Power system stability evaluation system and method suitable for large-scale new energy access

Abstract

The invention provides a power system stability evaluation system and method suitable for large-scale new energy access. Belongs to the technical field of power. The method comprises the following steps: acquiring multi-source real-time monitoring data, preprocessing the acquired multi-source real-time monitoring data, and integrating the preprocessed data with different sources and different formats by adopting a data fusion technology to obtain a fusion data set to be analyzed; and constructing a real-time state model based on the fused data set. Through acquisition and preprocessing of multi-source real-time monitoring data and a data fusion technology, the operation state of the large-scale new energy access power system can be comprehensively and accurately reflected, and a reliable data basis is provided for stability evaluation.

Description

Technical Field

[0001] The present invention proposes a power system stability assessment system and method suitable for large-scale renewable energy access, belonging to the field of power technology. Background Art

[0002] With the rapid development of new energy technologies, the integration of large-scale renewable energy sources such as wind and solar power into power systems has become a trend. However, the intermittent, volatile, and uncertain nature of renewable energy sources poses significant challenges to power system stability. Traditional power system stability assessment methods, often based on deterministic models and fixed parameters, struggle to accurately reflect the dynamic characteristics of power systems after the integration of large-scale renewable energy sources, and therefore cannot meet the demand for precise stability assessment in actual operation. Therefore, there is an urgent need for an innovative power system stability assessment method suitable for large-scale renewable energy integration to improve the accuracy and reliability of the assessment. Summary of the Invention

[0003] The present invention provides a power system stability assessment system and method suitable for large-scale renewable energy access, which is used to solve the problems mentioned in the above background technology:

[0004] The present invention proposes a method for evaluating the stability of a power system suitable for large-scale renewable energy access, the method comprising:

[0005] S1: Acquire multi-source real-time monitoring data, pre-process the acquired multi-source real-time monitoring data, and use data fusion technology to integrate the pre-processed data from different sources and formats to obtain a fused data set to be analyzed; based on the fused data set, build a real-time status model;

[0006] S2: Based on the real-time state model, dynamic simulation of the power system under multiple scenarios is performed. In each simulation scenario, the stability of the power system is analyzed using numerical calculation methods, key stability indicators are calculated, and a data set of key stability indicators for the power system under multiple scenarios is obtained. The data set of key stability indicators under multiple scenarios is deeply mined and analyzed, and a correlation model is established based on a machine learning algorithm. The correlation model is used to predict stability trends under different operating conditions and obtain power system stability trend prediction data.

[0007] S3: Based on the power system stability trend forecast data and the preset stability threshold, a preliminary assessment of the power system stability is conducted, and areas or nodes with stability risks are screened out to obtain data on the initially screened risk areas or nodes. Based on the data on the initially screened risk areas or nodes, relevant assessment strategy parameters are extracted from a preset stability assessment strategy database. Based on the extracted assessment strategy parameters, the test application parameters for subsequent stability assessments are adaptively adjusted to obtain a stability assessment scheme suitable for large-scale renewable energy access to the power system. The assessment scheme is then uploaded to the power system stability assessment management platform.

[0008] S4: According to the adjusted stability assessment plan, obtain real-time assessment test data of the large-scale renewable energy access power system. Based on the real-time assessment test data, use a comprehensive assessment method to conduct a detailed assessment of the stability of the power system, obtain detailed device performance assessment data, and then conduct a second screening of qualified devices based on the detailed device performance assessment data to select devices or subsystems with good stability during the assessment process, and obtain performance data of re-screened qualified devices.

[0009] S5: Based on the initial screening risk area or node data and the re-screening qualified device performance data, conduct a comprehensive summary analysis of the stability of large-scale renewable energy access to the power system; obtain a stability assessment report on the large-scale renewable energy access to the power system.

[0010] The present invention proposes a power system stability assessment system suitable for large-scale renewable energy access, including a memory, a processor, and a computer program stored on the memory and executable on the memory. The processor executes the program to implement a power system stability assessment method suitable for large-scale renewable energy access as described above.

[0011] The beneficial effects of the present invention are as follows: through the acquisition and preprocessing of multi-source real-time monitoring data, as well as data fusion technology, it can comprehensively and accurately reflect the operating status of large-scale renewable energy access to the power system, providing a reliable data basis for stability assessment.

[0012] By setting up dynamic simulations of power systems under multiple scenarios and applying advanced numerical calculation methods and machine learning algorithms, we can accurately calculate key stability indicators, predict system stability trends, and discover potential stability risks in advance.

[0013] Based on the preliminary evaluation results and evaluation strategy parameters, the test application parameters of the stability evaluation are adaptively adjusted and a personalized evaluation plan is formulated, which improves the pertinence and effectiveness of the evaluation.

[0014] A comprehensive assessment method is used to conduct a detailed assessment of the stability of the power system, and a comprehensive summary and analysis is carried out from the system as a whole, regional and device levels. This can deeply explore the factors affecting system stability and provide a scientific basis for system optimization and improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic flow chart of the steps of the method of the present invention;

[0016] Figure 2 for Figure 1 Detailed implementation steps of S1 in the flowchart;

[0017] Figure 3 for Figure 2 Detailed implementation steps of S14 are shown in the flowchart. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0019] One embodiment of the present invention, as Figure 1 As shown, a method for evaluating the stability of a power system applicable to large-scale renewable energy access is provided, the method comprising:

[0020] S1: Acquire multi-source real-time monitoring data; pre-process the acquired multi-source real-time monitoring data, and use data fusion technology to integrate the pre-processed data from different sources and formats to obtain a fused data set to be analyzed; and construct a real-time status model based on the fused data set.

[0021] S2: Based on the real-time state model, dynamic simulation of the power system under multiple scenarios is performed. In each simulation scenario, the stability of the power system is analyzed using numerical calculation methods, key stability indicators are calculated, and a data set of key stability indicators for the power system under multiple scenarios is obtained. The data set of key stability indicators under multiple scenarios is deeply mined and analyzed, and a correlation model is established based on a machine learning algorithm. The correlation model is used to predict stability trends under different operating conditions and obtain power system stability trend prediction data.

[0022] S3: Based on the power system stability trend forecast data and the preset stability threshold, a preliminary assessment of the power system stability is conducted, and areas or nodes with stability risks are screened out to obtain data on the initially screened risk areas or nodes. Based on the data on the initially screened risk areas or nodes, relevant assessment strategy parameters are extracted from a preset stability assessment strategy database. Based on the extracted assessment strategy parameters, the test application parameters for subsequent stability assessments are adaptively adjusted to obtain a stability assessment scheme suitable for large-scale renewable energy access to the power system. The assessment scheme is then uploaded to the power system stability assessment management platform.

[0023] S4: According to the adjusted stability assessment plan, obtain real-time assessment test data of the large-scale renewable energy access power system. Based on the real-time assessment test data, use a comprehensive assessment method to conduct a detailed assessment of the stability of the power system, obtain detailed device performance assessment data, and then conduct a second screening of qualified devices based on the detailed device performance assessment data to select devices or subsystems with good stability during the assessment process, and obtain performance data of re-screened qualified devices.

[0024] S5: Based on the initial screening risk area or node data and the re-screening qualified device performance data, conduct a comprehensive summary analysis of the stability of large-scale renewable energy access to the power system; obtain a stability assessment report on the large-scale renewable energy access to the power system.

[0025] The working principle of the above technical solution is to obtain multi-source real-time monitoring data from the power system with large-scale renewable energy integration. This data covers the output power of renewable energy power generation equipment, electrical quantities such as grid voltage, current, and frequency, as well as environmental meteorological data (such as wind speed and light intensity). This data serves as the basis for subsequent stability assessments, comprehensively reflecting the operating status of the power system and external environmental influences. The acquired data is preprocessed to remove noise and address missing values ​​to ensure data quality. Then, data fusion technology is used to integrate data from different sources and formats to form a fused dataset for analysis. Data fusion can comprehensively utilize information from various data types to more comprehensively describe the operation of the power system. Based on the fused dataset, a real-time status model is constructed that reflects the dynamic characteristics of the power system with large-scale renewable energy integration. This model can reflect the operating status and dynamic characteristics of the power system in real time, providing a foundation for subsequent stability simulation and analysis.

[0026] Based on a real-time state model, dynamic simulations of the power system are conducted under multiple scenarios. These scenarios include power fluctuations from various renewable energy sources, grid faults, and varying load variations. These multi-scenario simulations comprehensively examine the stability of the power system under various possible operating conditions. In each simulation scenario, numerical calculation methods are used to analyze the stability of the power system and calculate key stability indicators, such as transient stability margin, voltage stability margin, and frequency stability margin. This generates a dataset of key stability indicators for the power system under multiple scenarios. These datasets are then deeply mined and analyzed, and a correlation model is established using machine learning algorithms to link key stability indicators with system operating parameters and renewable energy generation characteristics. This correlation model is used to predict the stability trends of the power system under different operating conditions when large-scale renewable energy is integrated, generating power system stability trend forecast data. This helps to understand the stability status of the power system in advance, providing a basis for subsequent assessment and decision-making.

[0027] Based on power system stability trend forecast data and preset stability thresholds, a preliminary assessment of the power system's stability is conducted, screening areas or nodes that may pose stability risks and obtaining data for pre-screened risk areas or nodes. This step quickly locates potentially problematic areas or nodes, providing guidance for subsequent detailed assessments. Based on this pre-screened risk area or node data, relevant assessment strategy parameters, such as assessment intervals, assessment method weighting, and key monitoring point selection, are extracted from a pre-set stability assessment strategy database. These parameters can be used to tailor the subsequent assessment process to specific circumstances, improving its relevance and effectiveness. Based on these extracted assessment strategy parameters, subsequent stability assessment test parameters are adaptively adjusted, such as the fault type, fault duration, and renewable energy power fluctuation amplitude in dynamic simulations. This results in a stability assessment plan suitable for large-scale renewable energy integration in power systems. This plan is then uploaded to the power system stability assessment management platform for implementation. This adaptive adjustment of the assessment plan enables more accurate assessments of power system stability.

[0028] According to the adjusted stability assessment plan, real-time assessment test data for large-scale renewable energy integration into the power system is collected. This data covers electrical quantities such as voltage, current, and frequency at key monitoring points during the assessment process, as well as the operating status of renewable energy power generation equipment. This data provides a real-time reflection of the power system's operating status, providing a basis for a detailed assessment. Based on this real-time assessment test data, a comprehensive assessment method is used to conduct a detailed assessment of the power system's stability. This comprehensive assessment method combines multiple analytical techniques, including time-domain analysis, frequency-domain analysis, and the energy function method, to comprehensively consider the dynamic behavior and stability mechanisms of the power system. This data provides detailed device performance evaluation data reflecting the stability performance of each device during the assessment process. Based on this detailed device performance evaluation data, a further screening of qualified devices is conducted to identify devices or subsystems that demonstrate good stability during the assessment process, generating performance data for re-screened qualified devices. This step further identifies devices with good stability in the power system, ensuring stable system operation.

