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

Abstract

The application provides a power system stability evaluation system and method suitable for large-scale new energy access, and belongs to the technical field of electric power. The method comprises the following steps: acquiring multi-source real-time monitoring data, pre-processing the acquired multi-source real-time monitoring data, integrating the pre-processed data of different sources and different formats by using a data fusion technology to obtain a fusion data set to be analyzed, and constructing a real-time state model based on the fusion data set. Through the acquisition and preprocessing of multi-source real-time monitoring data and the 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] This invention proposes a power system stability assessment system and method applicable to large-scale new energy integration, belonging to the field of power technology. Background Technology

[0002] With the rapid development of new energy technologies, the integration of large-scale new energy sources such as wind and solar power into the power system has become a trend. However, the intermittency, volatility, and uncertainty of new energy sources pose a severe challenge to the stability of the power system. Traditional power system stability assessment methods are often based on deterministic models and fixed parameters, making it difficult to accurately reflect the dynamic characteristics of the power system after the integration of large-scale new energy sources, and failing to meet the need for precise stability assessment in actual operation. Therefore, there is an urgent need for an innovative power system stability assessment method applicable to the integration of large-scale new energy sources to improve the accuracy and reliability of the assessment. Summary of the Invention

[0003] This invention provides a power system stability assessment system and method suitable for large-scale renewable energy integration, in order to solve the problems mentioned in the background section above:

[0004] This invention proposes a power system stability assessment method applicable to large-scale renewable energy integration, the method comprising:

[0005] S1: Acquire multi-source real-time monitoring data, preprocess the acquired multi-source real-time monitoring data, and use data fusion technology to integrate the preprocessed data from different sources and in different formats to obtain a fused dataset to be analyzed; based on the fused dataset, construct a real-time status model;

[0006] S2: Based on the real-time state model, perform dynamic simulation of the power system under multiple scenarios; under each simulation scenario, analyze the stability of the power system through numerical calculation methods, calculate key stability indicators, and obtain a dataset of key stability indicators of the power system under multiple scenarios; conduct in-depth mining and analysis of the dataset of key stability indicators under multiple scenarios, establish a correlation model based on machine learning algorithms, predict the stability trend under different operating conditions through the correlation model, and obtain power system stability trend prediction data.

[0007] S3: Based on the power system stability trend prediction data and the preset stability threshold, conduct a preliminary assessment of the power system stability, screen out areas or nodes with stability risks, and obtain preliminary risk area or node data; based on the preliminary risk area or node data, extract relevant assessment strategy parameters from the preset stability assessment strategy database, and adaptively adjust the test application parameters for subsequent stability assessments according to the extracted assessment strategy parameters to obtain a stability assessment scheme suitable for large-scale new energy access to the power system, and upload the assessment scheme to the power system stability assessment management platform;

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

[0009] S5: Based on the initial screening of risk areas or nodes and the performance data of qualified devices after secondary screening, conduct a comprehensive summary and analysis of the stability of large-scale new energy access to the power system; and obtain a stability assessment report of large-scale new energy access to the power system.

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

[0011] The beneficial effects of this invention are: by acquiring and preprocessing multi-source real-time monitoring data, and through data fusion technology, it can comprehensively and accurately reflect the operating status of large-scale new energy access to the power system, providing a reliable data foundation for stability assessment.

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

[0013] Based on the preliminary assessment results and assessment strategy parameters, the test application parameters for stability assessment were adaptively adjusted to develop a personalized assessment plan, thereby improving the relevance and effectiveness of the assessment.

[0014] A comprehensive evaluation method is used to conduct a detailed assessment of the stability of the power system. This method involves a comprehensive analysis at the system as a whole, regional, and device levels, which can deeply explore the factors affecting system stability and provide a scientific basis for system optimization and improvement. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method steps described in this invention;

[0016] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of S1;

[0017] Figure 3 for Figure 2 A detailed flowchart illustrating the implementation steps of S14. Detailed Implementation

[0018] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

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

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

[0021] S2: Based on the real-time state model, perform dynamic simulation of the power system under multiple scenarios; under each simulation scenario, analyze the stability of the power system through numerical calculation methods, calculate key stability indicators, and obtain a dataset of key stability indicators of the power system under multiple scenarios; conduct in-depth mining and analysis of the dataset of key stability indicators under multiple scenarios, establish a correlation model based on machine learning algorithms, predict the stability trend under different operating conditions through the correlation model, and obtain power system stability trend prediction data.

[0022] S3: Based on the power system stability trend prediction data and the preset stability threshold, conduct a preliminary assessment of the power system stability, screen out areas or nodes with stability risks, and obtain preliminary risk area or node data; based on the preliminary risk area or node data, extract relevant assessment strategy parameters from the preset stability assessment strategy database, and adaptively adjust the test application parameters for subsequent stability assessments according to the extracted assessment strategy parameters to obtain a stability assessment scheme suitable for large-scale new energy access to the power system, and upload the assessment scheme to the power system stability assessment management platform;

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

[0024] S5: Based on the initial screening of risk areas or nodes and the performance data of qualified devices after secondary screening, conduct a comprehensive summary and analysis of the stability of large-scale new energy access to the power system; and obtain a stability assessment report of large-scale new energy access to the power system.

[0025] The working principle of the above technical solution is as follows: Multi-source real-time monitoring data is acquired from power systems with large-scale renewable energy integration. This data covers electrical quantities such as the output power of renewable energy generation equipment, grid voltage, current, and frequency, as well as environmental meteorological data (such as wind speed and solar irradiance). This data forms the basis for subsequent stability assessments, comprehensively reflecting the operating status of the power system and the impact of the external environment. The acquired data undergoes preprocessing, including noise removal and handling of missing values, to ensure data quality. Then, data fusion technology is used to integrate data from different sources and formats, forming a fused dataset for analysis. Through data fusion, information from various data sources can be comprehensively utilized to more fully describe the operating status of the power system. Based on the fused dataset, a real-time state model reflecting the dynamic characteristics of the power system with large-scale renewable energy integration is constructed. 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 fluctuations in power generation from different renewable energy sources, grid faults, and varying load changes. Through multi-scenario simulations, the stability of the power system under various possible operating conditions can be comprehensively examined. In each simulation scenario, numerical calculation methods are used to analyze the stability of the power system, calculating key stability indicators such as transient stability margin, voltage stability margin, and frequency stability margin, obtaining 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 based on machine learning algorithms between key stability indicators and system operating parameters and renewable energy generation characteristics. This correlation model is used to predict the stability trend of large-scale renewable energy integration into the power system under different operating conditions, obtaining power system stability trend prediction 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 prediction data and pre-set stability thresholds, a preliminary assessment of power system stability is conducted to identify areas or nodes potentially at risk, obtaining initial risk area or node data. This step quickly pinpoints potentially problematic areas or nodes, providing direction for subsequent detailed assessments. Based on this initial risk area or node data, relevant assessment strategy parameters, such as assessment time intervals, assessment method weighting, and key monitoring point selection, are extracted from a pre-set stability assessment strategy database. These parameters allow for adjustments to the subsequent assessment process based on specific circumstances, improving the assessment's relevance and effectiveness. According to the extracted assessment strategy parameters, the test application parameters for subsequent stability assessments are adaptively adjusted, such as adjusting fault types, fault durations, and fluctuations in renewable energy generation power in dynamic simulations, to obtain a stability assessment scheme suitable for large-scale renewable energy integration into the power system. This assessment scheme is then uploaded to the power system stability assessment management platform for implementation. By adaptively adjusting the assessment scheme, the stability of the power system can be assessed more accurately.

[0028] According to the adjusted stability assessment scheme, real-time assessment test data for large-scale renewable energy integration into the power system is obtained. 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 data of renewable energy power generation equipment. This data can reflect the real-time operating status of the power system and provide a basis for detailed assessment. Based on the real-time assessment test data, a comprehensive assessment method is used to conduct a detailed assessment of the power system's stability. The comprehensive assessment method combines multiple analytical techniques such as time-domain analysis, frequency-domain analysis, and energy function methods to comprehensively consider the dynamic behavior and stability mechanisms of the power system, obtaining detailed device performance assessment data. This data reflects the stability performance of each device during the assessment process. Based on the detailed device performance assessment data, qualified devices are screened again to identify devices or subsystems with good stability during the assessment process, obtaining performance data for the second-screened qualified devices. This step further identifies devices with good stability in the power system, providing a guarantee for the stable operation of the system.

