A stability analysis system and method for a flexible ac / dc interconnected system

Abstract

The application discloses a stability analysis system and method for a flexible DC interconnection system, and belongs to the technical field of DC power distribution. The stability analysis system for the flexible DC interconnection system comprises a stability analysis unit, an abnormality analysis unit and a visualization unit. The application solves the problem that the prior art cannot analyze the stability of the flexible DC interconnection system. The application predicts the operation state and behavior of the flexible DC interconnection system by constructing simulation calculation of a digital analysis model through the stability analysis unit, analyzes the stability of the flexible DC interconnection system according to the predicted operation state and behavior, establishes an abnormality diagnosis model to perform reliability analysis through the abnormality analysis unit, finds possible potential problems and risks in the flexible DC interconnection system, and thus can perform operation optimization and take preventive measures on the flexible DC interconnection system according to the obtained stability analysis result and reliability analysis result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of direct current power distribution, in particular to a stability analysis system and method for a flexible direct current interconnection system. BACKGROUND

[0002] The flexible direct current interconnection system is a system based on flexible direct current technology, which plays an important role in intelligent manufacturing, smart city, intelligent transportation and other fields. This system fundamentally avoids the problem of low-frequency oscillation by adopting a direct current asynchronous networking structure, and does not affect the short-circuit current level of the interconnected alternating current system. However, in actual application, the flexible direct current interconnection system also has some stability problems, such as voltage and current fluctuations, voltage surge and other problems of the flexible direct current interconnection system, which will affect the stability and safety of the system; therefore, the stability analysis of the flexible direct current interconnection system is very important, and for this purpose, we propose a stability analysis system and method for a flexible direct current interconnection system. SUMMARY

[0003] The purpose of the present application is to provide a stability analysis system and method for a flexible direct current interconnection system, which predicts the operating state and behavior of the flexible direct current interconnection system by constructing a simulation calculation of a digital analysis model, analyzes the stability of the flexible direct current interconnection system according to the predicted operating state and behavior, obtains a stability analysis result, performs reliability analysis by establishing an abnormal diagnosis model, finds potential problems and risks in the flexible direct current interconnection system, obtains a reliability analysis result, and visualizes the stability analysis result and the reliability analysis result, optimizes the operation of the flexible direct current interconnection system according to the stability analysis result, and takes preventive measures for the flexible direct current interconnection system according to the reliability analysis result, solving the problems raised in the above background technology.

[0004] To achieve the above purpose, the present application provides the following technical solution: a stability analysis system for a flexible direct current interconnection system, comprising:

[0005] a stability analysis unit for:

[0006] detailed modeling of the flexible direct current interconnection system, constructing a digital analysis model of the flexible direct current interconnection system, and performing stability analysis of the flexible direct current interconnection system based on the constructed digital analysis model;

[0007] an abnormal analysis unit for:

[0008] A large amount of historical data of the flexible DC interconnection system is collected, and data cleaning and preprocessing are performed, an abnormality diagnosis model of the flexible DC interconnection system is established by collecting and arranging a large amount of historical data, characteristics and rules of operation of the flexible DC interconnection system are extracted, and reliability analysis of the flexible DC interconnection system is realized;

[0009] The visualization unit is configured to:

[0010] The stability analysis result obtained by the stability analysis unit and the reliability analysis result obtained by the abnormality analysis unit are visualized and displayed, and operation optimization of the flexible DC interconnection system is performed according to the stability analysis result, and preventive measures are taken for the flexible DC interconnection system according to the reliability analysis result.

[0011] Further, the stability analysis unit comprises:

[0012] The stability modeling module is configured to:

[0013] According to each component and operating environment of the flexible DC interconnection system, a digital analysis model of the flexible DC interconnection system is constructed based on the RT-LAB simulation software, the constructed digital analysis model is debugged and optimized, and the optimized digital analysis model is exported for further analysis.

