An electromagnetic transient simulation efficiency improvement method and system for new energy station grid connection evaluation

Abstract

The application discloses a method and system for improving electromagnetic transient simulation efficiency for grid-connected evaluation of a new energy station, and belongs to the technical field of new energy grid connection and power system simulation analysis. In order to solve the problem of low efficiency of new energy station grid connection safety evaluation, the following steps are included: converting external modeling data into a device parameter-model parameter comparison data set and generating a digital fingerprint; deploying a message middleware to realize real-time synchronization of the data set and the digital fingerprint; loading parameter mapping configuration to perform consistency comparison on the device parameter-model parameter comparison data set, and triggering correction when inconsistency occurs; performing grid-connected dynamic characteristic simulation verification; constructing a station backbone network, hanging a verified device model instance and triggering simulation operation stage by stage, suspending hanging when an exception occurs, generating an exception positioning identifier, extracting an exception feature to match a solution, and generating a new solution and storing the new solution when there is no match; and creating a distributed shared storage volume. The method is used for electromagnetic transient simulation modeling process of large-scale new energy station access to a power grid.

Description

Technical Field

[0001] This invention relates to a method and system for improving the efficiency of electromagnetic transient simulation for grid connection assessment of new energy power plants, belonging to the field of new energy grid connection and power system simulation analysis technology. Background Technology

[0002] As the energy structure shifts towards cleaner and lower-carbon energy, new energy sources such as wind power and solar power have gradually become the main power sources. However, the randomness, intermittency, and volatility of new energy sources, as well as the characteristics of grid connection of power electronic equipment, have altered the dynamic behavior of traditional power systems, giving the power grid new characteristics of "fast dynamics, multi-scale, and strong nonlinearity." Traditional electromechanical transient simulation technology cannot accurately simulate the rapid transient processes at the switch level, and electromagnetic transient simulation must be relied upon to assess the impact of new energy power plant grid connection on the safety and stability of the power grid.

[0003] Before grid connection, renewable energy power plants need to undergo comprehensive evaluation using electromagnetic transient simulation to assess their impact on grid voltage, frequency, and power angle stability under various operating conditions, ensuring safe grid connection. However, current electromagnetic transient simulation applications for large-scale renewable energy power plant grid connection assessments face severe challenges, including complex models, difficulty in obtaining parameters, and extremely long computation times, directly restricting the timeliness and accuracy of grid connection safety assessments. A complete renewable energy power plant includes dozens or even hundreds of wind turbines / photovoltaic inverters, collector lines, transformers, reactive power compensation devices, etc., resulting in a huge workload for modeling and a high risk of errors, leading to excessively long grid connection assessment cycles and difficulty in providing timely decision-making basis for grid dispatching departments. Specifically, existing technologies suffer from the following pain points:

[0004] 1. Inconsistent data collection standards: Data provided by design units, equipment manufacturers, and other parties often has problems such as incomplete content, chaotic format, different versions, missing key parameters, or inconsistency with actual equipment. The lack of unified data collection standards and review mechanisms leads to extremely long time for subsequent sorting, verification, and correction, and frequent rework due to data problems.

[0005] 2. Repetitive work in single-device model debugging: The model parameters provided by the equipment manufacturer often do not match the actual operating settings. It is necessary to constantly check the external response and internal parameter settings with the equipment unit, resulting in a lot of time being spent on adjusting parameters or replacing models. There is a lack of efficient means of comparing parameter consistency.

[0006] 3. Lack of standardized procedures for model debugging: Model debugging mainly relies on the personal experience of operators, with arbitrary steps and no written debugging operation instructions or checklists. Operators are prone to failure due to omissions or incorrect operations, and troubleshooting is time-consuming and irregular, making it difficult to reproduce problems.

[0007] 4. Integrated whole-site model is prone to anomalies that are difficult to diagnose: After integrating multiple equipment models, anomalies such as "fault-free oscillation", "initialization failure", "multi-instance conflict", and "harmonic resonance" often occur. Due to the high degree of integration, the problem localization range is large, and the diagnosis process is like "finding a needle in a haystack". The average time for troubleshooting and resolution can exceed one day, which seriously restricts the overall simulation efficiency.

[0008] Therefore, there is an urgent need to provide a method that can systematically improve the efficiency of electromagnetic transient modeling and simulation for grid connection assessment of new energy power plants, thereby shortening the grid connection cycle and providing reliable technical support for power grid dispatching departments to timely assess the grid connection safety of new energy power plants and implement grid connection operation control strategies. Summary of the Invention

[0009] This invention addresses the problems of inconsistent data standards, difficulty in verifying parameter consistency, lack of standardized debugging procedures, and difficulty in troubleshooting integrated anomalies in the electromagnetic transient simulation of new energy power plants, which lead to low efficiency in grid connection safety assessment. It provides a method and system for improving the efficiency of electromagnetic transient simulation for grid connection assessment of new energy power plants.