[0029] Based on initial screening data of risk areas or nodes and performance data of qualified devices from subsequent screening, a comprehensive analysis of the stability of large-scale renewable energy integration into the power system is conducted. The stability of the power system is assessed from multiple dimensions, including the overall system level, regional level, and device level, comprehensively considering the impact of various factors on system stability. A stability assessment report for large-scale renewable energy integration into the power system is obtained, which comprehensively reflects the stability status of the power system and provides important basis for the planning, operation, and maintenance of the power system.

[0030] The above technical solution has the following effect: through the preprocessing and fusion of multi-source real-time monitoring data, it can comprehensively reflect the dynamic characteristics of large-scale new energy access to the power system, provide more accurate basic data for system stability analysis, and avoid the deviation 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 new energy power generation, grid faults, 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, regions or nodes that may pose 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 nodes, relevant assessment parameters are extracted and adjusted from the stability assessment strategy database, enabling the power system stability assessment scheme to adaptively adjust according to real-time conditions. This provides the optimal assessment scheme for different operating conditions, enhancing the relevance and real-time nature of the assessment.

[0034] By combining various analytical methods such as time-domain analysis, frequency-domain analysis, and energy function method, a detailed performance evaluation of each key component or subsystem in the power system can be performed. The hierarchical evaluation results enable comprehensive feedback on the stability status at each level, avoiding distortion of the overall stability evaluation due to neglecting a certain level.

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

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

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

[0038] S11. Real-time monitoring data from multiple sources is collected through various sensors;

[0039] S12. Preprocess 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, integrate the high-precision multi-source real-time monitoring data from different sources and in different formats to obtain the fusion dataset to be analyzed;

[0041] S14. Based on the fused dataset, a real-time state model reflecting the dynamic characteristics of large-scale new energy access to the power system is constructed using the system identification method.

[0042] The working principle of the above technical solution is as follows: various sensors are deployed on new energy power generation equipment (such as wind turbines, photovoltaic power stations, etc.) and key nodes of the power grid. These sensors are front-end devices for data acquisition, 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 acquire their output power data in real time, reflecting the real-time power output of wind power generation; voltage, current, and frequency sensors deployed at key nodes of the power grid can monitor changes in electrical quantities of the power grid in real time.

[0043] Environmental meteorological data, such as wind speed and solar irradiance, are acquired using meteorological monitoring equipment. This meteorological data is closely related to new energy power generation because the power output of wind power is proportional to the cube of wind speed, and the power output of photovoltaic power generation is closely related to solar irradiance. By acquiring this data, a comprehensive understanding of the external environmental factors affecting new energy power generation can be obtained, providing a basis for subsequent analysis of the uncertainties in new energy power generation.

[0044] Data collected from integrated sensors and meteorological monitoring equipment yields multi-source real-time monitoring data. This data covers electrical and environmental parameters of the power system, providing a rich source of information for subsequent data processing and analysis. Preprocessing of the collected multi-source real-time monitoring data aims to remove noise, outliers, and missing values ​​to improve data accuracy and quality. For example, due to potential external interference or sensor malfunctions, 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, providing a reliable foundation for subsequent data fusion and model building.

[0045] Based on data fusion algorithms, high-precision, multi-source real-time monitoring data from different sources and in different formats are integrated. Data from different sources may have different formats and precisions; data fusion algorithms can effectively integrate these data and extract more valuable information. For example, fusing the output power data of wind turbine generators with wind speed data can better analyze the power characteristics of wind power generation. Through data fusion, a fused dataset to be analyzed is obtained. This dataset integrates information from various types of data, providing a more comprehensive description of the operating status of the power system and offering richer data support for subsequent model building and stability assessment.

[0046] Based on a fused dataset, a real-time state model reflecting the dynamic characteristics of a large-scale renewable energy-integrated power system is constructed using a system identification method. The system identification method can establish 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 changes in environmental factors such as wind speed and solar intensity; the grid topology determines the transmission path and distribution of electrical energy in the power system; and the dynamic characteristics of each component reflect their response characteristics under different operating conditions. By comprehensively considering these factors, the model can more accurately simulate the operating state of the power system. The constructed real-time state model can simulate the operating state of the power system in real time, providing a foundation for subsequent stability assessments. During the stability assessment process, this model can be used to simulate and analyze various scenarios, calculate key stability indicators, and thus evaluate the stability status of the power system.

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

[0048] By preprocessing the collected multi-source real-time monitoring data, noise can be removed, data smoothed, and biases corrected, resulting in high-precision data. This preprocessing 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 differences between different data sources and obtaining a comprehensive, accurate, and consistent fused dataset. This data fusion enhances the accuracy and completeness of the overall dataset, making subsequent analysis more reliable.

[0050] By employing a system identification method based on fused datasets, a real-time state model capable of reflecting the dynamic characteristics of large-scale renewable energy integration into the power system was constructed. This model comprehensively considers the uncertainties of renewable energy generation, the grid topology, and the dynamic characteristics of components, improving the accuracy of power system operation state simulation, ensuring the model is closer to reality, and facilitating subsequent stability assessment.

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

[0052] Real-time state models can reflect the uncertainties of new energy power generation and take into account the topology of the power grid and the dynamic characteristics of each component. This helps the power system better adapt to the fluctuations brought about by the access of new energy sources, enhances the resilience of the system to cope with the access of new energy sources, and improves the stability and reliability of system operation.

[0053] By leveraging fused datasets and real-time state models, it is possible to monitor the dynamic changes of the power system in real time, promptly identify potential risks, and make 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 the stable operation of the power system.

[0054] Through dynamic simulation using a real-time state model, the evaluation strategy can be adjusted based on actual operating conditions, enhancing the adaptability and flexibility of the evaluation scheme. When faced with different operating states, the system can promptly optimize the evaluation method, improving the relevance and timeliness of stability assessments.

[0055] The combination of real-time state models and multi-source data fusion reduces the amount of computation 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] Real-time state models enable more detailed monitoring of every component and its dynamic performance in the power system. The hierarchical evaluation method allows for comprehensive feedback on the stability of each key component in the system, reducing the risk of neglecting any aspect and improving the accuracy of the overall stability assessment.

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

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

[0059] S132. Standardize the format of the high-precision multi-source real-time monitoring data collected, convert data from different sources and in different formats into a unified format standard, and retain and label the spatial coordinate information of each data point.

[0060] S133. Time alignment processing of multi-source real-time monitoring data will make the data from each data source correspond precisely on the time axis and perform spatial synchronization processing.

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

[0062] S135. Analyze the correlation between data from different spatial locations to obtain analysis results, namely, the correlation degree. Based on these results, perform data fusion using a spatial interpolation-based data fusion method. Then, conduct a spatial quality assessment on the fused dataset. Based on the spatial quality assessment results, iteratively optimize the data fusion process to obtain a fused dataset that meets the spatial analysis requirements. The correlation degree is obtained using the following formula:

[0063]

[0064] in, This represents the degree of correlation between spatial points i and j; Indicates spatial distance; Indicates the distance decay threshold; Indicates the spatial weighting coefficient; Represents the correlation coefficient of eigenvectors; and Represents spatial eigenvectors; Indicates the feature weight coefficient; Represents the elements of the topological adjacency matrix; This represents the topology weight coefficient.

[0065] The working principle of the above technical solution is as follows: Accurate spatial coordinates (latitude and longitude) are assigned to each data source, including new energy power generation equipment, sensors at key nodes of the power grid, and meteorological monitoring equipment. Spatial coordinates are crucial information for determining the geographical location of the data source, enabling subsequent data processing and analysis to correspond to the actual spatial location, providing a foundation for spatial analysis and fusion. Based on geographical location, power grid topology, or meteorological regional characteristics, the monitoring area is rationally divided into several sub-regions. This division facilitates a more detailed analysis of the power system operation and meteorological characteristics of different regions. For example, dividing sub-regions according to the power grid topology allows for a better understanding of power transmission and mutual influence between different regions; dividing according to meteorological regional characteristics is beneficial for 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 undergoes format standardization processing, converting data from different sources and in different formats into a unified format standard. Since data collected by different devices and systems may have different formats, a unified format standard facilitates subsequent data processing and analysis, avoiding problems caused by format inconsistencies. During the format conversion process, the spatial coordinate information of each data point is preserved and labeled to prevent the loss of spatial information during conversion. Spatial information is a crucial basis for data fusion and spatial analysis; preserving this information ensures that the spatial correlation of the data is not disrupted during subsequent processing.