[0014] The stability analysis module is configured to:

[0015] Based on the constructed digital analysis model, the operating state and behavior of the flexible DC interconnection system are predicted by simulation calculation of the digital analysis model, and the stability of the flexible DC interconnection system is analyzed according to the predicted operating state and behavior.

[0016] Further, the stability modeling module comprises the following processes:

[0017] The purpose and target of modeling are determined, and the specific components and operating environment of the flexible DC interconnection system to be simulated are determined.

[0018] Based on the RT-LAB simulation software, a semi-physical simulation platform of the flexible DC interconnection system is built.

[0019] According to the actual parameters of the flexible DC interconnection system, the modular equipment in the flexible DC interconnection system is modeled, and then a digital analysis model is constructed and exported for stability analysis.

[0020] Further, the constructed digital analysis model is debugged and optimized, specifically:

[0021] The constructed digital analysis model is simulated, the operation of the digital analysis model is checked and debugged, and the correctness and feasibility of the digital analysis model are verified through the simulation result.

[0022] According to the simulation results, find out the problems existing in the digital analysis model and optimize it;

[0023] The optimized digital analysis model is exported and used for the analysis module to analyze the stability of the flexible DC interconnection system.

[0024] Further, the modular device includes a battery energy storage module, an inverter module, a DC converter module, and a capacitor filter module, specifically:

[0025] According to the specific requirements of the flexible DC interconnection system, add the corresponding modules and make detailed parameter settings;

[0026] Connect each module to build a complete digital analysis model of the flexible DC interconnection system.

[0027] Further, the anomaly analysis unit comprises:

[0028] The data collection module is used for:

[0029] Collect a large amount of historical data of the flexible DC interconnection system, including operation data, state data and fault data of the flexible DC interconnection system;

[0030] The data processing module is used for:

[0031] The collected large amount of historical data is subjected to data cleaning and preprocessing, so as to improve the quality and reliability of the historical data, and further ensure the accuracy of subsequent modeling of the historical data;

[0032] The anomaly analysis module is used for:

[0033] The processed historical data is mined and analyzed by using data mining technology, the operation rules and characteristics of the flexible DC interconnection system are extracted, and an anomaly diagnosis model is established for reliability analysis, so as to find out potential problems and risks in the flexible DC interconnection system.

[0034] Further, the data processing module comprises:

[0035] The cleaning target data acquisition module is used for data cleaning of the historical data, and proposes cleaning target data in the historical data; wherein the data type of the cleaning target data includes incomplete data, error data, duplicate data and irrelevant data;

[0036] The first data amount extraction module is used for extracting the data amount of incomplete data and error data contained in the cleaning target data;

[0037] An initial quality judgment coefficient obtaining module is configured to obtain an initial quality judgment coefficient by using the data amount of incomplete data and error data contained in the cleaning target data, wherein the initial quality judgment coefficient is obtained by the following formula:

[0038]

[0039] wherein Q represents the initial quality judgment coefficient; D 01 and D 02 represent the data amount of incomplete data and the data amount of error data respectively; D z represents the total data amount of historical data; W 01 and W 02 represent the average value of weight coefficients of all key fields contained in the incomplete data and the average value of weight coefficients of all key fields of the error data respectively; S 01 and S 02 represent the severity coefficient of the incomplete data and the severity coefficient of the error data respectively, and the severity coefficient of the incomplete data is obtained by the following formula:

[0040]

[0041] wherein S 01 represents the severity coefficient of the incomplete data; k e01 represents a first adjustment coefficient, and the value range of the first adjustment coefficient is 0.35-0.78; n represents the number of key fields contained in the incomplete data; M represents the number of all key fields contained in the historical data; N qi represents the number of missing characters of the i-th key field; N fi represents the total number of characters of the i-th key field;

[0042] Meanwhile, the severity coefficient of the error data is obtained by the following formula:

[0043]

[0044] wherein S 02 represents the severity coefficient of the error data; k e02 represents a second adjustment coefficient, and the value range of the second adjustment coefficient is 0.46-0.81; m represents the number of key fields contained in the error data; M represents the number of all key fields contained in the historical data; M qi represents the number of characters with errors in the i-th key field N fi represents N fi represents the total number of characters of the i-th key field;

[0045] The first data quality determination module is configured to determine that the data quality of the historical data is poor and to perform data quality alarm when the initial quality determination coefficient is lower than the preset coefficient threshold.

