Micro-inverter remote parameter abnormality processing method and system

Abstract

The application discloses a kind of micro inverter remote parameter exception processing method and system, it is related to power grid equipment control technical field, method includes: obtaining the first running data of target micro inverter and the second running data of branch micro inverter;According to the first running data and the second running data, identify abnormal root;If the abnormal root is local power grid environment change, then according to the first running data and the second running data, identify current power grid environment change type, the current power grid environment change type includes power grid harmonic pollution aggravation or power grid impedance characteristic change;According to the current power grid environment change type, adjust the grid-connected control parameter of the target micro inverter.The application can be combined with inverter running data and power grid environment change type adjustment grid-connected control parameter, to realize parameter exception processing, improve parameter control accuracy and grid-connected stability.

Description

Technical Field

[0001] This invention relates to the field of power grid equipment control technology, and in particular to a method and system for handling remote parameter anomalies in micro inverters. Background Technology

[0002] In large-scale distributed photovoltaic power plants, microinverters are the core energy conversion devices, responsible for efficiently converting the direct current (DC) generated by solar panels into alternating current (AC) for grid connection. To ensure the stable operation of these microinverters, existing systems detect their output parameters and send commands such as remote reset and standard parameter calibration to correct abnormal parameters. However, in actual operation and maintenance, when remote reset and standard parameter calibration commands fail to effectively correct the problem, existing systems attribute the issue to hardware failure and initiate manual on-site troubleshooting. This approach not only suffers from high response delays and maintenance costs but also overlooks a crucial possibility: the anomaly may not be caused by a fault in the inverter itself, but by an unexpected change in the local grid environment. This environmental change renders the inverter's original grid-connected control parameters, set based on an ideal grid model, inapplicable, leading to unstable grid connection, low inverter parameter control accuracy, and low grid-connected stability.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose a remote parameter anomaly handling method and system for micro-inverters. This method can adjust grid-connected control parameters by combining inverter operating data and grid environment change types to achieve parameter anomaly handling, thereby improving parameter control accuracy and grid-connected stability.

[0005] On one hand, embodiments of the present invention provide a method for handling remote parameter anomalies in a micro inverter, comprising the following steps:

[0006] Acquire the first operating data of the target microinverter and the second operating data of the branch microinverter, wherein the branch microinverter is connected to the target microinverter on the same AC bus branch;

[0007] Based on the first operating data and the second operating data, the root cause of the anomaly is identified, including changes in the local power grid environment and individual equipment failures.

[0008] If the root cause of the anomaly is a change in the local power grid environment, then based on the first operating data and the second operating data, the type of change in the current power grid environment is identified, including an increase in power grid harmonic pollution or a change in power grid impedance characteristics.

[0009] Adjust the grid connection control parameters of the target microinverter according to the type of change in the current power grid environment.

[0010] On the other hand, embodiments of the present invention provide a remote parameter anomaly handling system for micro inverters, including:

[0011] The data acquisition module is used to acquire the first operating data of the target microinverter and the second operating data of the branch microinverter, wherein the branch microinverter is connected to the target microinverter on the same AC bus branch.

[0012] An anomaly root cause identification module is used to identify anomaly root causes based on the first operating data and the second operating data. The anomaly root causes include changes in the local power grid environment and individual equipment failures.

[0013] An environmental change type identification module is used to identify the current power grid environment change type based on the first operating data and the second operating data if the root cause of the anomaly is a change in the local power grid environment. The current power grid environment change type includes increased power grid harmonic pollution or changes in power grid impedance characteristics.

[0014] The grid connection control parameter adjustment module is used to adjust the grid connection control parameters of the target microinverter according to the current grid environment change type.

[0015] The embodiments of this application include at least the following beneficial effects: The embodiments of this application first obtain the first operating data of the target microinverter and the second operating data of the branch microinverter, and then identify the root cause of the anomaly based on the first operating data and the second operating data. If the root cause of the anomaly is a change in the local power grid environment, the current type of power grid environment change is identified based on the first operating data and the second operating data. Then, the grid-connected control parameters of the target microinverter are adjusted based on the current type of power grid environment change. This allows for the adjustment of grid-connected control parameters by combining inverter operating data and the type of power grid environment change, thereby achieving parameter anomaly handling and improving parameter control accuracy and grid-connected stability.

[0016] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0018] Figure 1 This is a flowchart of a remote parameter anomaly handling method for a micro inverter according to an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of a remote parameter anomaly handling system for a micro inverter according to an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0021] In related technologies, microinverters are the core energy conversion devices in large-scale distributed photovoltaic power plants, responsible for efficiently converting the direct current (DC) generated by solar panels into alternating current (AC) for grid connection. To ensure the stable operation of these microinverters, existing systems detect their output parameters and send commands such as remote reset and standard parameter calibration to correct abnormal parameters. However, in actual operation and maintenance, when remote reset and standard parameter calibration commands fail to effectively correct the problem, existing systems attribute the issue to hardware failure and initiate manual on-site troubleshooting. This approach not only has high response delays and maintenance costs but also ignores a crucial possibility: the anomaly may not be caused by a fault in the inverter itself, but by an unexpected change in the local grid environment. This environmental change renders the inverter's original grid-connected control parameters, set based on an ideal grid model, inapplicable, leading to unstable grid connection, low inverter parameter control accuracy, and low grid-connected stability.

[0022] For example, in a large rooftop distributed photovoltaic (PV) power station, hundreds of microinverters are densely deployed, each connected to one to four PV modules. Their operational data is collected by a data acquisition unit via power line carrier communication, and then uploaded to a remote monitoring center. The monitoring center's system platform continuously receives and analyzes this data, including the DC input voltage and current of each microinverter, the AC output voltage, frequency, active power, reactive power, and internal temperature. The system has preset normal operating ranges for each parameter. If any microinverter reports data exceeding these ranges, the system immediately generates an alarm message to alert maintenance personnel. In a typical maintenance process, when the monitoring center detects a slight but persistent deviation in the AC frequency output of microinverter A, exceeding the grid connection standard required by the power grid company, the system first executes a preset automated processing procedure. The first step of this procedure is to send a remote reset command to the inverter, attempting to restore its normal operating state by restarting the device.

[0023] However, in this incident, after the remote reset command was executed, although microinverter A restarted and reconnected to the grid, its output frequency fluctuated unstablely again within a short period and quickly exceeded the normal range once more. At this point, the automation process entered its second step. According to preset rules, the system sent a parameter fine-tuning command to microinverter A, attempting to forcibly correct its internal frequency reference point back to the standard frequency value of the grid. Surprisingly, this command also failed to achieve the expected effect. The output frequency of microinverter A continued to fluctuate significantly around the standard value and could not stabilize. Ultimately, to protect the equipment and grid safety, the internal protection mechanism of microinverter A was triggered, and the equipment automatically disconnected from the grid and shut down. According to the standard handling plan, the system would then generate a work order, requiring on-site maintenance personnel to go to the power station to conduct a detailed inspection and repair of microinverter A.

[0024] However, maintenance personnel retrieved the grid topology map and historical data for the area where microinverter A was located. They discovered that in the hours before microinverter A triggered its alarm, several other inverters connected to the same AC bus branch, while not exceeding the alarm threshold in terms of output frequency, also experienced more frequent and subtle fluctuations than usual. This phenomenon indicated that the problem might not lie with the hardware of microinverter A itself, but rather with a regional disturbance caused by external factors. Further investigation revealed that a new high-power equipment factory had recently commenced production in the industrial park where the rooftop photovoltaic power station was located. The large variable frequency motors on its production line injected a significant amount of harmonics and reactive power surges into the local power grid during start-up and shutdown, altering the grid characteristics and making the background "noise" of the grid more complex. This change rendered the original grid-connected control strategy of the microinverter inapplicable. The phase-locked loop (PLL) circuit inside the inverter, used to track the grid phase and frequency, has its control parameters set under the assumption of a relatively clean grid environment. When grid characteristics deteriorate, these fixed control parameters can no longer allow the inverter to quickly and accurately adapt to dynamic changes in the grid, resulting in frequency instability. Simply resetting the micro-inverter A or adjusting its frequency setpoint cannot solve the fundamental problem of its control system being incompatible with the current grid environment. Sending maintenance personnel to the site can only check whether the inverter hardware is intact, but cannot diagnose and resolve this software-level control mismatch caused by changes in the grid environment. Ultimately, the only temporary solution may be to replace the inverter, which is not only costly, but also risks other inverters in the same area developing the same problem in the future, failing to effectively address the root cause of the current grid environment.