[0067] Time alignment processing is performed on multi-source real-time monitoring data to ensure precise correspondence between data from different data sources on the timeline. The operation of a power system is a dynamic process, and data acquisition times from different data sources may differ. Time alignment ensures that data from the same point in time is used during analysis, thus accurately reflecting the operating status of the power system. Spatial synchronization processing is also performed, ensuring accurate correspondence between data from different spatial locations at the same point in time. This is particularly important for analyses requiring spatial continuity (such as spatial interpolation of meteorological data). Spatial synchronization processing guarantees that data from different spatial locations are matched in both time and space during analysis, improving the accuracy of the analysis results.

[0068] Spatially relevant features are extracted from standardized and time-aligned data, including wind speed distribution, light intensity gradient, and spatial topological relationships of power grid nodes across different regions. These spatial features reflect the spatial variation patterns 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. Simultaneously, it can identify anomalous spatial data, providing a basis for data cleaning and correction. The correlation between data from different spatial locations is analyzed, and data fusion is performed using spatial interpolation-based methods based on the analysis results. Spatial interpolation methods can infer data from unknown spatial locations based on data from known spatial locations, thus achieving data fusion. By analyzing the correlation between data, appropriate interpolation methods can be selected to improve the accuracy of data fusion.

[0069] Spatial quality assessment is performed on the fused dataset to check the completeness, consistency, and rationality of the spatial patterns. Spatial quality assessment ensures that the fused data meets the needs of spatial analysis and avoids inaccurate results due to data quality issues. Based on the spatial quality assessment results, the data fusion process is iteratively optimized, such as adjusting spatial interpolation parameters and improving spatial correlation analysis methods. Through iterative optimization, the quality of data fusion can be continuously improved, resulting in a fused dataset that meets the requirements of spatial analysis.

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

[0071] By standardizing the formats of high-precision, multi-source real-time monitoring data from different sources and in different formats, data loss or errors caused by inconsistent formats are avoided. This ensures the integrity of the data during the conversion process, avoids the loss of spatial information, and guarantees the consistency and availability of the data.

[0072] Time alignment and spatial synchronization processing are performed on multi-source real-time monitoring data to ensure that data from different spatial locations can accurately correspond at the same point in time. This is particularly crucial for analyses requiring spatial continuity, enhancing the accuracy of cross-temporal and spatial data analysis.

[0073] Spatial features, such as wind speed distribution, light intensity gradient, and spatial topological relationships of power grid nodes, are extracted from standardized and time-aligned data. These features help identify differences and trends between regions, improving the ability to understand and interpret complex spatial data.

[0074] Spatial statistical methods are used to identify spatial patterns or anomalies in the data. This approach helps uncover hidden spatial correlations and potential problems, thus providing more 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. The spatial quality assessment in this process ensures the integrity, consistency, and rationality of the data, improving the accuracy and reliability of the final fused data.

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

[0076] Because this solution employs an iterative optimization approach, it can flexibly adjust data processing and analysis strategies based on different data sources, analytical needs, and environmental changes. Therefore, the system can adapt to constantly changing monitoring conditions and continuously optimize itself during long-term operation.

[0077] The spatial distance and topological adjacency matrix in the above formulas effectively measure the geographical relationships between different spatial points, ensuring a full reflection of spatial relationships between data. By combining eigenvector correlation coefficients, the differences in spatial characteristics between different data sources can be better addressed, avoiding errors in correlation calculations caused by inconsistent data formats.

[0078] The introduction of spatial weighting coefficients, feature weighting coefficients, and topological weighting coefficients enables data from different sources and formats to be weighted and fused based on their spatial characteristics, feature attributes, and topological structure, thereby optimizing the fusion results and improving data quality. Spatial interpolation methods can achieve accurate data fusion, helping to reduce data errors caused by spatial differences and temporal asynchrony.

[0079] The format standardization and time alignment processes mentioned in S132 and S133 provide a unified data foundation for this formula, enabling data from different sources to be analyzed according to a unified standard when calculating correlation, avoiding inconsistencies caused by format or time asynchrony. These processing measures effectively improve the comparability of multi-source data, making the correlation calculation results more reliable.

[0080] The spatial distance decay mechanism in the formula, combined with spatial statistical methods, helps identify spatially correlated regions and their potential anomalies. Iterative optimization of the data fusion process through spatial quality assessment can further improve the accuracy of anomaly detection. Using this correlation calculation formula, patterns and potential problems in the data can be discovered on a larger scale, allowing for system optimization and adjustments.

[0081] Spatial quality assessment of the data fusion results allows us to identify shortcomings in the fusion process and iteratively optimize it. This feedback mechanism continuously improves the spatial analysis quality of the data, preventing error propagation caused by initial data bias. The spatial quality assessment and iterative optimization process helps provide more refined and reliable datasets, thus laying a solid foundation for subsequent spatial analysis.

[0082] In one embodiment of the present invention, 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; the data from each data source is timestamped based on the selected time base and labeled according to the unified time base; a time alignment algorithm is used to align the data from 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] Based on the requirements of spatial synchronization and the spatial distribution characteristics of data, spatial interpolation methods are used to perform spatial interpolation calculations on data points that require spatial synchronization, generating data values ​​at specified spatial locations and time points;

[0087] During time alignment and spatial synchronization processing, detect any possible anomalous data; process the detected anomalous data.

[0088] Evaluate the effectiveness of time alignment processing by checking the alignment accuracy and consistency of each data source on the timeline; evaluate the effectiveness of spatial synchronization processing by checking the correspondence accuracy and spatial continuity of data at different spatial locations at the same time point; and provide feedback and optimization to the processing based on the results of time alignment and spatial synchronization evaluations.

[0089] The working principle of the above technical solution is as follows: Since 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 of each data source. UTC time is a globally universal standard time. Using it as a reference standard for time alignment of all data can eliminate time differences between different data sources and provide a unified time framework for subsequent data processing.

[0090] Based on a selected time base, timestamp correction is applied to data from each data source. Data may contain inaccurate time information during collection; 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 processing. Interpolation and extrapolation time alignment algorithms are used to align data from different data sources along the time axis. In actual data collection, the data collection time intervals of different data sources may differ, resulting in missing data at the same time point. Interpolation algorithms can infer data for missing time points based on data at known time points; 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 can accurately correspond, providing accurate time series data for subsequent joint analysis.

[0091] Based on the needs of spatial analysis (such as spatial interpolation of meteorological data, spatial distribution analysis of power grid load, etc.), identify the data types and analysis scenarios requiring 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 load 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 to be synchronized, the synchronization time interval, and the synchronization accuracy requirements. A clear plan can guide subsequent spatial synchronization processing work, ensuring that the processing results meet the analysis requirements.

[0092] Establish a mapping relationship between spatial location and data value for each data point to ensure accurate association of data at the same spatial location. Spatial location is a crucial attribute of data; by establishing mapping relationships, data from the same spatial location across different data sources can be linked. For different data sources, identify their spatial coverage and overlapping areas, and establish a cross-data source spatial association mechanism. Different data sources may cover different spatial regions, and data from multiple data sources may exist in overlapping areas. By establishing association mechanisms, it is possible to ensure 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 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 from data at known spatial locations, generating data values ​​at specified spatial locations and time points, thus achieving spatial synchronization. The spatially interpolated data is then validated to ensure that data from different spatial locations at the same time point accurately correspond and meet the requirements of spatial continuity. Spatial continuity is a crucial characteristic of spatial data; the validation process checks the reasonableness of the interpolation results and identifies any discontinuities or anomalies.

[0094] During time alignment and spatial synchronization, potential anomalies are detected, such as timestamp errors, spatial location deviations, and abnormal data values. Timestamp errors can lead to inaccurate alignment of data on the timeline, spatial location deviations can affect the spatial correlation of data, and abnormal data values ​​may reflect problems during data acquisition or transmission. By detecting these anomalies, problems can be identified and addressed promptly, ensuring data quality.

[0095] Detected outlier data is processed through methods such as correcting timestamps, adjusting spatial positions, and removing or replacing outlier values. Correcting timestamps ensures accurate alignment of data on the timeline, adjusting spatial positions guarantees correct spatial correlation, and removing or replacing outlier values ​​prevents them from affecting subsequent analysis. These processing measures ensure the accuracy and reliability of the data, providing high-quality data for subsequent stability assessments.