[0046] The secondary quality determination module is configured to perform secondary quality determination on the historical data by using the repetitive data and irrelevant data in the cleaning target data and to obtain a determination result when the initial quality determination coefficient is not lower than the preset coefficient threshold.

[0047] Further, the secondary quality determination module comprises:

[0048] The second data amount extraction module is configured to extract repetitive data and irrelevant data in the cleaning target data when the initial quality determination coefficient is lower than the preset coefficient threshold.

[0049] The secondary quality determination coefficient acquisition module is configured to acquire a secondary quality determination coefficient by using the repetitive data and irrelevant data in the cleaning target data, wherein the secondary quality determination coefficient is acquired by the following formula:

[0050]

[0051] wherein E represents the secondary quality determination coefficient; D 03 and D 04 represent the data amount of repetitive data and the data amount of irrelevant data respectively; D z represents the total data amount of the historical data; W 03 and W 04 represent the average value of the weight coefficient of all key fields contained in the repetitive data and the average value of the weight coefficient of all key fields of the irrelevant data respectively; D 01 and D 02 represent the data amount of incomplete data and the data amount of error data respectively; D z represents the total data amount of the historical data; W 01 and W 02 represent the average value of the weight coefficient of all key fields contained in the incomplete data and the average value of the weight coefficient of all key fields of the error data respectively.

[0052] The second data quality determination module is configured to determine that the data quality of the historical data is poor and to perform data quality alarm when the secondary quality determination coefficient is lower than the preset secondary determination coefficient threshold.

[0053] Further, the visualization unit comprises:

[0054] The acquisition module is configured to:

[0055] obtaining a stability analysis result of the stability analysis unit and a reliability analysis result of the abnormality analysis unit;

[0056] an analysis module configured to:

[0057] analyze the obtained stability analysis result and reliability analysis result respectively, analyze the stability analysis result to formulate a corresponding stability control strategy, optimize the operation of the flexible DC interconnection system through the stability control strategy, analyze the reliability analysis result to determine potential problems and risks in the flexible DC interconnection system, and then take corresponding preventive measures for the flexible DC interconnection system;

[0058] a display module configured to:

[0059] visualize the obtained stability analysis result and reliability analysis result, and visualize the stability control strategy formulated by the analysis module and the preventive measures taken.

[0060] Further, the stability control strategy is a maximum power point tracking strategy and a DC side power supply control strategy, the maximum power point tracking strategy optimizes the output power of the battery of the flexible DC interconnection system to improve the stability of the flexible DC interconnection system, and the DC side power supply control strategy adjusts the output of the DC side power supply to improve the stability of the flexible DC interconnection system.

[0061] An analysis method of a stability analysis system for a flexible DC interconnection system, comprising the following steps:

[0062] detailed modeling of the flexible DC interconnection system to construct a digital analysis model of the flexible DC interconnection system;

[0063] debugging and optimization of the constructed digital analysis model, stability analysis of the flexible DC interconnection system based on the optimized digital analysis model, and finally obtaining a stability analysis result;

[0064] collecting a large amount of historical data of the flexible DC interconnection system and performing data cleaning and preprocessing;

[0065] establishing an abnormality diagnosis model of the flexible DC interconnection system by collecting and organizing a large amount of historical data to realize reliability analysis of the flexible DC interconnection system, and finally obtaining a reliability analysis result;

[0066] obtaining the stability analysis result and the reliability analysis result respectively and visualizing them;

[0067] optimizing the operation of the flexible DC interconnection system according to the stability analysis result, and taking preventive measures for the flexible DC interconnection system according to the reliability analysis result.