[0010] The present invention discloses a method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants, comprising:

[0011] Construct a standardized data structure template, convert external modeling data into a device parameter-model parameter comparison dataset that conforms to the template according to field mapping rules, and generate digital fingerprints for data entries in the dataset as version identifiers;

[0012] Deploy message middleware to establish publish-subscribe communication channels between simulation computing nodes, storage device parameter-model parameter comparison dataset nodes and digital fingerprint parameter dataset nodes, and model file nodes to achieve real-time synchronization of device parameter-model parameter comparison dataset and digital fingerprint;

[0013] Load the parameter mapping configuration and perform a consistency comparison of the synchronized device parameter-model parameter mapping dataset; if there is a discrepancy, trigger correction and update the digital fingerprint and model version identifier; update the device model instance based on the corrected parameters, perform grid-connected dynamic characteristic simulation verification, and mark instances whose response characteristics meet the grid operation threshold as verified; the model version identifier is associated with the digital fingerprint set of all data entries of the model.

[0014] The power station grid-connected backbone network is constructed based on the synchronized equipment parameter-model parameter comparison dataset. Verified equipment model instances are connected and simulation runs are triggered step by step. When abnormal features are detected, the connection is paused and an abnormal location identifier is generated.

[0015] Anomaly features are extracted based on anomaly location identifiers. Solutions in the anomaly case database are matched using feature vector similarity. If no match is found, a new solution is generated, and the anomaly features and the new solution are stored.

[0016] Create a distributed shared storage volume and configure access control policies.

[0017] Preferably, the field mapping rules include: predefining the position location method of parameters in different data source file formats, and the conversion coefficient required to convert the source parameter value into the target unit;

[0018] The aforementioned equipment parameter-model parameter comparison dataset includes: wind turbine generators, photovoltaic inverters, SVG, energy storage systems, synchronous condensers, collector lines, and transformers.

[0019] Preferably, the publish-subscribe communication channel transmits only the changed data entries and their corresponding digital fingerprints when data is updated, and maintains a global version number to identify the latest state of the dataset.

[0020] Preferably, the parameter mapping configuration includes: the correspondence between the parameter field names in the actual new energy power station electrical equipment parameter set and the parameter names in the internal model of the simulation platform, as well as the numerical conversion rules between the two.

[0021] Preferably, the grid-connected dynamic characteristic simulation verification includes:

[0022] Apply voltage step commands of different depths to the device model instance, verify its current response time, and verify whether the reactive current and voltage meet the low voltage ride-through strategy defined by the device during the low voltage ride-through; for devices with droop control, verify whether the reactive output meets the reactive response quantity formed by the droop coefficient.

[0023] Apply different active and reactive steady-state output quantities to the equipment and verify whether the active and reactive outputs meet the allowable error range;

[0024] Apply system frequencies with different frequency deviations to the equipment, and for equipment with primary frequency regulation, verify whether the active power regulation meets the active power change related to the active power regulation coefficient.

[0025] Apply system frequencies with different rates of change to the equipment, and for equipment with inertial response capability, verify whether the active power regulation satisfies the active power change related to the inertial time constant.

[0026] For dynamic reactive power compensation devices, when the positive sequence voltage drops below 0.85 pu, their reactive current injection characteristics satisfy:

[0027] ;

[0028] in, This indicates the reactive current command value. This represents the steady-state reactive current before the fault. This represents the per-unit value of the positive sequence voltage at the grid connection point. Indicates the peak value of the rated current;

[0029] For grid-type equipment, verify whether its voltage droop control characteristics meet the following requirements:

[0030] ;

[0031] in, Indicates reactive power output. Indicates the droop index, Indicates voltage deviation. Indicates the rated voltage. Indicates the rated active power;

[0032] For equipment with primary frequency regulation capability, verify whether the active power variation characteristics meet the following requirements:

[0033] ;

[0034] in, This represents the change in active power. This represents the active frequency regulation coefficient. This indicates the deviation between the frequency detected at the AC port and the system's rated frequency. Indicates the FM dead zone. Indicates the rated frequency;

[0035] For devices with inertial response performance, verify whether the active power change characteristics meet the following requirements:

[0036] ;

[0037] in, Indicates the frequency detected by the AC port. Indicates time, This represents the inertial time constant.

[0038] Preferably, the digital fingerprint is achieved by reading the content of a data entry and generating a fixed-length unique identifier, which is used to distinguish different versions of the same data entry;

[0039] The model version identifier is associated with the set of digital fingerprints of all data entries corresponding to the model, specifically:

[0040] A record is created that links the list of digital fingerprints of all parameter data entries referenced by the device model instance with the model version identifier to trace the consistency between the model and the parameter source.

