uses the sliding window mean filtering method to smooth the collected data to avoid the influence of short-term mutations on subsequent calculations.

[0035] Set the sliding window length to , then for a certain time of the power grid parameter Calculate the sliding mean: ; Wherein: is the smoothed parameter value at the current moment; is for the past measurement values at represents the length of the sliding window, which is usually set according to the dynamic change characteristics of the power grid.

[0036] In another possible implementation, this embodiment also calculates the change rate of the power grid state so that the subsequent power regulation modeling module can adjust the control strategy according to the trend information.

[0037] Suppose a certain power grid parameter at time has a value of , then the finite difference method is used to calculate the change rate: ; Wherein: is the sampling interval time; is the power grid parameter collected at the current moment , such as voltage or current; is the power grid parameter collected at the next moment .

[0038] This calculation can be used to analyze the short-term change trend of the power grid state and improve the response ability of the control system.

[0039] As an option, in some embodiments, in order to further evaluate the fluctuation of the power grid power factor, the power factor is calculated: ; Wherein: is the power factor, representing the ratio of active power to apparent power; is the active power; is the apparent power; is the phase angle difference between voltage and current.

[0040] If an abnormal power factor is detected, the system can perform reactive power compensation during the subsequent power regulation process (Steps S2 - S3) to optimize the power grid adaptability.

[0041] During this process, the combination of filtering processing, data smoothing, and short-term trend analysis enables the system to more stably adapt to the instantaneous fluctuations of the power grid while reducing the impact of measurement errors on subsequent calculations.

[0042] S2. Power regulation modeling. Based on the grid parameters provided by data acquisition and preprocessing, a variational optimization algorithm is used to construct a power regulation objective function, power error constraints and regulation rate constraints are set, and a power regulation mathematical model is generated. Since the grid operating environment is complex, power fluctuations, load changes, and external disturbances will all affect the stability of power transmission. Therefore, in this step, a rigorous mathematical modeling method is adopted, fully considering the grid dynamic characteristics, control inputs, and external disturbance factors to ensure that the power regulation system can maintain good adaptability and stability under different operating conditions.

[0043] Generally, power regulation modeling needs to cover the dynamic changes of grid voltage, current, and power, and combine state space equations, dynamic response characteristics, instantaneous power calculation, and reactive power compensation strategies to comprehensively describe the grid operating characteristics. In some embodiments, to improve the accuracy of power regulation modeling, this step introduces a multivariable state space modeling method and combines a power regulation strategy based on feedback correction to ensure that the regulation system can quickly respond to grid dynamic changes and improve grid connection stability.

[0044] In this embodiment, power regulation modeling first constructs grid state variables and uses state space equations to describe the dynamic evolution of grid power. Let the grid state variables be , and the control input be , then the state equation can be expressed as: ; where: is the grid state vector, including grid voltage , current , active power , reactive power , and frequency ; is the state transition matrix, representing the evolution relationship of the grid state over time; is the input matrix, describing the impact of control inputs on the grid state; is the control input variable, such as reactive power regulation signal, grid-connected inverter control quantity, etc.; is the external disturbance variable, such as load fluctuations, grid faults, etc.; is the disturbance impact matrix, reflecting the magnitude of the impact of external disturbances on the grid state.

[0045] In a possible implementation, to improve the controllability of the power regulation model, this embodiment linearizes the state equation and uses the small signal analysis method to model the small changes in state variables. Let the state variables and input variables at a certain steady-state operating point be and , the linearized form of the state equation can be expressed as: ; where: is the state variable deviation vector, representing the deviation of the system state variable relative to the steady-state operating point; is the state transition matrix, describing the dynamic evolution characteristics of the state variable, and its elements are determined by grid parameters (inductance, resistance, capacitance, etc.); is the input matrix (dimensionless), describing the influence of the control input on the system state, and its elements depend on the control topology; is the control input deviation vector (unit: V or var), representing the change of the system control variable relative to the set value; is the disturbance influence matrix (dimensionless), describing the influence of external disturbances on the system state; is the external disturbance vector (unit: different disturbances depend on specific components, such as V, A, Hz), including grid voltage fluctuations, current mutations, load disturbances, etc.