[0068] Compared with the prior art, the application has the following beneficial effects:

[0069] The stability analysis unit of the application constructs a digital analysis model of the flexible DC interconnected system according to each component and operating environment of the flexible DC interconnected system, and debugs and optimizes the constructed digital analysis model, so as to optimize the digital analysis model to the best, and further ensure the accuracy of subsequent analysis results. Based on the optimized digital analysis model, the simulation calculation of the digital analysis model is used to predict the operating state and behavior of the flexible DC interconnected system, and the stability of the flexible DC interconnected system is analyzed according to the predicted operating state and behavior. Meanwhile, the abnormality analysis unit collects a large amount of historical data and performs data cleaning and preprocessing, and then uses data mining technology to mine and analyze the processed historical data, extracts the rules and characteristics of the operation of the flexible DC interconnected system, and establishes an abnormal diagnosis model for reliability analysis, and further finds the potential problems and risks in the flexible DC interconnected system. Through the analysis of the stability and reliability of the flexible DC interconnected system, the dominant factors and abnormal problems of instability in the flexible DC interconnected system are found in time, so that the operation of the flexible DC interconnected system is optimized and corresponding preventive measures are taken in time. BRIEF DESCRIPTION OF DRAWINGS

[0070] Fig. 1 The figure is a structural schematic diagram of the stability analysis system for the flexible DC interconnected system of the application.

[0071] Fig. 2 The figure is a structural schematic diagram of the stability analysis unit, the abnormality analysis unit and the visualization unit of the application. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0073] In order to solve the technical problems that the existing flexible DC interconnected system has some stability problems such as voltage and current fluctuation, voltage surge and the like of the flexible DC interconnected system, and the prior art cannot analyze the stability of the flexible DC interconnected system, which affects the stability and safety of the system, please refer to Figs. 1-2 The embodiment provides the following technical solutions:

[0074] A stability analysis system for a flexible DC interconnected system, comprising:

[0075] The stability analysis unit is configured to:

[0076] The flexible DC interconnection system is modeled in detail, a digital analysis model of the flexible DC interconnection system is constructed, and stability analysis of the flexible DC interconnection system is performed based on the constructed digital analysis model;

[0077] The abnormality analysis unit is configured to:

[0078] A large amount of historical data of the flexible DC interconnection system is collected, data cleaning and preprocessing are performed, an abnormality diagnosis model of the flexible DC interconnection system is established by collecting and organizing a large amount of historical data, features and rules of operation of the flexible DC interconnection system are extracted, and reliability analysis of the flexible DC interconnection system is realized;

[0079] The visualization unit is configured to:

[0080] The stability analysis results obtained by the stability analysis unit and the reliability analysis results obtained by the abnormality analysis unit are visualized and displayed, the operation of the flexible DC interconnection system is optimized according to the stability analysis results, and preventive measures are taken for the flexible DC interconnection system according to the reliability analysis results.

[0081] The technical effects of the above are as follows: The stability analysis unit can predict the operation state and behavior of the flexible DC interconnection system through simulation calculation of the digital analysis model, so as to analyze the stability of the flexible DC interconnection system and finally obtain stability analysis results. The abnormality analysis unit can establish an abnormality diagnosis model for reliability analysis, so as to analyze whether there is a potential abnormality in the flexible DC interconnection system and finally obtain reliability analysis results. The stability analysis results and the reliability analysis results obtained above are visualized and displayed by the visualization unit, so as to facilitate the staff to intuitively understand and master the situation of the flexible DC interconnection system. In addition, the visualization unit can optimize the operation of the flexible DC interconnection system according to the stability analysis results and take preventive measures according to the reliability analysis results, so as to improve the stability and reliability of the flexible DC interconnection system.