[0025] The embodiments of this application will be explained in detail below with reference to the accompanying drawings:

[0026] Figure 1This is an optional flowchart of a remote parameter anomaly handling method for a micro inverter provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0027] Step S101: Obtain the first operating data of the target microinverter and the second operating data of the branch microinverter. The branch microinverter and the target microinverter are connected to the same AC bus branch.

[0028] Step S102: Identify the root cause of the anomaly based on the first operating data and the second operating data. The root cause of the anomaly includes changes in the local power grid environment and individual equipment failures.

[0029] Step S103: If the root cause of the anomaly is a change in the local power grid environment, then based on the first operating data and the second operating data, identify the type of change in the current power grid environment. The type of change in the current power grid environment includes increased harmonic pollution of the power grid or changes in the impedance characteristics of the power grid.

[0030] Step S104: Adjust the grid connection control parameters of the target micro-inverter according to the current grid environment change type.

[0031] Steps S101 to S104 shown in the embodiments of this application can adjust the grid connection control parameters by combining inverter operating data and grid environment change types, so as to realize parameter anomaly handling and improve parameter control accuracy and grid connection stability.

[0032] In some embodiments, steps S101-S104 can first acquire the first operating data of the target microinverter and the second operating data of the branch microinverter, which are connected to the target microinverter on the same AC bus branch. A data acquisition module can be integrated inside the microinverter to monitor and record various operating parameters in real time, and the data can be uploaded to a remote monitoring system via wired (e.g., power line carrier communication) or wireless (e.g., Wi-Fi, ZigBee) communication. Alternatively, independent sensors and data acquisition devices can be installed on the AC bus branch to centrally monitor and acquire the output parameters of the target microinverter and the branch microinverter. This data is typically stored in time-series format for subsequent analysis. It is understood that a microinverter is a device that converts direct current (DC) generated by solar panels into alternating current (AC) and feeds it into the power grid. The target microinverter refers to the specific microinverter that requires anomaly handling. The branch microinverter refers to other microinverters connected to the target microinverter on the same AC bus branch; they are typically in a similar power grid environment, and their operating data can be used as a reference. Operating data can include the DC input voltage and current of the microinverter, the AC output voltage, frequency, active power, reactive power, and internal temperature of the equipment. An AC bus branch refers to the power line that connects multiple microinverters and collects their AC output power into the power grid.

[0033] Then, based on the first and second operating data, the root cause of the anomaly is identified. The root cause includes changes in the local power grid environment and individual equipment failures. This can be achieved by analyzing the fluctuations in parameters such as frequency, voltage, and current in the first and second operating data. If the operating data of the target microinverter and the branch microinverters show similar fluctuation patterns, it may indicate that the anomaly is caused by a common external factor, namely, changes in the local power grid environment. Conversely, if only the operating data of the target microinverter shows anomalies, while the data of the branch microinverters remain normal, it is more likely to point to an individual equipment failure.

[0034] If the root cause of the anomaly is a change in the local power grid environment, the type of change in the current power grid environment is identified based on the first and second operating data. This type of change may include increased harmonic pollution or altered grid impedance characteristics. Spectral analysis of the operating data can detect the presence of abnormal harmonic components and their amplitude changes, thereby determining whether there is increased harmonic pollution. Alternatively, the phase relationship between the microinverter's output power and the grid voltage and current can be analyzed, or changes in grid impedance characteristics can be assessed by injecting test signals and analyzing the response. These analyses help the system more accurately determine the specific changes in the current power grid environment.

[0035] Then, based on the current changes in the power grid environment, the grid-connected control parameters of the target micro-inverter are adjusted. For example, if increased harmonic pollution from the power grid is detected, the harmonic suppression algorithm parameters within the micro-inverter can be adjusted to enhance its harmonic filtering capability. If changes in the power grid impedance characteristics are detected, the bandwidth and gain of the phase-locked loop (PLL), or the proportional-integral (PI) parameters of the current loop, can be adjusted to improve the grid-connected stability of the inverter in complex power grid environments. Parameter adjustments can be achieved through remote command issuance, eliminating the need for manual on-site intervention. It is understood that grid-connected control parameters are key parameters within the micro-inverter used to control its synchronization with the power grid, such as PLL parameters, current loop parameters, and voltage loop parameters.

[0036] In this embodiment, when an abnormal output parameter is detected in the target microinverter, the system first acquires its first operating data and second operating data from other microinverters on the same AC bus branch. This data contains the real-time operating status of the microinverters under the current grid environment. Subsequently, the system compares and analyzes this data to identify the root cause of the anomaly. For example, if the output frequencies of the target microinverter and the branch microinverters exhibit synchronous and similar fluctuations, this strongly suggests that the anomaly is not caused by a single device failure, but by a change in the local grid environment in which they share a common location. In this case, the root cause of the anomaly is identified as a change in the local grid environment. Once the root cause is determined to be a change in the local grid environment, the system further analyzes the first and second operating data to identify the specific type of change in the current grid environment. For example, by performing Fourier transforms on the voltage and current waveforms, the harmonic components and amplitudes in the grid can be analyzed to determine whether there is an increase in grid harmonic pollution. Alternatively, by analyzing the change patterns of active and reactive power, combined with a grid impedance model, it can be inferred whether the grid impedance characteristics have changed. This refined identification helps the system understand the specific nature of changes in the power grid environment.

[0037] Ultimately, based on the identified type of grid environment change, the system intelligently adjusts the grid-connected control parameters of the target microinverter. For example, if increased grid harmonic pollution is detected, the system may adjust the parameters of the digital filter inside the inverter to enhance its ability to suppress harmonics of specific frequencies. If a change in grid impedance characteristics is detected, the system may adjust the response speed of the phase-locked loop or the gain of the current controller to improve the inverter's stability under varying impedance environments. The entire process requires no on-site human intervention; parameter optimization and adjustment can be completed via remote commands, enabling the microinverter to adapt to the new grid environment and restore stable grid-connected operation.

[0038] Through the above technical solution, this embodiment, by acquiring and comparing the operating data of the target microinverter and the branch microinverters, can distinguish between two different root causes of anomalies: individual equipment faults and changes in the local power grid environment. If the anomaly is caused by an individual equipment fault, hardware repair or replacement may be required; however, if the anomaly is caused by changes in the local power grid environment, it can be resolved by adjusting the grid-connected control parameters. When the root cause of the anomaly is identified as a change in the local power grid environment, this embodiment can further identify the specific type of power grid environment change, such as increased harmonic pollution or changes in power grid impedance characteristics. This refined identification makes the adjustment of grid-connected control parameters more targeted. For example, for increased harmonic pollution, the harmonic suppression algorithm can be optimized; for changes in power grid impedance characteristics, the parameters of the phase-locked loop or current controller can be adjusted. This embodiment can more effectively enable microinverters to adapt to complex power grid environments and restore stable grid connection. Therefore, this embodiment, through intelligent anomaly root cause identification and power grid environment change type identification, achieves precise adjustment of microinverter grid-connected control parameters, avoids unnecessary on-site troubleshooting, significantly improves operation and maintenance efficiency, reduces operation and maintenance costs, and enhances the overall operational stability of distributed photovoltaic power stations.