[0046] In another possible implementation, in order to describe the dynamic relationship between voltage and current during the grid power regulation process, this embodiment adopts a current-voltage coupling equation to depict the influence of load changes on the grid voltage and current. The grid equivalent circuit can be described by the following equations: ; ; where: is the inductance, representing the inductance characteristics of the grid line; is the line resistance, representing the resistance loss of the grid; is the power supply voltage; is the line capacitance, representing the capacitance characteristics of the grid; is the load current, representing the transient change of the grid load; is the instantaneous voltage (unit: V); is the instantaneous current (unit: A).

[0047] In some embodiments, in order to calculate the instantaneous power change, this embodiment adopts the instantaneous power expression: ; ; where: is the instantaneous active power; is the instantaneous reactive power; is the phase angle between voltage and current; is the instantaneous voltage (unit: V); is the instantaneous current (unit: A).

[0048] As an option, in order to optimize the power factor of the power grid and reduce the reactive power loss, this embodiment adopts power factor optimization calculation. Let the power factor be calculated as follows: ; where: is the power factor; is the apparent power; is the root mean square value of voltage; is the root mean square value of current; is the active power.

[0049] In another possible implementation, this embodiment adopts a reactive power compensation modeling method to reduce the impact of power fluctuations. The reactive power compensation model can be described as: ; where: is the reactive power to be compensated; is the set value of the desired reactive power; is the actually measured reactive power value.

[0050] In some embodiments, in order to improve the power regulation accuracy, this embodiment adopts a control strategy based on error feedback. Let the power error be: ; where: is the power error; is the set active power target value; is the actually measured active power value.

[0051] Based on the error feedback, a proportional-integral (PI) controller can be used for correction: ; where: is the proportional gain coefficient; is the integral gain coefficient; is the control input signal (unit: V or var), representing the adjusted voltage or reactive power compensation amount; is the control error; is the error integral term (unit: W·s), representing the cumulative impact of historical errors, which helps to reduce the system steady-state error and improve the control accuracy.

[0052] Through the above modeling method, it is ensured that the power regulation system can quickly respond to the dynamic changes of the power grid and effectively reduce the impact of power fluctuations. Finally, the calculated power regulation strategy will be input into the optimization control strategy solution module to further optimize the power distribution scheme and ensure the stability and adaptability of the system.

[0053] S3. Optimize the control strategy solution. Based on the power regulation mathematical model of production, use the Pontryagin maximum principle to calculate the optimal control variables, and use the state space transformation method to map the optimal control variables to the actual control signal domain; The main purpose of S3 is to achieve the optimal power regulation strategy through an optimized control algorithm according to the power regulation modeling results obtained in step S2, ensuring the stability and efficient operation of the power grid under various dynamic conditions. The core of the optimized control is to minimize the difference between the target power demand of the power grid and the actual power output while meeting the stability requirements of the power grid.

[0054] Specifically, the solution process of the optimized control strategy needs to handle multiple objective functions and constraint conditions. In some embodiments, in order to balance the system response speed, power accuracy, and stability, the optimization process uses a multi-objective optimization method and combines a gradient optimization algorithm for iterative solution. This can respond to changes in the power grid state in real time during the dynamic adjustment process and then adjust the power regulation strategy.

[0055] In this embodiment, the goal of the optimized control strategy solution is to minimize the total power error in the power grid and optimize the power regulation control input. To this end, an optimization objective function is first constructed , and its expression is as follows: ; where: is the optimization objective function (unit: W 2 ·s), representing the cumulative error amount that needs to be minimized during the optimization process. The goal is to reduce the errors of active power and reactive power; is the reference set active power target value (unit: W), representing the ideal active power; is the actually measured active power value (unit: W); is the reference set reactive power target value (unit: var), representing the ideal reactive power; is the actually measured reactive power value (unit: var); is the control input vector, including the voltage regulation amount and the reactive power compensation amount , whose units are V and var, representing the control output of the inverter; , , are different weight coefficients in the optimization objective, adjusting the influence degrees of active power, reactive power, and control input on the objective function; is the optimization time interval (unit: s), representing the duration of the optimization process.

