Current-limiting fault ride-through method and system for network construction type converter and new energy unit

Abstract

The invention provides a current-limiting fault ride-through method and system for a network construction type converter and a new energy unit, and belongs to the technical field of converters and new energy units. The method comprises the steps of collecting multi-source heterogeneous data, and performing data preprocessing to obtain standardized data; coupling analysis is carried out on the fault risk of the new energy and the power grid and the safety redundancy capability of the device, and pre-adaptive parameters are initialized and set; continuously monitoring the state of the power grid, and judging and triggering fault occurrence; constructing a multi-parameter coupling analysis model, calculating an optimal current limiting target and adjusting core current limiting parameters; continuously monitoring a fault recovery state by a control core of the network-building type converter, and switching an operation mode of the network-building type converter; and constructing a multi-scene correlation analysis model, carrying out model and parameter optimization calculation, and updating the parameters after application optimization. The fault response time is shortened, different fault scenes are matched, the service life of a power device is prolonged, the whole life cycle of the fault ride-through capability is improved, and diversified operation requirements of a new energy power grid are met.

Description

Technical Field

[0001] This invention relates to the field of converter and new energy unit technology, and specifically to a current-limiting fault ride-through method and system for grid-connected converters and new energy units. Background Technology

[0002] With the large-scale grid connection of new energy units, the fault-limiting fault ride-through capability of grid-connected converters under grid fault conditions has become crucial for ensuring grid stability. Existing technologies revolve around "passive response after a fault," with mainstream methods including hardware current limiting and control strategy regulation. Hardware current limiting restricts fault current through devices such as series reactors and fuses, but suffers from high losses, slow response, and difficulty in adapting to different fault scenarios. Control strategy regulation, centered on dual-loop voltage and current control and model predictive control, achieves current limiting by dynamically adjusting the converter's internal potential or current limit. Some solutions incorporate grid condition monitoring data to improve adaptability.

[0003] In existing technologies, grid-connected converters and new energy units have poor fault response lag and poor adaptability to current limiting parameters, making it difficult to balance current limiting safety and grid support capabilities. Moreover, excessive thermal stress accumulation in power devices can easily lead to shortened device lifespan or damage during fault ride-through. In addition, the fault ride-through capability of converters and new energy units lacks a continuous optimization mechanism and has weak adaptability to multiple scenarios. Summary of the Invention

[0004] The purpose of this invention is to provide a current-limiting fault ride-through method and system for grid-connected converters and new energy units, in order to solve the technical problems of fault response lag, poor adaptability of current-limiting parameters, excessive thermal stress accumulation of power devices, and lack of continuous optimization mechanism for fault ride-through capability in the prior art.

[0005] To achieve the above objectives, this invention provides a current-limiting fault ride-through method for grid-connected converters and new energy units. The method includes: collecting multi-source heterogeneous data using data acquisition equipment deployed on the grid-connected converter and the new energy unit, and performing data preprocessing to obtain standardized data; based on the standardized data, performing a coupled analysis of the fault risk of new energy and the power grid, and the safety redundancy capability of devices, and initializing and setting pre-adaptive parameters to put the grid-connected converter into a standby state; using the control core of the grid-connected converter to continuously monitor the power grid status, determine and trigger fault occurrence, and strengthen the collection of fault characteristic data and thermal stress data based on the monitoring results; based on the fault characteristic data and thermal stress data, constructing a multi-parameter coupled analysis model, calculating the optimal current-limiting target and adjusting the core current-limiting parameters, and issuing and executing the adjusted core current-limiting parameters; the control core of the grid-connected converter continuously monitoring the fault recovery status, switching the operating mode of the grid-connected converter, and collecting and organizing the entire process data for data storage and backup.

[0006] Optionally, the acquisition of multi-source heterogeneous data and the data preprocessing include: for new energy units, acquiring core output characteristic parameters through a new energy power output fluctuation data acquisition module; for the grid side, acquiring weak grid disturbance signals and capturing early characteristics before grid faults by connecting to a grid-connected point synchronous phasor measurement unit through a grid disturbance precursor monitoring unit; for converter devices, acquiring relevant data of core power devices of grid-type converters through device thermal stress sensors; using filtering algorithms to filter and denoise the core output characteristic parameters, weak grid disturbance signals, and relevant data of core power devices of grid-type converters, and eliminating abnormal data; using interpolation to supplement data missing during the acquisition process, and normalizing multi-source heterogeneous data of different dimensions.

[0007] Optionally, the coupled analysis of fault risks between new energy sources and the power grid includes: fuzzifying the parameters of the intensity of power output fluctuations and the parameters of precursors to power grid disturbances after analysis, and mapping the parameter values ​​to fuzzy linguistic variables; establishing a fuzzy rule base based on historical fault data to clarify the risk tendencies corresponding to different combinations of new energy fluctuations and power grid disturbances; using neural networks to train on historical fault data, optimizing the weight coefficients of fuzzy rules, and improving the accuracy of risk assessment; and transforming the fuzzy inference results into clear fault risk levels through defuzzification.

[0008] Optionally, the coupled analysis of device safety redundancy capability includes: establishing a thermal stress safety margin calculation model based on the device thermal stress fundamental parameters in standardized data and combined with the rated technical parameters of the core power device; using the thermal stress safety margin calculation model, calculating the maximum thermal stress increment that the target device can withstand to evaluate the temperature tolerance capability of the target device under potential failure scenarios; and combining historical thermal stress accumulation to analyze the impact of the aging degree of the target device after long-term operation on the current safety redundancy, and assessing the current safety redundancy capability level of the target device.