[0039] In some embodiments, identifying the root cause of the anomaly based on the first running data and the second running data may include, but is not limited to, the following steps:

[0040] Calculate the first output frequency fluctuation curve based on the first operating data;

[0041] Calculate the second output frequency fluctuation curve based on the second operating data;

[0042] The fluctuation curves of the first and second output frequencies are compared to obtain the fluctuation matching degree.

[0043] If the fluctuation matching degree is greater than the preset matching degree threshold, the root cause of the anomaly is determined to be a change in the local power grid environment; otherwise, the root cause of the anomaly is determined to be an individual equipment failure.

[0044] In some embodiments, a first output frequency fluctuation curve can be calculated based on first operating data. Then, a second output frequency fluctuation curve can be calculated based on second operating data. The trajectory or characteristics of the output frequency changing over time can be extracted by performing time-domain or frequency-domain analysis on the first operating data of the target microinverter, serving as the first output frequency fluctuation curve. For example, short-time Fourier transform or wavelet transform methods can be used to analyze the frequency components and their dynamic changes, with the aim of quantifying the frequency stability or disturbance of the target microinverter within a specific time period. Similarly, the second output frequency fluctuation curve can be obtained by performing the same analysis on the second operating data of the branch microinverter, with the aim of providing a benchmark for comparison with the target microinverter.

[0045] The first and second output frequency fluctuation curves are then compared to obtain the fluctuation matching degree. Similarity or correlation indices between the two frequency fluctuation curves, such as Pearson correlation coefficient, dynamic time warping (DTW) distance, or the reciprocal of Euclidean distance, can be calculated to evaluate the consistency of frequency fluctuation behavior between the target microinverter and the branch microinverter.

[0046] If the fluctuation matching degree is greater than the preset matching degree threshold, the root cause of the anomaly is determined to be a change in the local power grid environment. Otherwise, the root cause is determined to be an individual equipment failure. The preset matching degree threshold is a pre-set value used to define the critical point of similarity between two curves. It can be set based on historical data, system design specifications, or expert experience, and its purpose is to provide a quantitative basis for judging the root cause of the anomaly. If the fluctuation matching degree is greater than the preset matching degree threshold, it indicates that the frequency fluctuation behavior of the target microinverter and the branch microinverter is highly consistent. This is usually due to their shared influence from changes in the external power grid environment, thus the root cause of the anomaly is determined to be a change in the local power grid environment. Conversely, if the fluctuation matching degree is not greater than the preset matching degree threshold, it indicates that there is a significant difference in the frequency fluctuation behavior between the two, which is more likely caused by a failure of the target microinverter itself, thus the root cause of the anomaly is determined to be an individual equipment failure.

[0047] This embodiment effectively distinguishes between anomaly root causes stemming from changes in the local power grid environment and individual device faults by introducing a comparison mechanism of the output frequency fluctuation curves of the target microinverter and the branch microinverters. When the local power grid environment changes, all microinverters connected to the same AC bus branch, including the target microinverter and the branch microinverters, are usually synchronously affected by this change, resulting in similar fluctuation patterns in their output frequencies. In this case, the calculated fluctuation matching degree will be high, exceeding the preset matching degree threshold, thus accurately indicating that the anomaly root cause is a change in the local power grid environment. Conversely, if the anomaly is caused by a device fault in the target microinverter itself (e.g., internal component damage, abnormal control algorithm, etc.), then its output frequency fluctuation pattern will differ significantly from that of a normally operating branch microinverter, resulting in a low fluctuation matching degree that fails to reach the preset matching degree threshold, thereby accurately identifying an individual device fault. This judgment mechanism based on the similarity of frequency fluctuation curves provides a clear and quantitative basis for identifying the root cause of anomalies.

[0048] To illustrate this technical solution more clearly, a specific example is used below. Assume a microinverter system includes a target microinverter B and a branch microinverter C connected to it on the same AC bus branch. When the system detects an anomaly, it first acquires the first operating data of microinverter B and the second operating data of microinverter C. Next, these two sets of data are processed to calculate the first output frequency fluctuation curve of microinverter B and the second output frequency fluctuation curve of microinverter C, respectively. For example, the output frequency can be sampled once per second, and its trend over the past minute can be recorded. Then, these two frequency fluctuation curves are compared, for example, by calculating the Pearson correlation coefficient between them as the fluctuation matching degree. If the calculated fluctuation matching degree is 0.95, and the preset matching degree threshold is 0.8, since 0.95 is greater than 0.8, the system will determine that the root cause of the anomaly is a change in the local power grid environment, such as grid voltage fluctuations or frequency drift. Conversely, if the fluctuation matching degree is 0.3, the system will determine that the root cause of the anomaly is an individual equipment fault, such as a phase-locked loop failure or power device damage inside microinverter B. In this way, the system can accurately determine the nature of the anomaly based on quantitative data analysis, providing the correct direction for subsequent fault handling.

[0049] Through the above technical solution, this embodiment can significantly improve the accuracy and reliability of anomaly root cause identification in the remote parameter anomaly handling method for microinverters. By performing a refined comparison of the output frequency fluctuation curves of the target microinverter and the branch microinverters, and introducing a judgment mechanism based on fluctuation matching degree and preset threshold, it can effectively avoid misjudging individual equipment faults as grid environment problems, or vice versa. This accurate root cause identification capability ensures the pertinence and effectiveness of subsequent grid-connected control parameter adjustments, avoiding unnecessary or erroneous parameter adjustments, thereby improving the stable operation capability and fault handling efficiency of the microinverter system.

[0050] In some embodiments, step S103, identifying the current power grid environment change type based on the first operating data and the second operating data, may include, but is not limited to, the following steps:

[0051] Step S201: Time-align the first running data and the second running data;

[0052] Step S202: Merge the time-aligned first running data and second running data to obtain merged running data;

[0053] Step S203: Verify and correct the fused operational data to obtain the target operational data;

[0054] Step S204: Identify the current power grid environment change type based on the target operating data.

[0055] In some embodiments, the first and second running data can be time-aligned. This can be achieved by synchronizing timestamps or using time interpolation algorithms to eliminate potential time delay differences during data acquisition or transmission from different microinverters, ensuring data consistency across the target microinverter and branch microinverters. The aim is to provide an accurate and synchronized data foundation for subsequent data fusion and analysis, avoiding misjudgments caused by time misalignment.

[0056] Then, the time-aligned first and second operating data are merged to obtain the merged operating data. Operating data from different microinverters, after time alignment, can be integrated. This can be achieved through simple data merging, weighted averaging, or other data fusion techniques to form a more comprehensive and representative dataset. The aim is to aggregate multi-source information, providing a more macroscopic and accurate view of the local power grid environment, thereby enhancing the ability to perceive changes in the power grid environment.

[0057] The merged operational data is then verified and corrected to obtain the target operational data. Quality checks and corrections can be performed on the merged data. This may include identifying and handling outliers, missing values, or noise in the data, for example, through statistical analysis methods (such as calculating the mode, mean, and standard deviation) and outlier detection algorithms (such as threshold-based, clustering, or machine learning methods) to identify and correct inconsistent data points. The purpose is to improve the reliability and accuracy of the data, ensuring that subsequent identification processes are based on high-quality data, thereby avoiding misjudgments caused by data quality issues. Finally, based on the target operational data, the type of change in the current power grid environment is identified.

[0058] This embodiment ensures that the operating status of different microinverters can be accurately compared and analyzed at the same point in time by aligning the first and second operating data, thereby eliminating errors introduced by data asynchrony. Subsequently, by fusing the time-aligned data, information from multiple microinverters can be integrated to form a more comprehensive and representative view of the power grid operation, which helps to more accurately capture the overall changing trends of the local power grid environment. Furthermore, verifying and correcting the fused operating data effectively removes noise, outliers, or inconsistencies, ensuring that the data used for identification is of high quality and reliability.