[0096] Evaluate the effectiveness of time alignment by checking the alignment accuracy and consistency of each data source on the timeline. Alignment accuracy can be measured by calculating the differences in data from different data sources at the same point in time, while consistency examines whether the trends of change of each data source over time are consistent. Evaluation can reveal whether the time alignment process has achieved its intended goals and whether there are any inaccuracies or inconsistencies in alignment.

[0097] The effectiveness of spatial synchronization processing is evaluated by checking the accuracy of correspondence and spatial continuity of data from different spatial locations at the same point in time. Accuracy of correspondence can be assessed by comparing data differences from different data sources at the same spatial location and time point, while spatial continuity checks whether the spatial variations in data are reasonable. The evaluation results can reflect whether spatial synchronization processing meets analytical requirements and whether there are any issues with data mismatch or spatial discontinuity.

[0098] Based on the results of time alignment and spatial synchronization assessments, feedback and optimization are performed on the processing. If deficiencies are found in the time alignment algorithm, algorithm parameters can be adjusted or a more suitable algorithm can be adopted; if the spatial interpolation method leads to inaccurate data, the interpolation method can be improved or the interpolation parameters optimized. 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 as follows: by selecting a unified time base, the data from each data source is corrected for timestamps and aligned in time, ensuring accurate synchronization of each data source in time, avoiding the impact of time deviation on the analysis results, and thus improving the accuracy of multi-source data integration.

[0100] Employing time alignment algorithms such as interpolation and extrapolation, precise timeline alignment is achieved for data from different data sources, 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, accurate correlation of data at the same spatial location is ensured, promoting the fusion 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 outlier data, the impact of erroneous data on the final analysis results is reduced, improving the reliability and accuracy of the results and ensuring data consistency in time and space. A cross-data source spatial correlation mechanism has been developed to address the spatial coverage and overlapping areas of different data sources, enabling flexible data extraction and comprehensive analysis across different data sources, thus providing broader support for diverse analytical needs.

[0102] By employing appropriate time alignment and spatial synchronization processing, the process of integrating multi-source data is simplified, reducing the complexity of manual correction and data cleaning, making data processing more efficient, and saving time and human resources. Through spatial interpolation calculations and subsequent verification of spatially synchronized data points, the accuracy of the spatial interpolation method is ensured, improving the correspondence accuracy between data from different spatial locations, and further guaranteeing the spatial continuity of the analysis results.

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

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

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

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

[0107] Suppose there are n data points in the space { If each data point contains m-dimensional feature data (e.g., voltage, current, frequency, meteorological parameters, etc.), then the correlation matrix R can be expressed as:

[0108]

[0109] in, Representing data points and The comprehensive correlation index between them (value range [-1, 1], the larger the absolute value, the stronger the correlation, positive and negative indicate the direction of the correlation); Represented as the weight coefficients of the k-th dimension feature data ( The weights are determined based on data reliability and importance to the analysis objective; for example, the weight of power grid topology data can be higher than that of meteorological data. Indicates the k-th dimension feature data in and The correlation coefficient between them is obtained using the following formula:

[0110]

[0111] Spatial clustering algorithms are used to cluster data points based on spatial correlation, identifying data groups with similar spatial characteristics; interpolation parameters are set based on interpolation methods.

[0112] Combining spatial correlation analysis and interpolation methods, a multi-source data fusion strategy is formulated, and the original data is preprocessed before data fusion;

[0113] According to the established data fusion strategy, the selected spatial interpolation method is used to interpolate the multi-source data to generate the fused dataset; the results of spatial quality assessment are fed back into 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. The optimized parameters and strategies are then used to perform new data fusion and spatial quality evaluation until a fusion dataset that meets the spatial analysis requirements is obtained.

[0115] The working principle of the above technical solution is as follows: Various correlations exist between data from different spatial locations, and these correlations are influenced by multiple factors, such as geographical location, meteorological conditions, and power grid topology. Based on data type (e.g., meteorological data, power grid data) and analysis objectives (e.g., accurately simulating the operating state of a power system), indicators are constructed to measure the correlations between data from different spatial locations. These indicators can be designed from the perspectives of data correlation, similarity, and causality. The purpose of spatial clustering algorithms is to group data points with similar spatial characteristics into one category. Based on the correlation matrix calculated earlier, spatial clustering algorithms can identify the inherent connections and patterns between data points.

[0116] Based on interpolation methods, interpolation parameters are set. Different interpolation methods have different parameters, which affect the accuracy and precision of the interpolation results. For example, in Kriging interpolation, the parameters of the variogram model need to be set, which describes the spatial variability of the data. Combining spatial correlation analysis with interpolation methods, a multi-source data fusion strategy is formulated. The weight allocation, interpolation range, and fusion rules for different data sources in the fusion process are clarified. Weight allocation 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 from a certain meteorological monitoring station is of higher quality and more stable, it can be given greater weight in the fusion process. The interpolation range determines which spatial locations need to be interpolated, and the fusion rules specify how to integrate data from different data sources.

[0117] Before data fusion, the raw data is preprocessed. Following the established data fusion strategy, a selected spatial interpolation method is used to perform interpolation calculations on the multi-source data. The interpolation process involves inferring the values ​​of unknown data points based on the known values ​​and spatial locations of those points. Through interpolation calculations, a fused dataset is generated. This dataset integrates information from different data sources and possesses better spatial continuity and completeness.

[0118] Spatial quality assessment is performed on the fused dataset. Assessment indicators may include data completeness (whether all required data points have values), consistency (whether data from different data sources are coordinated after fusion), and the rationality of spatial patterns (whether they conform to actual physical laws and spatial distribution characteristics). The results of the spatial quality assessment are fed back into the data fusion process to identify existing problems and deficiencies. For example, if the assessment finds significant discontinuities in the data fusion results for certain areas, or discrepancies with reality, it is necessary to identify the causes of these problems.

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

[0120] The effect of the above technical solution is that by accurately calculating the correlation between data from 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 dataset more accurate and reliable.

[0121] Spatial clustering algorithms are used to cluster data points, and interpolation is performed based on the clustering results. This simplifies the integration process of multi-source data, reduces the complexity of manual correction and data cleaning, and minimizes the computational and manual intervention required for data processing. By combining spatial correlation analysis and interpolation methods, higher continuity and consistency of data across different spatial locations are ensured, 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 allocation, 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 analytical objectives, ensuring the customization of the data fusion strategy and improving the system's adaptability. Preprocessing before data fusion, including anomaly detection and correction, 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 problems.

[0123] By optimizing spatial interpolation parameters and data fusion strategies, and combining the results of spatial quality assessment, the spatial quality was improved in each round of data fusion, thereby enhancing the reliability of the final dataset and the accuracy of the analysis results. A feedback mechanism was implemented to promptly relay the spatial quality assessment results to the data fusion process, enabling the strategies and parameters to be dynamically adjusted according to actual conditions. This optimized the data fusion process and improved overall processing efficiency and effectiveness.

[0124] By combining spatial clustering and interpolation, in-depth analysis can be performed at different spatial scales and multiple data dimensions. This allows the fused dataset to not only meet basic spatial analysis needs but also support more complex multi-dimensional analytical applications. The modular design ensures high scalability when handling different data sources and scenarios, enabling the system to flexibly adapt to new data sources or data types by adjusting parameters and strategies, reducing maintenance complexity. Through multiple rounds of spatial quality assessment and data fusion optimization, this solution continuously improves data quality, adapting to increasingly complex analytical needs and ensuring the long-term effectiveness of the data fusion process.

[0125] By comprehensively considering the weights of each feature dimension, different weights can be assigned to data in different dimensions based on the importance and reliability of each feature, making the correlation calculation more in line with the needs of practical applications. The correlation coefficient in the formula uses covariance calculation, which can accurately measure the similarity between data points, especially in the measurement of correlation between multidimensional data, and can effectively reflect the relationship between different features.

[0126] Weighting coefficients are set according to the importance of different characteristics to the analysis target, thereby enhancing the focus on key features. For example, in power grid analysis, the weight of power grid topology data can be higher than that of meteorological data; this design improves the relevance and effectiveness of the analysis. The design of weighting coefficients helps to handle differences between data sources and improves the accuracy of correlation calculations.