[0009] Optionally, the fault determination and triggering includes: when the control core of the grid-connected converter detects that the electrical parameters at the grid connection point exceed the preset fault determination threshold, and the abnormal state continues for more than a preset time period, a grid fault is determined to have occurred; after a grid fault occurs, a fault response signal is triggered, the pre-adaptive standby state is suspended, and a dedicated data analysis and control process in the fault mode is started; a fault warning signal is sent to the new energy unit control system to coordinate the unit to enter the fault collaborative operation state.

[0010] Optionally, the construction of a multi-parameter coupled analysis model to calculate the optimal current limiting target and adjust the core current limiting parameters includes: verifying the completeness and rationality of the data based on fault characteristic data and thermal stress data; constructing a four-dimensional coupled analysis model of voltage drop depth, phase change angle, junction temperature change rate, and thermal stress increment based on the verified fault condition data, and quantifying the degree of mutual influence among the four parameters; using the current limiting effect meeting the standard, the device thermal stress not exceeding the limit, and the grid support capacity maximizing as the multi-objective optimization function, and using the parameter constraint relationship obtained from the four-dimensional coupled analysis model as the optimization constraint condition; and using an optimization algorithm to solve the multi-objective optimization function to calculate the optimal current limiting target under the current fault condition.

[0011] Optionally, the switching operation mode of the grid-connected converter includes: when the electrical parameters at the grid connection point are monitored to meet the fault recovery judgment conditions and remain stable for a preset time, the grid fault is determined to be recovered; the internal potential reserve margin is gradually adjusted according to a preset transition strategy, and the parameter values ​​in the fault current-limiting state are slowly transitioned to the normal operating reference value; the current component distribution ratio is gradually adjusted, and the current-limiting optimization distribution mode in the fault state is restored to the power factor control mode in normal operation; during the switching process, the fluctuation of voltage and frequency at the grid connection point is monitored to avoid secondary disturbances to the grid caused by the mode switching; after all parameters have stabilized and reached the normal operating reference value, the grid-connected converter officially resumes its normal grid support function.

[0012] Optionally, the current limiting fault traversal method further includes: calling sample data from the full-process data, constructing a multi-scenario correlation analysis model, performing model and parameter optimization calculations, verifying the results of the optimization calculations, updating the optimized parameters, and realizing full lifecycle parameter iterative optimization.

[0013] Optionally, the construction of a multi-scenario correlation analysis model and the optimization calculation of the model and parameters include: constructing a four-dimensional correlation analysis model of fault scenario, pre-adaptation parameters, current limiting effect, and device thermal stress based on the multi-scenario fault ride-through data set in the sample data; using machine learning optimization algorithms, with the core optimization objectives of optimal current limiting effect, minimum device thermal stress, and strongest grid support capability under different fault scenarios, optimizing the core parameters; during the optimization process, using the correlation rules obtained from the four-dimensional correlation analysis model as constraints to ensure that the optimized parameters meet the actual working conditions, forming a scenario-based optimization parameter library.

[0014] On the other hand, the present invention provides a current-limiting fault ride-through system for grid-connected converters and new energy units. The current-limiting fault ride-through system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the current-limiting fault ride-through method described in any of the above claims.

[0015] Through the above technical solutions, the fault response time is significantly shortened by integrating fault risk level prediction with a four-dimensional coupled analysis model, avoiding transient inrush current. The dynamic current limiting parameter adaptive adjustment mechanism can accurately match different fault scenarios, ensuring that the output current does not exceed the limit, while also increasing the proportion of reactive current on the d-axis to support the grid voltage during faults, achieving a synergistic balance between current limiting safety and grid support capacity. By coupling the thermal stress safety margin calculation model with the thermal stress constraint in dynamic current limiting control, the dynamic changes of device thermal stress can be captured in real time, avoiding device damage caused by thermal stress exceeding the limit under fault conditions, reducing asymmetric heat loss of devices, reducing thermal fatigue damage, and extending the service life of power devices. Through the four-dimensional correlation analysis model and multi-scenario parameter optimization, the traditional fixed parameter mode is broken. Through full life cycle iterative optimization, the accuracy of pre-adaptive decision-making and the adaptability of current limiting control are continuously improved. Multi-scenario correlation analysis covers complex operating conditions such as new energy output fluctuations and different fault types, improving the versatility of the technical solution and achieving a steady improvement in fault ride-through capability throughout the entire life cycle, adapting to the diversified operation needs of the new energy grid.

[0016] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart illustrating the current-limiting fault ride-through method for grid-connected converters and new energy units according to the present invention. Figure 1 ;

[0019] Figure 2 This is a schematic diagram of the data preprocessing process in this invention;

[0020] Figure 3 This is a flowchart illustrating the coupling analysis of fault risks between new energy sources and the power grid in this invention.

[0021] Figure 4 This is a flowchart illustrating the coupling analysis of device safety redundancy capabilities in this invention.

[0022] Figure 5 This is a flowchart illustrating the fault occurrence determination and triggering process in this invention;

[0023] Figure 6 This is a flowchart illustrating the process of calculating the optimal current limiting target and adjusting the core current limiting parameters in this invention.

[0024] Figure 7This is a flowchart illustrating the process of switching the operating mode of a grid-type converter in this invention.

[0025] Figure 8 This is a schematic diagram of the process for model and parameter optimization calculations in this invention;

[0026] Figure 9 This is a flowchart illustrating the current-limiting fault ride-through method for grid-connected converters and new energy units according to the present invention. Figure 2 . Detailed Implementation

[0027] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0028] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0029] Please refer to Figure 1 and Figure 9 This invention provides a current-limiting fault ride-through method for grid-connected converters and new energy units. The current-limiting fault ride-through method may include:

[0030] Step S100: Collect multi-source heterogeneous data using the data acquisition equipment deployed on the grid-connected converter and new energy unit, and perform data preprocessing to obtain standardized data.

