Photovoltaic inverter grid-connected resonance suppression method, system, equipment and medium

Abstract

The invention relates to the technical field of power electronics, and discloses a photovoltaic inverter grid-connected resonance suppression method, system and device and a medium, and the method comprises the steps: collecting power grid and photovoltaic operation parameters in real time through deploying an intelligent monitoring system; the first-layer energy management strategy monitors frequency, voltage and load changes of the power grid, and the second-layer energy management strategy dynamically adjusts the output power of the inverter according to the changes; constructing a system dynamic prediction model in combination with a model prediction control algorithm, and optimizing power output in a future time period; operation data are continuously collected and fed back for adjustment, and the resonance phenomenon in the grid connection process is effectively restrained; and finally verifying the stability through an actual power grid integration test. According to the scheme, the dynamic response capability and the anti-interference performance of the photovoltaic grid-connected system are remarkably improved, and the method is suitable for a power system with high-proportion renewable energy access.

Description

A method, system, equipment, and dielectric for suppressing grid-connected resonance in a photovoltaic inverter. Technical Field

[0001] This invention relates to the field of power electronics technology, and in particular to a method, system, device and medium for suppressing grid-connected resonance in photovoltaic inverters. Background Technology

[0002] Currently, photovoltaic (PV) power generation has become an indispensable and important power source in the power system due to its advantages such as being clean, pollution-free, and widely distributed. However, under the current distribution network operating environment, large-scale PV grid connection faces some unavoidable problems. For example, PV power output is affected by natural factors such as sunlight intensity or ambient temperature, exhibiting significant intermittency and fluctuation. When this fluctuation is coupled with the inherent load changes, frequency regulation capabilities, and voltage dynamic characteristics of the power grid, grid resonance can be triggered. This grid resonance phenomenon poses a serious threat to the safe and stable operation of the power system.

[0003] Traditional photovoltaic inverters are typically designed using proportional-integral (PI) control or simple feedback control strategies, which adjust output power by setting fixed parameters beforehand. However, the current grid environment is complex and volatile, and traditional methods lack adaptability, making it difficult to respond promptly to rapid fluctuations in photovoltaic power generation and dynamic disturbances on the grid side.

[0004] To address the aforementioned issues, some researchers have attempted to introduce advanced strategies such as adaptive control or model predictive control (MPC) to overcome these shortcomings. MPC, in particular, constructs dynamic models of the power grid and photovoltaic system to predict short-term future states and optimizes current control commands accordingly, offering advantages in improving dynamic response speed and suppressing resonance. However, existing MPC schemes still suffer from low energy coordination efficiency when facing extreme grid faults, interactions between multiple inverters, or highly nonlinear operating conditions, making reliable grid-connected control under complex operating conditions difficult. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method, system, device and medium for suppressing grid-connected resonance of photovoltaic inverters, which can solve the problems in the prior art where resonance is easily caused by grid disturbances and photovoltaic power generation fluctuations during the grid connection of photovoltaic inverters, as well as the poor adaptability of traditional control methods in complex grid environments and the inability to achieve global stability and efficient energy management.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for suppressing grid-connected resonance in photovoltaic inverters, comprising: deploying an intelligent monitoring system during the grid connection process of the photovoltaic inverter, and collecting grid operating parameters and photovoltaic power generation system operating parameters in real time through sensors; designing a two-layer energy management control strategy based on the collected grid operating parameters, wherein the first layer is used to monitor grid frequency, voltage, and load changes in real time, and the second layer dynamically adjusts the output power of the photovoltaic inverter according to the monitoring results of the first layer; constructing a dynamic behavior prediction model of the grid and the photovoltaic power generation system, and employing a model predictive control algorithm to predict future power output based on the dynamic behavior prediction model. The system predicts grid conditions and calculates the optimal output power of the photovoltaic inverter for a future time period. It continuously collects grid frequency, grid voltage, and photovoltaic power generation data, inputting the collected data into a model predictive control algorithm to dynamically update the optimal output power and adjust the actual output power of the photovoltaic inverter accordingly to prevent resonance during grid connection. The system controls the photovoltaic inverter to operate according to the adjustment commands output by the model predictive control algorithm, responding to grid disturbances and photovoltaic power generation fluctuations, suppressing resonance, and maintaining system stability. The control system, including the above steps, is integrated into a real grid environment for operational testing to verify the resonance suppression effect and system stability under different grid operating conditions.

[0008] As a preferred embodiment of the photovoltaic inverter grid-connected resonance suppression method described in this invention, the deployment of the intelligent monitoring system includes the following operations: deploying sensors at key nodes of the power grid to collect power grid frequency, power grid voltage, load changes, photovoltaic power generation, DC side current and voltage; transmitting the collected data to the central processing unit via a communication module; using an intelligent monitoring terminal to collect power grid frequency, voltage, current and power in real time; selecting one of 5G NR, industrial Ethernet or Wi-Fi 6 communication methods according to the on-site power supply and network conditions to transmit the monitoring data to the control center in real time; integrating model predictive control algorithms and dual-layer energy management control strategies in the central processing unit to achieve joint monitoring and regulation of the power grid status and photovoltaic system output; configuring a system redundancy mechanism to automatically activate backup equipment or redundant paths when the main equipment fails, communication is interrupted or data is lost; encrypting the collected data, control signals and transmitted content, and implementing access control.