[0059] Through the above technical solution, this embodiment effectively addresses the limitations of inaccurate identification of power grid environment change types caused by issues such as asynchronous time distribution of multi-source data and poor data quality. Specifically, time alignment ensures data consistency, data fusion provides a more comprehensive perspective, and verification and correction guarantee data reliability. Therefore, this embodiment significantly improves the accuracy of identifying power grid environment change types, such as increased harmonic pollution or altered grid impedance characteristics, and enhances the system's robustness in handling complex power grid environments, providing a more accurate decision-making basis for adjusting the grid-connected control parameters of micro-inverters.

[0060] In some embodiments, step S203, verifying and correcting the fused operational data to obtain the target operational data, may include, but is not limited to, the following steps:

[0061] Iterate through and select each running parameter in the fused running data, and perform the following steps:

[0062] Use the currently selected operating parameters as the target operating parameters;

[0063] Obtain the sequence of numerical copies corresponding to the target running parameters. The sequence of numerical copies contains multiple numerical copies, which are used to represent the running parameter values.

[0064] If the numerical replicas in the numerical replica sequence are not completely identical, the mode of the numerical replica sequence is used as the value of the target running parameter to update the fused running data;

[0065] After the traversal is complete, the updated fused running data will be used as the target running data.

[0066] In some embodiments, each operating parameter in the fused operating data can be selected by traversing through the data. All monitoring parameters (such as voltage, current, frequency, power factor, etc.) contained in the fused operating data can be checked and processed one by one.

[0067] Then, using the currently selected operating parameter as the target operating parameter, a sequence of numerical copies corresponding to the target operating parameter is obtained. This sequence contains multiple numerical copies, which represent the operating parameter value. Multiple numerical representations can be obtained for the same target operating parameter from different data sources (e.g., the target microinverter and branch microinverters) or within a short timeframe after time alignment. These numerical copies collectively form a sequence reflecting the actual value of the operating parameter. For example, if the target microinverter and multiple branch microinverters all report the grid frequency at a certain moment, then these multiple grid frequency values ​​constitute a sequence of numerical copies of the grid frequency parameter.

[0068] If the numerical replicas in the numerical replica sequence are not completely identical, the mode of the numerical replica sequence is used as the value of the target running parameter to update the fused running data. When multiple numerical replicas obtained for the same running parameter differ, statistical methods are used for data correction to eliminate the impact of measurement errors or data transmission anomalies. The mode is the value that appears most frequently in a dataset. By selecting the mode as the final value of the running parameter, the reliability and accuracy of the data can be effectively improved, and the interference of outliers on the overall data judgment can be reduced. Updating the fused running data means replacing the corresponding parameter values ​​in the original fused data with the corrected parameter values.

[0069] After the traversal is complete, the updated fused operational data is used as the target operational data. Once all operational parameters in the fused operational data have undergone the aforementioned verification and correction process, the resulting cleaned and corrected dataset is determined as the final target operational data. This target operational data possesses higher accuracy and consistency, providing a reliable data foundation for subsequent identification of power grid environment change types.

[0070] This embodiment effectively solves the data inconsistency problem caused by measurement errors, transmission delays, or momentary equipment failures that may occur during data fusion by traversing each operational parameter in the fused operational data and performing consistency checks and corrections on its numerical replica sequence. Specifically, when multiple numerical replicas of the same operational parameter are not completely identical, the mode is used as the final value of the parameter. The principle is that the mode can reflect the most common and stable trend in the dataset, thus accurately estimating the true value of the parameter even in the presence of a small number of outliers. This mode-based verification and correction mechanism can filter out occasional inconsistent data points, ensuring the internal logical consistency and external representation accuracy of the fused data.

[0071] Through the above technical solution, this embodiment can significantly improve the quality and reliability of fused operation data. In the process of handling remote parameter anomalies in microinverters, accurate data is fundamental to identifying the root cause of the anomaly and the type of grid environment change. By rigorously verifying and correcting the fused operation data, especially by using the mode to handle inconsistent numerical copies, misjudgments caused by data errors or deviations can be effectively avoided. This ensures more accurate identification of subsequent grid environment change types, providing a more reliable decision-making basis for adjusting the microinverter's grid-connected control parameters. This not only improves the system's robustness but also reduces potential risks caused by data quality issues, thereby optimizing the operating efficiency and stability of the microinverter.

[0072] In some embodiments, step S204, identifying the type of change in the current power grid environment based on the target operating data, may include, but is not limited to, the following steps:

[0073] Extract electrical parameters, active power data, and reactive power data from the target operating data. Electrical parameters include voltage waveforms and current waveforms.

[0074] Perform a Fourier transform on the electrical parameters to obtain the harmonic components and harmonic amplitude values;

[0075] Analyze active and reactive power data to identify power change patterns;

[0076] The harmonic components, harmonic amplitudes, and power change patterns are matched with preset power grid disturbance characteristic rules to identify the current power grid environment change type.

[0077] In some embodiments, electrical parameters, active power data, and reactive power data can be extracted from the target operating data first. The target operating data is comprehensive data after time alignment, fusion, verification, and correction, containing operating status information of the target microinverter and branch microinverters. From this target operating data, key electrical parameters, active power data, and reactive power data need to be extracted. Electrical parameters specifically refer to electrical quantities reflecting grid quality and equipment operating status, such as voltage and current waveforms. These waveforms can intuitively reflect the instantaneous state of the grid and potential distortions. Active power data represents the power actually performed in the grid, while reactive power data represents the power exchanged between inductors and capacitors.

[0078] Then, a Fourier transform is performed on the electrical parameters to obtain the harmonic components and harmonic amplitudes. The Fourier transform is a mathematical method that converts a time-domain signal into a frequency-domain signal. Through this transform, complex voltage and current waveforms can be decomposed into harmonic components of different frequencies and their corresponding harmonic amplitudes. The presence of harmonic components and their magnitudes are important indicators for measuring the degree of harmonic pollution in the power grid.

[0079] The active and reactive power data are then analyzed to identify power variation patterns. These patterns reflect information such as grid load characteristics, power supply fluctuations, and grid impedance changes. For example, sustained reactive power fluctuations may indicate changes in grid impedance characteristics, while abnormal active power fluctuations may be related to load changes or power supply instability.

[0080] Finally, the harmonic components, harmonic amplitudes, and power variation patterns are matched with preset grid disturbance characteristic rules to identify the current type of grid environment change. These preset grid disturbance characteristic rules are established based on extensive historical data and expert experience, and include typical characteristic patterns corresponding to different types of grid environment changes (such as increased grid harmonic pollution or changes in grid impedance characteristics). By comparing the real-time monitored characteristics with these rules, the specific type of current grid environment change can be accurately identified.

[0081] This embodiment achieves accurate identification of current power grid environment change types through multi-dimensional and refined analysis of target operating data. Specifically, firstly, by extracting electrical parameters, active power data, and reactive power data, comprehensive raw information is provided for subsequent in-depth analysis. Secondly, Fourier transform of the electrical parameters quantifies the degree of harmonic pollution in the power grid, a key step in identifying the aggravation of harmonic pollution. Simultaneously, analysis of active and reactive power data reveals the power characteristics and load behavior of the power grid, thus helping to determine whether the grid impedance characteristics have changed. Finally, these processed and analyzed features are matched with preset power grid disturbance characteristic rules. Utilizing the knowledge and experience of the rule base, complex operating data can be mapped to specific types of power grid environment changes, thereby achieving accurate diagnosis of the root causes of anomalies.

[0082] Through the above technical solution, this embodiment can accurately identify the types of changes in the power grid environment in which the microinverter operates. This embodiment significantly improves the accuracy and reliability of identification by comprehensively analyzing the harmonic characteristics of electrical parameters, and the changing patterns of active and reactive power, and matching them with preset power grid disturbance characteristic rules. This multi-dimensional data analysis and rule matching mechanism enables the system to more accurately distinguish between the two different root causes of anomalies: increased power grid harmonic pollution and changes in power grid impedance characteristics. This provides a solid foundation for subsequent targeted adjustments to the grid-connected control parameters of the microinverter, thereby effectively improving the stability and adaptability of the microinverter in complex power grid environments.