[0031] In this embodiment of the invention, the three core acquisition devices—the new energy output fluctuation data acquisition module, the power grid disturbance precursor monitoring unit, and the device thermal stress sensor—can be activated first. A comprehensive initialization calibration of each device is performed, including sensor zero-point calibration, gain calibration, and linearity calibration, to eliminate inherent errors generated during equipment production and long-term operation. Simultaneously, a unified high-precision data acquisition synchronization clock system is established. A clock synchronization protocol is used to align the timestamps of all acquisition devices, preventing deviations in subsequent fusion analysis caused by timing discrepancies in data from different sources. After calibration, the effectiveness of each device is verified using a preset standard signal to ensure that the acquisition accuracy meets the requirements of subsequent analysis.

[0032] Please refer to Figure 2 In this embodiment of the invention, collecting multi-source heterogeneous data and performing data preprocessing may include:

[0033] Step S110: For new energy units, core output characteristic parameters are collected through the new energy output fluctuation data acquisition module.

[0034] In a preferred embodiment of the present invention, the photovoltaic unit in the new energy unit focuses on collecting real-time irradiance values ​​and continuous trend curves, as well as fluctuation data of output voltage and current of photovoltaic modules; the wind turbine unit focuses on collecting real-time wind speed values, wind speed change slopes, wind turbine speed changes, and real-time power output values ​​of the unit, while recording the duration and frequency of power output fluctuations.

[0035] Step S120: For the grid side, the grid disturbance precursor monitoring unit is connected to the grid connection point synchronous phasor measurement unit to collect weak grid disturbance signals (e.g., small fluctuation data of voltage amplitude at the grid connection point, frequency drift trend, slight phase offset and voltage harmonic content, etc.) to capture early characteristics before grid faults occur.

[0036] Step S130: For converter devices, collect relevant data of the core power devices of the grid-type converter through the device thermal stress sensor.

[0037] In a preferred embodiment of the present invention, the initial junction temperature, the cumulative amount of thermal stress during the historical operating cycle, and the real-time thermal stress change rate of the core power device of the grid converter can be collected by means of the device thermal stress sensor. At the same time, the heat loss data corresponding to the current operating load of the device is recorded, providing a basis for subsequent safety redundancy analysis.

[0038] Step S140: Use a filtering algorithm to filter and denoise the core output characteristic parameters, weak grid disturbance signals and related data of the core power devices of the grid-type converter, and remove abnormal data (e.g., abnormal spike data caused by environmental interference, sensor jitter and electromagnetic interference).

[0039] Step S150: Use interpolation to supplement missing data during the acquisition process and normalize multi-source heterogeneous data with different dimensions.

[0040] In a preferred embodiment of the present invention, data with different units such as irradiance, voltage amplitude, and junction temperature can be mapped to a unified order of magnitude range to eliminate the impact of dimensional differences on subsequent fusion algorithms. All preprocessed single-type data are precisely synchronized and aligned according to timestamps, and integrated to form a standardized data matrix containing three types of data, clarifying the temporal correspondence of each data point.

[0041] In a preferred embodiment of the present invention, standardized data can be transmitted to the pre-adaptive decision-making module and the dynamic current limiting control module via a high-speed, low-latency communication bus, ensuring that the two core control modules can obtain consistent basic data in real time. At the same time, a temporary data backup is established in the cache unit of the grid-type converter control core to form a data redundancy mechanism. When the main transmission link experiences a brief interruption or data loss, the cached backup data can be quickly called to ensure the normal operation of subsequent control modules. In addition, the pre-processed data is initially classified and stored, and indexes are established according to the new energy type, collection time, and data type to facilitate subsequent data traceability and retrieval.

[0042] Step S200: Based on standardized data, perform a coupled analysis of the fault risk of new energy and the power grid, and the safety redundancy capability of devices, and initialize and set pre-adaptation parameters to put the grid-type converter into standby state.

[0043] In a preferred embodiment of the present invention, the system's pre-adaptive decision module receives the transmitted standardized data and decomposes and extracts the data through a built-in data parsing submodule—separating three main categories of core parameters from the data: new energy output fluctuation characteristic parameters, grid disturbance precursor parameters, and device thermal stress basic parameters; performing a secondary validity check on each extracted parameter category, and removing invalid data (e.g., abnormal junction temperature data exceeding the physical characteristics of the device, voltage fluctuation data that does not conform to the grid operation rules) through preset parameter range thresholds; and standardizing the format of the qualified parameters, converting them into an analysis data format that the pre-adaptive decision module can directly call.

[0044] Please refer to Figure 3 In this embodiment of the invention, performing a coupled analysis of the fault risks of new energy sources and the power grid may include:

[0045] Step S210: Fuzzyize the parameters of the intensity of power output fluctuation of new energy sources and the parameters of the precursor of power grid disturbance after analysis, and map the parameter values ​​to fuzzy linguistic variables (e.g., "intense power output fluctuation", "slight voltage fluctuation").

[0046] Step S220: Establish a fuzzy rule base based on historical fault data to clarify the risk tendencies corresponding to different combinations of new energy fluctuations and grid disturbances.

[0047] Step S230: Use a neural network to train on historical fault data, optimize the weight coefficients of fuzzy rules, and improve the accuracy of risk assessment.

[0048] Step S240: Through defuzzification, the fuzzy inference result is transformed into a clear fault risk level (e.g., low, medium, high), and the confidence level of the risk level is output to ensure the reliability of the risk determination result.

[0049] Please refer to Figure 4 In this embodiment of the invention, performing a coupling analysis of device security redundancy capabilities may include:

[0050] Step S201: Based on the basic parameters of device thermal stress in the standardized data, and combined with the rated technical parameters of the core power device, establish a calculation model for thermal stress safety margin.