[0041] Preferably, the method for attaching the verified state device model instance and triggering the simulation run step by step includes:

[0042] First, connect the dynamic reactive power compensation device or synchronous condenser model and trigger the simulation to verify its ability to support the stability of the grid-connected voltage of the power station.

[0043] Then, wind turbines, photovoltaic or energy storage models of the same type are connected in batches. After each batch is connected, the simulation is triggered, and the active steady-state value and reactive steady-state value in the simulation results are read. The values ​​are compared with the given values ​​to determine whether the error is within the threshold range. If the error is within the allowable threshold range, the next type of equipment is connected until all are connected.

[0044] Preferably, the solution for matching anomaly case databases using feature vector similarity includes the following methods:

[0045] Key indicators extracted from abnormal features are used to construct a feature vector. The key indicators include at least one of the following: steady-state active and reactive power output with errors exceeding the threshold; the difference between the output active or reactive power and the target value divided by the rated power; the type of test with output error; the effective value of the voltage at the equipment access point; the ratio of the effective value of the voltage at the access point to the effective value of the voltage at the station access system bus; the instantaneous value of the response current; the value of the response reactive current; the value of the active current; the oscillation frequency; the harmonic content; and the initialization failure error code.

[0046] The similarity between the current abnormal feature vector and the feature vectors of historical cases in the abnormal case database is calculated using Euclidean distance or cosine similarity. Cases with similarity exceeding a preset threshold are output as matching results.

[0047] Preferably, the distributed shared storage volume is implemented using a server shared folder method, with storage directories divided according to project groups, and read / write permission lists configured based on users or user groups;

[0048] Modifications made by different users to the device parameter-model parameter comparison dataset under the same project are automatically saved as historical versions, and a version rollback function is provided.

[0049] The present invention discloses an electromagnetic transient simulation efficiency improvement system for grid connection evaluation of new energy power plants, comprising:

[0050] The data standardization module is used to construct a standardized data structure template, convert external modeling data into a device parameter-model parameter comparison dataset that conforms to the template, and generate digital fingerprints for data entries in the dataset as version identifiers.

[0051] The data synchronization module is configured with a message middleware to establish publish-subscribe communication channels between simulation computing nodes, storage device parameter-model parameter comparison dataset and its digital fingerprint parameter dataset nodes, and model file nodes.

[0052] The parameter comparison and verification module is used to load the parameter mapping configuration, compare the synchronized device parameters and model parameters against the dataset for consistency; if there is a discrepancy, it triggers correction and updates the digital fingerprint and model version identifier; based on the corrected parameters, it updates the device model instance, performs grid-connected dynamic characteristic simulation verification, and marks instances whose response characteristics meet the grid operation threshold as verified.

[0053] The integrated verification module is used to construct the power station grid-connected backbone network based on the synchronized equipment parameter-model parameter comparison dataset, connect the verified equipment model instances, and trigger simulation operation step by step.

[0054] The anomaly retrieval module is used to extract anomaly features based on anomaly location identifiers, match solutions in the anomaly case database based on feature vector similarity, and generate and store new solutions if no match is found.

[0055] The storage management module is used to create distributed shared storage volumes and configure access control policies to enable data read and write access.

[0056] This invention proposes a method and system for improving the efficiency of electromagnetic transient simulation for grid connection assessment of renewable energy power plants. It can quickly and accurately complete electromagnetic transient simulation analysis of renewable energy power plant grid connection, providing timely and reliable technical support for power grid dispatching departments to assess the grid connection safety of renewable energy power plants, determine whether grid connection conditions are met, and formulate grid connection operation control strategies. This effectively ensures the safe and stable operation of the power grid after a high proportion of renewable energy is connected, supporting the safe grid connection and efficient absorption of renewable energy power plants. It has the following advantages:

[0057] 1. By using standardized data conversion, automated parameter comparison, and incremental integrated verification technologies, the average time for the entire electromagnetic transient simulation process for grid connection assessment of new energy power plants has been reduced from 18 days to 13 days, an improvement of 27.8%, significantly shortening the grid connection assessment cycle.

[0058] 2. Through parameter mapping configuration and automated consistency comparison mechanism, automatic verification of parameters in the equipment parameter-model parameter comparison dataset and internal parameters of the model is realized, which reduces the deviation of grid connection simulation results caused by parameter inconsistency and improves the accuracy of grid connection evaluation.

[0059] 3. By generating digital fingerprints for data entries and associating and storing model version identifiers with digital fingerprints, a complete traceability chain from the parameter source to the model instance is achieved, ensuring the consistency between the model used for grid connection assessment and the actual equipment on site.

[0060] 4. By building an abnormal case database and supporting similarity retrieval based on feature vectors, historical solutions can be automatically matched when new anomalies occur, which greatly reduces the time for anomaly investigation and improves the continuity of grid connection assessment work.