[0082] The stability analysis unit comprises:

[0083] The stability modeling module is configured to:

[0084] A digital analysis model of the flexible DC interconnection system is constructed based on the RT-LAB simulation software according to each component and operating environment of the flexible DC interconnection system, the constructed digital analysis model is debugged and optimized, and the optimized digital analysis model is exported for further analysis;

[0085] The stability analysis module is configured to:

[0086] Based on the constructed digital analysis model, the operation state and behavior of the flexible DC interconnected system are predicted through simulation calculation of the digital analysis model, and the stability of the flexible DC interconnected system is analyzed according to the predicted operation state and behavior.

[0087] The stability modeling module includes the following processes:

[0088] The purpose and goal of modeling are determined, and the specific components and operating environment of the flexible DC interconnected system to be simulated are determined.

[0089] Based on the RT-LAB simulation software, a semi-physical simulation platform of the flexible DC interconnected system is built.

[0090] According to the actual parameters of the flexible DC interconnected system, the modular devices in the flexible DC interconnected system are modeled, and then the digital analysis model is constructed.

[0091] Among them, the modular device includes a battery energy storage module, an inverter module, a DC converter module and a capacitor filter module, according to the specific needs of the flexible DC interconnected system, the corresponding module is added and the detailed parameter setting is carried out, then the modules are connected to construct a complete digital analysis model of the flexible DC interconnected system.

[0092] The constructed digital analysis model is simulated, the operation of the digital analysis model is checked and debugged, and the correctness and feasibility of the digital analysis model are verified through the simulation results.

[0093] According to the simulation results, the problems existing in the digital analysis model are found out and optimized.

[0094] The optimized digital analysis model is exported and used for the analysis module to analyze the stability of the flexible DC interconnected system.

[0095] The technical effects of the above are: first, the stability modeling module needs to determine the specific components and operating environment of the flexible DC interconnected system to be simulated before modeling analysis, and then selects a suitable modeling tool according to the modeling requirements. The stability modeling module selects RT-LAB simulation software (according to actual requirements, Simulink software or Matlab software can also be selected). The RT-LAB simulation software can build a semi-physical simulation platform for the flexible DC interconnected system. According to the specific requirements and actual parameters of the flexible DC interconnected system, the corresponding modules are added and detailed parameter settings are made. Then, the modules are connected to build a complete digital analysis model of the flexible DC interconnected system. Before analysis of the digital analysis model, the digital analysis model can be debugged and optimized. The operation of the digital analysis model is checked through simulation, so that the digital analysis model can be debugged. According to the simulation results, the problems in the digital analysis model can be found and optimized to verify the correctness and feasibility of the digital analysis model, thereby ensuring the accuracy of the subsequent analysis results. The optimized digital analysis model is exported and run, so that the stability analysis module can predict the operating state and behavior of the flexible DC interconnected system through simulation calculation based on the digital analysis model, analyze the stability of the flexible DC interconnected system according to the predicted operating state and behavior, and find the dominant factor of instability in the flexible DC interconnected system in time, facilitating the staff to optimize the flexible DC interconnected system in time.

[0096] The anomaly analysis unit comprises:

[0097] The data collection module is configured to:

[0098] Collect a large amount of historical data of the flexible DC interconnected system, wherein the historical data comprises operating data, state data and fault data of the flexible DC interconnected system.

[0099] The data processing module is configured to:

[0100] The data processing module is configured to:

[0101] The anomaly analysis module is configured to:

[0102] The anomaly analysis module is configured to:

[0103] The technical effect of the above is that a large amount of data is required for the flexible DC interconnection system, a large amount of operation data, state data and fault data of the flexible DC interconnection system are collected through the data collection module, then the collected data is cleaned and preprocessed through the data processing module, the data quality and reliability are improved through the data cleaning and preprocessing, the credibility of the data is ensured, the subsequent modeling analysis is paved, the accuracy and reliability of the model and analysis are ensured, deviation of analysis caused by data quality problems is avoided, finally, abnormal analysis is performed through the abnormal analysis module, the processed historical data is mined and analyzed through the abnormal analysis module using data mining technology, the operation rules and characteristics of the flexible DC interconnection system are extracted, and an abnormal diagnosis model is established for reliability analysis, and then potential problems and risks in the flexible DC interconnection system are found, through the reliability analysis of the flexible DC interconnection system, the staff can understand the abnormal problems that may exist in the flexible DC interconnection system, and then prevent the abnormal problems that may exist.