[0051] Step S202: Calculate the maximum thermal stress increment that the target device can withstand using the thermal stress safety margin calculation model, in order to evaluate the temperature tolerance of the target device under potential failure scenarios.

[0052] Step S203: Combine historical thermal stress accumulation to analyze the impact of the aging degree of the target device after long-term operation on the current safety redundancy, assess the current safety redundancy capability level of the target device (e.g., high, medium, low), and clarify the safety boundary of the device under fault conditions.

[0053] In a preferred embodiment of the present invention, a two-dimensional coupling mapping table of "fault risk level - device safety redundancy capability" can be established to clarify the pre-adaptive parameter adjustment principles corresponding to different coupling combinations. Based on the fault risk level and the device safety redundancy capability level, the corresponding parameter adjustment strategy is matched in the coupling mapping table. The core pre-adaptive parameters—internal potential reserve margin and current limiting benchmark value—are initialized according to the adjustment strategy: if the risk level is high and the device safety redundancy is low, a larger internal potential reserve margin and a lower current limiting benchmark value are set to reserve sufficient current limiting redundancy space; if the risk level is medium and the device safety redundancy is medium, a medium level of internal potential reserve margin and current limiting benchmark value are set to balance current limiting requirements and grid support capability; if the risk level is low and the device safety redundancy is high, the internal potential reserve margin is appropriately reduced to improve the grid support capability of the unit, while a higher current limiting benchmark value is set to fully utilize the load-carrying potential of the device.

[0054] In a preferred embodiment of the present invention, the initialized pre-adaptive parameters (e.g., internal potential reserve margin, current limiting reference value) can be transmitted to the grid-type converter control core via a high-speed communication interface, along with a parameter validity description. After receiving the parameters, the grid-type converter control core verifies their rationality to confirm that they are within the converter's control capability range. After successful verification, the reference value of the internal control strategy is adjusted, and the original normal operation control logic is switched to pre-adaptive control logic, completing the pre-adaptive mode switch. After the mode switch, the converter enters a pre-adaptive standby state, monitors changes in the grid status in real time, waits for a fault trigger signal, and ensures a rapid response when a fault occurs.

[0055] For example, in a wind turbine-grid converter grid-connected system, the system's pre-adaptive decision module receives a standardized data matrix, parses out significant wind speed change slopes, continuous small voltage dips at the grid connection point over multiple cycles, and initial junction temperature data of core power devices. After verifying and removing one set of abnormal voltage data, the module completes format standardization. It then calls a fuzzy neural network fusion algorithm to fuzzify the wind speed change and small voltage dip parameters into "high fluctuations" and "slight disturbances." Through the fuzzy rules optimized by the neural network, it outputs a high fault risk level with a confidence level that meets preset requirements. Based on the rated thermal parameters and historical thermal stress accumulation data of the core power devices, it calculates the maximum thermal stress increment that the devices can currently withstand and assesses the safety redundancy capability as medium based on the device aging degree. Based on the coupling result of high risk and medium redundancy, it matches the strategy in the coupling mapping table and initializes a large internal potential reserve margin and a medium-to-low current limit benchmark value. The parameters are transmitted to the converter control core through a high-speed communication interface. After the core verifies that the parameters are reasonable, it switches to pre-adaptive control logic and enters a standby state to monitor the grid in real time.

[0056] Step S300: Utilize the control core of the grid-type converter to continuously monitor the grid status, determine and trigger fault occurrences, and enhance the collection of fault characteristic data and thermal stress data based on the monitoring results.

[0057] In a preferred embodiment of the present invention, the grid-connected converter control core continuously monitors the core electrical parameters of the grid connection point—voltage amplitude, phase, current amplitude, and frequency changes—based on the established grid data acquisition benchmark and using a higher sampling frequency; a fault judgment threshold system is pre-set, which includes parameter thresholds corresponding to different fault types (e.g., judgment criteria for voltage drop, current surge, and phase change); the control core compares the monitored electrical parameters with the preset fault judgment thresholds in real time, continuously tracks the changing trend of the grid state, and captures early signals of fault occurrence.

[0058] Please refer to Figure 5 In this embodiment of the invention, fault occurrence determination and triggering may include:

[0059] Step S310: When the control core of the grid-connected converter detects that the electrical parameters at the grid connection point exceed the preset fault judgment threshold (e.g., voltage amplitude is lower than the threshold, current amplitude is higher than the threshold, or phase change exceeds the threshold), and the abnormal state continues for more than the preset time period (e.g., ensuring that it is not a false abnormality caused by transient interference or measurement error), then the grid is judged to have a fault.

[0060] Step S320: After a power grid fault occurs, a fault response signal is triggered, the pre-adaptive standby state is suspended, and the dedicated data analysis and control process in the fault mode is started.

[0061] Step S330: Send a fault warning signal to the new energy unit control system to coordinate the unit to enter the fault cooperative operation state.

[0062] In a preferred embodiment of the present invention, after the fault response signal is triggered, an enhanced acquisition command is immediately sent to the power grid disturbance precursor monitoring unit and the device thermal stress sensor. The power grid disturbance precursor monitoring unit is controlled to increase the sampling frequency, focusing on enhancing the acquisition of core characteristic data of power grid faults, including voltage drop depth, phase change angle, and fault duration. At the same time, the fault type (e.g., symmetrical or asymmetrical fault) is determined by the sequence component decomposition algorithm to clarify the core characteristics of the fault. The device thermal stress sensor is controlled to acquire the real-time junction temperature, junction temperature change rate, and thermal stress increment of the core power device at a higher sampling frequency, accurately capturing the rapid change trend of device thermal stress in the early stage of the fault, and providing real-time data support to avoid device overheating damage. During the acquisition process, the fault timing stage of each data point (e.g., the initial stage of the fault and the stable stage of the fault) is marked simultaneously.