[0029] Based on data from initially screened risk areas or nodes and rescreened performance data from qualified devices, a comprehensive summary analysis of the stability of the power system with large-scale renewable energy integration is conducted. The stability of the power system is assessed from multiple perspectives, including the overall system level, the regional level, and the device level, comprehensively considering the impact of various factors on system stability. This results in a stability assessment report for the power system with large-scale renewable energy integration, which comprehensively reflects the stability of the power system and provides an important basis for power system planning, operation, and maintenance.

[0030] The effect of the above technical solution is: through the preprocessing and fusion of multi-source real-time monitoring data, it can fully reflect the dynamic characteristics of large-scale renewable energy access to the power system, provide more accurate basic data for system stability analysis, and avoid the deviations caused by inconsistent or incomplete data sources in traditional evaluation methods.

[0031] Multi-scenario dynamic simulation based on real-time state models can cover a variety of actual operating conditions, such as fluctuations in renewable energy power generation, grid failures, and load changes, ensuring the comprehensiveness of power system stability analysis and avoiding the risk that local simulation scenarios cannot reflect the overall stability of the system.

[0032] By combining preset stability thresholds with deep data mining, areas or nodes that may have stability risks can be accurately screened out, providing clear focus areas for subsequent detailed assessments, reducing human selection errors, and improving the reliability of risk warnings.

[0033] Based on the initial screening of risk areas or node data, relevant assessment parameters are extracted and adjusted from the stability assessment strategy database, so that the power system stability assessment scheme can be adaptively adjusted according to real-time conditions, providing the optimal assessment scheme under different operating conditions, and enhancing the targetedness and real-time nature of the assessment.

[0034] Through the comprehensive application of various analysis methods such as time domain analysis, frequency domain analysis, and energy function method, a detailed performance evaluation can be carried out on each key component or subsystem in the power system. The hierarchical evaluation results enable comprehensive feedback on the stability status of each level, avoiding the distortion of the overall stability assessment caused by neglecting a certain level.

[0035] During the power system stability assessment process, multi-dimensional risk analysis and qualified device screening can timely identify potential risk areas and unstable factors, providing strong support for the risk management of the power system and enhancing the system's resilience and response capabilities when facing large-scale new energy access.

[0036] The adaptive evaluation strategy adjustment mechanism reduces the impact of human intervention on the evaluation process, making the evaluation work more automated and objective, improving the overall evaluation efficiency and reducing the possibility of human operational errors.

[0037] One embodiment of the present invention, as Figure 2 As shown, the S1 includes:

[0038] S11, collect multi-source real-time monitoring data through various sensors;

[0039] S12, preprocessing the collected multi-source real-time monitoring data to obtain high-precision multi-source real-time monitoring data;

[0040] S13. Based on the data fusion algorithm, the high-precision multi-source real-time monitoring data obtained from different sources and in different formats are integrated to obtain a fused data set to be analyzed;

[0041] S14. Based on the fused data set, a system identification method is used to construct a real-time state model that reflects the dynamic characteristics of large-scale renewable energy access to the power system.

[0042] The working principle of the above technical solution is to deploy various sensors at renewable energy power generation equipment (such as wind turbines and photovoltaic power plants) and key nodes in the power grid. These sensors are front-end devices for data collection, and they can accurately sense the operating parameters of different locations and equipment in the power system. For example, sensors deployed on wind turbines can obtain real-time output power data, reflecting the real-time output of wind power generation; voltage, current, and frequency sensors deployed at key nodes in the power grid can monitor changes in electrical quantities in real time.

[0043] Meteorological monitoring equipment is used to obtain environmental meteorological data, such as wind speed and sunlight intensity. This data is closely related to renewable energy generation, as wind power generation is proportional to the cube of wind speed, and photovoltaic power generation is closely related to sunlight intensity. Obtaining this data provides a comprehensive understanding of the external environmental factors affecting renewable energy generation, providing a basis for subsequent analysis of uncertainties in renewable energy generation.

[0044] Data collected by sensors and meteorological monitoring equipment are integrated to generate multi-source real-time monitoring data. This data covers both electrical and environmental parameters of the power system, providing a rich source of information for subsequent data processing and analysis. Preprocessing is performed on this collected multi-source real-time monitoring data to remove noise, outliers, and missing values, thereby improving its accuracy and quality. For example, due to external interference or sensor failures, the collected data may contain some outliers that significantly deviate from the normal range. Preprocessing can remove or correct these outliers. After preprocessing, high-precision multi-source real-time monitoring data is obtained. This data more accurately reflects the actual operating status of the power system and provides a reliable foundation for subsequent data fusion and model construction.

[0045] Using a data fusion algorithm, high-precision, multi-source real-time monitoring data from various sources and formats is integrated. Data from different sources may have different formats and accuracies. The data fusion algorithm effectively integrates this data to extract more valuable information. For example, fusing wind turbine output power data with wind speed data can better analyze the power characteristics of wind power generation. Through data fusion, a fused dataset is generated for analysis. This dataset, incorporating information from various data types, provides a more comprehensive description of the power system's operating status, providing richer data support for subsequent model construction and stability assessment.

[0046] Based on the fused data set, a system identification method is used to construct a real-time state model that reflects the dynamic characteristics of the power system when large-scale renewable energy is integrated. System identification methods build a mathematical model of the system based on input and output data, thereby describing the system's dynamic behavior. This model comprehensively considers the uncertainties of renewable energy generation, the grid topology, and the dynamic characteristics of each component. Uncertainties in renewable energy generation include power fluctuations caused by environmental factors such as wind speed and light intensity; the grid topology determines the transmission path and distribution of electrical energy within the power system; and the dynamic characteristics of each component reflect the response characteristics of the component under different operating conditions. By comprehensively considering these factors, the model can more accurately simulate the operating status of the power system. The constructed real-time state model can simulate the operating status of the power system in real time, providing a foundation for subsequent stability assessments. During the stability assessment process, the model can be used to simulate and analyze various scenarios, calculate key stability indicators, and thus assess the stability of the power system.

[0047] The effect of the above technical solution is: by deploying various types of sensors at new energy power generation equipment and key nodes of the power grid, a variety of monitoring data can be obtained in real time, which greatly improves the data collection accuracy and real-time performance of the power system operation status, and provides more accurate basic data for subsequent stability analysis.

[0048] By preprocessing the collected multi-source real-time monitoring data, we can remove noise, smooth data, and correct for deviations, thereby obtaining high-precision data. This preprocessing process effectively reduces errors in data processing, ensures high data reliability, and lays a solid foundation for subsequent data fusion and modeling.

[0049] Based on data fusion algorithms, monitoring data from different sources and formats can be integrated, effectively eliminating discrepancies between data sources and obtaining a comprehensive, accurate, and consistent fused dataset. This data fusion enhances the accuracy and completeness of the overall dataset, making the basis for subsequent analysis more reliable.

[0050] Using a system identification method based on a fused data set, a real-time state model was constructed that reflects the dynamic characteristics of large-scale renewable energy integration into the power system. This model comprehensively considers the uncertainty of renewable energy generation, the grid topology, and the dynamic characteristics of components. This improves the accuracy of power system operating state simulation, ensures that the model is closer to reality, and facilitates subsequent stability assessments.

[0051] Based on the real-time state model, the operating status of the power system can be simulated in real time, providing clearer basic data for subsequent stability assessment. This method reduces the complex manual calculation and modeling steps in traditional stability assessment methods, improves efficiency, and reduces errors caused by human factors.

[0052] The real-time state model can reflect the uncertainty of renewable energy power generation and take into account the topological structure of the power grid and the dynamic characteristics of each component, helping the power system to better adapt to the fluctuations brought about by the access of renewable energy, enhancing the system's resilience to the access of renewable energy, and improving the stability and reliability of system operation.

[0053] By leveraging fused data sets and real-time state models, we can monitor the dynamic changes in the power system in real time, identifying potential risks and making scheduling adjustments. This real-time monitoring capability effectively enhances the power system's scheduling capabilities, enabling the system to respond quickly to emergencies and ensuring stable operation.

[0054] Through dynamic simulation of the real-time state model, the assessment strategy can be adjusted according to actual operating conditions, enhancing the adaptability and flexibility of the assessment scheme. When faced with different operating conditions, the system can promptly optimize the assessment method, improving the relevance and timeliness of the stability assessment.

[0055] The combination of real-time state models and multi-source data fusion reduces the computational and manual adjustments required in traditional assessment methods. Through data fusion and modeling, the computational burden of stability assessment is significantly reduced, improving work efficiency and processing speed.

[0056] Through the real-time status model, every component in the power system and its dynamic performance can be monitored more carefully. The hierarchical evaluation method enables comprehensive feedback on the stability status of each key component in the system, reducing the risk of neglecting a certain level and improving the accuracy of the overall stability assessment.

[0057] One embodiment of the present invention, as Figure 3 As shown, the S13 includes:

[0058] S131. Assign accurate spatial coordinates to each data source and reasonably divide the monitoring area into several sub-areas based on geographical location, power grid topology, or meteorological regional characteristics;

[0059] S132. Standardize the format of the collected high-precision multi-source real-time monitoring data, converting data from different sources and formats into a unified format standard while retaining and annotating the spatial coordinate information of each data point;

[0060] S133. Time alignment processing is performed on multi-source real-time monitoring data to ensure that the data from each data source accurately corresponds to each other on the time axis and perform spatial synchronization processing;

[0061] S134. Extract spatially relevant features from normalized and time-aligned data; use spatial statistical methods to identify spatial patterns or anomalies in the data;

[0062] S135. Analyze the correlation between data at different spatial locations to obtain an analysis result, which is a correlation degree. Perform data fusion based on the analysis result using a data fusion method based on spatial interpolation. Perform a spatial quality assessment on the fused data set. Based on the results of the spatial quality assessment, iteratively optimize the data fusion process to obtain a fused data set to be analyzed that meets the spatial analysis requirements. The correlation degree is obtained using the following formula:

[0063]

[0064] Among them, R(i,j) represents the correlation between spatial points i and j; d ij represents the spatial distance; θ represents the distance attenuation threshold; w1 represents the spatial weight coefficient; ρ(F i ,F j ) represents the eigenvector correlation coefficient; F i and F j represents the spatial feature vector; w2 represents the feature weight coefficient; A ij Represents the elements of the topological adjacency matrix; w3 represents the topological weight coefficient.