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

[0128] Interpolation of spatial data can fill in gaps in space, increasing data continuity and completeness. By setting interpolation parameters, values ​​at different spatial locations can be smoothed, reducing the impact of missing data. The accuracy and reasonableness of interpolation depend on the quality of the spatial correlation matrix; therefore, accurate correlation calculations directly improve the precision and reliability of the interpolation results.

[0129] In multi-source data analysis, different data points typically possess different data types and characteristics. By establishing this correlation matrix, a unified correlation analysis can be performed on data from multiple dimensions, avoiding inconsistencies between data points and providing more comprehensive analytical results. This method effectively addresses the fusion issues caused by differences between data sources, improving the accuracy and reliability of the comprehensive analysis results.

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

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

[0132] S22. Under each set simulation scenario, the stability of the power system is analyzed using numerical calculation methods; the dynamic response process of the system under the simulation scenario is simulated by solving the differential-algebraic equations of the power system; and a dataset of key stability indicators of the power system under multiple scenarios is obtained.

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

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

[0135] The working principle of the above technical solution is as follows: After large-scale integration of new energy sources into the power system, the system's operation becomes more complex, and various faults and abnormal conditions may occur. Setting up dynamic simulation scenarios for the power system under multiple scenarios based on actual operating conditions and potential fault types is to comprehensively and realistically simulate the operating state 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 under the simulated scenario by step-by-step solving the differential-algebraic equations of the power system, which can intuitively reflect the state changes of the system at different times. The direct rule is based on theories such as the system's energy function to quickly determine the system's stability.

[0136] Transient stability margin is determined by calculating the critical condition under which the system can recover to a stable operating state after a fault is cleared; it reflects the system's resilience after a fault impact. Voltage stability margin uses voltage stability criteria to calculate the critical point of voltage instability in the system, used to assess the stability of the system voltage. Frequency stability margin analyzes the frequency changes of the system under load variations or power generation fluctuations, determines the boundaries of frequency stability, and ensures that the system frequency remains within a reasonable range. By calculating these key stability indicators, a dataset of key power system stability indicators under multiple scenarios is obtained, providing data support for subsequent in-depth analysis.

[0137] This study conducts in-depth mining and analysis of key stability indicator datasets across multiple scenarios to uncover hidden patterns and regularities within the data. Due to the vast and complex nature of power system operation data, data mining techniques can extract valuable information, such as the changing trends of stability indicators under 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 levels, and grid topology parameters) and the characteristics of renewable energy generation (such as power prediction errors and fluctuation characteristics of renewable energy generation). Machine learning algorithms can learn the intrinsic relationships between variables from large amounts of data. By training the model, the complex connections between key stability indicators and various influencing factors can be accurately described. For example, neural network algorithms can handle nonlinear relationships, and regression algorithms can establish quantitative relationships between variables.

[0138] By utilizing an established correlation model and inputting different operating parameters and renewable energy generation characteristic data, the stability trend of large-scale renewable energy integration into the power system under various operating conditions can be predicted. The correlation model has learned the relationships between key stability indicators and various influencing factors; when new operating parameters and renewable energy generation characteristic data are input, the model can make predictions based on these relationships. By analyzing the prediction results, power system stability trend prediction data can be obtained. This prediction data can help power system operators understand the system's stability under different operating conditions in advance and take corresponding measures to ensure stable system operation. For example, if it is predicted that the system's transient stability margin is 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 that 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 understanding of the overall stability of the power system.

[0140] By conducting stability analysis under different fault scenarios, potential fault risks can be identified and analyzed in advance, thereby enabling the development of effective preventive measures to reduce the risk of power system instability loss during faults.

[0141] Considering the strong volatility of energy sources such as wind and solar power after large-scale integration of new energy sources, analyzing the impact of new energy power generation fluctuations on the power system can enhance the dynamic response capability of the power system in the face of new energy fluctuations and ensure the 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 the characteristics of new energy power generation, thereby enabling trend prediction of 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 transient stability margins, we can assess the system's recovery capability after fault clearance, thereby improving the recovery speed and efficiency after system faults, reducing power outage time, and enhancing the reliability of power supply.

[0145] Based on stability trend prediction data, power systems can better optimize operating parameters and dispatch strategies, ensuring optimal stability performance under different scenarios and reducing unnecessary energy waste and instability risks.

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

[0147] Based on the actual topology and component characteristics of large-scale new energy access to the power system, a dynamic model of the power system is established, and initial parameters are set for each component in the model.

[0148] Based on the characteristics of the simulation scenario and the analysis requirements, a numerical calculation method is selected and applied to the constructed power system model to perform stability analysis.

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

[0150] The transient stability margin is determined by calculating the critical conditions under which the system can recover to a stable operating state after a fault is cleared; the critical point of voltage instability of the system is calculated by using voltage stability criteria; and the frequency change of the system under load changes or power generation fluctuations is analyzed to determine the boundary of frequency stability.

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

[0152] The working principle of the above technical solution is as follows: After large-scale integration of new energy sources into the power system, the system's topology and component characteristics become more complex. To accurately simulate the system's operation, a dynamic model of the power system needs to be established based on the actual topology and component characteristics. This model covers all key components, including generators, transformers, lines, loads, and new energy power generation equipment (such as wind turbines and photovoltaic power plants), and accurately describes their dynamic behavior. Appropriate numerical calculation methods are 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 can simulate the dynamic changes of the system over time in detail. By progressively solving the differential-algebraic equations of the power system, the state changes of the system at different times can be clearly observed, making it suitable for analyzing transient stability. For example, in simulating the occurrence and clearing of faults, time-domain simulation can accurately record the changes in variables such as generator rotor angle, voltage, and frequency, thereby evaluating the stability of the system during transient processes. Direct methods quickly derive stability criteria for power systems by solving their differential-algebraic equations. They focus more on analyzing steady-state stability from a theoretical perspective, eliminating the need for lengthy simulations like time-domain methods. This makes them suitable for rapidly determining system stability under steady-state conditions. Applying selected numerical methods to the constructed power system model for stability analysis allows for the selection of appropriate methods based on different needs, improving analytical efficiency and accuracy.

[0153] By solving the differential-algebraic equations of the power system, the dynamic response process of the system under a set simulation scenario is simulated. During the simulation, various components in the system interact, leading to acceleration and deceleration of the generator rotor, voltage and frequency fluctuations, and changes in renewable energy power generation. The changes in key variables, such as generator rotor angle, voltage amplitude and phase, frequency, and renewable energy power generation, are recorded in real time. These key variables are important indicators reflecting system stability; by recording their changes, the operating state of the system at different times can be intuitively understood. For example, changes in the generator rotor angle can reflect the transient stability of the system; if the rotor angle swings too much and cannot return to a stable state, it indicates that the system may have experienced transient instability. Changes in voltage amplitude and phase can reflect the voltage stability of the system; excessively low voltage amplitude or excessive phase changes can lead to voltage instability. Changes in frequency can reflect the frequency stability of the system; excessive deviation of the frequency from the rated value may affect the normal operation of power equipment.

[0154] The transient stability margin is determined by calculating the critical conditions under which the system can recover to a stable operating state after a fault is cleared. This involves analyzing the system's energy balance and rotor angle oscillation after the fault is cleared. When a fault occurs, the system accumulates a certain amount of energy. After the fault is cleared, the system needs to release this energy and recover to a stable operating state. If the system can gradually reduce the rotor angle oscillation and return to a stable value through its own regulation mechanisms (such as the generator's speed control system and excitation system) after the fault is cleared, the system is considered transiently stable; otherwise, the system may experience transient instability. By analyzing the amplitude and trend of the rotor angle oscillation, the critical conditions for the system to recover to a stable operating state can be determined, thereby calculating the transient stability margin. The voltage stability criterion is used to calculate the critical point of system voltage instability. This is achieved by analyzing the voltage changes when the system load changes and the boundary conditions of voltage collapse. When the system load increases, the voltage gradually decreases. If the load increases to a certain extent, the voltage may drop sharply, leading to voltage instability. By analyzing the relationship between load changes and voltage changes, and the critical conditions for voltage collapse (such as voltage amplitude falling below a certain threshold), the critical point for system voltage instability, i.e., the voltage stability margin, can be calculated. Analyzing the frequency changes of the system under load changes or fluctuations in power generation determines the boundary of frequency stability. The system frequency is related to the balance between power generation and load power; when power generation exceeds load power, the frequency increases; when power generation is less than load power, the frequency decreases. By analyzing the relationship between frequency changes and changes in power generation and load power, and the boundary conditions for frequency stability (such as the range of frequency deviation from the rated value), the boundary of frequency stability, i.e., the frequency stability margin, can be determined.