[0009] As a preferred embodiment of the photovoltaic inverter grid-connected resonance suppression method described in this invention, the execution of the dual-layer energy management control strategy includes the following operations: acquiring grid frequency, grid voltage, and load change information through grid status monitoring equipment, and using the information for grid status assessment; based on the grid status assessment results, formulating a power regulation strategy for the photovoltaic inverter, dynamically adjusting its output power to maintain grid frequency and voltage within allowable ranges; introducing a real-time feedback mechanism during the control process, immediately adjusting the output power of the photovoltaic inverter when abnormal fluctuations in grid frequency are detected, and optimizing control parameters based on feedback data; configuring redundant components, including backup power supplies, backup sensors, and backup computing units, automatically switching when the main component fails; setting an automatic backup mechanism for critical operating data, enabling data recovery from backups when the main storage device fails.

[0010] As a preferred embodiment of the photovoltaic inverter grid-connected resonance suppression method described in this invention, the model predictive control algorithm includes the following operations: constructing a predictive model describing the joint dynamic behavior of the power grid and the photovoltaic system based on real-time collected grid frequency, grid voltage, load power, and photovoltaic power generation; using the predictive model to predict the grid state over a future period; solving an optimization problem based on the prediction results to determine the optimal output power sequence of the photovoltaic inverter over the future period; and applying the control quantity of the current moment in the optimal output power sequence to the photovoltaic inverter.

[0011] As a preferred embodiment of the photovoltaic inverter grid-connected resonance suppression method described in this invention, the operation of continuously acquiring grid frequency, grid voltage, and photovoltaic power generation includes: forming current system state information, comparing the current state information with a preset reference state, and generating a deviation signal; based on the deviation signal, generating an adjustment command through a model predictive control algorithm to control the photovoltaic inverter to adjust its output power; and using a time synchronization protocol to perform clock calibration on multiple data acquisition points to ensure time consistency of multi-source data.

[0012] As a preferred embodiment of the photovoltaic inverter grid-connected resonance suppression method described in this invention, the step of controlling the photovoltaic inverter to operate according to the adjustment command output by the model predictive control algorithm includes: setting a target output power according to the adjustment command output by the model predictive control algorithm to match grid frequency fluctuations and load changes; monitoring grid frequency changes in real time and identifying potential resonant frequency components; and when a resonant frequency component is detected, actively adjusting the power output characteristics of the photovoltaic inverter near that frequency to weaken the resonant response.

[0013] As a preferred embodiment of the photovoltaic inverter grid-connected resonance suppression method described in this invention, the operational test includes: simulating grid load fluctuations and photovoltaic power generation fluctuations in a simulation platform to verify the effectiveness of the model predictive control algorithm and the two-layer energy management control strategy on grid frequency stability and resonance suppression; optimizing the prediction step size and feedback gain parameters in the model predictive control algorithm based on the test results; conducting robustness tests under extreme grid disturbance conditions to evaluate the stability performance of the system under photovoltaic power generation fluctuations or load abrupt changes, and improving the adaptability of the algorithm accordingly.

[0014] Secondly, the present invention provides a photovoltaic inverter grid-connected resonance suppression system, comprising: an intelligent monitoring unit for real-time acquisition of grid operating parameters and photovoltaic power generation system operating parameters; a dual-layer energy management control unit, including a grid status monitoring subunit and a power regulation subunit, wherein the grid status monitoring subunit is used to acquire grid frequency, voltage, and load information, and the power regulation subunit is used to generate power regulation commands for the photovoltaic inverter based on the information; a model prediction control unit for establishing a dynamic prediction model based on the operating parameters and generating optimized power output commands based on the prediction results; a data acquisition and feedback unit for continuously acquiring operating data and feeding it back to the model prediction control unit to form a closed-loop regulation; a photovoltaic inverter control execution unit for receiving the power output commands and adjusting the actual output power; and a system integration and testing unit for integrating the above units into the actual grid environment and performing operational tests.

[0015] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0017] Compared with existing technologies, the beneficial effects of this invention are that it proposes a method for suppressing grid-connected resonance in photovoltaic inverters. This method involves deploying an intelligent monitoring system to collect real-time grid and photovoltaic operating parameters; a first-layer energy management strategy monitors grid frequency, voltage, and load changes; a second layer dynamically adjusts the inverter output power accordingly; a model predictive control algorithm is combined to construct a dynamic prediction model for the system, optimizing power output for future periods; continuous collection of operating data and feedback adjustment effectively suppress resonance phenomena during grid connection; and finally, stability is verified through actual grid integration testing. This solution significantly improves the dynamic response capability and anti-interference performance of photovoltaic grid-connected systems and is suitable for power systems with a high proportion of renewable energy integration. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 is a flowchart of a method for suppressing grid-connected resonance of a photovoltaic inverter according to an embodiment of the present invention.