[0065] The working principle of the above technical solution is to assign accurate spatial coordinates (latitude and longitude) to each data source such as new energy power generation equipment, sensors at key nodes of the power grid, and meteorological monitoring equipment. Spatial coordinates are key information for determining the geographical location of the data source. It enables subsequent data processing and analysis to correspond to the actual spatial location, providing a basis for spatial analysis and fusion. The monitoring area is reasonably divided into several sub-areas based on the geographical location, power grid topology or meteorological regional characteristics. This division helps to analyze the power system operation and meteorological characteristics of different regions in more detail. For example, dividing sub-areas according to the power grid topology can better understand the power transmission and mutual influence between different regions; dividing according to meteorological regional characteristics is conducive to analyzing the impact of meteorological factors on new energy power generation in different regions.

[0066] The collected high-precision, multi-source, real-time monitoring data is standardized, converting data from different sources and formats into a unified format. Since data collected by different devices and systems may have different formats, a unified format standard facilitates subsequent data processing and analysis, avoiding issues caused by inconsistent formats. During the format conversion process, the spatial coordinate information of each data point is retained and annotated to prevent spatial information loss during the conversion process. Spatial information is a key basis for data fusion and spatial analysis, and preserving this information ensures that the spatial correlation of the data is not compromised during subsequent processing.

[0067] Time alignment is performed on multi-source real-time monitoring data to ensure that the data from each data source accurately corresponds on the time axis. The operation of the power system is a dynamic process, and the data collection time of different data sources may differ. Time alignment can ensure that data at the same time point is used during analysis, thereby accurately reflecting the operating status of the power system. Spatial synchronization processing is performed, that is, data at different spatial locations at the same time point can accurately correspond. This is especially important for analyses that require spatial continuity (such as spatial interpolation of meteorological data). Spatial synchronization processing can ensure that data at different spatial locations are matched in time and space during the analysis process, thereby improving the accuracy of the analysis results.

[0068] Spatially relevant features are extracted from the standardized and time-aligned data, including wind speed distribution across different regions, light intensity gradients, and the spatial topological relationships of power grid nodes. These spatial features can reflect the spatial variations of power systems and meteorological factors, providing richer information for subsequent data fusion and analysis. Spatial statistical methods (such as spatial autocorrelation analysis) are used to identify spatial patterns or anomalies in the data. Spatial autocorrelation analysis can determine whether data from adjacent regions are correlated, thereby discovering spatial patterns in the data. Furthermore, this analysis can identify anomalous spatial data, providing a basis for data cleaning and correction. The correlation between data at different spatial locations is analyzed, and based on the analysis results, data fusion is performed using spatial interpolation-based data fusion methods. Spatial interpolation methods can infer data at unknown locations from data at known locations, thereby achieving data fusion. By analyzing the correlation between data, an appropriate interpolation method can be selected to improve the accuracy of data fusion.

[0069] Perform a spatial quality assessment on the fused dataset to check the integrity and consistency of the spatial data and the rationality of the spatial patterns. This ensures that the fused data meets the requirements of spatial analysis and avoids inaccurate analysis results due to data quality issues. Based on the results of the spatial quality assessment, the data fusion process is iteratively optimized, such as by adjusting spatial interpolation parameters and refining spatial correlation analysis methods. This iterative optimization continuously improves the quality of data fusion and yields a fused dataset that meets the requirements of spatial analysis.

[0070] The effect of the above technical solution is: by assigning accurate spatial coordinates to each data source, the solution effectively improves the spatial positioning accuracy of the data, ensures the accurate correspondence between the monitoring data and the geographic location, and provides accurate basic data for subsequent analysis.

[0071] By standardizing the format of high-precision, multi-source, real-time monitoring data from different sources and formats, we avoid missing or incorrect data due to inconsistent formats. This ensures data integrity during conversion, avoids loss of spatial information, and guarantees data consistency and availability.

[0072] Time alignment and spatial synchronization of multi-source real-time monitoring data ensures that data from different spatial locations accurately correspond to each other at the same time point. This is particularly important for analyses requiring spatial continuity and enhances the accuracy of cross-temporal and spatial data analysis.

[0073] Spatially relevant features, such as wind speed distribution, light intensity gradients, and the spatial topology of power grid nodes, are extracted from the standardized and time-aligned data. These features help identify differences and trends between regions, improving the understanding and interpretation of complex spatial data.

[0074] Spatial statistical methods are used to identify spatial patterns or anomalies in data. This approach helps uncover hidden spatial correlations and potential problems, providing valuable insights for decision-making. By analyzing the correlations between data from different spatial locations and applying spatial interpolation data fusion methods, data from different sources can be accurately merged. This spatial quality assessment ensures data integrity, consistency, and rationality, improving the accuracy and reliability of the final fused data.

[0075] Iterative optimization during spatial quality assessment and data fusion reduces inconsistencies in data fusion. Through repeated optimization, the data fusion process becomes more refined, and the final dataset better meets spatial analysis requirements. Optimized data fusion and spatial quality assessment reduce analysis complexity and improve efficiency. Accurate spatial data and fusion results make subsequent analysis more efficient and provide more accurate decision support.

[0076] Because the solution utilizes an iterative optimization approach, data processing and analysis strategies can be flexibly adjusted based on different data sources, analysis requirements, and environmental changes. As a result, the system can adapt to changing monitoring conditions and continuously optimize over time.

[0077] The spatial distance and topological adjacency matrix in the above formula effectively measure the geographic relationships between different spatial points, fully reflecting the spatial relationships between data. By combining the eigenvector correlation coefficient, we can better handle the differences in spatial characteristics between different data sources and avoid errors in correlation calculations caused by inconsistent data formats.

[0078] The introduction of spatial, feature, and topological weight coefficients allows for weighted fusion of data from different sources and formats based on their spatial characteristics, feature attributes, and topological structure, thereby optimizing fusion results and improving data quality. Spatial interpolation methods enable precise data fusion, helping to reduce data errors caused by spatial differences and temporal asynchrony.

[0079] The format standardization and time alignment mentioned in S132 and S133 provide a unified data foundation for this formula. This allows data from different sources to be analyzed according to a unified standard when calculating correlation, avoiding inconsistencies caused by format or time synchronization issues. These measures effectively enhance the comparability of multi-source data and provide a higher degree of confidence in the correlation calculation results.

[0080] The spatial distance decay mechanism in this formula, combined with spatial statistical methods, can help identify spatially significant regions and potential outliers. Spatial quality assessment and iterative optimization of the data fusion process can further improve the accuracy of anomaly detection. Using this correlation calculation formula, patterns and potential issues in data can be discovered on a larger scale, enabling system optimization and adjustment.

[0081] By assessing the spatial quality of data fusion results, deficiencies in the fusion process can be identified and iteratively optimized. This feedback mechanism continuously improves the quality of spatial analysis and avoids error propagation caused by initial data bias. Spatial quality assessment and iterative optimization help provide a more refined and reliable dataset, laying a solid foundation for subsequent spatial analysis.

[0082] In one embodiment of the present invention, the step S133 includes:

[0083] Based on the clock synchronization of each data source, a unified time base is selected as the reference standard for time alignment of all data. Based on the selected time base, the data of each data source is timestamped and annotated according to the unified time base. A time alignment algorithm is used to align the data of different data sources on the time axis.

[0084] Based on the needs of spatial analysis, identify the data types and analysis scenarios that require spatial synchronization;

[0085] Establish a mapping relationship between spatial location and data value for each data point, identify the spatial coverage and overlapping areas of different data sources, and establish a spatial association mechanism across data sources;

[0086] According to the requirements of spatial synchronization and the spatial distribution characteristics of the data, spatial interpolation calculations are performed on the data points that require spatial synchronization through spatial interpolation methods to generate data values ​​at the specified spatial location and time point;

[0087] During the time alignment and spatial synchronization process, possible abnormal data is detected; the detected abnormal data is processed;

[0088] Evaluate the effectiveness of time alignment processing, check the alignment accuracy and consistency of each data source on the time axis; evaluate the effectiveness of spatial synchronization processing, check the corresponding accuracy and spatial continuity of data in different spatial positions at the same time point; provide feedback and optimize the processing process based on the results of time alignment and spatial synchronization evaluation.

[0089] The working principle of the above technical solution is that because different data sources may use their own clock systems and have time deviations, it is necessary to select a unified time base (such as UTC time) based on the clock synchronization status of each data source. UTC time is the global standard time. Using it as the reference standard for all data time alignment can eliminate time differences between different data sources and provide a unified time frame for subsequent data processing.

[0090] Based on the selected time base, data from each data source is timestamped. Data may contain inaccurate time information during the data collection process. This correction ensures that each data point carries accurate time information and is labeled according to a unified time base. This makes data from different data sources comparable in the time dimension, laying the foundation for subsequent time alignment. Time alignment algorithms such as interpolation and extrapolation are used to align data from different data sources along the time axis. In actual data collection, data from different data sources may have different collection intervals, resulting in the possibility of no corresponding data at the same time point. Interpolation algorithms can infer data from missing time points based on data from known time points, while extrapolation algorithms can predict data for future time points based on existing data trends. This ensures that data from different data sources at the same time point are accurately aligned, providing accurate time series data for subsequent joint analysis.

[0091] Based on the needs of spatial analysis (such as spatial interpolation of meteorological data and spatial distribution analysis of power grid loads), identify the data types and analysis scenarios that require spatial synchronization. Different spatial analysis needs have different data requirements. For example, spatial interpolation of meteorological data requires precise spatial location data, while spatial distribution analysis of power grid loads may need to consider the load characteristics of different regions. Based on these needs, develop a specific plan for spatial synchronization, including determining the spatial locations that require synchronization, the synchronization time interval, and the synchronization accuracy requirements. A clear plan can guide subsequent spatial synchronization processing and ensure that the processing results meet the analysis requirements.

[0092] A mapping relationship between spatial location and data value is established for each data point to ensure accurate association of data at the same spatial location. Spatial location is a key attribute of data. By establishing this mapping relationship, data at the same spatial location from different data sources can be linked. For different data sources, their spatial coverage and overlapping areas are identified, and a spatial association mechanism across data sources is established. Different data sources may cover different spatial regions, and overlapping areas may contain data from multiple sources. This association mechanism ensures that data at the same spatial location can be obtained from different data sources and comprehensively analyzed, improving data utilization efficiency and accuracy.

[0093] Based on the requirements for spatial synchronization and the spatial distribution characteristics of the data, spatial interpolation methods are used to perform spatial interpolation calculations on data points requiring spatial synchronization. Spatial interpolation methods can infer data at unknown spatial locations based on data at known spatial locations, generating data values ​​at specified spatial locations and time points, thereby achieving spatial synchronization of the data. The interpolated data is verified to ensure that data at different spatial locations at the same time point accurately correspond and meet spatial continuity requirements. Spatial continuity is a key characteristic of spatial data, and the verification process can check the rationality of the interpolation results and whether there are any discontinuities or anomalies.