[0083] In some embodiments, step S104, adjusting the grid-connected control parameters of the target micro-inverter according to the current type of grid environment change, may include, but is not limited to, the following steps:

[0084] Acquire historical remote intervention operation information, historical operation response data, and historical grid environment change types of the target microinverter. The historical remote intervention operation information includes remote reset command information and parameter adjustment command information. The remote reset command information includes the first issuance time and the first execution result. The parameter adjustment command information includes the second issuance time, the second execution result, and the adjustment parameter value.

[0085] Perform statistical analysis on historical operational response data and calculate statistical parameters, including mean, standard deviation, and fluctuation range.

[0086] Based on historical remote intervention operation information, statistical parameters, and historical power grid environment change types, the effectiveness of parameter adjustment is evaluated, and the effectiveness evaluation results are obtained.

[0087] Based on the effectiveness assessment results, the current type of changes in the power grid environment, and the initial operating data, adjust the grid connection control parameters of the target microinverter.

[0088] In some embodiments, if the effects of historical adjustments and the long-term operational response of the system are not fully considered, using only a single, immediate adjustment method may lead to blind parameter adjustments, making it difficult to guarantee the optimality or lasting effectiveness of the adjustment scheme. It may even affect the stable operation and grid-connected performance of the microinverter due to repeated trial and error. If these problems are not addressed, the efficiency and accuracy of adjusting the grid-connected control parameters of the microinverter will be limited when facing complex and ever-changing grid environments, making it difficult to achieve optimal operating conditions.

[0089] To this end, we can first obtain historical remote intervention operation information, historical operational response data, and historical grid environment change types of the target microinverter. Historical remote intervention operation information refers to all remote operation records performed on the target microinverter, aiming to record the type, time, and result of each intervention for subsequent evaluation of the effectiveness of these intervention measures. Historical remote intervention operation information includes remote reset command information and parameter adjustment command information. Remote reset command information includes the first issuance time and the first execution result; parameter adjustment command information includes the second issuance time, the second execution result, and the adjusted parameter value. This information provides the basic data for evaluating historical adjustment strategies. Historical operational response data refers to the changes in the target microinverter's operating status and performance indicators after historical remote intervention operations, such as real-time or periodic data of parameters like output power, voltage, current, and frequency.

[0090] Then, statistical analysis is performed on the historical operational response data to calculate statistical parameters, aiming to quantify the performance of the microinverter under different interventions. Statistical parameters include the mean, standard deviation, and fluctuation range, which effectively reflect the stability and response characteristics of the system operation. The mean indicates the average performance level of the system under specific conditions, the standard deviation measures the degree of data dispersion and reflects the stability of system operation, and the fluctuation range reveals the maximum and minimum performance changes of the system within a specific time period.

[0091] Then, based on historical remote intervention operation information, statistical parameters, and historical grid environment change types, an effectiveness assessment of the parameter adjustments is conducted to obtain the effectiveness assessment results. The success or failure of past parameter adjustment strategies and their impact on the micro-inverter's operating performance can be comprehensively judged based on historical remote intervention operation information, statistical parameters, and historical grid environment change types. For example, if, after a parameter adjustment, under a specific grid environment change type, the fluctuation range of historical operating response data significantly decreases and the mean tends to stabilize, then the adjustment can be considered effective. The effectiveness assessment results will guide subsequent parameter adjustment decisions, avoiding repeated ineffective adjustments.

[0092] Finally, based on the effectiveness assessment results, the current type of grid environment change, and the initial operating data, the grid-connected control parameters of the target microinverter are adjusted. This can be achieved by comprehensively considering the effectiveness assessment results, the current type of grid environment change, and the initial operating data, prioritizing adjustment strategies proven effective in similar grid environments, and then combining these with the target microinverter's current real-time operating data for refined adjustments to ensure the relevance and timeliness of the adjustment plan.

[0093] This embodiment overcomes the potential blindness and inefficiency of parameter adjustments by introducing historical remote intervention operation information, historical operational response data, and historical grid environment change types, and performing statistical analysis and effectiveness evaluation on these data. Specifically, upon identifying the current grid environment change type, the system first analyzes past remote intervention operations performed under similar grid environments and their corresponding micro-inverter operational responses. By performing statistical analysis on historical operational response data, such as calculating the mean, standard deviation, and fluctuation range, the actual effectiveness of past parameter adjustments can be quantitatively evaluated. For example, if the output power fluctuation range of the micro-inverter significantly decreases after a parameter adjustment, it indicates that the adjustment is effective. Based on this historical data and statistical analysis, the system can evaluate the effectiveness of parameter adjustments, thereby identifying which parameter adjustment strategies are effective and which are ineffective under a specific grid environment. Therefore, when facing new grid environment changes, the system can prioritize those proven effective adjustment strategies and fine-tune them in conjunction with the target micro-inverter's current first operational data, thereby achieving more precise, efficient, and predictive grid-connected control parameter adjustments. This adjustment mechanism, based on historical experience and performance evaluation, enables microinverters to adapt more intelligently to grid changes, avoiding repeated trial and error and unnecessary system disturbances.

[0094] To illustrate this technical solution more clearly, a specific example is used below. Suppose that a target microinverter has repeatedly encountered grid environment changes characterized by "intensified grid harmonic pollution" over a period of time. During these historical events, the system attempted various grid-connected control parameter adjustment schemes, such as adjusting the phase-locked loop bandwidth and changing the proportional-integral parameters of the current loop. After each adjustment, the system records the corresponding parameter adjustment command information (including the adjusted parameter values, issuance time, and execution result) and the target microinverter's operational response data after the adjustment (e.g., output current harmonic content, output power fluctuations, etc.).

[0095] Specifically, when the system identifies "intensified grid harmonic pollution" as the current grid environment change type again, it first acquires and analyzes historical remote intervention operation information, historical operation response data, and historical grid environment change types. For example, the system might find that in the past three "intensified grid harmonic pollution" events, after the first adjustment scheme (e.g., adjusting the phase-locked loop bandwidth from XHz to YHz), the output current harmonic content decreased significantly over a period of time, and the fluctuation range decreased, indicating that the adjustment was effective. After the second adjustment scheme (e.g., changing the proportional-integral parameters of the current loop), the harmonic content did not change significantly, or even worsened, indicating that the adjustment was ineffective. The third adjustment scheme was similar to the first and also achieved good results.

[0096] Based on this historical data, the system will perform statistical analysis on the historical operational response data. For example, for the first and third adjustments, the mean, standard deviation, and fluctuation range of the output current harmonic content after the adjustments are calculated, revealing that the mean is low, and the standard deviation and fluctuation range are small. However, for the second adjustment, the statistical parameters may show a higher mean and larger fluctuations. Therefore, the system evaluates the effectiveness of parameter adjustments and concludes that the strategy of adjusting the phase-locked loop bandwidth from XHz to YHz is effective in an environment of "intensified grid harmonic pollution," while the strategy of changing the proportional-integral parameters of the current loop is ineffective.

[0097] Ultimately, based on the effectiveness assessment results, the current type of grid environment change (increased grid harmonic pollution), and the target microinverter's initial operating data, the system will prioritize and execute the adjustment strategy assessed as effective, namely, adjusting the target microinverter's phase-locked loop bandwidth to YHz. Simultaneously, considering the initial operating data, the system may fine-tune the YHz setting to adapt to the specific operating conditions, thereby achieving precise and efficient grid-connected control parameter adjustment.

[0098] Through the above technical solution, this embodiment can significantly improve the intelligence and effectiveness of microinverter grid-connected control parameter adjustment. By fully utilizing historical remote intervention operation information, historical operation response data, and historical grid environment change types, and conducting rigorous statistical analysis and effectiveness evaluation, the system can learn from past experience, avoiding repeated ineffective adjustments, thereby significantly reducing the trial-and-error costs and time of parameter adjustment. Therefore, when facing complex grid environment changes, the microinverter can converge to the optimal grid-connected control parameters more quickly and accurately, effectively improving its grid-connected stability, power generation efficiency, and adaptability to grid disturbances. This adjustment mechanism based on historical data and effect evaluation enables the microinverter to achieve more robust and adaptive operation, thereby extending equipment life, reducing operation and maintenance costs, and improving the overall grid reliability.