[0020] Figure 2 is a partial system topology diagram of a photovoltaic inverter grid-connected resonance suppression method provided in an embodiment of the present invention.

[0021] Figure 3 is a schematic diagram of a two-layer energy management control method for a photovoltaic inverter grid-connected resonance suppression method provided in an embodiment of the present invention.

[0022] Figure 4 is a schematic diagram of the MPC algorithm for a photovoltaic inverter grid-connected resonance suppression method provided in an embodiment of the present invention.

[0023] Figure 5 is a schematic diagram of the system response under varying illumination conditions and active power step fluctuations in a photovoltaic inverter grid-connected resonance suppression method according to an embodiment of the present invention.

[0024] Figure 6 is a schematic diagram of the system response under varying illumination conditions and active power step changes in a photovoltaic inverter grid-connected resonance suppression method according to an embodiment of the present invention.

[0025] Figure 7 shows the results of current harmonic analysis at the PCC point before and after the application of a photovoltaic inverter grid-connected resonance suppression method according to an embodiment of the present invention.

[0026] Figure 8 shows the results of current harmonic analysis at the PCC point before and after the application of a photovoltaic inverter grid-connected resonance suppression method according to an embodiment of the present invention.

[0027] Figure 9 is an internal structure diagram of an electronic device for a photovoltaic inverter grid-connected resonance suppression method provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0029] Example 1, referring to Figure 1, is the first embodiment of the present invention. This embodiment provides a method for suppressing grid-connected resonance of a photovoltaic inverter, including: The present invention provides a method that can effectively solve the problems mentioned above. The following will describe in detail how to implement this method for suppressing grid-connected resonance of a photovoltaic inverter using multiple embodiments. Figure 1 shows a flowchart of a method for suppressing grid-connected resonance of a photovoltaic inverter, including: S101, during the grid connection process of the photovoltaic inverter, deploying an intelligent monitoring system to collect grid operating parameters and photovoltaic power generation system operating parameters in real time through sensors; In this embodiment of the present invention, deploying the intelligent monitoring system includes the following operations: deploying sensors at key nodes of the grid to collect grid frequency, grid voltage, load changes, photovoltaic power generation, DC current and voltage; transmitting the collected data to the central processing unit through a communication module; using an intelligent monitoring terminal to collect grid frequency, voltage, current and power in real time; selecting 5G NR, industrial Ethernet or Wi-Fi according to the on-site power supply and network conditions. One of the communication methods in section 6 transmits monitoring data to the control center in real time; the central processing unit integrates model predictive control algorithms and two-layer energy management control strategies to achieve joint monitoring and regulation of grid status and photovoltaic system output; a system redundancy mechanism is configured to automatically activate backup equipment or redundant paths when the main equipment fails, communication is interrupted, or data is lost; the collected data, control signals, and transmitted content are encrypted and access control is implemented.

[0030] In some embodiments, a grid condition monitoring network can be designed based on the characteristics of the photovoltaic inverter and the power grid. Sensors and data acquisition devices are deployed to acquire key data such as grid frequency, voltage, load changes, and the photovoltaic system's power generation, current, and voltage in real time. The data is transmitted to a central processing unit for analysis and decision-making via a communication module.

[0031] Furthermore, an intelligent monitoring terminal integrating high-precision sensors and real-time data acquisition modules is selected. The terminal has the capability to acquire high-precision power grid parameters, including but not limited to parameters such as frequency, voltage, current, and power. It also has data storage and backup functions to ensure stable operation during power grid fluctuations or changes in the photovoltaic system's status.

[0032] Furthermore, based on the on-site power and network conditions, an appropriate communication method (such as 5G NR, industrial Ethernet, Wi-Fi 6, etc.) is selected to transmit the monitoring data to the control center. High-bandwidth, low-latency network support ensures that the collected data can be transmitted in real time, meeting the real-time processing requirements of the control algorithm.

[0033] Furthermore, model predictive control (MPC) algorithms and a two-layer energy management control strategy are integrated into the central processing unit to monitor the grid status and the power output of the photovoltaic system in real time. Through real-time data updates and control algorithm optimization, the system can operate efficiently and stably under various grid disturbances and load changes.

[0034] Furthermore, to improve system stability and reliability, a system redundancy mechanism is designed. In the event of equipment failure, communication interruption, or data loss, the system can automatically activate backup devices or redundant paths to ensure the continuity and stability of monitoring data and avoid system paralysis caused by single points of failure.

[0035] Furthermore, advanced encryption technologies are employed during data acquisition and transmission to ensure data security and privacy compliance. Video images, sensor data, and control signals are encrypted, and access control is implemented during data storage and transmission to guarantee the secure operation of the system.