[0056] By solving this objective function, the control system can dynamically adjust the power regulation strategy to minimize the gap between the actual power and the set power and ensure that the system operates under stability constraints.

[0057] In a possible implementation, the optimization process is solved using the gradient descent method. The core of this method is to update the control input according to the gradient information of the objective function to gradually reduce the error. The update formula for the control input is: ; where: is the control input value after the th iteration; is the control input value at the th iteration; is the learning rate, which controls the size of each iteration step; is the partial derivative of the objective function with respect to the control input .

[0058] This gradient can be obtained according to the specific form of the objective function, where: ; where: and respectively represent the partial derivatives of the active power and the reactive power with respect to the control input, and their calculations involve the specific characteristics of the power grid, such as voltage, current, and power factor, etc.; is the control input, representing the adjustment signal of the inverter; is the optimization objective function (unit: W 2 ·s), representing the cumulative error amount that needs to be minimized during the optimization process; is the partial derivative of the objective function with respect to the control input (unit: W·s / V), representing the change amount of the objective function when the control input changes during the optimization process, indicating the influence of the control input on the optimization objective; , , are different weight coefficients in the optimization objective, which adjust the influence degrees of the active power, the reactive power, and the control input on the objective function; is the actually measured active power (unit: W), representing the active power output of the power grid at the current moment; is the active power reference value (unit: W), which is the desired active power value of the power grid; is the actually measured reactive power (unit: var), representing the reactive power output of the power grid at the current moment; is the set reference reactive power (unit: var), which is the reactive power value expected by the power grid.

[0059] As an option, when performing optimization control in this embodiment, in addition to the traditional method based on gradient optimization, a strategy based on model predictive control (MPC) is introduced to improve the response speed and accuracy of the control system. The model predictive control method calculates the system behavior in a future period in advance and adjusts the control strategy according to the prediction results. Let the state variables of the power grid be , and the control input be . The prediction model can be represented by the following state-space equation: ; ; where: is the state vector at the th time step, containing various information about the operation of the power grid, such as voltage , current , active power and reactive power ; is the control input at the th time step; is the system output at the th time step, including regulation indexes such as power; is the state transition matrix, representing the evolution of the system state; is the control input matrix, representing the influence of the control signal on the system state; is the output matrix, representing the mapping relationship from the system state to the output.

[0060] In addition, during the optimization control process, for reactive power regulation, this embodiment adopts an optimization strategy based on reactive power compensation, aiming to optimize the power factor of the power grid and reduce the waste of reactive power. The calculation formula of the reactive power compensation strategy is: ; where: is the reactive power compensation amount (unit: var), representing the reactive power compensated by the control system; is the reference reactive power target value (unit: var); is the actually measured reactive power value (unit: var).

[0061] To further optimize the power factor of the power grid , this embodiment adjusts the control strategy of the system through the following power factor optimization formula, using the "Power Factor Optimization Calculation" disclosed in S2 By optimizing the power factor, this embodiment effectively reduces the reactive power loss and improves the operation efficiency and stability of the power grid. Finally, the control command obtained through optimization and solution will be input into the inverter control unit to perform corresponding power regulation tasks, ensuring the stable operation of the power grid under changing load and grid conditions.

[0062] By comprehensively adopting a variety of optimization control strategies and methods, not only can accurate power regulation be achieved, but also the stability and adaptability of grid connection can be improved. These optimization methods combine traditional power regulation models and modern predictive control technologies to ensure that the system can achieve the best performance in various practical application scenarios.