[0063] In a preferred embodiment of the present invention, the enhanced fault feature data and real-time thermal stress data are time-aligned to ensure that the two types of data are accurately matched in the time dimension, and to clarify the thermal stress change law of the device corresponding to different fault features. The aligned data is format-standardized and converted into a format that the dynamic current limiting control module can directly parse. The fault occurrence signal, the standardized fault feature data and the real-time thermal stress data are synchronously transmitted to the dynamic current limiting control module through a low-latency communication link. At the same time, the batch of data is backed up in the cache unit of the grid-type converter control core to ensure that the control module can obtain fault condition information in a timely and reliable manner.

[0064] For example, the control core of the grid-connected converter continuously monitors the voltage at the grid connection point. When the voltage amplitude is detected to be lower than the preset fault threshold and the abnormal state continues for more than the preset time period, a short-circuit fault is determined, triggering a fault response signal. The grid disturbance precursor monitoring unit increases the sampling frequency to collect fault characteristic data such as voltage drop depth and phase change angle, and determines the fault type through sequence component decomposition. The thermal stress sensor of the control device collects the real-time junction temperature, junction temperature change rate, and thermal stress increment of the core power device at a higher frequency. After aligning and standardizing the fault signal, fault characteristic data, and thermal stress data in time sequence, the data is transmitted to the dynamic current limiting control module through a low-latency communication link, while the data is backed up in the control core cache unit.

[0065] Step S400: Based on fault characteristic data and thermal stress data, construct a multi-parameter coupled analysis model, calculate the optimal current limiting target and adjust the core current limiting parameters, and then issue and execute the adjusted core current limiting parameters.

[0066] Please refer to Figure 6In this embodiment of the invention, constructing a multi-parameter coupled analysis model to calculate the optimal current limiting target and adjust the core current limiting parameters may include:

[0067] Step S410: Verify the completeness and rationality of the data based on the fault characteristic data and thermal stress data.

[0068] In a preferred embodiment of the present invention, the system's dynamic current limiting and control module receives the transmitted fault occurrence signal, fault characteristic data, and real-time thermal stress data. First, it verifies the integrity of the data, checking for missing data, data transmission errors, or other issues. For missing or erroneous data, a redundant data retrieval mechanism is activated, calling backup data cached in the grid-type converter control core to supplement or correct it. Next, the data's rationality is verified, and abnormal data exceeding reasonable limits is eliminated based on the physical characteristics of the converter and the power grid. After verification, a complete and reliable fault condition dataset is formed, serving as the basis for subsequent analysis and control.

[0069] Step S420: Based on the verified fault condition data, construct a four-dimensional coupled analysis model of voltage drop depth, phase change angle, junction temperature change rate, and thermal stress increment, and quantify the degree of mutual influence among the four parameters.

[0070] In a preferred embodiment of the present invention, the influence of voltage drop depth and phase change angle on the junction temperature change rate and thermal stress increment of the device is clearly defined. At the same time, the constraint relationship between the thermal stress state of the device and the current limiting capability is analyzed in reverse. During the model construction process, the coupling rules in historical fault data are incorporated, and the fitting accuracy of the model is improved through parameter iteration optimization to ensure that the model can accurately reflect the multi-parameter interaction relationship under the current fault condition.

[0071] Step S430: Using the current limiting effect meeting the standard, the thermal stress of the device not exceeding the limit, and the grid support capacity maximizing as the multi-objective optimization function, the parameter constraint relationship obtained from the four-dimensional coupled analysis model is used as the optimization constraint condition.

[0072] Step S440: Use an optimization algorithm to solve the multi-objective optimization function and calculate the optimal current limiting target under the current fault condition (e.g., the maximum allowable output current, the minimum internal potential adjustment amplitude, the optimal reactive power support share, and other core indicators), and clarify the direction and boundary of current limiting control.

[0073] In a preferred embodiment of the present invention, based on the optimal current limiting target, the core current limiting parameters are adaptively adjusted in two main dimensions: First, the internal potential adjustment trajectory of the grid-connected converter is dynamically adjusted. A smooth nonlinear trajectory planning algorithm is used to plan the gradual adjustment path of the internal potential amplitude and phase, avoiding transient inrush currents caused by sudden changes in internal potential and ensuring that current changes are stable and controllable. Second, the current component allocation ratio is optimized. Based on the determined fault type (symmetric / asymmetric) and real-time thermal stress state, the current components are adjusted in a targeted manner through sequence component decomposition and d / q axis decoupling control technology: In the case of asymmetric faults, the negative sequence current component is limited first to reduce the asymmetric thermal stress of the device; in the case of voltage deep drop, the allocation ratio of d-axis reactive current is increased to enhance the support capability for grid voltage. Throughout the adjustment process, the total current is monitored in real time to ensure that it does not exceed the maximum allowable output current.

[0074] In a preferred embodiment of the present invention, the adjusted internal potential adjustment trajectory parameters and current component distribution ratio parameters can be standardized and converted into a control command format that can be directly executed by the grid-type converter control core. The standardized control commands are transmitted to the grid-type converter control core in real time via a high-speed communication link. After receiving the commands, the control core verifies their validity and feasibility to confirm that they are within the hardware capabilities of the converter. After the verification is passed, the power module of the converter is driven to perform current limiting actions according to the new parameters. At the same time, the output current and voltage data of the converter are collected in real time and fed back to the dynamic current limiting control module to form a control closed loop, ensuring that the current limiting effect meets the optimal target.