[0036] In some embodiments, model predictive control (MPC) algorithms are integrated with a two-layer energy management control strategy into the central processing unit to achieve real-time monitoring and dynamic adjustment of the photovoltaic system and grid status. Specifically, this includes the following steps: First, a dynamic model of the grid and photovoltaic system is established based on real-time collected data such as grid frequency, voltage, and photovoltaic inverter output. Then, using the MPC control algorithm, parameters such as grid load changes and grid frequency fluctuations are monitored in real time, and the changing trend of the grid status is calculated and input into the two-layer energy management module.

[0037] Furthermore, the MPC algorithm is used to predict the grid state and calculate the optimal output power of the photovoltaic inverter within a certain future timeframe. The two-layer energy management strategy dynamically adjusts the inverter's power output based on the grid load and the photovoltaic system's operating status, ensuring the stability of grid frequency and voltage during photovoltaic grid connection and reducing the occurrence of resonance phenomena.

[0038] In some embodiments, to improve system stability and reliability, system redundancy and fault-tolerance mechanisms are designed. Specifically, this includes the following steps: deploying backup equipment in the system to handle system switching in the event of a primary equipment failure. The redundant equipment maintains the same operating state as the primary equipment through the same control signals and data inputs. When the primary equipment fails, the system can automatically switch to the backup equipment and continue controlling the power grid and photovoltaic inverter, ensuring uninterrupted system operation.

[0039] Furthermore, a data recovery mechanism is employed to back up critical data in the system (such as inverter control signals and grid status data) in real time. In the event of data loss or anomalies, the system can recover data from the backup, ensuring the stability and continuity of the control system.

[0040] S102, based on the collected grid operating parameters, a two-layer energy management control strategy is designed. The first layer is used to monitor grid frequency, voltage, and load changes in real time, and the second layer dynamically adjusts the output power of the photovoltaic inverter based on the monitoring results of the first layer. In this embodiment of the invention, the execution of the two-layer energy management control strategy includes the following operations: acquiring grid frequency, grid voltage, and load change information through grid status monitoring equipment and using the information for grid status assessment; formulating a power regulation strategy for the photovoltaic inverter based on the grid status assessment results, and dynamically adjusting its output power to maintain grid frequency and voltage within the allowable range; introducing a real-time feedback mechanism in the control process, immediately adjusting the output power of the photovoltaic inverter when abnormal fluctuations in grid frequency are detected, and optimizing control parameters based on feedback data; configuring redundant components, including backup power supply, backup sensors, and backup computing units, and automatically switching when the main component fails; setting an automatic backup mechanism for critical operating data, enabling data recovery from backup when the main storage device fails.

[0041] In some embodiments, grid condition monitoring equipment can be deployed to collect information such as grid frequency, voltage, and load changes in real time. The real-time data from the grid is transmitted to a control center via a data acquisition module for grid condition assessment and analysis. Based on changes in the grid condition, energy management strategies are dynamically adjusted to optimize inverter power output.

[0042] Furthermore, based on the results of grid condition monitoring and analysis, a power regulation strategy for photovoltaic inverters is designed and optimized. The output power of the photovoltaic inverters is dynamically adjusted according to grid load and frequency fluctuations to ensure grid voltage and frequency stability and optimize the energy efficiency of the photovoltaic system.

[0043] Furthermore, a two-layer control strategy is designed and combined with a real-time feedback mechanism to further improve the response speed and stability of the control system.

[0044] Furthermore, redundancy and fault tolerance mechanisms are incorporated into the system design to ensure stable system operation in the event of equipment failure or communication interruption.

[0045] Preferably, power grid condition monitoring equipment is deployed to collect real-time information such as frequency, voltage, and load changes in the power grid. The real-time data is transmitted to the control center via a data acquisition module for power grid condition assessment and analysis. Based on changes in the power grid condition, energy management strategies are dynamically adjusted to optimize inverter power output. Specifically, this includes the following steps: selecting high-precision power grid parameter monitoring sensors, such as frequency sensors, current and voltage sensors, and deploying them at key nodes of the power grid to collect real-time data on the grid's operating status. These sensors transmit data to the data acquisition unit via wireless or wired communication technology, forming a complete power grid monitoring network.

[0046] Furthermore, by utilizing the collected real-time grid data, mathematical modeling and data analysis methods are employed to predict the trends of grid load and frequency fluctuations. This prediction will serve as the basis for subsequent energy management and control strategies, helping photovoltaic inverters adjust their output power according to the future state of the grid and avoid instability caused by excessively high or low grid frequencies.

[0047] Preferably, based on the results of grid status monitoring and analysis, a power regulation strategy for the photovoltaic inverter is designed and optimized, specifically including the following steps: The photovoltaic inverter regulates its power output according to real-time grid status data. After receiving signals of grid load changes and voltage fluctuations, the control unit calculates the optimal output power using an algorithm and achieves stable power output by adjusting the inverter's operating parameters (such as DC / AC power ratio, switching frequency, etc.).

[0048] Furthermore, based on grid load forecasts, the response rate of the photovoltaic inverter is dynamically adjusted to optimize its load adaptability. In the event of sudden changes in grid load, the inverter can respond promptly and perform rapid power regulation to ensure stable system operation and prevent grid resonance caused by drastic load fluctuations.