[0063] S4. Generation of the optimal control signal: Adopt a rolling optimization algorithm to calculate the time-sequence optimal control signal according to the results of the optimization control strategy solution steps, and dynamically adjust the control signal in combination with the real-time data of the grid state; S4 is a key step to ensure the stable grid connection of the inverter according to the optimization control strategy. Through this step, the inverter not only performs power regulation according to the optimization control command obtained in step S3, but also adjusts the control strategy in real time through a feedback mechanism to adapt to the dynamic changes of the grid, thus ensuring the stable operation of the grid.

[0064] Generally, the process of control command execution and system feedback is the application of a real-time closed-loop control system, aiming to accurately match the output power of the inverter with the actual demand of the power grid. Specifically, the inverter will adjust its active power and reactive power output according to the optimization control command, and adjust the control input through real-time feedback signals to achieve the best power distribution and regulation. The feedback control mechanism can timely correct the deviation between the grid state and the target setting, thus ensuring system stability and preventing possible over-regulation or response lag.

[0065] In this embodiment, the implementation process of step S4 can be divided into several important parts. First, the inverter adjusts its power output according to the optimization control command calculated in step S3 to make it as close as possible to the reference value and . The control input includes the regulating voltage and the reactive power compensation amount , and the inverter controls the power flow in the power grid according to these inputs.

[0066] The output power and reactive power of the inverter can be calculated by the following formulas: ; ; Wherein: is the active power output of the inverter (unit: W), representing the actual active power output from the inverter to the power grid; is the reactive power output of the inverter (unit: var), representing the actual reactive power output from the inverter to the power grid; is the active power reference value (unit: W), representing the active power target desired by the power grid or system; is the reactive power reference value (unit: var), representing the reactive power target desired by the power grid or system; is the measured active power error (unit: W), representing the deviation between the actual power of the power grid and the reference power; is the actually measured reactive power error (unit: var), representing the deviation between the actual reactive power of the power grid and the reference value; is the compensation amount of active power (unit: W), representing the power compensation achieved by adjusting the control input; is the compensation amount of reactive power (unit: var), representing the reactive power compensation achieved by adjusting the control input.

[0067] In this process, the inverter determines whether to adjust its output power by real-time monitoring of the status data of the power grid, such as voltage, current, power, etc., so as to reduce the deviation from the reference value.

[0068] Next, the inverter adjusts the control input through a real-time feedback mechanism . The core of the feedback control strategy is to compare the measured system output with the set target output, thereby obtaining an error and adjusting the control input according to the error. The feedback control can be expressed by the following formula: ; Wherein: is the actual control input (unit: V), that is, the control input adjusted by the inverter according to the feedback signal; is the optimized control instruction (unit: V), that is, the ideal control input obtained through step S3; is the feedback gain coefficient (unitless), used to adjust the influence of the feedback signal on the control input; is the actually measured system output (unit: V, A, W), including voltage, current, power, etc.; is the reference set value (unit: V, A, W), that is, the target value desired by the system.

[0069] In this formula, the error represents the difference between the actual measured value and the desired target value. The feedback gain coefficient Through dynamic adjustment, it is possible to ensure that the system can quickly respond to errors, thereby improving the control accuracy.

[0070] As an option, this embodiment adopts a dynamic feedback gain adjustment mechanism, that is, adjusts the feedback gain according to the real-time state change of the power grid. . This can avoid overcompensation or hysteresis phenomena and improve the response speed of the system when the power grid suddenly changes. The adjustment formula of the feedback gain coefficient can be: ; Where: is the feedback gain coefficient (unitless), representing the feedback gain at time ; is the initial feedback gain coefficient; is the gain adjustment coefficient, used to control the degree of change of the gain with the error; is the error (unit: V, A, W or var). It represents the difference between the actual measured value and the reference set value at time . The larger the error value, the larger the feedback gain, thereby improving the adjustment response speed.

[0071] This gain adjustment method can make the system increase the feedback response when the error is large, ensure a faster adjustment speed, and reduce the gain when the error is small to avoid overregulation.