[0061] 5. Real-time data synchronization between multiple nodes is achieved by deploying message middleware, and centralized management and version tracking of project data are achieved through distributed shared storage. Attached Figure Description

[0062] Figure 1 This is an overall flowchart of the electromagnetic transient simulation efficiency improvement method for grid connection evaluation of new energy power plants as described in this invention;

[0063] Figure 2 This is a schematic diagram illustrating the standardized data conversion and version identification of this invention;

[0064] Figure 3 This is a schematic diagram of the structure of the multi-node data synchronization channel of the present invention;

[0065] Figure 4 This is a flowchart of the automated parameter comparison and dynamic verification of the single-device model of the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0068] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0069] Example 1:

[0070] like Figures 1-4 As shown in this embodiment, a method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants includes the following steps:

[0071] Step 1: Construct a standardized data structure template, convert external modeling data into a device parameter-model parameter mapping dataset that conforms to the template according to field mapping rules, and generate digital fingerprints for data entries in the dataset as version identifiers;

[0072] Step 2: Deploy message middleware to establish publish-subscribe communication channels between simulation computing nodes, storage device parameter-model parameter comparison dataset nodes and digital fingerprint parameter dataset nodes, and model file nodes to achieve real-time synchronization of device parameter-model parameter comparison dataset and digital fingerprint;

[0073] Step 3: Load the parameter mapping configuration and perform a consistency comparison on the synchronized device parameter-model parameter comparison dataset; if there is a discrepancy, trigger correction and update the digital fingerprint and model version identifier; update the device model instance based on the corrected parameters, perform grid-connected dynamic characteristic simulation verification, and mark instances whose response characteristics meet the grid operation threshold as verified; the model version identifier is associated with the digital fingerprint set of all data entries of the model.

[0074] Step 4: Construct the power station grid-connected backbone network based on the synchronized equipment parameter-model parameter comparison dataset, connect the verified status equipment model instances and trigger simulation operation level by level, pause connection and generate anomaly location identifier when abnormal features are detected;

[0075] Step 5: Extract abnormal features based on the abnormal location identifier, match the solutions in the abnormal case database by feature vector similarity, if no match is found, generate a new solution, and store the abnormal features and the new solution;

[0076] Step 6: Create a distributed shared storage volume and configure access control policies.

[0077] The digital fingerprint is used to uniquely identify a single data entry in the device parameter-model parameter comparison dataset. When the digital fingerprint of any data entry is updated, the version identifier of the device model instance associated with that data entry is updated synchronously, thereby realizing complete version traceability from the parameter level to the model level.

[0078] The equipment parameter-model parameter comparison dataset includes three aspects: first, the electrical and control parameter values ​​from the model specifications provided by the equipment manufacturer; second, the operating parameters from the actual equipment setpoint tables on site; and third, the mapping relationship between the above parameters and the internal model parameters of the simulation platform, as well as consistency verification records. The core function of this dataset is to correlate and compare actual electrical quantities on site with the internal parameters of the model, providing a data foundation for automated consistency verification.

[0079] Taking an electromagnetic transient simulation project of a wind farm grid connection system as an example, this method includes the following six steps: standardized data conversion and version identification, multi-node data synchronization, automated parameter comparison and dynamic verification of single-device models, incremental whole-site model integration and simulation verification, construction and retrieval of anomaly case library, and distributed shared storage. Each step is explained in detail below.

[0080] Step 1: Standardized data conversion and version identification.

[0081] The first issue to address is the difficulty in processing data from different equipment manufacturers and in different formats in a unified manner.

[0082] A standardized data structure template refers to a predefined data storage format that specifies the access system plan, equipment electrical parameters (such as rated voltage, rated capacity, impedance voltage, load loss, etc.), model file path, and the specific location and data type of integrity verification fields. Specifically, the standardized template in this embodiment includes the project access system plan, the main electrical wiring diagram within the station, parameters of the transformer substation and step-up transformer, and parameters of the 35kV collection line.

[0083] Field mapping rules refer to predefined field location paths for different file formats in external data sources, as well as conversion coefficients for converting source field values ​​to target field units. For example, for an Excel-formatted parameter table provided by a device manufacturer, the mapping rules define the "rated capacity" field as being located in column 3, row 2, and stipulate that if the original unit is kW, it needs to be divided by 1000 to convert it to MW; for a PDF document provided by the manufacturer, the data is extracted by parsing the numbers following the keyword "rated capacity".

[0084] Digital fingerprinting refers to generating a fixed-length unique identifier by reading the content of a data entry. In this embodiment, a unique digital fingerprint is generated for each data entry in the equipment parameter-model parameter comparison dataset (such as the rated capacity parameter of a wind turbine, the length parameter of a collector line, etc.) to distinguish different versions of the same data entry. When a parameter is modified, its digital fingerprint is updated accordingly, thereby achieving accurate tracking of parameter version changes. The digital fingerprint can be calculated using a hash algorithm to generate a fixed-length unique identifier for distinguishing different versions of the same data entry.