[0049] Preferably, a two-layer control strategy is designed, combined with a real-time feedback mechanism, to further improve the response speed and stability of the control system. Specifically, the two-layer energy management control strategy comprises two main modules: the first layer monitors grid load, frequency, and voltage changes; the second layer calculates and adjusts the output power of the photovoltaic inverter based on the data from the first layer. This control strategy ensures dynamic balance between the grid and the photovoltaic inverter, avoiding resonance or power fluctuations caused by system misalignment.

[0050] Furthermore, by incorporating a feedback mechanism, the system can adjust its energy management strategy based on real-time monitoring data. For example, when abnormal fluctuations occur in the grid frequency, the control system can quickly adjust the inverter's output power and optimize control parameters based on feedback data to achieve a rapid response. Through real-time feedback, the system can automatically self-adjust when the grid state changes, ensuring that the system always maintains stability and efficient operation.

[0051] Preferably, redundancy and fault-tolerance mechanisms are incorporated into the system design to ensure stable system operation in the event of equipment failure or communication interruption. This includes the following steps: configuring redundant modules, such as backup power supplies, sensors, and computing modules, to ensure that backup modules can take over control functions and guarantee continuous system operation when the main equipment fails. Through self-diagnosis and automatic switching mechanisms, the system can detect faults in real time and activate backup modules, avoiding system paralysis due to single points of failure.

[0052] Furthermore, to prevent data loss, the system performs regular data backups. For critical power grid data and photovoltaic system control data, an automatic backup mechanism is implemented. If the main data storage device fails, the system can recover the data from the backup, ensuring the continuity and accuracy of the control process.

[0053] S103, a dynamic behavior prediction model of the power grid and photovoltaic power generation system is constructed, and a model predictive control algorithm is used to predict the future state of the power grid based on the dynamic behavior prediction model, and then calculate the optimal output power of the photovoltaic inverter in the future time period. In this embodiment of the invention, the model predictive control algorithm includes the following operations: based on the real-time collected power grid frequency, power grid voltage, load power and photovoltaic power generation, a prediction model describing the joint dynamic behavior of the power grid and photovoltaic system is constructed; the prediction model is used to predict the state of the power grid in the future time period; the optimization problem is solved based on the prediction results to determine the optimal output power sequence of the photovoltaic inverter in the future time period; the control quantity of the current moment in the optimal output power sequence is applied to the photovoltaic inverter.

[0054] In some embodiments, firstly, a dynamic model needs to be constructed for the power grid and the photovoltaic system, taking into account grid frequency fluctuations, grid load changes, and the intermittent nature of photovoltaic power generation. Assuming the output power of the photovoltaic inverter is Ppv, the grid load is Pload, and the grid frequency is fgrid, this invention uses the following model to describe the dynamic behavior of the photovoltaic grid-connected system: in, This is the system state vector, which includes grid frequency, photovoltaic power generation, etc. The control input is the power output of the inverter. External disturbances (such as changes in grid load); and The system dynamic matrix is ​​obtained through system linearization.

[0055] By collecting real-time data, a predictive model of the system is constructed to predict the state of the power grid at future times, providing input for the MPC control algorithm.

[0056] Furthermore, Model Predictive Control (MPC) calculates the optimal control input based on a predictive model of the system. The goal of MPC is to minimize the deviation and energy loss during the control process while avoiding system resonance by solving an optimization problem. The optimization objective function can be expressed as: in, and These are the reference values ​​for grid frequency and inverter power, respectively; Q and R are weighting matrices that adjust the weights of system state and control input; N is the prediction step size.

[0057] The MPC algorithm solves this optimization problem iteratively, adjusting the power output of the photovoltaic inverter in real time to ensure that the system is as close as possible to the reference state in the future.

[0058] S104, continuously collect grid frequency, grid voltage, and photovoltaic power generation, and input the collected data into the model predictive control algorithm to dynamically update the optimal output power, and adjust the actual output power of the photovoltaic inverter accordingly to prevent resonance during grid connection; in this embodiment of the invention, the operation of continuously collecting grid frequency, grid voltage, and photovoltaic power generation includes: forming current system state information, comparing the current state information with a preset reference state, and generating a deviation signal; based on the deviation signal, generating adjustment commands through the model predictive control algorithm to control the photovoltaic inverter to adjust its output power; and using a time synchronization protocol to calibrate the clocks of multiple data collection points to ensure the time consistency of multi-source data.

[0059] In some embodiments, real-time data acquisition of the power grid and photovoltaic system can be performed first, collecting data such as grid frequency, load changes, and photovoltaic output power in real time, generating a real-time state vector x(t), and transmitting it to the control unit. The monitored grid parameters include grid frequency fgrid(t), grid voltage Vgrid(t), photovoltaic power Ppv(t), and load power Pload(t).

[0060] According to real-time data The system calculates the current error. = - And generate control signals based on the error.