[0094] During the time alignment and spatial synchronization process, we detect possible data anomalies, such as timestamp errors, spatial position deviations, and abnormal data values. Timestamp errors can lead to inaccurate data alignment on the time axis, spatial position deviations can affect the spatial correlation of data, and abnormal data values ​​can indicate problems during data collection or transmission. By detecting these anomalies, we can promptly identify and address issues and ensure data quality.

[0095] Processing detected anomalous data involves correcting timestamps, adjusting spatial positions, and removing or replacing anomalous data values. Correcting timestamps ensures accurate alignment of data on the timeline, adjusting spatial positions ensures accurate spatial correlation, and removing or replacing anomalous data values ​​prevents them from impacting subsequent analysis. These measures ensure data accuracy and reliability, providing high-quality data for subsequent stability assessments.

[0096] Evaluate the effectiveness of the time alignment process and check the alignment accuracy and consistency of each data source on the time axis. Alignment accuracy can be measured by calculating the difference in data from different data sources at the same time point, while consistency examines whether the changing trends of each data source over the time series are consistent. This evaluation can determine whether the time alignment process has achieved its intended goals and whether there are any inaccuracies or inconsistencies in the alignment.

[0097] Evaluate the effectiveness of spatial synchronization processing by examining the correspondence accuracy and spatial continuity of data at different spatial locations at the same point in time. Correspondence accuracy can be assessed by comparing data differences between different data sources at the same spatial location and time point. Spatial continuity examines whether the spatial variation of data is reasonable. The evaluation results can indicate whether the spatial synchronization processing meets the analysis requirements and whether there are any data mismatches or spatial discontinuities.

[0098] Based on the results of the time alignment and spatial synchronization assessment, feedback and optimization are provided to the processing process. If the time alignment algorithm is found to be inadequate, the algorithm parameters can be adjusted or a more appropriate algorithm can be adopted. If the spatial interpolation method results in inaccurate data, the interpolation method or parameters can be improved. Through feedback and optimization, the effectiveness of time alignment and spatial synchronization processing can be continuously improved, ensuring the accuracy and reliability of the data and providing more reliable data support for subsequent power system stability assessments.

[0099] The effect of the above technical solution is: by selecting a unified time base, the data from each data source is timestamped and time aligned, ensuring the precise time synchronization of each data source, avoiding the impact of time deviation on the analysis results, and thus improving the accuracy of multi-source data integration.

[0100] Using time alignment algorithms such as interpolation and extrapolation, data from different data sources are precisely aligned on their time axes, reducing errors caused by data time differences and effectively improving data processing accuracy. By establishing a mapping relationship between spatial location and data values ​​and a spatial association mechanism across data sources, data at the same spatial location can be accurately associated, facilitating the integration and analysis of spatial data. Spatial interpolation calculations further improve the spatial consistency of data and enhance the continuity of data at different spatial locations.

[0101] By detecting and correcting or replacing abnormal data, the impact of erroneous data on the final analysis results is reduced, the reliability and accuracy of the analysis results are improved, and the consistency of data in time and space is ensured. A cross-data source spatial association mechanism has been developed to address the spatial coverage and overlapping areas of different data sources. This allows for flexible data extraction and comprehensive analysis across different data sources, providing broader support for diverse analytical needs.

[0102] Through reasonable time alignment and spatial synchronization processing, the multi-source data integration process is simplified, reducing the complexity of manual correction and data cleaning, making the data processing process more efficient, saving time and human resources. Through spatial interpolation calculation and post-validation of spatially synchronized data points, the accuracy of the spatial interpolation method is ensured, the correspondence accuracy between data at different spatial locations is improved, and the spatial continuity of the analysis results is further guaranteed.

[0103] This technical solution enhances the system's adaptability to different data sources and scenarios by processing time synchronization, spatial synchronization, and abnormal data, enabling the system to provide more accurate and reliable analysis results and better scalability when faced with complex multidimensional data.

[0104] By evaluating and optimizing aspects such as time alignment, spatial synchronization, and data anomaly detection, the data quality management level has been improved, ensuring that high-quality data is always maintained during the processing process to meet the needs of high-precision analysis.

[0105] In one embodiment of the present invention, the step S135 includes:

[0106] Based on the data type and analysis objectives, we construct an index to measure the correlation between data at different spatial locations. We calculate the correlation matrix between the data points at each spatial location and determine the strength and direction of the correlation between the data points. The correlation matrix is ​​calculated as follows:

[0107] Assume that there are n data points {P1, P2…, P n}, each data point contains m-dimensional feature data (such as voltage, current, frequency, meteorological parameters, etc.), then the correlation matrix R can be expressed as:

[0108]

[0109] Among them, r ij Represents the data point P i With P j Comprehensive correlation index between (value range [-1,1], the larger the absolute value, the stronger the correlation, and positive and negative values ​​indicate the direction of correlation); ω k Expressed as the weight coefficient of the k-th dimension feature data ( The weight is determined based on the reliability of the data and its importance to the analysis objective. For example, the weight of power grid topology data can be higher than that of meteorological data. Indicates that the k-th dimension feature data is in P i With P j The correlation coefficient between them is obtained by the following formula:

[0110]

[0111] Use spatial clustering algorithms to cluster data points based on spatial correlation and identify data groups with similar spatial characteristics; set interpolation parameters based on interpolation methods;

[0112] Combining spatial correlation analysis with interpolation methods, a multi-source data fusion strategy is developed, and the original data is pre-processed before data fusion;

[0113] According to the formulated data fusion strategy, the selected spatial interpolation method is used to interpolate the multi-source data to generate a fused data set; the results of the spatial quality assessment are fed back to the data fusion process to identify existing problems and deficiencies;

[0114] Based on the evaluation results, the spatial interpolation parameters and data fusion strategies are adjusted and optimized, and the optimized parameters and strategies are used to perform new data fusion and spatial quality assessment until a fused data set to be analyzed that meets the spatial analysis requirements is obtained.

[0115] The working principle of the above technical solution is: there are various associations between data in different spatial locations, and this association is affected by many factors, such as geographical location, meteorological conditions, grid topology, etc. Based on the data type (such as meteorological data, grid data, etc.) and the analysis goal (such as accurately simulating the operating status of the power system), indicators are constructed to measure the association between data in different spatial locations. These indicators can be designed from the perspectives of data correlation, similarity, causality, etc. The purpose of the spatial clustering algorithm is to group data points with similar spatial characteristics into one category. Based on the correlation matrix calculated previously, the spatial clustering algorithm can identify the inherent connections and patterns between data points.

[0116] Based on the interpolation method, set the interpolation parameters. Different interpolation methods have different parameters, which affect the accuracy and precision of the interpolation results. For example, in Kriging interpolation, it is necessary to set the variogram model parameters, which describe the spatial variation characteristics of the data. Combine spatial correlation analysis with interpolation methods to formulate a multi-source data fusion strategy. Clarify the weight distribution, interpolation range, and fusion rules of different data sources in the fusion process. The weight distribution can be determined based on the reliability, accuracy, and importance of the data source to the analysis objective. For example, for meteorological data, if the data quality of a certain meteorological monitoring station is higher and more stable, it can be given a greater weight in the fusion process. The interpolation range determines which spatial locations need to perform interpolation calculations, and the fusion rules stipulate how to integrate data from different data sources.

[0117] Before data fusion, the raw data is preprocessed. Following the established data fusion strategy, the selected spatial interpolation method is used to interpolate the multi-source data. Interpolation infers the values ​​of unknown data points based on the values ​​and spatial locations of known data points. This interpolation generates a fused dataset that integrates information from different data sources and exhibits improved spatial continuity and integrity.

[0118] Perform a spatial quality assessment on the fused dataset. This assessment can include metrics such as data completeness (whether all required data points have values), consistency (whether data from different data sources are coordinated after fusion), and the rationality of the spatial pattern (whether it conforms to actual physical laws and spatial distribution characteristics). Feedback the results of this spatial quality assessment back into the data fusion process to identify any problems or deficiencies. For example, if the assessment reveals significant discontinuities in the data fusion results in certain areas, or discrepancies with actual conditions, the causes of these problems need to be identified.

[0119] Based on the evaluation results, the spatial interpolation parameters and data fusion strategies are adjusted and optimized. If inappropriate interpolation parameters lead to inaccurate interpolation results, the parameters are adjusted; if the weight distribution in the data fusion strategy is unreasonable, the weights are redefined. A new round of data fusion and spatial quality assessment is conducted using the optimized parameters and strategies until a fused dataset that meets the requirements of spatial analysis is obtained. Through this iterative optimization process, the quality of data fusion is continuously improved, so that the fused data can more accurately reflect the actual operating status of the power system and provide reliable data support for subsequent stability assessments.

[0120] The effect of the above technical solution is: by accurately calculating the correlation of data at different spatial locations, an effective correlation matrix is ​​established, which can clearly reflect the strength and direction between data points, thereby reducing errors in the spatial clustering and interpolation process, making the fused data set more accurate and reliable.

[0121] Using spatial clustering algorithms to cluster data points and interpolating the data based on the clustering results simplifies the integration of multi-source data, reduces the complexity of manual corrections and data cleaning, and reduces the computational and manual intervention required for data processing. By combining spatial correlation analysis with interpolation methods, we ensure greater continuity and consistency between data in different spatial locations, thereby improving the effectiveness of cross-spatial data integration and making the spatial fusion of data from different sources smoother.

[0122] By precisely determining the weight distribution, interpolation range, and fusion rules for each data source during the fusion process, the system can flexibly adapt to the needs of different data sources and analysis objectives, ensuring the customization of data fusion strategies and improving the system's adaptability. Preprocessing data before fusion to detect and correct anomalies avoids potential quality issues during the fusion process, such as missing values ​​and outliers, thereby effectively improving the quality of the final dataset and reducing analytical errors caused by data quality issues.

[0123] By optimizing spatial interpolation parameters and data fusion strategies, combined with the results of spatial quality assessment, we ensured improved spatial quality during each round of data fusion, thereby strengthening the reliability of the final dataset and the accuracy of the analysis results. A feedback mechanism promptly incorporated spatial quality assessment results into the data fusion process, enabling dynamic adjustment of strategies and parameters based on actual conditions. This optimized the data fusion process and improved overall processing efficiency and effectiveness.

[0124] By combining spatial clustering with interpolation, in-depth analysis can be performed at different spatial scales and multiple data dimensions, so that the fused dataset can not only meet basic spatial analysis needs, but also support more complex multi-dimensional analysis applications. This technical solution ensures high scalability in processing different data sources and different scenarios through a modular design concept, allowing the system to flexibly adapt to new data sources or data types by adjusting parameters and strategies, reducing the difficulty of maintenance. Through multiple rounds of spatial quality assessment and data fusion optimization, this technical solution has the ability to continuously improve data quality, can continuously adapt to increasingly complex analysis needs, and ensure the long-term effectiveness of the data fusion process.