[0155] By integrating the calculated key indicators such as transient stability margin, voltage stability margin, and frequency stability margin, a dataset of key power system stability indicators under multiple scenarios is formed. These key indicators are important bases for evaluating the stability of the power system under different simulation scenarios. By integrating indicator data from different scenarios, a comprehensive understanding of the system's stability under various operating conditions can be achieved. For example, stability indicators can be compared under different scenarios of fluctuating renewable energy generation power, grid fault scenarios, and load changes, analyzing the degree of influence of various factors on system stability and providing decision support for power system planning and operation.

[0156] The effects of the above technical solution are as follows: By accurately modeling and dynamically simulating the large-scale integration of renewable energy into the power system, the stability of the system under different fault and load change conditions can be evaluated more effectively. It 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] Numerical calculation methods such as time-domain simulation and direct methods can be used to quickly and accurately simulate the dynamic response of a system after a fault occurs, and record the changes in key variables in real time. By simulating the acceleration and deceleration of the generator rotor, voltage and frequency fluctuations, the system's recovery capability can be assessed in a timely manner 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 is especially important under conditions of power system load fluctuations or power generation fluctuations, allowing for a more precise assessment of the impact of renewable energy generation stability and power prediction errors on the system. Such detailed modeling improves the operational reliability of renewable energy generation equipment and enhances the system's adaptability to renewable energy fluctuations.

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

[0160] By simulating the system's response under various fault and load change conditions, potential risks and vulnerabilities can be identified in advance, helping to develop more reliable emergency response strategies. Calculating the critical conditions after fault clearing allows for the rapid determination of the conditions required for the system to return to a stable state when a fault occurs, enhancing the system's resilience to disturbances during unexpected events.

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

[0162] This technical solution can adapt to the needs of power systems of different scales, and as the proportion of new energy sources continues to increase, the solution can be flexibly adjusted to meet the diversified and complex needs of future power systems. With the improvement and expansion of different types of energy access models, 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. Based on the power system stability trend prediction data and combined with the preset stability threshold, conduct a preliminary assessment of the power system stability.

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

[0166] S33. Based on the initial screening of risk areas or node data, extract relevant assessment strategy parameters from the preset stability assessment strategy database; adaptively adjust the test application parameters for subsequent stability assessments according to the extracted assessment strategy parameters; obtain a stability assessment scheme suitable for large-scale new 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 the 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 power system's safe operation standards, equipment tolerance, and past operating experience, providing a clear boundary for judging system stability. By comparing the power system stability trend prediction data with the preset stability threshold, a preliminary assessment of the power system's stability can be made. Based on the preliminary assessment, the predicted stability indicators are compared with the stability threshold in detail. Under different operating conditions (such as different fluctuations in new energy power generation, load changes, grid topology, etc.), the system's stability performance may differ. Through comparison, it can be determined whether the system meets the stability requirements under different operating conditions. For areas or nodes that do not meet the stability requirements, i.e., areas or nodes that may have stability risks, they are screened out to obtain preliminary risk area or node data.

[0169] Based on the initial screening of risk areas or nodes, relevant assessment strategy parameters are extracted from a pre-defined stability assessment strategy database. This database stores assessment strategy parameters for different risk areas or nodes and different operating scenarios. These parameters are derived from long-term practice and research and can provide reasonable assessment guidance based on different risk situations. The assessment frequency is determined according to the risk level, with shorter assessment intervals for high-risk areas or nodes. For example, for some critical wind power connection points, if their stability risk is high, more frequent assessments are required to promptly identify potential stability issues.

[0170] By comprehensively considering the results of multiple evaluation methods, reasonable weights are assigned to different methods. Different evaluation methods (such as time-domain simulation, direct methods, and machine learning-based prediction methods) have their own advantages, disadvantages, and applicable scopes. By reasonably allocating weights, the advantages of various methods can be comprehensively utilized to improve the accuracy of evaluation results. The electrical quantities and equipment that need to be monitored in the evaluation process are identified. 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 evaluation strategy parameters, the test application parameters for subsequent stability assessments are adaptively adjusted. For example, the fault types in dynamic simulations are adjusted (simulating more severe fault types, such as three-phase short-circuit faults instead of single-phase ground faults, for high-risk areas) to examine the system's stability under more severe conditions; the fault duration is extended to observe the system's response and recovery capabilities under long-term faults; more severe fluctuations in renewable energy power generation are simulated to test the system's ability to withstand the uncertainties of renewable energy. Through these adjustments, a stability evaluation scheme suitable for large-scale renewable energy integration into the power system is obtained, which can more accurately assess the system's stability in actual operation.

[0171] The adjusted stability assessment plan is uploaded to the power system stability assessment management platform. This platform is an integrated system capable of unified scheduling and management of assessment tasks. Based on the adjusted stability assessment plan, the platform schedules and manages the tasks, assigning 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. Reasonable task allocation ensures the smooth progress of the assessment work, improving efficiency and quality. Simultaneously, the platform can monitor and record the assessment process to promptly identify and adjust any issues.

[0172] The effect of the above technical solution is that 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, reducing the error of human judgment and improving the accuracy of system assessment.

[0173] In the initial screening stage, regions or nodes that may pose stability risks can be effectively identified, providing a foundation for more accurate subsequent assessments. By comparing predicted stability indicators with stability thresholds, regions or nodes with stability risks are automatically screened, avoiding erroneous assessments caused by inaccurate or missed human judgments.

[0174] After identifying the risk areas, the relevant assessment strategy parameters were extracted and adaptively adjusted to further reduce errors, making the assessment results more consistent with actual operating conditions.

[0175] The assessment strategy is flexibly adjusted according to different regions, nodes, and operating scenarios during the evaluation process. In particular, the simulation of fluctuations in renewable energy power generation effectively improves the stability assessment capability when large-scale renewable energy is integrated into the power system. By dynamically simulating different types of faults, the system's anti-interference capability in the face of various complex situations is ensured, enhancing the adaptability of the power system 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 execution of the assessment process. This reduces wasted time in task allocation and coordination issues during execution. Adaptive adjustment of assessment parameters allows for optimization based on the needs of risk areas, improving the speed and flexibility of assessments and shortening the execution cycle of assessment tasks.

[0177] The assessment strategy database includes weightings for various assessment methods and selections 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 the accuracy and operability of the assessments. Flexible adjustments to assessment time intervals and methods enable the development of more targeted assessment strategies based on the characteristics of different risk areas, thus enhancing system stability.

[0178] By automating risk screening and assessment strategy parameter extraction, manual intervention and complex decision-making processes are reduced, making risk management more systematic and standardized. A systematic risk assessment and task management platform simplifies the risk management process in power systems, reduces the complexity of risk identification and assessment, and improves the efficiency of emergency response.

[0179] By simulating more severe fault types and extending fault duration, the performance of the power system under extreme conditions can be identified in advance, enhancing the power system's responsiveness and stability recovery capabilities in emergency situations. Adjusting assessment strategy parameters to address new energy fluctuations improves the system's flexibility and robustness in responding to emergencies, thereby enhancing the system's disturbance resistance.

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

[0181] S41. In accordance with the adjusted stability assessment scheme, real-time assessment test data of large-scale new energy access to the power system is obtained through real-time monitoring equipment; based on the real-time assessment test data, the stability of the power system is assessed using a comprehensive assessment method.

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

[0183] S43. Based on the established evaluation criteria for device stability, compare the performance evaluation data of each device with the evaluation criteria, screen out devices or subsystems with good stability during the evaluation process, and obtain the performance data of qualified devices after re-screening.

[0184] The working principle of the above technical solution is as follows: Based on the adjusted stability assessment scheme, real-time monitoring equipment is used to collect data on the large-scale integration of renewable energy into the 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 basic parameters reflecting the operating status of the power system, and their changes directly reflect the dynamic behavior of the system. Simultaneously, operating status data of renewable energy power generation equipment is also acquired, such as generator speed, rotor angle, and output characteristics of photovoltaic inverters. Renewable energy power generation equipment is an important component of the power system, and its operating status has a significant impact on system stability. Acquiring this data allows for a more comprehensive understanding of the system's operation after the integration of renewable energy.