[0072] In addition, the system also adjusts the reactive power compensation through feedback control. The reactive power compensation amount needs to be adjusted according to the real-time reactive power error of the power grid. The adjustment formula of the reactive power compensation is: ; Where: is the reactive power compensation amount at time (unit: var), calculated by the system control strategy; is the set reference reactive power value (unit: var); is the actually measured reactive power at time (unit: var); is the reactive power compensation amount adjusted according to the control strategy (unit: var).

[0073] By accurately calculating and adjusting the reactive power compensation, the inverter can effectively improve the power factor, reduce the reactive power loss, thereby enhancing the stability and operation efficiency of the power grid.

[0074] Through the combination of real-time control input and feedback mechanism in S4, the inverter can quickly respond to the changes in the power grid and effectively adjust the power output, thereby achieving the stability and high efficiency of the power grid grid connection.

[0075] S5. Control signal execution: Use a finite state machine to parse the dynamically adjusted control signal, drive the power adapter to adjust the power output, and control the inverter output power by adjusting the DC-side power conversion parameters. S5 is a key step to ensure a smooth and stable operation during the grid connection process. Through this step, the inverter can evaluate the stability of the grid status in real time and dynamically adjust the power output according to the actual operating conditions, thereby ensuring the stability and efficiency of the grid. During the grid connection process, the grid may be affected by factors such as load changes and external disturbances. Therefore, it is crucial to identify and adjust the power output in a timely manner to maintain the system stability.

[0076] Generally, the process of stability evaluation and regulation requires the system to be able to respond quickly when evaluating the grid stability, especially in the case of grid fluctuations or sudden load changes. As an option, a stability evaluation method based on real-time data monitoring and feedback mechanism is adopted. By monitoring key parameters such as grid voltage, current, and power, dynamic adjustment can be achieved. Specifically, in this step, the system determines whether the system faces unstable risks by analyzing the grid status in real time, and adjusts the power output of the inverter when necessary to prevent unstable phenomena from occurring.

[0077] In this embodiment, the specific implementation process of step S5 includes several main links: First, the inverter needs to obtain the status data of the grid in real time (such as voltage, current, power, etc.). By analyzing these data, the system can determine whether there are potential unstable risks in the current grid, such as voltage fluctuations, frequency deviations, load overloading, and other factors. The stability of the grid can be preliminarily evaluated by comparing the power error with the allowable error threshold. Specifically, the following formula can be used: ; ; Where: is the measured active power error (unit: W), which represents the difference between the actual power of the grid and the reference power, usually obtained by sensors; is the allowable maximum active power error (unit: W), which is the stability standard set by the system; is the measured reactive power error (unit: var), which represents the difference between the actual reactive power of the grid and the reference target; is the allowable maximum reactive power error (unit: var), which is the stability standard set by the system.

[0078] If the power error in the grid exceeds these preset thresholds, the system will consider the current grid status unstable and trigger the power regulation and adjustment process.

[0079] In actual implementation, the inverter automatically adjusts its power output according to the grid stability assessment result to ensure that the grid can recover stability when there are fluctuations or load changes. The power regulation of the inverter can be described by the following formula: ; ; Where: is the active power output of the inverter (unit: W), representing the actual active power after the inverter adjustment; is the reactive power output of the inverter (unit: var), representing the actual reactive power after the inverter adjustment; is the reference active power target value (unit: W), representing the active power expected by the grid; is the reference reactive power target value (unit: var), representing the reactive power expected by the grid; is the active power regulation coefficient (unitless), representing the sensitivity of power regulation; is the reactive power regulation coefficient (unitless), representing the sensitivity of reactive power regulation; is the measured active power error (unit: W), that is, the difference between the actual active power of the grid and the reference target; is the measured reactive power error (unit: var), that is, the difference between the actual reactive power of the grid and the reference target.