[0125] By comprehensively considering the weights of each dimension, we can assign different weights to data from different dimensions based on the importance and reliability of each feature, making correlation calculations more suitable for practical applications. The correlation coefficient in the formula, calculated using covariance, can accurately measure the similarity between data points. This is particularly effective in measuring the relationship between different features when measuring correlations between multidimensional data.

[0126] Weight coefficients are set based on the importance of different features to the analysis objective, increasing focus on key features. For example, in power grid analysis, grid topology data can be given a higher weight than meteorological data. This design can improve the relevance and effectiveness of the analysis. The design of weight coefficients helps to address differences between data sources and improve the accuracy of correlation calculations.

[0127] Spatial clustering algorithms, based on the correlations between data points, can group data points with similar spatial characteristics into the same group for further analysis. This clustering not only aggregates data based on spatial location but also considers multidimensional features between data, making clustering results more reasonable and accurate. This clustering method can identify potential patterns in space, optimize data distribution, and help discover hidden patterns in data.

[0128] Interpolation of spatial data using interpolation methods can fill gaps in space and increase data continuity and integrity. By setting interpolation parameters, values ​​at different spatial locations can be smoothed to reduce the impact of missing data. The accuracy and rationality of interpolation depend on the quality of the spatial correlation matrix. Therefore, accurate correlation calculations directly improve the accuracy and reliability of interpolation results.

[0129] In multi-source data analysis, different data points often have different data types and characteristics. By establishing this correlation matrix, we can conduct unified correlation analysis across multiple dimensions of data, avoiding data inconsistencies and providing more comprehensive analysis results. This approach effectively addresses integration issues caused by differences between data sources and improves the accuracy and reliability of comprehensive analysis results.

[0130] In one embodiment of the present invention, the S2 includes:

[0131] S21. Based on the actual operation and fault types of large-scale renewable energy access to the power system, set up dynamic simulation scenarios of the power system under multiple scenarios;

[0132] S22. Analyze the stability of the power system using numerical calculation methods in each set simulation scenario; simulate the dynamic response process of the system in the simulation scenario by solving the differential-algebraic equations of the power system; and obtain a data set of key indicators of power system stability in multiple scenarios;

[0133] S23. Conduct in-depth mining and analysis of key stability indicator datasets across multiple scenarios; discover hidden patterns and patterns in the data; and establish correlation models between key stability indicators, system operating parameters, and renewable energy generation characteristics based on machine learning algorithms.

[0134] S24. Utilize the established correlation model, input different operating parameters and new energy power generation characteristic data, and predict the stability trend of large-scale new energy access to the power system under different operating conditions; and obtain power system stability trend prediction data by analyzing the prediction results.

[0135] The working principle of the above technical solution is as follows: after large-scale renewable energy is connected to the power system, the system's operation becomes more complex, and various faults and abnormal conditions may occur. The purpose of setting dynamic simulation scenarios for the power system under multiple scenarios based on actual operating conditions and possible fault types is to comprehensively and realistically simulate the operating status of the power system under different conditions. In each set simulation scenario, numerical calculation methods are used to analyze the stability of the power system. The time domain simulation method simulates the dynamic response process of the system in the simulation scenario by gradually solving the differential-algebraic equations of the power system, which can intuitively reflect the changes in the system state at different times. The direct rule is based on theories such as the system's energy function to quickly determine the stability of the system.

[0136] The transient stability margin is determined by calculating the critical conditions for the system to return to a stable operating state after fault removal. It reflects the system's ability to recover after a fault impact. The voltage stability margin uses voltage stability criteria to calculate the critical point at which the system voltage destabilizes, and is used to assess system voltage stability. The frequency stability margin analyzes how the system frequency changes with load changes or power generation fluctuations, determining the boundaries of frequency stability and ensuring that the system frequency remains within a reasonable range. By calculating these key stability indicators, a dataset of key power system stability indicators is obtained under multiple scenarios, providing data support for subsequent in-depth research and analysis.

[0137] Deep mining and analysis of key stability indicator datasets in multiple scenarios aims to discover hidden laws and patterns in the data. Since power system operation data is large and complex, valuable information can be extracted through data mining technology, such as the changing trends of stability indicators in different scenarios and the degree of influence of different factors on stability indicators. Based on machine learning algorithms, a correlation model is established between key stability indicators and system operating parameters (such as generator output, load level, grid topology parameters, etc.) and renewable energy power generation characteristics (such as power prediction error and fluctuation characteristics of renewable energy power generation). Machine learning algorithms can learn the intrinsic relationship between variables from large amounts of data. By training the model, it can accurately describe the complex relationship between key stability indicators and various influencing factors. For example, neural network algorithms can handle nonlinear relationships, and regression algorithms can establish quantitative relationships between variables.

[0138] Using the established correlation model, different operating parameters and renewable energy generation characteristics are input to predict the stability trends of large-scale renewable energy-connected power systems under different operating conditions. The correlation model has learned the relationships between key stability indicators and various influencing factors. When new operating parameters and renewable energy generation characteristics are input, the model can make predictions based on these relationships. By analyzing the prediction results, power system stability trend forecasts are obtained. This prediction data can help power system operators understand the system stability under different operating conditions in advance and take appropriate measures to ensure stable system operation. For example, if the system transient stability margin is predicted to be low under certain operating conditions, operators can adjust generator output and optimize the grid topology in advance to improve system stability.

[0139] The effect of the above technical solution is: by simulating and analyzing the dynamic response of the power system under multiple scenarios, the stability of the system under different faults and load fluctuations can be accurately evaluated, thereby improving the grasp of the overall stability of the power system.

[0140] By conducting stability analysis under different fault scenarios, we can identify and analyze potential fault risks in advance, and then formulate effective preventive measures to reduce the risk of loss of stability of the power system when a fault occurs.

[0141] Considering that wind power, photovoltaic power and other energy sources are highly volatile after large-scale new energy access, by analyzing the impact of new energy power generation fluctuations on the power system, the dynamic response capability of the power system in the face of new energy fluctuations can be enhanced to ensure stable operation of the system.

[0142] By using machine learning algorithms to build correlation models, accurate predictions can be made based on different system operating parameters and new energy power generation characteristics, thereby achieving trend predictions on power system stability and improving the ability to control future operating conditions.

[0143] Analyzing the stability of the power grid under load change scenarios can effectively identify the impact of load changes on the stability of the power system, improve the system's ability to respond to sudden load changes, and ensure the continuity and stability of power supply.

[0144] By analyzing the transient stability margin, we can evaluate the system's recovery capability after fault removal, thereby improving the system's recovery speed and efficiency after a fault, reducing power outage time, and improving the reliability of power supply.

[0145] Based on stability trend prediction data, the power system can better optimize operating parameters and scheduling strategies to ensure that the system can achieve optimal stability performance in different scenarios and reduce unnecessary energy waste and instability risks.

[0146] In one embodiment of the present invention, the step S22 includes:

[0147] Based on the actual topological structure and component characteristics of the power system connected to large-scale renewable energy, a dynamic model of the power system is established, and initial parameters are set for each component in the model;

[0148] Select a numerical calculation method based on the characteristics of the simulation scenario and analysis requirements, and apply the selected numerical calculation method to the constructed power system model to perform stability analysis;

[0149] By solving the differential-algebraic equations of the power system, the dynamic response process of the system under the set simulation scenario is simulated; during the simulation process, the changes of key variables are recorded in real time;

[0150] Determine the transient stability margin by calculating the critical conditions for the system to return to a stable operating state after fault removal; calculate the critical point of system voltage instability using voltage stability criteria; and analyze the frequency changes of the system when the load changes or the power generation fluctuates to determine the frequency stability boundary.

[0151] The calculated key indicators are integrated to form a data set of key indicators of power system stability under multiple scenarios.

[0152] The working principle of the above technical solution is that the integration of large-scale renewable energy into the power system increases the complexity of the system's topology and component characteristics. To accurately simulate the system's operation, a dynamic model of the power system must be established based on the actual topology and component characteristics. This model covers all key components, including generators, transformers, lines, loads, and renewable energy generation equipment (such as wind turbines and photovoltaic power plants), and accurately describes their dynamic behavior. The appropriate numerical calculation method is selected based on the characteristics of the simulation scenario and the analysis requirements. Different numerical calculation methods are suitable for different stability analysis scenarios. Time-domain simulation methods can simulate the dynamic changes of the system over time in detail. By gradually solving the differential-algebraic equations of the power system, the changes in the system state at different moments can be clearly observed, making them suitable for transient stability analysis. For example, during the simulation of fault occurrence and removal, time-domain simulation methods can accurately record the changes in variables such as the generator rotor angle, voltage, and frequency, thereby assessing the stability of the system during transient processes. The direct method solves the differential-algebraic equations of the power system to quickly derive the stability criteria for the system. It focuses on analyzing the steady-state stability of the system from a theoretical perspective, eliminating the need for lengthy simulations like time-domain simulation methods. It is suitable for quickly determining the stability of the system under steady-state conditions. Applying the selected numerical calculation method to the constructed power system model for stability analysis allows for the selection of appropriate methods based on specific needs, improving analysis efficiency and accuracy.

[0153] By solving the differential-algebraic equations of the power system, the system's dynamic response is simulated under a set simulation scenario. During the simulation, various components in the system interact, resulting in acceleration and deceleration of the generator rotor, fluctuations in voltage and frequency, and changes in renewable energy generation. Changes in key variables such as the generator rotor angle, voltage amplitude and phase, frequency, and renewable energy generation are recorded in real time. These key variables are important indicators of system stability, and recording their changes provides an intuitive understanding of the system's operating status at different moments. For example, changes in the generator rotor angle can reflect the system's transient stability. If the rotor angle swings excessively and fails to return to a stable state, it indicates that the system may be experiencing transient instability. Changes in voltage amplitude and phase reflect the system's voltage stability. Excessively low voltage amplitude or large phase variations can lead to voltage instability. Changes in frequency reflect the system's frequency stability. Excessive deviations from the rated frequency can affect the normal operation of power equipment.

[0154] The transient stability margin is determined by calculating the critical conditions for the system to return to a stable state after fault removal. This involves analyzing the system's energy balance and rotor angle swing after fault removal. When a fault occurs, the system accumulates a certain amount of energy. After the fault is removed, the system needs to release this energy and return to a stable state. If the system can gradually reduce the rotor angle swing and return to a stable value after the fault is removed through its own regulatory mechanisms (such as the generator's speed control system and excitation system), the system is transiently stable; otherwise, the system may experience transient instability. By analyzing the amplitude and trend of the rotor angle swing, the critical conditions for the system to return to a stable state can be determined, thereby calculating the transient stability margin. Using voltage stability criteria, the critical point of system voltage instability is calculated. This is achieved by analyzing the voltage changes as the system load changes and the boundary conditions for voltage collapse. As the system load increases, the voltage gradually decreases. If the load increases to a certain level, the voltage may drop sharply, leading to voltage instability. By analyzing the relationship between load changes and voltage changes, as well as the critical conditions for voltage collapse (such as voltage amplitude falling below a certain threshold), the critical point of system voltage instability, namely the voltage stability margin, can be calculated. Analyze the frequency changes of the system when the load changes or the power generation power fluctuates to determine the boundaries of frequency stability. The frequency of the system is related to the balance between power generation and load power. When the power generation power exceeds the load power, the frequency will increase; when the power generation power is less than the load power, the frequency will decrease. By analyzing the relationship between frequency changes and power generation and load power changes, as well as the boundary conditions for frequency stability (such as the range within which the frequency deviates from the rated value), the boundaries of frequency stability, namely the frequency stability margin, can be determined.