[0104] Specifically, the data processing module comprises:

[0105] The cleaning target data acquisition module is configured to clean the historical data and obtain cleaning target data in the historical data; and the data type of the cleaning target data comprises incomplete data, error data, duplicate data and irrelevant data.

[0106] The first data amount extraction module is configured to extract the data amount of the incomplete data and the error data contained in the cleaning target data.

[0107] The initial quality determination coefficient acquisition module is configured to acquire an initial quality determination coefficient by using the data amount of the incomplete data and the error data contained in the cleaning target data, wherein the initial quality determination coefficient is acquired by the following formula:

[0108]

[0109] Wherein, Q represents the initial quality determination coefficient; D 01 and D 02 represent the data amount of the incomplete data and the data amount of the error data, respectively; D z represents the total data amount of the historical data; W 01 and W 02 represent the weight coefficient average value of all key fields contained in the incomplete data and the weight coefficient average value of all key fields of the error data, respectively; S 01 and S 02 represent the severity coefficient of the incomplete data and the severity coefficient of the error data, respectively, and the severity coefficient of the incomplete data is acquired by the following formula:

[0110]

[0111] wherein, S 01 represents the severity coefficient of incomplete data; k e01 represents the first adjustment coefficient, and the value range of the first adjustment coefficient is 0.35-0.78; n represents the number of key fields contained in the incomplete data; M represents the number of all key fields contained in the historical data; N qi represents the number of missing characters of the i-th key field; N fi represents the total number of characters of the i-th key field;

[0112] Meanwhile, the severity coefficient of the error data is obtained by the following formula:

[0113]

[0114] wherein, S 02 represents the severity coefficient of error data; k e02 represents the second adjustment coefficient, and the value range of the second adjustment coefficient is 0.46-0.81; m represents the number of key fields contained in the error data; M represents the number of all key fields contained in the historical data; M qi represents the number of characters in the i-th key field that appear to be errors N fi represents N fi represents the total number of characters of the i-th key field;

[0115] The first data quality determination module is configured to determine that the data quality of the historical data is poor and perform data quality alarm when the initial quality determination coefficient is lower than the preset coefficient threshold.

[0116] The secondary quality determination module is configured to perform secondary quality determination on the historical data by using the repeated data and irrelevant data in the cleaning target data when the initial quality determination coefficient is not lower than the preset coefficient threshold, and obtain a determination result.

[0117] The technical effect of the above technical solution is that the cleaning target data acquisition module can accurately identify and extract incomplete data, error data, repeated data and irrelevant data in the historical data, thereby providing a high-quality data source for subsequent data processing. This precise data cleaning process helps to reduce data noise and improve the accuracy and reliability of data analysis.

[0118] By introducing an initial quality judgment coefficient (Q) and combining the data volume of incomplete data and error data, the average weight coefficient of key fields, and the severity coefficient, a scientific evaluation of historical data quality is achieved. This method not only considers the quantity dimension of data, but also takes into account the quality dimension of data (such as the completeness and accuracy of key fields), making the evaluation of data quality more comprehensive and accurate.

[0119] By introducing adjustment coefficients (such as the first adjustment coefficient ke01 and the second adjustment coefficient ke02), the quality evaluation model can be adjusted according to different business scenarios and needs, enhancing the flexibility and adaptability of the model. The range of values for these adjustment coefficients also reflects the consideration of the complexity of actual business scenarios.

[0120] The first data quality judgment module can immediately determine that the data quality is poor and issue an alarm when the initial quality judgment coefficient is below the preset threshold, which helps to promptly discover and solve data quality problems and avoid errors in subsequent analysis or decision-making.