[0061] Furthermore, the real-time feedback mechanism compares the actual state of the system. and reference state The inverter power output is adjusted in real time using the MPC algorithm. This causes the system to tend towards the reference state. The control algorithm can be adjusted using the following feedback formula: in, The feedback gain matrix is ​​used to adjust the system's response speed and stability. By continuously adjusting the inverter's output power, the system can quickly respond to changes in grid load, maintain grid frequency stability, and prevent grid resonance.

[0062] Furthermore, to ensure accurate real-time data transmission, the system incorporates a data synchronization mechanism. Using a clock synchronization protocol (such as PTP), the time between multiple devices is calibrated to ensure consistent data transmission and processing times across all devices, thereby improving the overall system efficiency.

[0063] S105, controlling the photovoltaic inverter to operate according to the adjustment command output by the model predictive control algorithm, responding to grid disturbances and photovoltaic power generation fluctuations, suppressing resonance and maintaining system stability; in this embodiment of the invention, controlling the photovoltaic inverter to operate according to the adjustment command output by the model predictive control algorithm includes: setting a target output power according to the adjustment command output by the model predictive control algorithm to match grid frequency fluctuations and load changes; monitoring grid frequency changes in real time and identifying potential resonant frequency components; when a resonant frequency component is detected, actively adjusting the power output characteristics of the photovoltaic inverter near that frequency to weaken the resonant response.

[0064] In some embodiments, the inverter's output power Pinv is determined by a control signal and matched to the grid frequency fluctuation fgrid and load change Pload. A target power output is set as (Pinv, ref), and adjusted in real time using an MPC control algorithm. in, To adjust the power output in real time, ensuring that the inverter can adjust its output promptly when the grid load fluctuates.

[0065] Furthermore, the system identifies the resonant frequency by monitoring grid frequency fluctuations in real time and suppresses it by adjusting the inverter's power output. The resonant frequency is set to... When resonance occurs, the system adjusts the power output according to the resonance characteristics: By adjusting the inverter power, its output near the resonant frequency is suppressed, thereby avoiding excessive fluctuations in the system's frequency and voltage.

[0066] S106. Integrate the control system, which includes the above steps, into the actual power grid environment for operation testing to verify the suppression effect of resonance and the system stability under different power grid operating conditions.

[0067] In this embodiment of the invention, the operational test includes: simulating grid load fluctuations and photovoltaic power generation fluctuations in a simulation platform to verify the effectiveness of the model predictive control algorithm and the two-layer energy management control strategy on grid frequency stability and resonance suppression; optimizing the prediction step size and feedback gain parameters in the model predictive control algorithm based on the test results; conducting robustness tests under extreme grid disturbance conditions to evaluate the stability performance of the system under photovoltaic power generation fluctuations or load abrupt changes, and improving the adaptability of the algorithm accordingly.

[0068] In some embodiments, the effectiveness of the MPC algorithm and the two-layer energy management control strategy is verified using a simulation platform. The simulations account for dynamic conditions such as grid load fluctuations and photovoltaic system power generation fluctuations, evaluating the control strategy's effect on grid frequency and photovoltaic system power output regulation. Experimental tests are conducted to analyze the system's response time, power output stability, and resonance suppression effect under different load and grid conditions.

[0069] Furthermore, based on experimental data, the control parameters are optimized, including the prediction step size and feedback gain matrix K in the MPC algorithm. By adjusting these parameters, the system's response speed and stability are improved. For example, optimizing the control gain K ensures that the system can respond to changes in the grid frequency and maintain stable output in the shortest possible time.

[0070] Furthermore, robustness tests were conducted under different grid disturbance conditions to analyze the system's stability performance under extreme load changes and extreme fluctuations in photovoltaic power generation. By improving the fault tolerance and adaptability of the MPC algorithm, the system's stable operation capability in complex grid environments was enhanced.

[0071] Example 2, referring to Figures 2 to 8, to verify the photovoltaic inverter grid-connected resonance suppression system and method based on model predictive control and two-layer energy management proposed in this invention, combined with the actual power grid operation scenario, a complete functional structure block diagram, key algorithm flow and before-and-after control comparison images were constructed, as shown in Figures 2 to 8.

[0072] As shown in Figure 2, this photovoltaic grid-connected system consists of a front-end Boost converter and a rear-end three-phase inverter, achieving efficient energy conversion from the photovoltaic array to the grid. The front-end uses a combination of MPPT algorithm and PI control, adjusting the duty cycle via PWM to ensure the photovoltaic modules operate at their maximum power point and stabilize the DC bus voltage. The rear-end inverter employs a model predictive control (MPC) algorithm, selecting the optimal switching vector in real-time based on system state prediction, achieving rapid current tracking and harmonic suppression. The system achieves decoupled control of active and reactive power through dq coordinate transformation, and effectively suppresses high-frequency harmonics using an LCL filter. This structure features fast dynamic response, high control accuracy, and strong anti-interference capability, making it suitable for high-performance photovoltaic grid-connected scenarios.