[0185] The stability of a system is determined by analyzing its dynamic response over time. During the evaluation process, real-time monitored electrical quantities and equipment operating status data change over time. Time-domain analysis can observe the trends of these data at different moments, such as whether the generator rotor angle gradually stabilizes after a fault, and whether voltage and frequency can return to normal ranges, thus assessing the system's stability during transient processes. The response characteristics of the system at different frequencies are studied to assess its frequency stability. Power systems contain various harmonics and oscillations. Frequency-domain analysis can analyze the system's response to these frequency components to determine whether frequency instability will occur, such as low-frequency oscillations. The system's energy function is constructed, and energy changes are analyzed to determine system stability. The energy function method, from an energy perspective, calculates the 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. By comprehensively utilizing these three analytical methods, the dynamic behavior and stability mechanisms of the power system can be fully considered, improving the accuracy and reliability of stability assessments.

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

[0187] Based on detailed device performance evaluation data, qualified devices are screened again. "Qualified" here refers to the device's stability meeting the standards, meaning the device can meet the requirements for stable system operation during the evaluation process. By setting certain screening criteria, such as whether the device's various performance indicators are within normal ranges and whether there are any obvious instability phenomena, devices that do not meet the standards are filtered 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 past operating experience, providing a clear basis for judging the stability of the devices.

[0188] By comparison, devices or subsystems exhibiting good stability during the evaluation process are selected. For example, if a generator's speed fluctuation range is less than a set threshold, its rotor angle oscillation converges quickly, and other performance indicators meet requirements, then the generator is considered a device with good stability. The performance data of these stable devices or subsystems are then compiled and recorded to obtain performance data for devices that pass the re-screening. This data can provide important references for power system operation and maintenance, equipment selection, and system optimization.

[0189] The advantages of the above technical solution are: by acquiring real-time evaluation and testing data of large-scale renewable energy integration into the power system through real-time monitoring equipment, it is possible to comprehensively capture the dynamic response of the system during operation. This makes the evaluation process no longer dependent on static models, but rather incorporates the dynamic behavior of the system in actual operation, ensuring the accuracy of stability assessment.

[0190] By employing a comprehensive evaluation approach that combines time-domain analysis, frequency-domain analysis, and the energy function method, the scientific rigor and accuracy of the evaluation are further enhanced, enabling in-depth analysis of the system's diverse response characteristics and stability mechanisms. Detailed device performance evaluation data allows for precise performance analysis of key equipment in the power system, reducing evaluation errors caused by differences in equipment characteristics. The combination of multiple analytical methods effectively reduces uncertainty in the evaluation process, making the evaluation results more reliable.

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

[0192] The comprehensive evaluation method, by considering different frequencies, dynamic responses, and energy variations, enhances the system's adaptability to fluctuations in renewable energy sources, load changes, and external disturbances, thereby improving the system's overall stability. This method provides detailed time, frequency, and energy characteristic analyses of the system's dynamic behavior, enabling rapid problem identification and timely countermeasures in the event of emergencies, thus enhancing the system's emergency response capabilities.

[0193] Through real-time data feedback and evaluation, assessors can obtain detailed information promptly and respond quickly when a fault occurs, improving the power system's resilience and anti-interference capabilities. Detailed performance evaluations of individual components (such as generators, transformers, and transmission lines) enable a more precise understanding of each component's status and operating characteristics, thereby achieving refined management of power system components and ensuring the long-term stability of the system.

[0194] By reviewing the performance data of qualified components, components with poor stability are eliminated, reducing the risk of overall system failures caused by equipment performance issues and improving the efficiency of overall power system equipment management. Detailed data from the evaluation process helps power system planners and operators better understand the real-time performance of the system under large-scale renewable energy integration, thus providing data support for subsequent optimization and adjustments.

[0195] By combining real-time monitoring with various analytical methods, a more comprehensive basis is provided for system planning and scheduling, enabling more precise adjustments and decisions, and improving the power system's adaptability to new energy sources. Through continuous stability assessment, the system can detect problems in advance and take preventive measures before potential faults occur, thereby reducing maintenance workload and the frequency of sudden failures. Selecting stable components avoids equipment failures caused by insufficient component performance, reducing safety hazards and economic losses caused by equipment failures, and ensuring the safe operation of the power system.

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

[0197] S51. Based on the data of the initial screening of risk areas or nodes and the performance data of qualified devices after rescreening, a comprehensive summary and analysis of the stability of large-scale new energy access to the power system is conducted.

[0198] S52. Based on the comprehensive summary and analysis results, obtain a stability assessment report on the large-scale integration of new energy sources into the power system;

[0199] The working principle of the above technical solution is as follows: It combines data from the initial screening of risk areas or nodes with performance data from the secondary screening of qualified components, taking a comprehensive approach from the perspective of the entire system. The initial screening data identifies areas in the system where stability issues may exist, while the performance data from the secondary screening of qualified components reflects the stability status of each key component or subsystem. Through comprehensive analysis of this data, the overall stability level of the system can be assessed. For example, by statistically analyzing the number, distribution, and degree of risk areas in the system, combined with the proportion and distribution of qualified components, the overall stability of the system can be determined. Simultaneously, weak links in system stability can be identified, which may be a critical piece of equipment, a transmission line, or a specific operating scenario. Furthermore, a thorough analysis of the main factors affecting system stability is conducted, such as the fluctuation characteristics of new energy power generation, the topology of the power grid, and the changing patterns of load. These factors interact and jointly influence system stability.

[0200] For different risk areas, stability is analyzed independently for each area using data from initial screening of risk areas or nodes and performance data from subsequent screening of qualified devices. Different risk areas may have different geographical environments, grid structures, and renewable energy integration ratios, which can lead to variations in their stability performance. The stability characteristics of each risk area are analyzed; for example, some areas may be more susceptible to fluctuations in renewable energy generation, while others may be more susceptible to load changes. The risk level of each area is assessed by setting corresponding risk indicators, such as the probability of risk occurrence and potential losses. Simultaneously, the interactions between different areas are studied; for example, a fault in one area may propagate to other areas through grid coupling, leading to a decrease in the overall system stability.

[0201] Based on the performance data of qualified components after rescreening, and combined with the analysis results at the system-wide and regional levels, this study investigates the contribution of each component's stability to the overall system stability and the coordination between components. A power system is a complex system composed of numerous components, and the performance and operating status of each component affect system stability. Furthermore, there are mutual cooperation and coordination relationships between components. By analyzing the contribution of each component's stability to the overall system stability, we can identify which components are key supports for system stability and which component performance improvements have the most significant impact on system stability. Simultaneously, we study the coordination between components, such as power matching between generators and transformers, and coordinated control between new energy power generation equipment and energy storage devices. Good coordination can improve system stability and reliability. By comprehensively considering the impact of different factors on system stability, and interrelating the analysis results at the system-wide, regional, and component levels, a comprehensive and systematic stability analysis framework is formed.

[0202] Based on the comprehensive analysis results, various aspects of system stability are summarized and categorized to form a stability assessment report for large-scale renewable energy integration into the power system. This assessment report presents the final outcome of the entire stability assessment process, expressing complex analytical results in a clear and concise manner, providing a scientific basis for the planning, operation, and maintenance of the power system. It provides an overall evaluation of system stability, clarifying whether the system is stable, has potential risks, or has already experienced instability; it details the stability characteristics and risk level of each risk area, as well as the stability status of each component, facilitating understanding of the system's specific operational status by maintenance personnel and managers.

[0203] A thorough analysis of the main causes of system stability issues provides direction for subsequent improvement measures. Based on the analysis of key factors, specific improvement measures and suggestions are proposed, such as optimizing the power grid structure, adjusting the control strategies of new energy power generation equipment, and strengthening equipment maintenance and upgrading. These measures will help improve the stability of large-scale new energy integration into the power system and ensure the safe and reliable operation of the power system.

[0204] The aforementioned technical solution achieves the following results: By comprehensively summarizing and analyzing power system stability based on initial screening of risk areas, node data, and performance data of qualified devices after secondary screening, it can accurately identify weak links and key influencing factors in the power system, thereby ensuring high accuracy of the assessment results. At the regional level, analyzing the stability characteristics and mutual influences of different risk areas helps to refine the stability differences between regions and avoid large-scale generalization errors in system assessment.