[0085] Step 2: Multi-node data synchronization.

[0086] To address the data consistency issue in multi-user, multi-node collaboration, this embodiment deploys a message middleware.

[0087] The publish-subscribe communication channel refers to the real-time communication link established between the simulation calculation node, the parameter dataset node, and the model file node. Among them, the parameter dataset node is specifically used to store the device parameter-model parameter comparison dataset and its digital fingerprint generated in step 1.

[0088] In practice, when a data entry on a parameter dataset node changes (e.g., the digital fingerprint is updated due to parameter correction), the message middleware automatically broadcasts the changed data entry and its updated digital fingerprint to all nodes subscribed to that data topic (such as simulation calculation nodes and model file nodes), and increments the global version number. This mechanism ensures that all nodes use the same version of data, avoiding simulation result deviations caused by data inconsistency. In this embodiment, unlike methods that rely on manual communication to transmit data, this invention uses an automated message middleware for data synchronization, achieving data consistency maintenance between nodes without manual intervention.

[0089] Step 3: Automated parameter comparison and dynamic verification of single-device models.

[0090] This step involves parameter verification and dynamic validation of a single device model, which is crucial for ensuring the accuracy of the subsequent whole-site model.

[0091] Parameter consistency check:

[0092] The modeling data includes three aspects: First, the model specification, which includes the basic structure of the model, the method of setting important parameters, the values ​​of important electrical and control parameters, and the verification formulas corresponding to the control strategy (such as high and low voltage ride-through characteristics); Second, the model parameter mapping table, which gives the correspondence between the actual names of the parameters and the names of the model parameters inside the simulation platform (in this embodiment, the ADPSS platform is used); Third, the program that can run on the ADPSS platform.

[0093] Parameter mapping configuration establishes a correspondence between parameter field names in the device parameter-model parameter mapping dataset and model parameter names within the simulation platform, and determines the numerical conversion rules between the two. This embodiment extracts parameter values ​​from the specification, parameter names from the parameter mapping table, and actual parameter values ​​from the ADPSS model, and performs consistency checks by associating these three. If inconsistencies exist, a list of inconsistent parameters is output, triggering manual review. Upon successful review, the first verification is completed, and the corresponding digital fingerprint and model version identifier are updated.

[0094] Dynamic simulation verification:

[0095] Dynamic simulation verification is performed on the corrected equipment model instance, including steady-state control command verification and dynamic response characteristic verification.

[0096] Steady-state control command verification: Apply steady-state control commands, such as active power commands and reactive power commands, to the model; apply voltage control targets to grid-type equipment, and verify whether the error between the actual output of the model and the given value is within the preset threshold range. Apply different active and reactive steady-state output values ​​(such as 0%, 20%, 50%, 80%, 100% of rated values) to the equipment, and verify whether the active and reactive outputs meet the allowable error range.

[0097] Dynamic response characteristic verification: Based on the function and response characteristics of each device, and in accordance with published technical standards and specifications, key response characteristics and verification formulas are extracted to verify different performance levels. Specifically, this includes:

[0098] (1) Apply voltage step commands of different depths (such as drop to) to the device model instance. , (etc.), verify its current response time, and whether the reactive current and voltage meet the low-voltage ride-through strategy defined by the device during low-voltage ride-through; for devices with droop control, verify whether the reactive output meets the reactive response quantity formed by the droop coefficient.

[0099] (2) Simulate grid voltage dips or rises to verify whether the fault ride-through response characteristics meet grid connection standards. For example, for SVG equipment, it is necessary to verify its performance when the positive sequence voltage dips to... The reactive current injection characteristics at that time, namely:

[0100] .

[0101] (3) For grid-type equipment, verify the voltage droop control formula, the primary frequency regulation formula, and the overcurrent capacity. The voltage droop control formula is:

[0102] .

[0103] (4) For equipment with primary frequency regulation capability, verify the active power variation characteristics:

[0104] Apply system frequencies with different frequency deviations (e.g., ±0.05Hz, ±0.1Hz, ±0.2Hz) to the equipment. For equipment with primary frequency regulation capabilities, verify whether the active power regulation meets the active power change requirements related to the active power regulation coefficient.

[0105] ;

[0106] in, This represents the change in active power. This represents the active frequency regulation coefficient. This indicates the deviation between the frequency detected at the AC port and the system's rated frequency. Indicates the FM dead zone. Indicates the rated frequency.

[0107] (5) For equipment with inertial response performance, verify the characteristics of active power change:

[0108] Apply system frequencies with different rates of change (e.g., 0.5Hz / s, 1Hz / s, 2Hz / s) to the equipment. For equipment with inertial response capabilities, verify whether the active power regulation satisfies the active power change related to the inertial time constant.

[0109] ;

[0110] in, Indicates the frequency detected by the AC port. Indicates time, This represents the inertial time constant.