[0075] For example, in a single-phase grounding asymmetrical fault scenario, the dynamic current limiting control module receives fault signals (single-phase grounding), voltage sag characteristic data, phase change data, and real-time junction temperature and junction temperature change rate data of the core power devices. It verifies that the data is complete and valid, with no abnormalities or missing data. A four-dimensional coupled analysis model is constructed to quantify the positive correlation between voltage sag depth and junction temperature change rate, clarifying the constraint boundaries of device thermal stress on current limiting parameters. With the objectives of current not exceeding the limit benchmark value set in step two, junction temperature not exceeding the safety threshold, and maximizing grid support capacity, the optimal current limiting target is calculated through an optimization algorithm. Based on the optimal target, the internal potential is planned to gradually change along a smooth nonlinear trajectory to avoid phase changes. The negative sequence current component is preferentially limited through sequence component decomposition to increase the proportion of the positive sequence reactive current component, ensuring that the total current meets the optimal target. The adjustment parameters are converted into control commands and transmitted to the converter control core. The core drives the power module to execute current limiting actions, while simultaneously providing real-time feedback of output current and voltage data, forming a control closed loop.

[0076] Step S500: The control core of the grid-type converter continuously monitors the fault recovery status, switches the operating mode of the grid-type converter, and collects and organizes the data of the entire process for data storage and backup.

[0077] In this embodiment of the invention, the grid-connected converter control core continuously monitors the electrical status parameters (e.g., voltage amplitude, frequency, phase, and current) at the grid connection point, and sets a fault recovery judgment condition system—including voltage amplitude recovery threshold, frequency stability range, phase offset zeroing standard, and parameter stability duration requirements; it compares the monitored electrical parameters with the recovery judgment conditions in real time to determine whether the grid has recovered from the fault state to the normal operation state; at the same time, it monitors the output status of the converter, assesses the continued necessity of current limiting action, and provides a basis for mode switching.

[0078] Please refer to Figure 7 In this embodiment of the invention, switching the operating mode of a grid-type converter may include:

[0079] Step S510: When the electrical parameters at the grid connection point are monitored to meet the fault recovery judgment conditions and remain stable for a preset time, the grid fault is judged to be recovered.

[0080] Step S520: Gradually adjust the internal potential reserve margin according to the preset transition strategy, and slowly transition the parameter value from the fault current limiting state to the normal operation reference value.

[0081] Step S530: Gradually adjust the current component distribution ratio to restore the power factor control mode from the current limiting optimization distribution mode under fault conditions to the normal operation mode under normal power factor control.

[0082] Step S540: During the switching process, monitor the fluctuations in voltage and frequency at the grid connection point to avoid secondary disturbances to the power grid caused by the mode switching.

[0083] Step S550: After all parameters have stabilized and reached the normal operating baseline value, the grid-type converter officially resumes its normal grid support function.

[0084] In a preferred embodiment of the present invention, after the mode switch is completed, a full-process data recording instruction is triggered to comprehensively collect all the data of this fault ride from the grid-type converter control core cache, the pre-adaptive decision module database, and the dynamic current limiting control module database. The collected data covers six core data: basic acquisition data and preprocessing results, fault risk level assessment results and pre-adaptive parameters, fault judgment basis and enhanced acquisition data, coupling analysis results, current limiting parameter adjustment trajectory and execution status feedback data, fault recovery time, recovery process parameters and mode switch status data. The collected data is systematically organized according to the logical structure of "fault scenario - operation steps - core parameters - control results", duplicate data is removed, data descriptions are added, and standardized fault ride data samples are formed.

[0085] In a preferred embodiment of the present invention, the standardized data samples after being sorted are stored in a dedicated database of the iterative optimization module according to preset classification rules (e.g., new energy type, fault type, occurrence time, etc.) to establish a clear data index for easy subsequent quick retrieval and analysis; at the same time, a dual backup mechanism is activated to back up the data samples on the local hard drive and the cloud server respectively, and data encryption technology is used to ensure data security; data retention period management rules are established to ensure the long-term traceability of historical fault-crossing data samples, and to accumulate sufficient multi-scenario data resources for the iterative optimization module.

[0086] For example, the control core of the grid-connected converter continuously monitors the voltage and frequency at the grid connection point. When the parameters are detected to have returned to the normal range and remained stable for a preset time, the fault is determined to have been recovered. The control converter gradually adjusts the internal potential reserve margin and the current component distribution ratio according to a smooth transition strategy, switching to the normal grid support mode. The data recording command is triggered to collect the basic acquisition data, risk assessment results, pre-adaptation parameters, fault characteristic data, current limiting adjustment trajectory, recovery process parameters, and other full-process data for this fault ride. The data is then categorized into "photovoltaic-single-phase grounding fault" to form standardized data samples, which are stored in the dedicated database of the iterative optimization module. At the same time, dual backups are performed locally and in the cloud, and a data index is established.

[0087] Step S600: Call the sample data in the full process data, build a multi-scenario correlation analysis model, perform model and parameter optimization calculations, verify the results of the optimization calculations, update the optimized parameters, and realize full lifecycle parameter iterative optimization.

[0088] In this embodiment of the invention, the system's iterative optimization module retrieves the full-process data samples of the current fault ride from a dedicated database. Simultaneously, based on the scenario characteristics of the current fault (e.g., new energy type, fault type, grid disturbance characteristics, etc.), it extracts historical fault ride data samples of the same and different types stored in the database to form a fault ride data set covering multiple scenarios. The retrieved data samples are then screened for validity, and invalid samples with incomplete data or abnormal operating conditions are removed to ensure the reliability of the data set. The screened valid samples are then classified and labeled according to scenario type, laying the foundation for subsequent multi-scenario analysis.

[0089] Please refer to Figure 8 In this embodiment of the invention, a multi-scenario correlation analysis model is constructed, and model and parameter optimization calculations are performed, including:

[0090] Step S610: Based on the multi-scenario fault ride-through data set in the sample data, construct a four-dimensional correlation analysis model of fault scenario, pre-adaptation parameters, current limiting effect, and device thermal stress.