[0080] Through these formulas, the inverter adjusts the power output according to the actual power error of the grid to restore the stability of the grid. The regulation coefficients and are mainly used to control the sensitivity of the regulation response to ensure that the grid can quickly return to a stable state.

[0081] To further improve the evaluation accuracy of grid stability, this embodiment introduces an adaptive stability evaluation mechanism. This mechanism automatically adjusts the stability evaluation criteria according to the historical operation data and real-time state changes of the grid. For example, the system can dynamically adjust the allowable range of power error according to information such as the load condition and volatility of the grid. Specifically, the adaptive stability evaluation algorithm dynamically adjusts the power regulation strategy by monitoring the historical data and real-time feedback of the grid to ensure that the inverter always maintains an optimal regulation response under different time periods or different load conditions.

[0082] For example, the adaptive adjustment formula can be written as: ; ; Where: is the moment The maximum active power error after dynamic adjustment (unit: W); is the moment The maximum reactive power error after dynamic adjustment (unit: var); is the initially set maximum active power error (unit: W); is the initially set maximum reactive power error (unit: var); and are adaptive gain coefficients (unitless), respectively controlling the adjustment degree of the allowable range of active and reactive power errors; is the absolute value of the currently measured active power error (unit: W), indicating the deviation of the actual active power during the grid operation; is the absolute value of the currently measured reactive power error (unit: var), indicating the deviation of the actual reactive power during the grid operation.

[0083] Through this dynamic adjustment mechanism, the system can flexibly adjust its stability evaluation criteria according to the real-time operation state changes of the power grid, thereby improving the accuracy of the inverter regulation response and the stability of the grid connection.

[0084] In summary, step S5 ensures that the inverter can make the optimal power regulation response under different grid conditions through the real-time evaluation and adjustment of the grid state. By dynamically evaluating the stability and adjusting the power output, this embodiment can effectively cope with unstable factors such as grid load fluctuations and external disturbances, thereby maintaining the long-term stable operation of the system.

[0085] S6. Real-time feedback and adjustment, monitor the output power state of the power adapter, collect the real-time changing data of the power grid, calculate the power error using the mean square error method, adjust the parameters of the optimal control strategy according to the error, and feedback the adjusted data to the data acquisition and preprocessing set step to form a closed-loop adjustment mechanism; S6 is an important link to ensure the long-term stable operation of the power grid. The main goal of this step is to adjust the output power of the inverter based on the real-time grid state after completing the stability evaluation (step S5) to make it meet the grid demand and reduce the impact of power fluctuations on the system stability. At the same time, this step also involves the fine compensation of the power error to optimize the power quality and improve the system response ability.

[0086] In general, the power grid load has the characteristic of dynamic change. Therefore, the inverter needs to have a certain adaptive adjustment ability to ensure that the output power can still meet the operation requirements of the power grid under different load conditions. As an option, the power optimization adjustment of the inverter can adopt a dynamic optimization strategy based on the power grid state prediction model and combine the voltage-current-power coupling control method to improve the adjustment accuracy. Specifically, this step calculates the optimal power output target value by obtaining the power grid parameters in real time and combining the system operation constraint conditions, and then adjusts the operation state of the inverter.

[0087] In this embodiment, the power optimization adjustment of the inverter is mainly calculated based on the following mathematical model: ; ; Where: is the active power output of the inverter at time (unit: W); is the reactive power output of the inverter at time (unit: var); is the set reference active power target value (unit: W), which is determined by the power dispatch strategy; is the set reference reactive power target value (unit: var), which is determined by the power dispatch strategy; is the real-time grid voltage (unit: V), which is measured by the voltage sensor; is the grid target voltage (unit: V), usually the rated voltage value; is the real-time grid current (unit: A), which is measured by the current sensor; is the grid target current (unit: A), which is determined by the current reference value preset by the system; is the real-time grid apparent power (unit: VA), and the calculation formula is: ; Where and are the measured active power and reactive power respectively; is the grid target apparent power (unit: VA), which is determined by the power target value preset by the system; is the regulation coefficient of voltage on active power (unit: W / V), which determines the influence degree of voltage change on active power output; is the regulation coefficient of voltage on reactive power (unit: var / V), which determines the influence degree of voltage change on reactive power output; is the regulation coefficient of current on active power (unit: W / A), which determines the influence degree of current change on active power output; is the regulation coefficient of current to reactive power (unit: var / A), which determines the influence degree of current change on reactive power output; is the regulation coefficient of apparent power to active power (unit: W / VA), which determines the influence degree of apparent power change on active power output; is the regulation coefficient of apparent power to reactive power (unit: var / VA), which determines the influence degree of apparent power change on reactive power output.