[0155] The calculated key indicators, such as transient stability margin, voltage stability margin, and frequency stability margin, are integrated to form a dataset of key indicators for power system stability under multiple scenarios. These key indicators are an important basis for evaluating the stability of the power system under different simulation scenarios. By integrating indicator data from different scenarios, we can fully understand the stability of the system under different operating conditions. For example, we can compare stability indicators under different scenarios of renewable energy power fluctuations, grid faults, and load changes, analyze the impact of various factors on system stability, and provide decision support for power system planning and operation.

[0156] The benefits of this technical solution are: by accurately modeling and dynamically simulating the large-scale integration of renewable energy into the power system, we can more effectively assess the system's stability under various fault and load conditions. This provides more accurate transient stability margins, voltage stability margins, and frequency stability margins, thereby enhancing the power system's ability to cope with various disturbances.

[0157] Using numerical calculation methods such as time-domain simulation and direct methods, we can quickly and accurately simulate the system's dynamic response after a fault occurs and record changes in key variables in real time. By simulating the acceleration and deceleration of the generator rotor, as well as voltage and frequency fluctuations, we can promptly assess the system's recovery capabilities after a fault occurs, thereby reducing system recovery time.

[0158] Accurate dynamic modeling of renewable energy equipment, such as wind turbines and photovoltaic power plants, helps analyze their interactions within the system. This allows for more precise assessment of the stability of renewable energy generation and the impact of power forecast errors on the system, particularly in the event of load or power fluctuations in the power system. This detailed modeling can improve the operational reliability of renewable energy generation equipment and enhance the system's adaptability to fluctuations in renewable energy.

[0159] The model encompasses the dynamic characteristics of key components such as generators, transformers, lines, loads, and renewable energy generation equipment, providing a scientific basis for real-time dispatch and optimization of the power system. By analyzing various simulation scenarios, it can provide data support for grid operation dispatch, load forecasting, power optimization, and other aspects, improving the efficiency and accuracy of dispatch decisions.

[0160] By simulating the system's response to various faults and load changes, potential system risks and vulnerabilities can be identified in advance, helping to develop more reliable emergency response strategies. By calculating critical conditions after fault removal, the conditions required to restore stability can be quickly determined in the event of a system failure, enhancing the system's ability to withstand unexpected events.

[0161] Numerical simulation allows for rapid evaluation of system behavior under different scenarios without conducting actual physical tests. This not only reduces the cost of actual debugging and testing, but also enables extensive experimentation in a virtual environment, enabling early identification of problems and adjustments.

[0162] This technical solution can adapt to the needs of power systems of varying sizes, and as the proportion of renewable energy continues to increase, the solution can be flexibly adjusted to meet the diverse and complex needs of future power systems. As different energy access models are improved and expanded, this solution can provide continuous optimization support for the dynamic scheduling and stability of future power grids.

[0163] In one embodiment of the present invention, S3 includes:

[0164] S31. Conduct a preliminary assessment of the stability of the power system based on the power system stability trend forecast data and a preset stability threshold;

[0165] S32. Compare the predicted stability index with the stability threshold to determine whether the system meets the stability requirements under different operating conditions; screen out areas or nodes with stability risks and obtain preliminary screening risk area or node data;

[0166] S33. Based on the initial screening of risk areas or node data, extract relevant assessment strategy parameters from a preset stability assessment strategy database; adaptively adjust the test application parameters of subsequent stability assessments based on the extracted assessment strategy parameters; and obtain a stability assessment scheme suitable for large-scale renewable energy access to the power system.

[0167] S34. Upload the adjusted stability assessment plan to the power system stability assessment management platform; the assessment management platform schedules and manages the assessment tasks based on the adjusted stability assessment plan, and assigns the assessment plan to corresponding assessment equipment and personnel.

[0168] The working principle of the above technical solution is as follows: the power system stability trend prediction data reflects the changing trend of system stability under different operating conditions. The preset stability threshold is determined based on factors such as the safe operation standards of the power system, the tolerance of the equipment, and previous operating experience. It provides a clear boundary for judging the stability of the system. By comparing the power system stability trend prediction data with the preset stability threshold, a preliminary assessment of the stability of the power system can be made. On the basis of the preliminary assessment, the predicted stability index is carefully compared with the stability threshold. Under different operating conditions (such as different new energy power generation power fluctuations, load changes, grid topology, etc.), the stability performance of the system may be different. Through comparison, it can be clarified whether the system meets the stability requirements under different operating conditions. For areas or nodes that do not meet the stability requirements, that is, areas or nodes that may have stability risks, they are screened out to obtain preliminary screening risk area or node data.

[0169] Based on the data from the initially screened risk areas or nodes, relevant assessment strategy parameters are extracted from a pre-set stability assessment strategy database. This database stores assessment strategy parameters for different risk areas or nodes and different operating scenarios. These parameters, developed through long-term practice and research, provide reasonable assessment guidance based on different risk situations. The frequency of assessments is determined based on the level of risk, with shorter assessment intervals for high-risk areas or nodes. For example, for critical wind turbine access points with high stability risks, more frequent assessments are required to promptly identify potential stability issues.

[0170] Comprehensively consider the results of multiple assessment methods and assign appropriate weights to each method. Different assessment methods (such as time-domain simulation, direct methods, and machine learning-based prediction methods) have their own advantages and disadvantages and scopes of application. By assigning appropriate weights, the advantages of various methods can be leveraged to improve the accuracy of assessment results. Identify the electrical quantities and equipment that require key monitoring during the assessment process. For example, in risk areas near high-voltage transmission lines, it may be necessary to focus on monitoring electrical quantities such as line current, voltage, and power, as well as the operating status of key equipment such as transformers and circuit breakers. Based on the extracted assessment strategy parameters, adaptively adjust the test application parameters for subsequent stability assessments. For example, adjust the fault type in the dynamic simulation (simulate more severe fault types, such as three-phase short circuit faults instead of single-phase ground faults, for high-risk areas) to examine system stability under more severe conditions; extend the fault duration to observe the system's response and recovery capabilities under long-term faults; and simulate more severe fluctuations in renewable energy generation to test the system's ability to withstand the uncertainties of renewable energy. Through these adjustments, a stability assessment scheme suitable for power systems with large-scale renewable energy integration is developed, which can more accurately assess the system's stability in actual operation.

[0171] Upload the adjusted stability assessment plan to the power system stability assessment management platform. The assessment management platform is an integrated system that centrally schedules and manages assessment tasks. Based on the adjusted stability assessment plan, the assessment management platform schedules and manages assessment tasks and assigns the plan to the appropriate assessment equipment and personnel. Different assessment tasks may require different equipment and professionals. For example, some complex dynamic simulation analyses may require high-performance computing equipment and specialized power system analysts. Through reasonable task allocation, the smooth progress of the assessment work is ensured, and the efficiency and quality of the assessment are improved. The assessment management platform also monitors and records the assessment process to identify problems and make adjustments in a timely manner.

[0172] The effect of the above technical solution is: by combining the predicted stability trend data with the preset stability threshold, a preliminary and accurate stability assessment of the power system can be performed, which reduces the error of human judgment and improves the accuracy of system assessment.

[0173] During the initial screening phase, it can effectively identify areas or nodes that may pose stability risks, providing a foundation for more accurate subsequent assessments. By comparing predicted stability indicators with stability thresholds, it automatically screens out areas or nodes with stability risks, avoiding erroneous assessments caused by inaccurate or missed manual judgments.

[0174] After screening out the risk areas, the errors are further reduced by extracting and adaptively adjusting the relevant assessment strategy parameters, making the assessment results more consistent with the actual operating conditions.

[0175] Flexible adjustments to assessment strategies based on different regions, nodes, and operational scenarios during the assessment process, particularly the simulation of fluctuations in renewable energy generation power, can effectively improve stability assessment capabilities when large-scale renewable energy is integrated into the power system. Dynamically simulating different types of faults ensures the system's ability to withstand various complex situations, enhancing the power system's adaptability to the uncertainties of renewable energy generation.

[0176] The assessment management platform schedules and manages assessment plans, assigning tasks to appropriate equipment and personnel to ensure efficient assessment execution. This reduces time wasted in task allocation and coordination issues during execution. Adaptive adjustment of assessment parameters allows for optimization based on the needs of risk areas, improving assessment speed and flexibility while reducing the execution cycle of assessment tasks.

[0177] The assessment strategy database includes weightings for various assessment methods and selection of key monitoring points, making stability assessments more scientific and targeted. It allows for the selection of appropriate assessment methods and key monitoring equipment based on different risk conditions, improving assessment accuracy and operability. Flexible adjustments to assessment intervals and methods allow for the development of more targeted assessment strategies tailored to the characteristics of different risk areas, enhancing system stability.

[0178] Automated risk screening and assessment strategy parameter extraction reduces manual intervention and complex decision-making, making risk management more systematic and standardized. A systematic risk assessment and task management platform can streamline the power system's risk management process, reduce the complexity of risk identification and assessment, and improve the efficiency of emergency response.

[0179] By simulating more severe fault types and extending fault duration, we can proactively identify the power system's performance under extreme conditions, enhancing its responsiveness and resilience in emergencies. Adjusting assessment strategy parameters to account for fluctuations in renewable energy sources increases the system's flexibility and robustness in responding to emergencies, improving its ability to cope with disturbances.

[0180] In one embodiment of the present invention, the S4 includes:

[0181] S41. In accordance with the adjusted stability assessment plan, obtain real-time assessment test data of large-scale renewable energy access to the power system through real-time monitoring equipment; and evaluate the stability of the power system using a comprehensive assessment method based on the real-time assessment test data;

[0182] S42. Obtain detailed device performance evaluation data through a comprehensive evaluation method; and screen qualified devices again based on the detailed device performance evaluation data.

[0183] S43. Based on the set device stability evaluation standard, the performance evaluation data of each device is compared with the evaluation standard, and the devices or subsystems with good stability during the evaluation process are screened out to obtain the performance data of the re-screened qualified devices.