[0091] In a preferred embodiment of the present invention, data mining algorithms can be used to uncover the inherent correlation between pre-adaptive parameters (e.g., internal potential reserve margin, current limiting reference value, etc.) and current limiting effects (e.g., current control accuracy, grid voltage support effect, etc.) and device thermal stress (e.g., thermal stress increment, junction temperature peak, etc.) under different fault scenarios; identify the deficiencies in the current pre-adaptive parameter settings (e.g., parameters that are too conservative leading to insufficient grid support, parameters that are too aggressive leading to excessive device thermal stress, etc.), and clarify the core direction and adjustment range of parameter optimization.

[0092] Step S620: Using machine learning optimization algorithms, with the core optimization objectives of achieving the best current limiting effect, minimizing device thermal stress, and maximizing grid support capability under different fault scenarios, the core parameters are optimized and calculated.

[0093] In a preferred embodiment of the present invention, the two types of core parameters include: first, the fusion algorithm parameters of the pre-adaptive decision module of the system, including the weight coefficients and thresholds of the fuzzy neural network, to improve the accuracy of fault risk level determination; second, the initial pre-adaptive parameter benchmark values ​​of the system, which optimize the corresponding internal potential reserve margin and current limit benchmark values ​​for different fault scenarios (e.g., severe fluctuations in new energy output + slight grid disturbances, stable new energy output + severe grid faults, etc.).

[0094] Step S630: During the optimization process, the correlation rules obtained from the four-dimensional correlation analysis model are used as constraints to ensure that the optimization parameters meet the actual working conditions and form a scenario-based optimization parameter library to clarify the optimal parameter combination for different scenarios.

[0095] In a preferred embodiment of the present invention, the optimized fusion algorithm parameters and the baseline values ​​of the scenario-based pre-adaptive parameters can be substituted into a dedicated simulation model, and simulation verification can be performed using multiple sets of historical fault data samples from different scenarios. During the simulation, the three core indicators of the optimized parameters under different fault scenarios are evaluated: current limiting effect (e.g., whether the current control accuracy meets the standard), device thermal stress control effect (e.g., whether the thermal stress increment is reduced), and grid support capability (e.g., whether the voltage stability at the grid connection point is improved). If the simulation verification results do not meet the preset optimization target, the optimization algorithm parameters and constraints are readjusted, and a second optimization calculation is performed. The optimization-verification process is repeated until the optimized parameters can meet the preset target in multiple scenarios, ensuring the reliability and universality of the optimization results.

[0096] In a preferred embodiment of the present invention, the verified optimized parameters (e.g., parameters of the pre-adaptive decision module fusion algorithm and the baseline value of the scenario-based pre-adaptive parameters) can be updated to the pre-adaptive decision module through a high-speed communication interface, covering and replacing the original parameter setting baseline; at the same time, the scenario recognition logic of the pre-adaptive decision module is updated, and the scenario feature extraction algorithm is optimized to ensure that when encountering the corresponding fault scenario in the future, the optimized parameters can be quickly and accurately identified and called for initialization settings; after the update is completed, the pre-adaptive decision module is functionally tested to confirm that the parameter update is effective and the module is operating normally; when the next fault risk occurs, the pre-adaptive decision module operates based on the optimized parameters, realizing continuous improvement of fault traversal capability and completing the full life cycle closed loop of "data-analysis-optimization-application".

[0097] For example, the system's iterative optimization module calls upon the current fault data sample of "severe fluctuations in new energy output + slight voltage drop," as well as multiple sets of historical fault data samples of the same and different types, and filters them to form an effective data set. Based on the data set, a four-dimensional correlation analysis model is constructed. Through data mining, it is found that the fusion algorithm parameters of the current pre-adaptive decision module lead to a conservative risk level judgment, and the corresponding internal potential reserve margin benchmark value is unreasonable. A machine learning optimization algorithm is used to optimize and adjust the fusion algorithm parameters and the internal potential reserve margin benchmark value under this scenario. The optimized parameters are substituted into the simulation model and verified using multiple sets of historical data. The results show that the current limiting effect, device thermal stress control effect, and grid support capability all meet the preset optimization goals. The optimized parameters are updated to the pre-adaptive decision module to optimize the scenario recognition logic and complete functional testing. When the same fault risk occurs again, the pre-adaptive decision module calls the optimized parameters to initialize the settings, improving the overall effect of fault ride-through.

[0098] On the other hand, embodiments of the present invention also provide a current-limiting fault ride-through system for grid-connected converters and new energy units. The current-limiting fault ride-through system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the current-limiting fault ride-through method of any of the above claims.

[0099] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0100] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0101] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0102] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0103] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0104] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0105] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0106] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0107] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for current-limiting fault ride-through of a grid-connected converter and a new energy unit, characterized in that, The current-limiting fault ride-through method includes: Based on the data acquisition equipment deployed on grid-connected converters and new energy units, multi-source heterogeneous data is collected and preprocessed to obtain standardized data; Based on the standardized data, a coupled analysis of the fault risk of new energy and the power grid and the safety redundancy capability of devices is performed, and pre-adaptive parameters are initialized to put the grid-type converter into standby mode. By utilizing the control core of the grid-type converter, the grid status is continuously monitored, fault occurrence is determined and triggered, and the collection of fault characteristic data and thermal stress data is enhanced based on the monitoring results. Based on fault characteristic data and thermal stress data, a multi-parameter coupled analysis model is constructed to calculate the optimal current limiting target and adjust the core current limiting parameters. The adjusted core current limiting parameters are then issued and executed. The control core of the grid-type converter continuously monitors the fault recovery status, switches the operating mode of the grid-type converter, and collects and organizes the data of the entire process for data storage and backup.