[0111] Once all verification items for this type of device are completed and the response characteristics meet the preset thresholds, the device model instance is marked as verified.

[0112] A model version identifier is a code used to uniquely identify a specific version of a model instance.

[0113] In this embodiment, the model version identifier is associated with the digital fingerprint of the corresponding device parameter-model parameter comparison dataset. Specifically, an association record is established between the digital fingerprint list of all parameter data entries referenced by the model instance and the model version identifier, thereby achieving complete traceability of the model and parameter sources.

[0114] Step 4: Incremental integration and simulation verification of the whole site model.

[0115] This step assembles the verified individual device models into a complete new energy power station model, using an incremental integration strategy to quickly locate integration anomalies.

[0116] First, the main network of the wind farm is constructed based on the equipment parameter-model parameter comparison dataset synchronized in step 2. This includes the electrical connection relationships and component parameters of the main transformer of the booster station, the busbars, and each collector line. In this embodiment, the specific paths, conductor types, lengths, and other parameters of the five collector lines of a wind farm are all derived from standardized parameter templates.

[0117] Secondly, the equipment model instances are connected sequentially: First, key equipment models such as Dynamic Var Compensator (SVG) or Synchronous Condenser are connected, triggering a simulation run for each connected device to verify its compatibility with the backbone network; then, wind turbine, photovoltaic, or energy storage models of the same type are connected in batches, triggering a simulation after each batch is connected, and reading the active steady-state value and reactive steady-state value from the simulation results, comparing them with the given values, and determining whether the error is within the threshold range. If the error is within the allowable threshold range, the next batch of devices is connected; if the error exceeds the threshold, an anomaly location indicator is triggered.

[0118] Step-by-step simulation execution: After each or a batch of device instances is successfully connected, the simulation program is automatically executed once. During each simulation run, the system monitors the initialization flag and steady-state voltage and current waveform characteristics. If abnormal characteristics are detected (such as "fault-free oscillation," "initialization failure," "multi-instance conflict," "harmonic resonance," etc.), the connection of subsequent devices is paused, and an anomaly location identifier is generated. This anomaly location identifier includes the name of the device model instance that caused the anomaly and its digital fingerprint in the device parameter-model parameter comparison dataset, thereby quickly locating the source of the problem.

[0119] Step 5: Construction and retrieval of the exception case library.

[0120] This step stores the anomalies encountered in step 4 in a structured manner and supports automated retrieval and matching, enabling the reuse of anomaly knowledge.

[0121] The exception case database is a structured storage system, and each record contains:

[0122] (1) Abnormal characteristics, including but not limited to: steady-state active and reactive power output with errors exceeding the threshold, the difference between the output active or reactive power and the target value divided by the rated power, the test type with output error, the effective value of the voltage at the equipment access point, the ratio of the effective value of the voltage at the access point to the effective value of the voltage at the station access system bus, the instantaneous value of the response current, the value of the response reactive current, the value of the active current, the oscillation frequency, the harmonic content, the initialization failure error code and other key indicators; when a new abnormality occurs, the system extracts the above key indicators to form a feature vector.

[0123] (2) Error log identifier;

[0124] (3) Corresponding solutions;

[0125] (4) Associated device model version identifier and digital fingerprint set. This embodiment archives typical problems such as "multi-instance conflict", "initialization oscillation" and "harmonic resonance" and their solutions in the form of text and data files.

[0126] Feature vector-based similarity retrieval works as follows: When a new anomaly occurs, the system extracts the waveform features and error codes of the anomaly to form a feature vector; the feature vector is then compared with the feature vectors of historical cases in the anomaly case database (e.g., using Euclidean distance or cosine similarity); cases with similarity exceeding a preset threshold are output as matching results, and the corresponding solutions are returned.

[0127] In this embodiment, when the "initial oscillation" anomaly occurs again, the system can automatically match historical solutions (such as checking for multiple instance number conflicts, adjusting step size parameters, etc.), avoiding repeated troubleshooting.

[0128] Step 6: Distributed shared storage.

[0129] This step provides a unified data storage infrastructure for the entire modeling process.

[0130] A distributed shared storage volume is a centralized storage space implemented using a server-wide shared folder approach. Storage directories are divided by project group, and read / write permission lists are configured based on users or user groups. In this embodiment, a shared data storage area is created on the simulation server to store device parameter-model parameter comparison datasets, digital fingerprints, model version identifiers and their relationships, anomaly case data, and simulation results.

[0131] Access control policies are used to manage the data operation permissions of different users. Modifications made by different users within the same project team to the device parameter-model parameter comparison dataset are automatically saved as historical versions, and a version rollback function is provided. In this embodiment, through this shared storage space, users do not need to transfer data via removable storage media, significantly improving the convenience of daily work.