[0088] The above mathematical model is used to dynamically adjust the inverter output, so that the power can adaptively match the real-time demand of the power grid and reduce the influence brought by power disturbance. In order to further improve the adaptability of power optimization control, this embodiment adopts an adaptive regulation coefficient optimization mechanism, so that the system can dynamically adjust the regulation coefficient according to the power grid fluctuation situation. 、 、 、 、 、 。 Its mathematical description is as follows: ; ; ; Where: is the active power voltage regulation coefficient at time (unit: W / V); is the active power current regulation coefficient at time (unit: W / A); is the active power apparent power regulation coefficient at time (unit: W / VA); , , is the initially set power regulation coefficient (unit: W / V, W / A, W / VA); , , is the adaptive gain coefficient (unitless), which determines the adjustment rate of the regulation coefficient; , , respectively represent the deviations of grid voltage, current and apparent power (unit: V, A, VA).

[0089] S6 realizes the precise control of the inverter power output through the power optimization adjustment model and the adaptive regulation mechanism, makes it match the real-time demand of the power grid, improves the dynamic response ability of the inverter, and enhances the stability and adaptability of the power grid operation.

[0090] The power adapter control system of the stable grid-connected inverter system described below can be correspondingly referred to the power adapter control method of the stable grid-connected inverter system described above.

[0091] Please refer to the attached Figure 2 , the present invention also provides a power adapter control system for a stable grid-connected inverter system, including: A grid status monitoring module for obtaining grid voltage, current, frequency, and load information; An optimization algorithm module for solving the optimal control strategy based on the variational optimization theory and the Pontryagin maximum principle; A feedback and adjustment module for adjusting the power output of the power adapter based on the rolling optimization algorithm; An execution and regulation module for adjusting the power output according to the control signal to enable the power adapter to operate stably in grid connection.

[0092] The system of this embodiment can be used to execute the above method embodiment, and its principle and technical effect are similar, which will not be elaborated here.

[0093] A power adapter described below can be correspondingly referred to the power adapter control method of the stable grid-connected inverter system described above.

[0094] Please refer to the attached Figure 3 , the present invention also provides a power adapter, including: An input interface for connecting to a DC power source; A power conversion circuit using a DC-DC conversion topology for converting direct current into inverted alternating current; An output interface for connecting to an inverter and providing stable power; A sensor module for collecting grid status data; A control unit for executing the optimal control signal generated by the optimization algorithm module and adjusting the power output.

[0095] The power adapter of this embodiment can be used to execute the above method embodiment, and its principle and technical effect are similar, which will not be elaborated here.

[0096] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power adapter control method and a power adapter for a stable grid-connected inverter system, characterized in that, It includes the following steps: S1. Data acquisition and preprocessing: Acquire grid voltage, current, frequency, and load power information, and preprocess the acquired data; S2. Power regulation modeling: Based on the grid parameters provided by data acquisition and preprocessing, use the variational optimization algorithm to construct a power regulation objective function, set power error constraints and regulation rate constraints, and generate a power regulation mathematical model; S3. Optimization control strategy solution: Based on the generated power regulation mathematical model, use the Pontryagin maximum principle to calculate the optimal control variables, and use the state space transformation method to map the optimal control variables to the actual control signal domain; S4. Optimal control signal generation: Use the rolling optimization algorithm to calculate the time-series optimal control signal according to the results of the optimization control strategy solution step, and dynamically adjust the control signal in combination with the real-time data of the grid state; S5. Control signal execution: Use a finite state machine to parse the dynamically adjusted control signal, drive the power adapter to adjust the power output, and control the inverter output power by adjusting the DC-side power conversion parameters; S6. Real-time feedback and adjustment: Monitor the output power state of the power adapter, acquire the real-time changing data of the grid, calculate the power error using the mean square error method, adjust the parameters of the optimization control strategy according to the error, and feedback the adjusted data to the data acquisition and preprocessing step to form a closed-loop regulation mechanism.