2. The current-limiting fault ride-through method for grid-connected converters and new energy units according to claim 1, characterized in that, The process of collecting multi-source heterogeneous data and performing data preprocessing includes: For new energy generating units, core output characteristic parameters are collected through the new energy output fluctuation data acquisition module; For the power grid side, the grid disturbance precursor monitoring unit is connected to the grid connection point synchronous phasor measurement unit to collect weak grid disturbance signals and capture early characteristics before grid faults occur. For converter devices, relevant data of the core power devices of the grid-type converter are collected through device thermal stress sensors; A filtering algorithm is used to filter and denoise the core output characteristic parameters, weak grid disturbance signals, and related data of the core power devices of the grid-type converter, and to remove abnormal data. Interpolation is used to supplement missing data during the acquisition process, and multi-source heterogeneous data with different dimensions are normalized.

3. The current-limiting fault ride-through method for grid-connected converters and new energy units according to claim 1, characterized in that, The coupling analysis of fault risks between new energy sources and the power grid includes: The parameters of the intensity of fluctuations in new energy output and the parameters of precursors to power grid disturbances after analysis are fuzzified, and the parameter values ​​are mapped to fuzzy linguistic variables; A fuzzy rule base is established based on historical fault data to clarify the risk tendencies corresponding to different combinations of new energy fluctuations and grid disturbances. By using neural networks to train on historical fault data, the weight coefficients of fuzzy rules are optimized to improve the accuracy of risk assessment. By defuzzifying the results, the fuzzy reasoning results are transformed into a clear fault risk level.

4. The current-limiting fault ride-through method for grid-type converters and new energy units according to claim 3, characterized in that, The coupling analysis of device safety redundancy capabilities includes: Based on the basic parameters of device thermal stress in standardized data, and combined with the rated technical parameters of core power devices, a calculation model for thermal stress safety margin is established. Using a thermal stress safety margin calculation model, the maximum thermal stress increment that the target device can withstand is calculated to evaluate the temperature tolerance of the target device under potential failure scenarios. By combining historical thermal stress accumulation, the impact of the aging degree of the target device after long-term operation on the current safety redundancy is analyzed, and the current safety redundancy capability level of the target device is evaluated.

5. The current-limiting fault ride-through method for grid-connected converters and new energy units according to claim 1, characterized in that, The fault occurrence determination and triggering process includes: When the control core of the grid-connected converter detects that the electrical parameters at the grid connection point exceed the preset fault judgment threshold, and the abnormal state continues for more than the preset time period, it is determined that a grid fault has occurred. When a power grid failure occurs, a fault response signal is triggered, the pre-adaptive standby state is suspended, and a dedicated data analysis and control process in fault mode is initiated. Send fault warning signals to the control system of the new energy unit to coordinate the unit to enter the fault cooperative operation state.

6. The current-limiting fault ride-through method for grid-connected converters and new energy units according to claim 1, characterized in that, The construction of a multi-parameter coupled analysis model, calculation of the optimal current limiting target, and adjustment of the core current limiting parameters include: The completeness and rationality of the data are verified based on fault characteristic data and thermal stress data. Based on the verified fault condition data, a four-dimensional coupled analysis model of voltage drop depth, phase change angle, junction temperature change rate, and thermal stress increment is constructed, and the degree of mutual influence among the four parameters is quantitatively analyzed. The optimization function is based on the objectives of achieving the current limiting effect, ensuring that the thermal stress of the device does not exceed the limit, and maximizing the grid support capacity. The parameter constraint relationship obtained from the four-dimensional coupled analysis model is used as the optimization constraint condition. An optimization algorithm is used to solve the multi-objective optimization function, and the optimal current limiting target under the current fault condition is calculated.

7. The current-limiting fault ride-through method for grid-connected converters and new energy units according to claim 1, characterized in that, The switching grid-type converter operating modes include: When the monitored electrical parameters at the grid connection point meet the fault recovery judgment conditions and remain stable for a preset time, the grid fault is judged to have been recovered. The internal potential reserve margin is gradually adjusted according to the preset transition strategy, and the parameter value of the fault current limiting state is slowly transitioned to the normal operation reference value. Gradually adjust the current component distribution ratio to restore the power factor control mode from the current limiting optimization distribution mode under fault conditions to the normal operation power factor control mode; During the switching process, monitor the fluctuations in voltage and frequency at the grid connection point to avoid secondary disturbances to the power grid caused by the mode switching; Once all parameters have stabilized and reached normal operating baseline values, the grid-type converter officially resumes its normal grid support function.

8. The current-limiting fault ride-through method for grid-connected converters and new energy units according to claim 1, characterized in that, The current-limiting fault ride-through method further includes: By calling sample data from the entire process data, a multi-scenario correlation analysis model is constructed, the model and parameters are optimized and calculated, the results of the optimization calculation are verified, the optimized parameters are updated and applied, and the parameter iterative optimization is achieved throughout the entire lifecycle.

9. The current-limiting fault ride-through method for grid-connected converters and new energy units according to claim 8, characterized in that, The construction of a multi-scenario correlation analysis model and the optimization calculation of the model and parameters include: Based on the multi-scenario fault ride-through dataset in the sample data, a four-dimensional correlation analysis model of fault scenario, pre-adaptation parameter, current limiting effect, and device thermal stress is constructed. Machine learning optimization algorithms are used to optimize core parameters with the core optimization objectives of achieving the best current limiting effect, the minimum thermal stress of devices, and the strongest grid support capability under different fault scenarios. During the optimization process, the correlation patterns derived from the four-dimensional correlation analysis model are used as constraints to ensure that the optimization parameters meet the actual working conditions and form a scenario-based optimization parameter library.

10. A current-limiting fault ride-through system for a grid-connected converter and a new energy unit, characterized in that, The current-limiting fault-crossing system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the current-limiting fault-crossing method according to any one of claims 1-9.

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