[0099] In some embodiments, after identifying the root cause of the anomaly based on the first running data and the second running data, the method may further include, but is not limited to, the following steps:

[0100] Step S301: Obtain the current grid frequency, current grid current, DC bus voltage data, and the internal parameters of the target micro-inverter, including the phase-locked loop estimated frequency, output current, and internal protection mechanism trigger count.

[0101] Step S302: Calculate the internal state information of the target micro-inverter based on the current grid frequency, current grid current, DC bus voltage data, phase-locked loop estimated frequency, output current, and internal protection mechanism trigger count;

[0102] Step S303: Compare the internal state information of the target micro-inverter with the corresponding internal state information of the branch micro-inverter to identify state differences.

[0103] Step S304: Update the root cause of the exception based on the state differences.

[0104] In some embodiments, the current grid frequency, current grid current, DC bus voltage data, and internal parameters of the target microinverter can be acquired first. The purpose is to collect deeper operational information to assess the internal health of the microinverter. Internal parameters refer to key indicators of the microinverter's internal control system and protection mechanisms, including the phase-locked loop (PLL) estimated frequency, output current, and internal protection mechanism trigger count. The PLL estimated frequency reflects the inverter's accuracy and stability in tracking the grid frequency; the output current directly reflects the power injected into the grid by the inverter; and the internal protection mechanism trigger count indicates whether the device frequently encounters internal faults or abnormal operating conditions.

[0105] Then, based on the current grid frequency, current grid current, DC bus voltage data, PLL estimated frequency, output current, and internal protection mechanism trigger counts, the internal state information of the target microinverter is calculated. This internal state information is a comprehensive description of the microinverter's current operating status, going beyond externally observable grid parameters to reflect the actual operating state of the inverter's internal control, power conversion, and protection mechanisms. For example, through combined analysis of these parameters, the inverter's frequency tracking performance, current output stability, DC bus voltage fluctuations, and the frequency and type of internal protection actions can be evaluated.

[0106] Next, the internal state information of the target microinverter is compared with the corresponding internal state information of the branch microinverters to identify state differences. The internal state information of the branch microinverters, as other normally operating devices on the same AC bus branch, can be used as a benchmark. Through comparison, it can be discovered whether the target microinverter exhibits significantly different internal operating characteristics from normal equipment. These differences are key evidence for distinguishing between individual device faults and changes in the local power grid environment.

[0107] Finally, the root cause of the anomaly is updated based on the state differences. This means that even if the initial external data analysis points to a certain root cause of the anomaly, if the internal state comparison results show internal differences that do not match the root cause, the root cause of the anomaly can be corrected, thereby obtaining a more accurate diagnostic result.

[0108] This embodiment effectively overcomes the limitations of relying solely on external operating data to identify the root cause of anomalies by introducing in-depth analysis and comparison of the internal state information of the microinverter. In the basic scheme, the identification of the root cause of anomalies is mainly based on the matching degree of the output frequency fluctuation curves of the target microinverter and the branch microinverters. However, in some complex scenarios, such as grid disturbances, all microinverters connected to the same branch may exhibit similar external frequency fluctuations. In this case, it is difficult to accurately determine whether the problem is a grid issue or a fault in a specific device based solely on external data. By acquiring the current grid frequency, current grid current, DC bus voltage data, and internal parameters of the target microinverter such as the phase-locked loop estimated frequency, output current, and internal protection mechanism trigger count, a more comprehensive internal state information of the target microinverter can be constructed. These internal parameters can directly reflect the actual working status of the inverter's internal control loop, power conversion circuit, and protection logic. For example, abnormal fluctuations in the phase-locked loop estimated frequency may indicate a decline in its tracking performance, while a high-frequency increase in the internal protection mechanism trigger count may suggest a potential fault in internal components. Furthermore, comparing the internal state information of the target microinverter with that of other microinverters on the same branch can effectively identify unique state differences of the target device. If the internal state information of the target microinverter differs significantly from that of the branch microinverters, even with similar external frequency fluctuations, it is more likely to indicate an individual device fault in the target device. Conversely, if the internal state information is highly consistent, it is more likely to confirm that the root cause of the anomaly is a change in the local power grid environment. Thus, this embodiment can perform secondary verification and correction of the root cause of the anomaly from an internal perspective, ensuring the accuracy of the diagnostic results.

[0109] To illustrate this technical solution more clearly, a specific example is used below. Assume that in the initial identification of the anomaly's root cause, based on a comparison of the output frequency fluctuation curves of the target microinverter and the branch microinverters, the fluctuation matching degree is high, initially indicating that the root cause of the anomaly is a change in the local power grid environment. However, to further verify and correct this judgment, the system acquires the target microinverter's current grid frequency, current grid current, DC bus voltage data, and its internal parameters, such as the phase-locked loop (PLL) estimated frequency, output current, and internal protection mechanism trigger count. Simultaneously, it also acquires the corresponding internal parameters of other branch microinverters on the same branch. By calculating these data, the internal state information of the target microinverter is obtained, such as its PLL frequency tracking deviation, output current stability index, and internal protection action frequency. Subsequently, this internal state information is compared with the internal state information of the branch microinverters. For example, if the comparison results show that the target microinverter's internal protection mechanism trigger count is significantly higher than that of the branch microinverter, and its phase-locked loop estimated frequency has a persistently large deviation, while the branch microinverter performs normally, then even if the external frequency fluctuations are similar, the system will update the root cause of the anomaly from "local grid environment change" to "individual equipment fault" based on these internal state differences. Therefore, the remote processing system can perform a more in-depth diagnosis of the target microinverter or take specific parameter adjustments, rather than making grid environment-related adjustments to the entire branch, thus achieving more accurate and efficient fault handling.

[0110] Through the above technical solution, this embodiment can significantly improve the accuracy of anomaly root cause identification in remote parameter anomaly handling of microinverters. This embodiment introduces in-depth analysis and comparison of internal state information, enabling the system to more precisely distinguish between local grid environment changes and individual equipment faults. This more accurate identification capability effectively avoids invalid or inappropriate remote intervention operations caused by misjudgment, thereby reducing operation and maintenance costs and improving fault handling efficiency. Furthermore, continuous monitoring and analysis of internal parameters helps to detect potential equipment faults in advance, further enhancing the overall reliability and operational stability of the microinverter system.

[0111] In some embodiments, in step S302, calculating the internal state information of the target micro-inverter based on the current grid frequency, current grid current, DC bus voltage data, phase-locked loop estimated frequency, output current, and internal protection mechanism trigger count may include, but is not limited to, the following steps:

[0112] Calculate the frequency deviation based on the current power grid frequency and the frequency estimated by the phase-locked loop;

[0113] Calculate the rate of change of the output current based on the output current and the current grid current;

[0114] Calculate the voltage fluctuation amplitude based on the DC bus voltage data;

[0115] By integrating frequency deviation, output current change rate, voltage fluctuation amplitude, and internal protection mechanism trigger count, the internal state information of the target micro-inverter is obtained.

[0116] In some embodiments, the frequency deviation can be calculated first based on the current grid frequency and the frequency estimated by the phase-locked loop (PLL). The frequency deviation refers to the difference between the frequency estimated by the PLL inside the target microinverter and the actual grid frequency. This difference can reflect the microinverter's ability to track changes in grid frequency or the frequency stability of the grid itself. For example, the frequency deviation can be obtained by subtracting the current grid frequency from the PLL-estimated frequency.