[0184] The working principle of the above technical solution is as follows: Based on the adjusted stability assessment plan, real-time monitoring equipment is used to collect data from the large-scale renewable energy integration power system. The real-time monitoring equipment is deployed at key monitoring points in the system, which are determined based on previously screened risk areas or nodes and stability assessment requirements. The collected data includes electrical quantities such as voltage, current, and frequency at each key monitoring point. These electrical quantities are fundamental parameters reflecting the operating status of the power system, and their changes can directly reflect the system's dynamic behavior. Simultaneously, operating status data of renewable energy power generation equipment is also collected, such as generator speed, rotor angle, and output characteristics of photovoltaic inverters. Renewable energy power generation equipment is a critical component of the power system, and its operating status has a significant impact on system stability. Acquiring this data provides a more comprehensive understanding of the system's operation after renewable energy integration.

[0185] System stability is assessed by analyzing the system's dynamic response over time. During the assessment process, real-time monitored electrical quantities and equipment operating status data change over time. Time-domain analysis can observe these trends at different moments, such as whether the generator rotor angle gradually stabilizes after a fault, and whether the voltage and frequency return to normal ranges. This allows for the assessment of system stability during transient conditions. The system's response characteristics at different frequencies are studied to assess its frequency stability. Power systems contain harmonics and oscillations of various frequencies. Frequency-domain analysis can analyze the system's response to these frequency components and determine whether the system will experience frequency instability, such as low-frequency oscillations. An energy function is constructed to analyze energy changes to assess system stability. The energy function method, from an energy perspective, calculates energy changes during system operation to determine whether the system has sufficient energy to maintain stable operation or whether energy accumulation will lead to instability. The combined application of these three analytical techniques comprehensively considers the dynamic behavior and stability mechanisms of the power system, improving the accuracy and reliability of stability assessments.

[0186] Based on real-time evaluation test data and comprehensive evaluation methods, the performance of key components or subsystems in the power system (such as generators, transformers, transmission lines, new energy power generation equipment, etc.) is evaluated. The evaluation results obtained by the comprehensive evaluation method can reflect the dynamic behavior and stability performance of each device during the evaluation process, thereby obtaining detailed device performance evaluation data. For example, for generators, evaluate whether the changes in their speed and rotor angle are within a reasonable range and whether they can quickly recover to a stable operating state; for transformers, evaluate the stability of their voltage conversion and current transmission; for transmission lines, evaluate whether their voltage drop and power transmission capacity meet the requirements; for new energy power generation equipment, evaluate the stability of their output characteristics and their impact on system stability, etc.

[0187] Based on detailed device performance evaluation data, qualified devices are screened again. Qualified devices here refer to devices meeting stability standards, meaning they meet the system's requirements for stable operation during the evaluation process. By setting specific screening criteria, such as whether various device performance indicators are within the normal range and whether there are no obvious signs of instability, substandard devices are screened out, leaving only those with good stability. Based on the established device stability evaluation criteria, the performance evaluation data of each device is compared with the evaluation criteria. The evaluation criteria are formulated based on factors such as the safe operation requirements of the power system, the technical specifications of the equipment, and previous operating experience, providing a clear basis for judging device stability.

[0188] Through comparison, components or subsystems with excellent stability are screened out during the evaluation process. For example, if the speed fluctuation range of a generator is less than a set threshold, the rotor angle oscillation converges quickly, and other performance indicators meet requirements, then the generator is considered to have good stability. The performance data of these components or subsystems with good stability is collated and recorded to obtain performance data for re-screened qualified components. This data provides important reference for power system operation and maintenance, equipment selection, and system optimization.

[0189] The effect of this technical solution is that, through real-time monitoring equipment, real-time evaluation and test data for large-scale renewable energy integration into the power system can fully capture the dynamic response of the system during operation. This eliminates the need for static models in the evaluation process and instead incorporates the dynamic behavior of the system in actual operation, ensuring the accuracy of the stability assessment.

[0190] The use of comprehensive evaluation methods, including time-domain analysis, frequency-domain analysis, and energy function analysis, further enhances the scientific nature and accuracy of the assessment, enabling in-depth analysis of the system's diverse response characteristics and stability mechanisms. Detailed device performance evaluation data enables precise performance analysis of key equipment in the power system, reducing evaluation errors caused by differences in device characteristics. The combination of multiple analytical methods effectively reduces uncertainty in the assessment process, making the results more reliable.

[0191] By comparing device performance evaluation data with the stability requirements of the evaluation standards, qualified devices or subsystems with good stability can be screened, reducing the risk of equipment failure or non-compliance with standards. Real-time monitoring and evaluation of the operating status data of renewable energy power generation equipment (such as the output characteristics of photovoltaic inverters) can promptly identify potential stability risks and ensure that the power system can maintain stable operation after the integration of renewable energy.

[0192] By considering different frequencies, dynamic responses, and energy variations, the comprehensive assessment method enhances the system's adaptability to renewable energy fluctuations, load variations, and external interference, improving its overall stability. The comprehensive assessment method provides a detailed analysis of the system's dynamic behavior based on time, frequency, and energy characteristics. This allows for rapid identification and timely response in the event of an emergency, enhancing the system's emergency response capabilities.

[0193] Through real-time data feedback and assessment, assessors can obtain detailed information and respond quickly to faults, improving the power system's resilience and anti-interference capabilities. Detailed performance assessments of individual components (such as generators, transformers, and transmission lines) provide a more precise understanding of each component's status and operating characteristics, enabling refined management of power system components and ensuring long-term system stability.

[0194] By rescreening qualified device performance data and eliminating devices with poor stability, the risk of overall system failures caused by device performance issues is reduced, and the efficiency of overall power system equipment management is improved. The detailed data generated during the evaluation process can help power system planners and operators better understand the real-time performance of the system under large-scale renewable energy integration, providing data support for subsequent optimization and adjustment.

[0195] The combination of real-time monitoring and multiple analytical methods provides a more comprehensive basis for system planning and scheduling, enabling more precise adjustments and decisions, and improving the power system's adaptability to renewable energy. Through continuous stability assessments, the system can identify potential problems and take preventative measures before they occur, reducing maintenance workload and the frequency of sudden failures. Screening for components with excellent stability avoids equipment failures caused by insufficient performance, reduces safety hazards and economic losses caused by equipment failures, and ensures the safe operation of the power system.

[0196] In one embodiment of the present invention, the S5 includes:

[0197] S51. Based on the data of the initial screening risk areas or nodes and the performance data of the re-screened qualified devices, conduct a comprehensive summary analysis of the stability of large-scale renewable energy access to the power system;

[0198] S52. Based on the results of comprehensive summary analysis, obtain a stability assessment report on the large-scale renewable energy access power system;

[0199] The working principle of the above technical solution is to combine the data of the initial screening of risk areas or nodes with the performance data of the re-screened qualified devices, and conduct a comprehensive consideration from the perspective of the entire system. The data of the initial screening of risk areas or nodes clearly identify the areas in the system where stability problems may exist, while the performance data of the re-screened qualified devices reflects the stability status of each key component or subsystem. Through the comprehensive analysis of these data, the overall stability level of the system can be evaluated. For example, the number, distribution and risk level of risk areas in the system are counted, and the overall stability status of the system is judged in combination with the proportion and distribution of qualified devices. At the same time, the weak links in the stability of the system are found, which may be a key device, a transmission line or a specific operating scenario. In addition, the main factors affecting the stability of the system are deeply analyzed, such as the fluctuation characteristics of the power generation power of renewable energy, the topological structure of the power grid, the change pattern of the load, etc. These factors interact with each other and jointly affect the stability of the system.

[0200] For different risk areas, the stability of each area is analyzed independently using the data of the initial screening risk areas or nodes and the performance data of the re-screened qualified devices. Different risk areas may have different geographical environments, grid structures, proportions of new energy access, and other factors, which will lead to differences in their stability performance. Analyze the stability characteristics of each risk area. For example, some areas may be more susceptible to fluctuations in renewable energy power generation, while other areas may be more susceptible to load changes. The risk level of each area can be assessed by setting corresponding risk indicators, such as the probability of risk occurrence and the possible losses. At the same time, study the mutual influence between different areas. For example, a fault in one area may spread to other areas through the coupling effect of the power grid, resulting in a decrease in the stability of the entire system.

[0201] Based on the performance data of re-screened qualified devices, combined with the analysis results at the system-wide and regional levels, the contribution of each device's stability to the overall system stability and the coordination between devices are studied. The power system is a complex system composed of numerous devices. The performance and operating status of each device will affect the stability of the system, and there is a relationship of mutual cooperation and coordination between devices. By analyzing the contribution of the stability of each device to the overall stability of the system, it is possible to determine which devices are the key support points for system stability and which device performance improvements will most significantly improve system stability. At the same time, the coordination between devices is studied, such as the power matching between generators and transformers, and the coordinated control between new energy power generation equipment and energy storage devices. Good coordination can improve the stability and reliability of the system. The impact of different factors on system stability is comprehensively considered, and the analysis results at the system-wide, regional, and device levels are interconnected to form a comprehensive and systematic stability analysis system.

[0202] Based on the results of this comprehensive analysis, all aspects of system stability are summarized and summarized to form a stability assessment report for large-scale renewable energy access to the power system. This report represents the final outcome of the entire stability assessment process, presenting complex analysis results in a clear and concise manner, providing a scientific basis for power system planning, operation, and maintenance. It provides an overall assessment of the system's stability, clarifying whether the system is stable, facing potential risks, or already unstable. It also details the stability characteristics and risk level of each risk area, as well as the stability status of each component, making it easier for operators and managers to understand the specific operating conditions of the system.

[0203] In-depth analysis of the primary causes of system stability issues provides guidance for subsequent improvement measures. Based on the key factor analysis results, specific improvement measures and suggestions are proposed, such as optimizing the grid structure, adjusting the control strategy of renewable energy power generation equipment, and strengthening equipment maintenance and upgrades. These measures will help improve the stability of the power system when large-scale renewable energy is integrated, ensuring the safe and reliable operation of the power system.

[0204] The effectiveness of this technical solution is: by comprehensively analyzing power system stability based on initial risk area screening, node data, and re-screened qualified device performance data, it can accurately identify weak links and key influencing factors in the power system, thereby ensuring high-precision assessment results. At the regional level, analyzing the stability characteristics and mutual influences of different risk areas helps to refine regional stability differences and avoid large-scale generalization errors in system assessments.

[0205] By thoroughly analyzing each component's contribution to system stability and the coordination between them, we can promptly identify potential incoordination and system instability risks, allowing us to take measures to prevent system failures. By identifying potential weak links based on the risk characteristics of each region, we can help prevent global failures caused by local instability during system operation.

[0206] The assessment report provides a scientific basis for power system planning, operation, and maintenance. It enables targeted adjustments during the integration of renewable energy, thereby improving the power system's adaptability to renewable energy fluctuations and load changes. By analyzing key factors, it can provide optimized decision support for power system dispatchers, enabling more flexible response to emergencies in complex power networks.