[0121] After the initial quality judgment, if the data quality meets the basic requirements (i.e., the initial quality judgment coefficient is not lower than the preset threshold), further quality judgment is performed using duplicate data and irrelevant data. This multi-level quality judgment mechanism can more comprehensively evaluate the quality of historical data, ensuring the accuracy and effectiveness of subsequent data processing and analysis.

[0122] Through modular design, each module can run independently or in parallel, improving the overall efficiency of data processing. At the same time, data transmission and result feedback between modules are automated and intelligent, reducing the possibility of human intervention and errors.

[0123] In summary, this technical solution achieves comprehensive, accurate, and efficient evaluation and monitoring of historical data quality through scientific data cleaning, quality evaluation, and multi-level quality judgment mechanisms, providing a solid data foundation for subsequent data analysis and decision-making.

[0124] Specifically, the secondary quality judgment module includes:

[0125] The second data volume extraction module is used to extract duplicate data and irrelevant data from the cleaning target data when the initial quality judgment coefficient is lower than the preset coefficient threshold.

[0126] The secondary quality judgment coefficient acquisition module is used to acquire a secondary quality judgment coefficient using duplicate data and irrelevant data in the cleaning target data, wherein the secondary quality judgment coefficient is acquired by the following formula:

[0127]

[0128] where E represents a secondary quality determination coefficient; D 03 and D 04 represent the data volume of duplicate data and the data volume of irrelevant data, respectively; D z represents the total data volume of historical data; W 03 and W 04 represent the average weight coefficient of all key fields contained in duplicate data and the average weight coefficient of all key fields of irrelevant data, respectively; D 01 and D 02 represent the data volume of incomplete data and the data volume of error data, respectively; D z represents the total data volume of historical data; W 01 and W 02 represent the average weight coefficient of all key fields contained in incomplete data and the average weight coefficient of all key fields of error data, respectively;

[0129] a second data quality determination module, configured to determine that the data quality of the historical data is poor and perform data quality alarm when the secondary quality determination coefficient is lower than a preset secondary determination coefficient threshold.

[0130] The technical effect of the above technical solution is that by introducing a secondary quality determination module, the technical solution further considers the impact of duplicate data and irrelevant data on the overall data quality after the initial quality determination. This multi-level and multi-dimensional evaluation method can more comprehensively reflect the quality status of historical data and improve the accuracy and reliability of evaluation.

[0131] In the secondary quality determination, not only the data volume of duplicate data and irrelevant data (D03 and D04) is considered, but also the average weight coefficient of key fields in these data (W03 and W04), as well as the information of previously evaluated incomplete data (D01, W01) and error data (D02, W02) is combined. This determination logic considers both the quantity dimension and the quality dimension of data (such as the importance of key fields), making the determination result more scientific and reasonable.

[0132] When the secondary quality determination coefficient is lower than the preset secondary determination coefficient threshold, the system can immediately determine that the data quality of the historical data is poor and perform data quality alarm. This real-time feedback mechanism helps to discover and solve potential data quality problems in a timely manner, preventing problem data from having a negative impact on subsequent analysis or decision-making.

[0133] Through more comprehensive data quality evaluation, it can be ensured that the data used in subsequent data processing and analysis processes is of high quality and reliable. This helps to reduce errors and biases caused by data quality problems and improves the accuracy and efficiency of data processing and analysis.

[0134] The modules in this technical solution are relatively independent and can be expanded or adjusted according to actual needs. For example, the weighting coefficients and adjustment coefficients of key fields can be adjusted based on different business scenarios and data characteristics to adapt to different data quality assessment requirements. This design enhances the system's adaptability and scalability.

[0135] In summary, this technical solution, by introducing a secondary quality assessment module, achieves a more comprehensive and scientific evaluation and monitoring of historical data quality. This multi-level, multi-dimensional assessment method not only improves the accuracy and reliability of data quality assessment but also helps to promptly identify and resolve potential data quality issues, providing a solid data foundation for subsequent data processing and analysis.