[0073] Figure 3 illustrates the overall structure of the photovoltaic grid-connected control system based on MPC and two-layer energy management. The system consists of a photovoltaic array, an inverter, an MPC control unit, a grid monitoring unit, and a two-layer energy management module. The upper-layer energy management is responsible for grid status monitoring and power allocation strategies, while the lower-layer MPC control enables real-time optimization control of the inverter, ensuring dynamic stability and efficient energy transmission of the system.

[0074] Figure 4 shows the block diagram of the MPC control principle, which mainly includes a predictive model, an optimization algorithm, and a controlled object. The controller estimates the future behavior of the system through the predictive model and minimizes the objective function by combining the optimization algorithm, thereby achieving precise regulation of state variables such as current and voltage, and thus achieving fast dynamic response and low harmonic control effects.

[0075] Figures 5 and 6 show the dynamic response of active and reactive power at the PCC point under varying illumination conditions and power step disturbances. The results show that the system using MPC control can achieve fast convergence and small overshoot, exhibiting excellent dynamic performance and stability.

[0076] Figures 7 and 8 show the spectral analysis results. It can be seen that after adopting the proposed MPC and energy management strategy, the dominant harmonics of the system are significantly reduced, harmonic energy is concentrated in the low-frequency band, and high-frequency components are significantly reduced, indicating that this method has a significant effect on resonance suppression and power quality improvement.

[0077] Example 3, referring to Figure 9, also provides a photovoltaic inverter grid-connected resonance suppression system, including: an intelligent monitoring unit for real-time acquisition of grid operating parameters and photovoltaic power generation system operating parameters; a dual-layer energy management control unit, including a grid status monitoring subunit and a power regulation subunit, wherein the grid status monitoring subunit is used to acquire grid frequency, voltage and load information, and the power regulation subunit is used to generate power regulation commands for the photovoltaic inverter based on the information; a model prediction control unit for establishing a dynamic prediction model based on operating parameters and generating optimized power output commands based on the prediction results; a data acquisition and feedback unit for continuously acquiring operating data and feeding it back to the model prediction control unit to form a closed-loop regulation; a photovoltaic inverter control execution unit for receiving power output commands and adjusting the actual output power; and a system integration and testing unit for integrating the above units into the actual grid environment and performing operational tests.

[0078] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0079] This embodiment also provides an electronic device, which can be a terminal, and its internal structure diagram is shown in Figure 8. The electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for suppressing grid-connected resonance in a photovoltaic inverter. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0080] This embodiment also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the following steps: During the grid connection process of the photovoltaic inverter, an intelligent monitoring system is deployed to collect grid operating parameters and photovoltaic power generation system operating parameters in real time through sensors; based on the collected grid operating parameters, a two-layer energy management control strategy is designed, wherein the first layer is used to monitor grid frequency, voltage, and load changes in real time, and the second layer dynamically adjusts the output power of the photovoltaic inverter according to the monitoring results of the first layer; a dynamic behavior prediction model of the grid and photovoltaic power generation system is constructed, and a model predictive control algorithm is used to predict the future grid behavior based on the dynamic behavior prediction model. The system predicts the state and calculates the optimal output power of the photovoltaic inverter in the future time period; it continuously collects grid frequency, grid voltage, and photovoltaic power generation, and inputs the collected data into the model predictive control algorithm to dynamically update the optimal output power and adjust the actual output power of the photovoltaic inverter accordingly to prevent resonance during grid connection; it controls the photovoltaic inverter to operate according to the adjustment commands output by the model predictive control algorithm, responds to grid disturbances and photovoltaic power generation fluctuations, suppresses resonance, and maintains system stability; the control system including the above steps is integrated into the actual grid environment for operation testing to verify the resonance suppression effect and system stability under different grid operating conditions.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0082] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0083] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for suppressing grid-connected resonance in a photovoltaic inverter, characterized in that, include: During the grid connection process of photovoltaic inverters, an intelligent monitoring system is deployed to collect grid operation parameters and photovoltaic power generation system operation parameters in real time through sensors; Based on the collected grid operating parameters, a two-layer energy management control strategy is designed. The first layer is used to monitor grid frequency, voltage, and load changes in real time, while the second layer dynamically adjusts the output power of the photovoltaic inverter based on the monitoring results of the first layer. A dynamic behavior prediction model of the grid and the photovoltaic power generation system is constructed, and a model predictive control algorithm is used to predict the future grid state based on the dynamic behavior prediction model, thereby calculating the optimal output power of the photovoltaic inverter in the future time period. Grid frequency, grid voltage, and photovoltaic power generation are continuously collected, and the collected data are input into the model predictive control algorithm to dynamically update the optimal output power and adjust the actual output power of the photovoltaic inverter accordingly to prevent resonance phenomena during grid connection. The photovoltaic inverter is controlled to operate according to the adjustment commands output by the model predictive control algorithm, responding to grid disturbances and photovoltaic power generation fluctuations, suppressing resonance and maintaining system stability. The control system, which includes the above steps, is integrated into the actual power grid environment for operation testing to verify the suppression effect of resonance and system stability under different power grid conditions.