[0132] Example verification:

[0133] The method of this invention was used to perform electromagnetic transient simulation on a grid-type energy storage system. Before implementation, the total time for all stages of the project was approximately 18 working days; after implementation, the total time was shortened to 13 working days, with the initial data collection period reduced from 5 days to 4 days, single-device model debugging from 5.3 days to 4 days, and overall station modeling and simulation from 6 days to 4 days. Verification results show that the method of this invention can significantly improve the efficiency of electromagnetic transient modeling and simulation for new energy power plants.

[0134] Example 2:

[0135] This embodiment describes an electromagnetic transient simulation efficiency improvement system for grid connection evaluation of new energy power plants, comprising:

[0136] The data standardization module is used to construct a standardized data structure template, convert external modeling data into a device parameter-model parameter comparison dataset that conforms to the template, and generate digital fingerprints for data entries in the dataset as version identifiers.

[0137] The data synchronization module is configured with a message middleware to establish publish-subscribe communication channels between simulation computing nodes, storage device parameter-model parameter comparison dataset and its digital fingerprint parameter dataset nodes, and model file nodes.

[0138] The parameter comparison and verification module is used to load the parameter mapping configuration, compare the synchronized device parameters and model parameters against the dataset for consistency; if there is a discrepancy, it triggers correction and updates the digital fingerprint and model version identifier; based on the corrected parameters, it updates the device model instance, performs grid-connected dynamic characteristic simulation verification, and marks instances whose response characteristics meet the grid operation threshold as verified.

[0139] The integrated verification module is used to construct the power station grid-connected backbone network based on the synchronized equipment parameter-model parameter comparison dataset, connect the verified equipment model instances, and trigger simulation operation level by level.

[0140] The anomaly retrieval module is used to extract anomaly features based on anomaly location identifiers, match solutions in the anomaly case database based on feature vector similarity, and generate and store new solutions if no match is found.

[0141] The storage management module is used to create distributed shared storage volumes and configure access control policies to enable data read and write access.

[0142] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants, characterized in that, include: Construct a standardized data structure template, convert external modeling data into a device parameter-model parameter comparison dataset that conforms to the template according to field mapping rules, and generate digital fingerprints for data entries in the dataset as version identifiers; Deploy message middleware to establish publish-subscribe communication channels between simulation computing nodes, storage device parameter-model parameter comparison dataset nodes and digital fingerprint parameter dataset nodes, and model file nodes to achieve real-time synchronization of device parameter-model parameter comparison dataset and digital fingerprint; Load the parameter mapping configuration and perform a consistency comparison of the synchronized device parameter-model parameter comparison dataset; if there is a discrepancy, trigger correction and update the digital fingerprint and model version identifier; update the device model instance based on the corrected parameters, perform grid-connected dynamic characteristic simulation verification, and mark instances whose response characteristics meet the grid operation threshold as verified; The model version identifier is a set of digital fingerprints associated with all data entries of the model; The power station grid-connected backbone network is constructed based on the synchronized equipment parameter-model parameter comparison dataset. Verified equipment model instances are connected and simulation runs are triggered step by step. When abnormal features are detected, the connection is paused and an abnormal location identifier is generated. Anomaly features are extracted based on anomaly location identifiers. Solutions in the anomaly case database are matched using feature vector similarity. If no match is found, a new solution is generated, and the anomaly features and the new solution are stored. Create a distributed shared storage volume and configure access control policies.

2. The method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants according to claim 1, characterized in that, The field mapping rules include: predefining the position location method of parameters in different data source file formats, and the conversion coefficient required to convert the source parameter value to the target unit; The aforementioned equipment parameter-model parameter comparison dataset includes: wind turbine generators, photovoltaic inverters, SVG, energy storage systems, synchronous condensers, collector lines, and transformers.

3. The method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants according to claim 1, characterized in that, The publish-subscribe communication channel transmits only the changed data entries and their corresponding digital fingerprints when data is updated, and maintains a global version number to identify the latest state of the dataset.

4. The method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants according to claim 1, characterized in that, The parameter mapping configuration includes: the correspondence between the parameter field names in the actual new energy power station electrical equipment parameter set and the parameter names in the simulation platform internal model, as well as the numerical conversion rules between the two.