[0117] Then, based on the output current and the current grid current, the output current change rate is calculated. The output current change rate refers to the rate or magnitude of change of the target microinverter's output current relative to the current grid current. This parameter indicates the current response characteristics of the microinverter during grid-connected operation, such as whether overcurrent, undercurrent, or abnormal fluctuations exist. For example, the output current can be divided by the current grid current to obtain the output current change rate.

[0118] Next, based on the DC bus voltage data, the voltage fluctuation amplitude is calculated. Specifically, the voltage fluctuation amplitude refers to the difference between the maximum and minimum values ​​of the DC bus voltage data within a certain time range, or its deviation from the average value. This amplitude reflects the stability of the DC power supply and the inverter's ability to suppress DC voltage fluctuations. For example, the voltage fluctuation amplitude can be obtained by subtracting the maximum and minimum values ​​from the DC bus voltage data.

[0119] Finally, the frequency deviation, output current change rate, voltage fluctuation amplitude, and internal protection mechanism trigger count are integrated to obtain the internal state information of the target microinverter. This can be achieved by constructing parameter sequences to form a comprehensive internal state information that accurately reflects the internal operating status of the target microinverter. The internal protection mechanism trigger count directly reflects the device's self-protection behavior under abnormal conditions and is an important indicator for assessing the device's health status.

[0120] This embodiment calculates the internal state information of the target microinverter by refining it into multiple dimensions, including frequency deviation, output current change rate, voltage fluctuation amplitude, and internal protection mechanism trigger count. This allows for a more comprehensive and accurate assessment of the microinverter's operating status. Calculating the frequency deviation helps determine the accuracy of the microinverter's synchronization with the grid; calculating the output current change rate reveals the stability of the inverter's output current and its matching degree with the grid current; calculating the voltage fluctuation amplitude reflects the stability of the DC-side power supply and the inverter's ability to handle DC voltage fluctuations; and the internal protection mechanism trigger count directly provides a record of the device's self-protection behavior under abnormal conditions. By independently calculating and ultimately integrating these key parameters, a multi-dimensional internal state profile can be formed, providing more reliable and detailed data support for subsequent identification of state differences and updating the root causes of anomalies.

[0121] Through the above technical solution, this embodiment decomposes the internal state information of the target microinverter into multiple specific, quantifiable indicators for calculation, significantly improving the precision and accuracy of internal state assessment. This multi-dimensional data acquisition and analysis method enhances the ability to identify potential problems within the microinverter, such as distinguishing between internal state anomalies caused by frequency synchronization issues, abnormal current output, or DC-side voltage fluctuations. Therefore, when subsequently comparing the internal state information of the target microinverter with the corresponding internal state information of the branch microinverters, state differences can be identified more accurately, providing a more targeted basis for updating the root cause of anomalies. This avoids misjudgments or omissions caused by insufficient assessment of a single indicator, improving the efficiency and reliability of remote parameter anomaly handling.

[0122] In some embodiments, after identifying the type of change in the current power grid environment based on the first operating data and the second operating data, the method may further include, but is not limited to, the following steps:

[0123] The monitoring shows the first trend of frequency deviation, the second trend of output current change rate, the third trend of voltage fluctuation amplitude, and the fourth trend of internal protection mechanism trigger count.

[0124] If both the first and second trends show an increase, then update the current power grid environment change type to increased power grid harmonic pollution; otherwise, assess the third and fourth trends.

[0125] If both the third and fourth trends show an increase, then the current power grid environment change type is updated to a change in power grid impedance characteristics.

[0126] In some embodiments, because changes in the power grid environment are often dynamic and complex, operational data or internal state information at a single point in time may not fully capture the evolution trend of grid disturbances, potentially leading to untimely or inaccurate identification of the type of grid environment change. If this problem is not addressed, it may affect the accuracy and effectiveness of adjusting the microinverter's grid-connected control parameters, thereby reducing system stability and power generation efficiency.

[0127] To this end, we can first monitor the first trend of frequency deviation, the second trend of output current change rate, the third trend of voltage fluctuation amplitude, and the fourth trend of internal protection mechanism trigger count. The first trend of frequency deviation refers to the direction of continuous or periodic change in the deviation between the target microinverter's phase-locked loop estimated frequency and the current grid frequency over a period of time, such as continuously increasing, decreasing, or remaining stable. The second trend of output current change rate refers to the direction of evolution of the rate of change between the target microinverter's output current and the current grid current over a period of time. The third trend of voltage fluctuation amplitude refers to the direction of change of the voltage fluctuation amplitude reflected by the DC bus voltage data over a period of time. The fourth trend of internal protection mechanism trigger count refers to the direction of change of the number of times the target microinverter's internal protection mechanism is triggered over a period of time. These trends can be monitored and evaluated by methods such as moving average, differential calculation, or trend fitting of historical data.

[0128] If both the first and second trends show an increase, it indicates a continuously increasing harmonic component in the power grid, posing a greater challenge to the inverter in tracking the grid frequency and output current. In this case, the current power grid environment change type can be updated to "intensified grid harmonic pollution." Otherwise, assess the third and fourth trends. If both the third and fourth trends show an increase, it indicates a significant change in the grid impedance characteristics, leading to decreased voltage stability and potentially frequent triggering of the inverter's internal protection mechanisms. In this case, the current power grid environment change type can be updated to "change in grid impedance characteristics."

[0129] To illustrate this technical solution more clearly, a specific example is used below. Suppose that a target microinverter, in its initial stage, is identified as experiencing a localized change in the power grid environment based on first and second operating data, initially judged to be an increase in grid harmonic pollution. However, during subsequent operation, the system continuously monitors a sustained increase in the first trend of frequency deviation, while the second trend of output current change rate also shows a significant increase. For example, over several minutes, the frequency deviation gradually rises from 0.1Hz to 0.3Hz, while the root mean square value of the output current change rate increases from 5% to 15%. Based on this continuous and synchronous increasing trend, the system further confirms and updates the current power grid environment change type to an increase in grid harmonic pollution, rather than merely a temporary disturbance.

[0130] For example, in another scenario, after initial identification of the target microinverter, the system detects a continuously increasing third trend in voltage fluctuation amplitude, along with a significant increase in the fourth trend in the internal protection mechanism trigger count—for instance, the number of protection mechanism triggers increasing from once per hour to five per hour within a short period. This phenomenon indicates that the grid impedance characteristics may be deteriorating, leading to a continuous decrease in voltage stability and frequent triggering of the inverter's overvoltage or undervoltage protection. In this case, the system updates the current grid environment change type to a change in grid impedance characteristics. In this way, this embodiment can real-time correct and confirm the grid environment change type based on the dynamic evolution of grid disturbances, thereby providing more accurate and timely decision support for adjusting the grid-connected control parameters of the microinverter.

[0131] Through the above technical solution, this embodiment enables more accurate and dynamic identification of the types of changes in the power grid environment in which the microinverter operates. This embodiment introduces monitoring of the changing trends of internal state parameters, allowing the system to capture the evolution of power grid disturbances and potential deterioration trends, thereby avoiding parameter adjustment errors caused by information lag or inaccurate judgment. This dynamic update mechanism significantly improves the adaptability of the microinverter to complex and variable power grid environments, providing a more reliable basis for the precise adjustment of subsequent grid-connected control parameters, and thus effectively ensuring the stable operation and efficient power generation of the microinverter under various power grid conditions.

[0132] The beneficial effects of implementing the embodiments of the present invention include: the embodiments of this application first obtain the first operating data of the target microinverter and the second operating data of the branch microinverter, and then identify the root cause of the anomaly based on the first operating data and the second operating data. If the root cause of the anomaly is a change in the local power grid environment, the current type of power grid environment change is identified based on the first operating data and the second operating data. Then, the grid-connected control parameters of the target microinverter are adjusted based on the current type of power grid environment change. This allows for the adjustment of grid-connected control parameters by combining inverter operating data and the type of power grid environment change, thereby achieving parameter anomaly handling and improving parameter control accuracy and grid-connected stability.