2. The photovoltaic inverter grid-connected resonance suppression method as described in claim 1, characterized in that, The deployment of the intelligent monitoring system includes the following operations: deploying sensors at key nodes of the power grid to collect data on grid frequency, grid voltage, load changes, photovoltaic power generation, DC current, and voltage; transmitting the collected data to the central processing unit via a communication module; using intelligent monitoring terminals to collect grid frequency, voltage, current, and power in real time; selecting one of the following communication methods—5G NR, Industrial Ethernet, or Wi-Fi 6—based on on-site power supply and network conditions to transmit the monitoring data to the control center in real time; integrating model predictive control algorithms and a two-layer energy management control strategy in the central processing unit to achieve joint monitoring and regulation of the grid status and photovoltaic system output; configuring a system redundancy mechanism to automatically activate backup equipment or redundant paths when the main equipment fails, communication is interrupted, or data is lost; encrypting the collected data, control signals, and transmitted content, and implementing access control.

3. The photovoltaic inverter grid-connected resonance suppression method as described in claim 2, characterized in that, The execution of the dual-layer energy management control strategy includes the following operations: acquiring grid frequency, grid voltage, and load change information through grid status monitoring equipment, and using the information for grid status assessment; based on the grid status assessment results, formulating a power regulation strategy for the photovoltaic inverter, dynamically adjusting its output power to maintain grid frequency and voltage within allowable ranges; introducing a real-time feedback mechanism during the control process, immediately adjusting the output power of the photovoltaic inverter when abnormal fluctuations in grid frequency are detected, and optimizing control parameters based on feedback data; configuring redundant components, including backup power supplies, backup sensors, and backup computing units, automatically switching when the main component fails; Set up an automatic backup mechanism for critical operational data so that data can be restored from backups in the event of a failure of the main storage device.

4. The photovoltaic inverter grid-connected resonance suppression method as described in claim 3, characterized in that, The model predictive control algorithm includes the following operations: based on real-time collected grid frequency, grid voltage, load power, and photovoltaic power generation, a predictive model describing the joint dynamic behavior of the grid and the photovoltaic system is constructed; the predictive model is used to predict the grid state in the future period; the optimization problem is solved according to the prediction results to determine the optimal output power sequence of the photovoltaic inverter in the future period; and the control quantity of the current moment in the optimal output power sequence is applied to the photovoltaic inverter.

5. The photovoltaic inverter grid-connected resonance suppression method as described in claim 4, characterized in that, The operation of continuously collecting grid frequency, grid voltage, and photovoltaic power generation includes: forming current system state information, comparing the current state information with a preset reference state, and generating a deviation signal; based on the deviation signal, generating adjustment commands through a model predictive control algorithm to control the photovoltaic inverter to adjust its output power; and using a time synchronization protocol to calibrate the clocks of multiple data acquisition points to ensure the time consistency of multi-source data.

6. The photovoltaic inverter grid-connected resonance suppression method as described in claim 5, characterized in that, The control of the photovoltaic inverter to operate according to the adjustment command output by the model predictive control algorithm includes: setting a target output power according to the adjustment command output by the model predictive control algorithm to match the grid frequency fluctuations and load changes; monitoring grid frequency changes in real time and identifying potential resonant frequency components; and when a resonant frequency component is detected, actively adjusting the power output characteristics of the photovoltaic inverter near that frequency to weaken the resonant response.

7. The photovoltaic inverter grid-connected resonance suppression method as described in claim 6, characterized in that, The operational tests include: simulating grid load fluctuations and photovoltaic power generation fluctuations in a simulation platform to verify the effectiveness of the model predictive control algorithm and the two-layer energy management control strategy on grid frequency stability and resonance suppression; optimizing the prediction step size and feedback gain parameters in the model predictive control algorithm based on the test results; conducting robustness tests under extreme grid disturbance conditions to evaluate the stability performance of the system under photovoltaic power generation fluctuations or load abrupt changes, and improving the adaptability of the algorithm accordingly.

8. A photovoltaic inverter grid-connected resonance suppression system, using the method described in any one of claims 1 to 7, characterized in that, include: The intelligent monitoring unit is used to collect power grid operating parameters and photovoltaic power generation system operating parameters in real time; A dual-layer energy management and control unit includes a grid status monitoring subunit and a power regulation subunit. The grid status monitoring subunit is used to acquire grid frequency, voltage and load information, and the power regulation subunit is used to generate power regulation commands for the photovoltaic inverter based on the information. The model prediction control unit is used to establish a dynamic prediction model based on the operating parameters and generate an optimized power output command based on the prediction results. The data acquisition and feedback unit is used to continuously acquire operating data and feed it back to the model prediction and control unit to form a closed-loop regulation; the photovoltaic inverter control and execution unit is used to receive the power output command and adjust the actual output power; the system integration and testing unit is used to integrate the above units into the actual power grid environment and perform operation tests.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the photovoltaic inverter grid-connected resonance suppression method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the photovoltaic inverter grid-connected resonance suppression method according to any one of claims 1 to 7.

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