[0205] By thoroughly analyzing the contribution of each component's stability to the system, as well as the coordination between components, potential inconsistencies and system instability risks can be identified in a timely manner, allowing for measures to be taken to prevent system failures. Identifying potential weaknesses in advance, based on the risk characteristics of each region, helps to avoid global failures caused by local instability during system operation.

[0206] The assessment report provides a scientific basis for the planning, operation, and maintenance of the power system, enabling targeted adjustments during the integration of new energy sources and thus improving the power system's adaptability to fluctuations in new energy supply and load changes. Through key factor analysis, it provides power system dispatchers with optimized decision support, allowing for more flexible responses to emergencies in complex power networks.

[0207] Component-level performance evaluation and stability contribution analysis provide clear guidance for equipment selection and procurement in power systems, ensuring that the stability of critical equipment meets requirements. Based on the evaluation report results, more detailed equipment maintenance and upkeep plans can be developed, identifying issues such as equipment aging and performance degradation in advance, thereby extending equipment lifespan 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 power generation equipment (such as wind power and photovoltaics) and their impact on system stability, the system design is optimized to ensure that renewable energy fluctuations do not affect the overall system stability. The comprehensive evaluation results provide data support for subsequent system adjustments, effectively improving the system's operational efficiency and stability after renewable energy integration.

[0209] The 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 specific improvement recommendations make system management more scientific and systematic, providing a more stable management framework during the rapid integration of new energy sources.

[0210] By scientifically assessing the system stability after the integration of new energy sources, the system has been able to continuously expand the integration of new energy sources while ensuring system stability, providing data support for the green and sustainable development of the power system. Early identification and resolution of the potential impacts of new energy fluctuations on the power system have promoted the harmonious coexistence of new and traditional energy sources and driven the green transformation of the power system.

[0211] An embodiment of the present invention provides a power system stability assessment system suitable for large-scale renewable energy integration, 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 integration as described above.

[0212] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A power system stability assessment method applicable to large-scale renewable energy integration, characterized in that, The method includes: S1: Acquire multi-source real-time monitoring data, preprocess the acquired multi-source real-time monitoring data, and use data fusion technology to integrate the preprocessed data from different sources and in different formats to obtain a fused dataset to be analyzed; based on the fused dataset, construct a real-time status model; S2: Based on the real-time state model, perform dynamic simulation of the power system under multiple scenarios; under each simulation scenario, analyze the stability of the power system through numerical calculation methods, calculate key stability indicators, and obtain a dataset of key stability indicators of the power system under multiple scenarios; conduct in-depth mining and analysis of the dataset of key stability indicators under multiple scenarios, establish a correlation model based on machine learning algorithms, predict the stability trend under different operating conditions through the correlation model, and obtain power system stability trend prediction data. S3: Based on the power system stability trend prediction data and the preset stability threshold, conduct a preliminary assessment of the power system stability, screen out areas or nodes with stability risks, and obtain preliminary risk area or node data; based on the preliminary risk area or node data, extract relevant assessment strategy parameters from the preset stability assessment strategy database, and adaptively adjust the test application parameters for subsequent stability assessments according to the extracted assessment strategy parameters to obtain a stability assessment scheme suitable for large-scale new energy access to the power system, and upload the assessment scheme to the power system stability assessment management platform; S4: According to the adjusted stability assessment scheme, obtain real-time assessment test data of large-scale new energy access to the power system. Based on the real-time assessment test data, use the comprehensive assessment method to conduct a detailed assessment of the stability of the power system and obtain detailed device performance assessment data. Based on the detailed device performance assessment data, conduct qualified device screening again and select devices or subsystems with good stability during the assessment process to obtain the performance data of the re-screened qualified devices. S5: Based on the initial screening of risk areas or nodes and the performance data of qualified devices after secondary screening, conduct a comprehensive summary and analysis of the stability of large-scale new energy access to the power system; and obtain a stability assessment report of large-scale new energy access to the power system.

2. The power system stability assessment method applicable to large-scale renewable energy integration according to claim 1, characterized in that, S1 includes: S11. Real-time monitoring data from multiple sources is collected through various sensors; S12. Preprocess 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, integrate the high-precision multi-source real-time monitoring data from different sources and in different formats to obtain the fusion dataset to be analyzed; S14. Based on the fused dataset, a real-time state model reflecting the dynamic characteristics of large-scale new energy access to the power system is constructed using the system identification method.

3. The power system stability assessment method applicable to large-scale renewable energy integration according to claim 2, characterized in that, S13 includes: S131. Assign accurate spatial coordinates to each data source, and rationally divide the monitoring area into several sub-regions based on geographical location, power grid topology, or meteorological regional characteristics. S132. Standardize the format of the high-precision multi-source real-time monitoring data collected, convert data from different sources and in different formats into a unified format standard, and retain and label the spatial coordinate information of each data point. S133. Time alignment processing of multi-source real-time monitoring data will make the data from each data source correspond precisely 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 from different spatial locations, and perform data fusion based on the analysis results using a spatial interpolation-based data fusion method; conduct spatial quality assessment on the fused dataset, and iteratively optimize the data fusion process based on the spatial quality assessment results to obtain a fused dataset that meets the spatial analysis requirements.

4. The power system stability assessment method applicable to large-scale renewable energy integration according to claim 3, characterized in that, 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; the data from each data source is timestamped based on the selected time base and labeled according to the unified time base; a time alignment algorithm is used to align the data from 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; Based on the requirements of spatial synchronization and the spatial distribution characteristics of data, spatial interpolation methods are used to perform spatial interpolation calculations on data points that require spatial synchronization, generating data values ​​at specified spatial locations and time points; During time alignment and spatial synchronization processing, detect any possible anomalous data; process the detected anomalous data. Evaluate the effectiveness of time alignment processing by checking the alignment accuracy and consistency of each data source on the timeline; evaluate the effectiveness of spatial synchronization processing by checking the correspondence accuracy and spatial continuity of data at different spatial locations at the same time point; and provide feedback and optimization to the processing based on the results of time alignment and spatial synchronization evaluations.

5. The power system stability assessment method applicable to large-scale renewable energy integration according to claim 3, characterized in that, S135 includes: Based on the data type and analysis objectives, an index is constructed to measure the correlation between data from different spatial locations. The correlation matrix between data points at each spatial location is calculated to determine the strength and direction of the correlation between data points. Spatial clustering algorithms are used to cluster data points based on spatial correlation, identifying data groups with similar spatial characteristics; interpolation parameters are set based on interpolation methods. Combining spatial correlation analysis and interpolation methods, a multi-source data fusion strategy is formulated, and the original data is preprocessed before data fusion; According to the established data fusion strategy, the selected spatial interpolation method is used to interpolate the multi-source data to generate the fused dataset; the results of spatial quality assessment are fed back into 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. The optimized parameters and strategies are then used to conduct a new round of data fusion and spatial quality evaluation until a fusion dataset that meets the spatial analysis requirements is obtained.

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

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

8. The power system stability assessment method applicable to large-scale renewable energy integration according to claim 1, characterized in that, The S4 includes: S41. In accordance with the adjusted stability assessment scheme, real-time assessment test data of large-scale new energy access to the power system is obtained through real-time monitoring equipment; based on the real-time assessment test data, the stability of the power system is assessed using a comprehensive assessment method. S42. Obtain detailed device performance evaluation data through a comprehensive evaluation method; based on the detailed device performance evaluation data, screen qualified devices again. S43. Based on the established evaluation criteria for device stability, compare the performance evaluation data of each device with the evaluation criteria, screen out devices or subsystems with good stability during the evaluation process, and obtain the performance data of qualified devices after re-screening.

9. The power system stability assessment method applicable to large-scale renewable energy integration according to claim 1, characterized in that, The S5 includes: S51. Based on the data of the initial screening of risk areas or nodes and the performance data of qualified devices after rescreening, a comprehensive summary and analysis of the stability of large-scale new energy access to the power system is conducted. S52. Based on the comprehensive summary and analysis results, a stability assessment report on the large-scale integration of new energy sources into the power system is obtained.

10. A power system stability assessment system suitable for large-scale renewable energy integration, characterized in that, It includes a memory, a processor, and a computer program stored on and executable on the memory, wherein the processor executes the program to implement claim 1. A power system stability assessment method applicable to large-scale new energy integration, as described in any of the nine points.

Images