[0207] Component-level performance evaluation and stability contribution analysis provide clear guidance for power system equipment selection and procurement, ensuring that critical equipment meets stability requirements. Combined with the results of the evaluation report, more refined equipment maintenance and repair plans can be developed, enabling early detection of issues such as equipment aging and performance degradation, thereby extending equipment life and reducing failure rates.

[0208] By analyzing the stability of renewable energy integration into the power system, evaluating the output characteristics of different renewable energy generation equipment (such as wind power and photovoltaics) and their impact on system stability, we can optimize system design to ensure that renewable energy fluctuations do not affect overall system stability. The comprehensive evaluation results provide data support for subsequent system adjustments, effectively improving system operating efficiency and stability after renewable energy integration.

[0209] This comprehensive analysis report provides detailed stability assessment data for the power system, enabling managers to take targeted management measures based on actual conditions and optimize resource allocation and scheduling. The report's targeted improvement suggestions make system management more scientific and systematic, providing a more stable management framework during the rapid development of renewable energy integration.

[0210] By scientifically assessing system stability after the integration of new energy sources, we ensure the continued expansion of new energy integration while maintaining system stability, providing data support for the green and sustainable development of the power system. By proactively identifying and addressing the potential impact of new energy fluctuations on the power system, we promote the harmonious coexistence of new and traditional energy sources and drive the green transformation of the power system.

[0211] One embodiment of the present invention is a power system stability assessment system suitable for large-scale renewable energy access, characterized in that it includes a memory, a processor, and a computer program stored on the memory and executable on the memory, and the processor executes the program to implement a power system stability assessment method suitable for large-scale renewable energy access as described above.

[0212] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for evaluating the stability of a power system suitable for large-scale renewable energy access, characterized in that: The method comprises: S1: Acquire multi-source real-time monitoring data, pre-process the acquired multi-source real-time monitoring data, and use data fusion technology to integrate the pre-processed data from different sources and formats to obtain a fused data set to be analyzed; based on the fused data set, build a real-time status model; S2: Based on the real-time state model, dynamic simulation of the power system under multiple scenarios is performed. In each simulation scenario, the stability of the power system is analyzed using numerical calculation methods, key stability indicators are calculated, and a data set of key stability indicators for the power system under multiple scenarios is obtained. The data set of key stability indicators under multiple scenarios is deeply mined and analyzed, and a correlation model is established based on a machine learning algorithm. The correlation model is used to predict stability trends under different operating conditions and obtain power system stability trend prediction data. S3: Based on the power system stability trend forecast data and the preset stability threshold, a preliminary assessment of the power system stability is conducted, and areas or nodes with stability risks are screened out to obtain data on the initially screened risk areas or nodes. Based on the data on the initially screened risk areas or nodes, relevant assessment strategy parameters are extracted from a preset stability assessment strategy database. Based on the extracted assessment strategy parameters, the test application parameters for subsequent stability assessments are adaptively adjusted to obtain a stability assessment scheme suitable for large-scale renewable energy access to the power system. The assessment scheme is then uploaded to the power system stability assessment management platform. S4: According to the adjusted stability assessment plan, obtain real-time assessment test data of the large-scale renewable energy access power system. Based on the real-time assessment test data, use a comprehensive assessment method to conduct a detailed assessment of the stability of the power system, obtain detailed device performance assessment data, and then conduct a second screening of qualified devices based on the detailed device performance assessment data to select devices or subsystems with good stability during the assessment process, and obtain performance data of re-screened qualified devices. S5: Based on the initial screening risk area or node data and the re-screening qualified device performance data, conduct a comprehensive summary analysis of the stability of large-scale renewable energy access to the power system; obtain a stability assessment report on the large-scale renewable energy access to the power system.

2. A method for evaluating the stability of a power system suitable for large-scale renewable energy access according to claim 1, characterized in that: Said S1 comprises: S11, collect multi-source real-time monitoring data through various sensors; S12, preprocessing the collected multi-source real-time monitoring data to obtain high-precision multi-source real-time monitoring data; S13. Based on the data fusion algorithm, the high-precision multi-source real-time monitoring data obtained from different sources and in different formats are integrated to obtain a fused data set to be analyzed; S14. Based on the fused data set, a system identification method is used to construct a real-time state model that reflects the dynamic characteristics of large-scale renewable energy access to the power system.

3. The method for evaluating the stability of a power system suitable for large-scale renewable energy access according to claim 2, characterized in that: Said S13 comprises: S131. Assign accurate spatial coordinates to each data source and reasonably divide the monitoring area into several sub-areas based on geographical location, power grid topology, or meteorological regional characteristics; S132. Standardize the format of the collected high-precision multi-source real-time monitoring data, converting data from different sources and formats into a unified format standard while retaining and annotating the spatial coordinate information of each data point; S133. Time alignment processing is performed on multi-source real-time monitoring data to ensure that the data from each data source accurately corresponds to each other on the time axis and perform spatial synchronization processing; S134. Extract spatially relevant features from the standardized and time-aligned data and use spatial statistical methods to identify spatial patterns or anomalies in the data; S135. Analyze the correlation between data at different spatial locations, and perform data fusion based on the analysis results using a data fusion method based on spatial interpolation; and perform spatial quality assessment on the fused dataset. Based on the results of the spatial quality assessment, iteratively optimize the data fusion process to obtain a fused dataset to be analyzed that meets the needs of spatial analysis.

4. The method for evaluating the stability of a power system suitable for large-scale renewable energy access according to claim 3, characterized in that: The S133 includes: Based on the clock synchronization of each data source, a unified time base is selected as the reference standard for time alignment of all data. Based on the selected time base, the data of each data source is timestamped and annotated according to the unified time base. A time alignment algorithm is used to align the data of different data sources on the time axis. Based on the needs of spatial analysis, identify the data types and analysis scenarios that require spatial synchronization; Establish a mapping relationship between spatial location and data value for each data point, identify the spatial coverage and overlapping areas of different data sources, and establish a spatial association mechanism across data sources; According to the requirements of spatial synchronization and the spatial distribution characteristics of the data, spatial interpolation calculations are performed on the data points that require spatial synchronization through spatial interpolation methods to generate data values ​​at the specified spatial location and time point; During the time alignment and spatial synchronization process, possible abnormal data is detected; the detected abnormal data is processed; Evaluate the effectiveness of time alignment processing, check the alignment accuracy and consistency of each data source on the time axis; evaluate the effectiveness of spatial synchronization processing, check the corresponding accuracy and spatial continuity of data in different spatial positions at the same time point; provide feedback and optimize the processing process based on the results of time alignment and spatial synchronization evaluation.

5. The method for evaluating the stability of a power system suitable for large-scale renewable energy access according to claim 3, characterized in that: The S135 includes: Based on the data type and analysis objectives, construct indicators to measure the correlation between data at different spatial locations, calculate the correlation matrix between data points at each spatial location, and determine the strength and direction of the correlation between data points; Use spatial clustering algorithms to cluster data points based on spatial correlation and identify data groups with similar spatial characteristics; set interpolation parameters based on interpolation methods; Combining spatial correlation analysis with interpolation methods, a multi-source data fusion strategy is developed, and the original data is pre-processed before data fusion; According to the formulated data fusion strategy, the selected spatial interpolation method is used to interpolate the multi-source data to generate a fused data set; the results of the spatial quality assessment are fed back to the data fusion process to identify existing problems and deficiencies; Based on the evaluation results, the spatial interpolation parameters and data fusion strategies are adjusted and optimized, and a new round of data fusion and spatial quality assessment is carried out using the optimized parameters and strategies until a fused data set to be analyzed that meets the spatial analysis requirements is obtained.

6. The method for evaluating the stability of a power system suitable for large-scale renewable energy access according to claim 1, characterized in that: Said S2 comprises: S21. Based on the actual operation and fault types of large-scale renewable energy access to the power system, set up dynamic simulation scenarios of the power system under multiple scenarios; S22. Analyze the stability of the power system using numerical calculation methods in each set simulation scenario; simulate the dynamic response process of the system in the simulation scenario by solving the differential-algebraic equations of the power system; and obtain a data set of key indicators of power system stability in multiple scenarios; S23. Conduct in-depth mining and analysis of key stability indicator datasets across multiple scenarios; discover hidden patterns and patterns in the data; and establish correlation models between key stability indicators, system operating parameters, and renewable energy generation characteristics based on machine learning algorithms. S24. Utilize the established correlation model, input different operating parameters and new energy power generation characteristic data, and predict the stability trend of large-scale new energy access to the power system under different operating conditions; and obtain power system stability trend prediction data by analyzing the prediction results.

7. The method for evaluating the stability of a power system suitable for large-scale renewable energy access according to claim 1, characterized in that: Said S3 comprises: S31. Conduct a preliminary assessment of the stability of the power system based on the power system stability trend forecast data and a preset stability threshold; S32. Compare the predicted stability index with the stability threshold to determine whether the system meets the stability requirements under different operating conditions; screen out areas or nodes with stability risks and obtain preliminary screening risk area or node data; S33. Based on the initial screening of risk areas or node data, extract relevant assessment strategy parameters from a preset stability assessment strategy database; adaptively adjust the test application parameters of subsequent stability assessments based on the extracted assessment strategy parameters; and obtain a stability assessment scheme suitable for large-scale renewable energy access to the power system. S34. Upload the adjusted stability assessment plan to the power system stability assessment management platform; the assessment management platform schedules and manages the assessment tasks based on the adjusted stability assessment plan, and assigns the assessment plan to corresponding assessment equipment and personnel.

8. The method for evaluating the stability of a power system suitable for large-scale renewable energy access according to claim 1, characterized in that: Said S4 comprises: S41. In accordance with the adjusted stability assessment plan, obtain real-time assessment test data of large-scale renewable energy access to the power system through real-time monitoring equipment; and evaluate the stability of the power system using a comprehensive assessment method based on the real-time assessment test data; S42. Obtain detailed device performance evaluation data through a comprehensive evaluation method; and screen qualified devices again based on the detailed device performance evaluation data. S43. Based on the set device stability evaluation standard, the performance evaluation data of each device is compared with the evaluation standard, and the devices or subsystems with good stability during the evaluation process are screened out to obtain the performance data of the re-screened qualified devices.

9. The method for evaluating the stability of a power system suitable for large-scale renewable energy access according to claim 1, characterized in that: Said S5 comprises: S51. Based on the data of the initial screening risk areas or nodes and the performance data of the re-screened qualified devices, conduct a comprehensive summary analysis of the stability of large-scale renewable energy access to the power system; S52. Based on the results of comprehensive summary and analysis, obtain a stability assessment report on the access of large-scale renewable energy to the power system.

10. A power system stability assessment system suitable for large-scale renewable energy access, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the memory, wherein the processor executes the program to implement a power system stability assessment method suitable for large-scale renewable energy access as described in any one of claims 1 to 9.

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