[0133] like Figure 2 As shown, this embodiment of the invention also provides a remote parameter anomaly handling system for micro-inverters, comprising:

[0134] The data acquisition module 401 is used to acquire the first operating data of the target microinverter and the second operating data of the branch microinverter, wherein the branch microinverter and the target microinverter are connected to the same AC bus branch.

[0135] The anomaly root cause identification module 402 is used to identify the anomaly root cause based on the first operating data and the second operating data. The anomaly root cause includes changes in the local power grid environment and individual equipment failures.

[0136] The environmental change type identification module 403 is used to identify the current power grid environment change type based on the first operating data and the second operating data if the root cause of the anomaly is a change in the local power grid environment. The current power grid environment change type includes increased power grid harmonic pollution or changes in power grid impedance characteristics.

[0137] The grid connection control parameter adjustment module 404 is used to adjust the grid connection control parameters of the target micro-inverter according to the current grid environment change type.

[0138] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0139] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A method for handling remote parameter anomalies in a micro inverter, characterized in that, Includes the following steps: Acquire the first operating data of the target microinverter and the second operating data of the branch microinverter, wherein the branch microinverter is connected to the target microinverter on the same AC bus branch; Based on the first operating data and the second operating data, the root cause of the anomaly is identified, including changes in the local power grid environment and individual equipment failures. If the root cause of the anomaly is a change in the local power grid environment, then based on the first operating data and the second operating data, the type of change in the current power grid environment is identified, including an increase in power grid harmonic pollution or a change in power grid impedance characteristics. Adjust the grid connection control parameters of the target microinverter according to the current type of power grid environment change; The step of adjusting the grid-connected control parameters of the target micro-inverter according to the current grid environment change type includes: The historical remote intervention operation information, historical operation response data, and historical grid environment change types of the target microinverter are obtained. The historical remote intervention operation information includes remote reset command information and parameter adjustment command information. The remote reset command information includes a first issuance time and a first execution result. The parameter adjustment command information includes a second issuance time, a second execution result, and an adjustment parameter value. Statistical analysis is performed on the historical operational response data to calculate statistical parameters, including the mean, standard deviation, and fluctuation range. Based on the historical remote intervention operation information, the statistical parameters, and the historical power grid environment change types, an effectiveness assessment of parameter adjustment is conducted to obtain the effectiveness assessment results; Based on the effectiveness assessment results, the current grid environment change type, and the first operating data, adjust the grid connection control parameters of the target microinverter.

2. The method according to claim 1, characterized in that, The step of identifying the root cause of the anomaly based on the first operating data and the second operating data includes: Calculate the first output frequency fluctuation curve based on the first operating data; Calculate the second output frequency fluctuation curve based on the second operating data; The first output frequency fluctuation curve and the second output frequency fluctuation curve are compared to obtain the fluctuation matching degree; If the fluctuation matching degree is greater than the preset matching degree threshold, then the root cause of the anomaly is determined to be a change in the local power grid environment; Otherwise, the root cause of the anomaly is determined to be an individual equipment malfunction.

3. The method according to claim 1, characterized in that, The step of identifying the current power grid environment change type based on the first operating data and the second operating data includes: Time alignment is performed on the first running data and the second running data; The time-aligned first and second running data are merged to obtain merged running data; The fused operational data is verified and corrected to obtain the target operational data; Based on the target operating data, identify the type of change in the current power grid environment.

4. The method according to claim 3, characterized in that, The process of verifying and correcting the fused operational data to obtain the target operational data includes: Iterate through and select each running parameter in the fused running data, and perform the following steps: Use the currently selected operating parameters as the target operating parameters; Obtain a sequence of numerical copies corresponding to the target operating parameters, wherein the sequence of numerical copies contains multiple numerical copies, and the numerical copies are used to represent the operating parameter values; If the numerical replicas in the numerical replica sequence are not completely identical, the mode of the numerical replica sequence is used as the value of the target running parameter to update the fused running data; After the traversal is complete, the updated fusion running data is used as the target running data.

5. The method according to claim 3, characterized in that, The step of identifying the type of change in the current power grid environment based on the target operating data includes: Electrical parameters, active power data, and reactive power data are extracted from the target operating data. The electrical parameters include voltage waveforms and current waveforms. Perform a Fourier transform on the electrical parameters to obtain the harmonic components and harmonic amplitude values; The active power data and the reactive power data are analyzed to identify power change patterns. The harmonic components, harmonic amplitudes, and power change patterns are matched with preset power grid disturbance characteristic rules to identify the current power grid environment change type.

6. The method according to claim 1, characterized in that, After identifying the root cause of the anomaly based on the first running data and the second running data, the method further includes: Acquire the current grid frequency, current grid current, DC bus voltage data, and the internal parameters of the target microinverter, including the phase-locked loop estimated frequency, output current, and internal protection mechanism trigger count; The internal state information of the target microinverter is calculated based on the current grid frequency, the current grid current, the DC bus voltage data, the estimated frequency of the phase-locked loop, the output current, and the trigger count of the internal protection mechanism. The internal state information of the target microinverter is compared with the internal state information of the corresponding branch microinverter to identify state differences; Update the root cause of the anomaly based on the state differences.

7. The method according to claim 6, characterized in that, The calculation of the internal state information of the target microinverter based on the current grid frequency, the current grid current, the DC bus voltage data, the estimated frequency of the phase-locked loop, the output current, and the trigger count of the internal protection mechanism includes: The frequency deviation is calculated based on the current power grid frequency and the frequency estimated by the phase-locked loop. Calculate the rate of change of the output current based on the output current and the current grid current; Calculate the voltage fluctuation amplitude based on the DC bus voltage data; By integrating the frequency deviation, the output current change rate, the voltage fluctuation amplitude, and the internal protection mechanism trigger count, the internal state information of the target micro-inverter is obtained.

8. The method according to claim 7, characterized in that, After identifying the type of change in the current power grid environment based on the first operating data and the second operating data, the method further includes: The monitoring includes the first trend of the frequency deviation, the second trend of the output current change rate, the third trend of the voltage fluctuation amplitude, and the fourth trend of the internal protection mechanism trigger count. If both the first and second trends show an increase, then the current power grid environment change type is updated to increased power grid harmonic pollution; otherwise, the third and fourth trends are assessed. If the third trend of change is increasing and the fourth trend of change is increasing, then the current power grid environment change type is updated to change in power grid impedance characteristics.

9. A remote parameter anomaly handling system for a micro inverter, characterized in that, include: The data acquisition module is used to acquire the first operating data of the target microinverter and the second operating data of the branch microinverter, wherein the branch microinverter is connected to the target microinverter on the same AC bus branch. An anomaly root cause identification module is used to identify anomaly root causes based on the first operating data and the second operating data. The anomaly root causes include changes in the local power grid environment and individual equipment failures. An environmental change type identification module is used to identify the current power grid environment change type based on the first operating data and the second operating data if the root cause of the anomaly is a change in the local power grid environment. The current power grid environment change type includes increased power grid harmonic pollution or changes in power grid impedance characteristics. The grid connection control parameter adjustment module is used to adjust the grid connection control parameters of the target micro-inverter according to the current grid environment change type. The step of adjusting the grid-connected control parameters of the target micro-inverter according to the current grid environment change type includes: The historical remote intervention operation information, historical operation response data, and historical grid environment change types of the target microinverter are obtained. The historical remote intervention operation information includes remote reset command information and parameter adjustment command information. The remote reset command information includes a first issuance time and a first execution result. The parameter adjustment command information includes a second issuance time, a second execution result, and an adjustment parameter value. Statistical analysis is performed on the historical operational response data to calculate statistical parameters, including the mean, standard deviation, and fluctuation range. Based on the historical remote intervention operation information, the statistical parameters, and the historical power grid environment change types, an effectiveness assessment of parameter adjustment is conducted to obtain the effectiveness assessment results; Based on the effectiveness assessment results, the current grid environment change type, and the first operating data, adjust the grid connection control parameters of the target microinverter.

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