5. The method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants according to claim 1, characterized in that, The grid-connected dynamic characteristic simulation verification includes: Apply voltage step commands of different depths to the device model instance, verify its current response time, and verify whether the reactive current and voltage meet the low voltage ride-through strategy defined by the device during the low voltage ride-through; for devices with droop control, verify whether the reactive output meets the reactive response quantity formed by the droop coefficient. Apply different active and reactive steady-state output quantities to the equipment and verify whether the active and reactive outputs meet the allowable error range; Apply system frequencies with different frequency deviations to the equipment, and for equipment with primary frequency regulation, verify whether the active power regulation meets the active power change related to the active power regulation coefficient. Apply system frequencies with different rates of change to the equipment, and for equipment with inertial response capability, verify whether the active power regulation satisfies the active power change related to the inertial time constant. For dynamic reactive power compensation devices, when the positive sequence voltage drops below 0.85 pu, their reactive current injection characteristics satisfy: ； in, This indicates the reactive current command value. This represents the steady-state reactive current before the fault. This represents the per-unit value of the positive sequence voltage at the grid connection point. Indicates the peak value of the rated current; For grid-type equipment, verify whether its voltage droop control characteristics meet the following requirements: ； in, Indicates reactive power output. Indicates the droop index, Indicates voltage deviation. Indicates the rated voltage. Indicates the rated active power; For equipment with primary frequency regulation capability, verify whether the active power variation characteristics meet the following requirements: ； in, This represents the change in active power. This represents the active frequency regulation coefficient. This indicates the deviation between the frequency detected at the AC port and the system's rated frequency. Indicates the FM dead zone. Indicates the rated frequency; For devices with inertial response performance, verify whether the active power change characteristics meet the following requirements: ； in, Indicates the frequency detected by the AC port. Indicates time, This represents the inertial time constant.

6. The method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants according to claim 1, characterized in that, The digital fingerprint is achieved by reading the content of a data entry and generating a fixed-length unique identifier, which is used to distinguish different versions of the same data entry; The model version identifier is associated with the set of digital fingerprints of all data entries corresponding to the model, specifically: A record is created that links the list of digital fingerprints of all parameter data entries referenced by the device model instance with the model version identifier to trace the consistency between the model and the parameter source.

7. The method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants according to claim 1, characterized in that, The specific method for attaching verified device model instances and triggering simulation execution step by step includes: First, connect the dynamic reactive power compensation device or synchronous condenser model and trigger the simulation to verify its ability to support the stability of the grid-connected voltage of the power station. Then, wind turbines, photovoltaic or energy storage models of the same type are connected in batches. After each batch is connected, the simulation is triggered, and the active steady-state value and reactive steady-state value in the simulation results are read. The values ​​are compared with the given values ​​to determine whether the error is within the threshold range. If the error is within the allowable threshold range, the next type of equipment is connected until all are connected.

8. The method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants according to claim 1, characterized in that, The solution for matching anomaly case databases using feature vector similarity includes the following specific methods: Key indicators extracted from abnormal features are used to construct a feature vector. The key indicators include at least one of the following: steady-state active and reactive power output with errors exceeding the threshold; the difference between the output active or reactive power and the target value divided by the rated power; the type of test with output error; the effective value of the voltage at the equipment access point; the ratio of the effective value of the voltage at the access point to the effective value of the voltage at the station access system bus; the instantaneous value of the response current; the value of the response reactive current; the value of the active current; the oscillation frequency; the harmonic content; and the initialization failure error code. The similarity between the current abnormal feature vector and the feature vectors of historical cases in the abnormal case database is calculated using Euclidean distance or cosine similarity. Cases with similarity exceeding a preset threshold are output as matching results.

9. The method for improving the efficiency of electromagnetic transient simulation for grid connection evaluation of new energy power plants according to claim 1, characterized in that, The distributed shared storage volume is implemented using a server shared folder approach, with storage directories divided by project group and read / write permission lists configured based on users or user groups; Modifications made by different users to the device parameter-model parameter comparison dataset under the same project are automatically saved as historical versions, and a version rollback function is provided.

10. An electromagnetic transient simulation efficiency improvement system for grid connection evaluation of new energy power plants, the system being used to implement the method described in any one of claims 1 to 9, characterized in that, include: The data standardization module is used to construct a standardized data structure template, convert external modeling data into a device parameter-model parameter comparison dataset that conforms to the template, and generate digital fingerprints for data entries in the dataset as version identifiers. The data synchronization module is configured with a message middleware to establish publish-subscribe communication channels between simulation computing nodes, storage device parameter-model parameter comparison dataset and its digital fingerprint parameter dataset nodes, and model file nodes. The parameter comparison and verification module is used to load the parameter mapping configuration, compare the synchronized device parameters and model parameters against the dataset for consistency; if there is a discrepancy, it triggers correction and updates the digital fingerprint and model version identifier; based on the corrected parameters, it updates the device model instance, performs grid-connected dynamic characteristic simulation verification, and marks instances whose response characteristics meet the grid operation threshold as verified. The integrated verification module is used to construct the power station grid-connected backbone network based on the synchronized equipment parameter-model parameter comparison dataset, connect the verified equipment model instances, and trigger simulation operation level by level. The anomaly retrieval module is used to extract anomaly features based on anomaly location identifiers, match solutions in the anomaly case database based on feature vector similarity, and generate and store new solutions if no match is found. The storage management module is used to create distributed shared storage volumes and configure access control policies to enable data read and write access.

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