2. The power adapter control method and power adapter of the stable grid-connected inverter system according to claim 1, characterized in that, The data acquisition and preprocessing include: Acquire grid voltage, current, frequency, and load power information through a voltage sensor, current sensor, phase-locked loop circuit, and power meter; Use the Kalman filtering algorithm to filter the noise and preprocess the acquired data; Use the finite difference method to calculate the grid state change rate and generate data analysis results.

3. The power adapter control method and power adapter of the stable grid-connected inverter system according to claim 1, characterized in that The power regulation modeling includes: Use the variational optimization algorithm to construct a power regulation objective function; Limit the control variables through the Lagrangian functional to constrain the power error and regulation rate; Set power output constraints to ensure that the output of the power adapter meets the grid requirements.

4. The power adapter control method and power adapter of the stable grid-connected inverter system according to claim 1, characterized in that, The optimization control strategy solution includes: Use the Pontryagin maximum principle to calculate the optimal control variables; Determine the extreme value conditions of the Hamiltonian function; Calculate the optimal solution of the control signal in combination with the state variables and co-state variables.

5. The power adapter control method and power adapter of the stable grid-connected inverter system according to claim 1, characterized in that The optimal control signal generation includes: Use the rolling optimization algorithm to calculate the optimal control signal at each time step; Adjust the control strategy in real time according to the dynamic changes of the grid state; Use the finite-time convergence control algorithm to iteratively optimize and adjust the control signal to ensure fast response.

6. The power adapter control method and power adapter of the stable grid-connected inverter system according to claim 1, characterized in that, The control signal execution includes: Parse the optimal control signal calculated by the optimization algorithm module; Adjust the DC-side power conversion parameters; Regulate the grid-connected power through the inverter output terminal to achieve stable power supply.

7. The power adapter control system of a stable grid-connected inverter system, characterized in that, Using the power adapter control method of the stable grid-connected inverter system according to any one of claims 1-6, includes: A grid state monitoring module for acquiring grid voltage, current, frequency, and load information; An optimization algorithm module for solving the optimal control strategy based on the variational optimization theory and the Pontryagin maximum principle; A feedback and adjustment module for adjusting the power output of the power adapter based on the rolling optimization algorithm. An execution and regulation module, which is used to adjust the power output according to a control signal so that the power adapter operates stably in parallel with the grid.

8. The power adapter control system of the stable grid-connected inverter system according to claim 7, characterized in that The optimization algorithm module includes: A variational optimization solving unit; A Pontryagin maximum value optimization unit; A rolling optimization control unit.

9. A power adapter, characterized in that, Using the power adapter control method of the stable grid-connected inverter system according to any one of claims 1-6, including: An input interface, which is used to connect to a DC power supply; A power conversion circuit, adopting a DC-DC conversion topology, which is used to convert direct current into inverted alternating current; An output interface, which is used to connect to an inverter and provide stable power; A sensor module, which is used to collect grid state data; A control unit, which is used to execute the optimal control signal generated by the optimization algorithm module and adjust the power output.

10. A power adapter according to claim 9, characterized in that, The control unit includes: A data processing unit, which is used to receive and analyze grid state data; An optimal control unit, which is used to execute the optimization algorithm and generate a control signal; An adjustment execution unit, which is used to adjust the power output so that the power adapter operates stably.

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