[0136] Visualization units include:

[0137] The acquisition module is used for:

[0138] Obtain the stability analysis results of the stability analysis unit and the reliability analysis results of the anomaly analysis unit;

[0139] Analysis module, used for:

[0140] The obtained stability analysis results and reliability analysis results are analyzed separately. Based on the stability analysis results, corresponding stability control strategies are formulated and the operation of the flexible DC interconnection system is optimized through the stability control strategies. Based on the reliability analysis results, potential problems and risks in the flexible DC interconnection system are identified, and corresponding preventive measures are taken for the flexible DC interconnection system.

[0141] Among them, the stability control strategies are the maximum power point tracking strategy and the DC-side power supply control strategy. The maximum power point tracking strategy improves the stability of the flexible DC interconnect system by optimizing the output power of the battery in the flexible DC interconnect system, while the DC-side power supply control strategy improves the stability of the flexible DC interconnect system by adjusting the output of the DC-side power supply.

[0142] The display module is used for:

[0143] The obtained stability and reliability analysis results are visualized, and the stability control strategies and preventive measures formulated by the analysis module are also visualized.

[0144] The technical effects of the above content are as follows: The acquisition module obtains stability analysis results and reliability analysis results respectively. The analysis module then analyzes these results. By analyzing the stability analysis results, the dominant factors causing instability in the flexible DC interconnect system can be identified, allowing for the formulation of corresponding stability control strategies (examples of stability control strategies include: maximum power point tracking strategy, which optimizes the output power of the battery in the flexible DC interconnect system to improve its stability; and DC-side power supply control strategy, which adjusts the output of the DC-side power supply to improve its stability). By analyzing the reliability analysis results, potential problems and risks in the flexible DC interconnect system can be identified, enabling the implementation of corresponding preventative measures. Finally, the display module provides a visual representation of the acquired stability and reliability analysis results, as well as the formulated stability control strategies and preventative measures, facilitating timely understanding and control of the dominant factors and abnormal problems in the flexible DC interconnect system by staff.

[0145] Specifically, this embodiment also proposes an analysis method for a stability analysis system of a flexible DC interconnection system, including the following steps:

[0146] A detailed model of the flexible DC interconnection system is constructed, and a digital analysis model of the flexible DC interconnection system is built.

[0147] The constructed digital analysis model is debugged and optimized, and the stability analysis of the flexible DC interconnection system is performed based on the optimized digital analysis model to obtain the final stability analysis results.

[0148] Collect a large amount of historical data from the flexible DC interconnection system, and perform data cleaning and preprocessing;

[0149] By collecting and organizing a large amount of historical data, an anomaly diagnosis model for the flexible DC interconnection system is established, enabling reliability analysis of the flexible DC interconnection system and ultimately obtaining the reliability analysis results.

[0150] The stability analysis results and reliability analysis results are obtained and visualized.

[0151] Based on the stability analysis results, the operation of the flexible DC interconnection system is optimized, and based on the reliability analysis results, preventive measures are taken for the flexible DC interconnection system.

[0152] Working Principle: The stability analysis unit constructs a digital analysis model of the flexible DC interconnect system based on its various components and operating environment. This model is then debugged and optimized to ensure the accuracy of subsequent analysis results. Based on the optimized model, simulation calculations predict the operating state and behavior of the flexible DC interconnect system. The stability of the system is analyzed based on these predictions, allowing for the timely identification of key factors leading to instability. The anomaly analysis unit collects and cleans and preprocesses a large amount of historical data. Data mining techniques are used to mine and analyze processed historical data, extracting the patterns and characteristics of the flexible DC interconnection system's operation. Based on this, an anomaly diagnosis model is established for reliability analysis, thereby identifying potential problems and risks in the flexible DC interconnection system. The results of both stability and reliability analyses are visualized through visualization units, facilitating timely understanding and control of the actual situation of the flexible DC interconnection system by staff. Furthermore, by analyzing the stability and reliability analysis results, the dominant factors and anomalies causing instability in the flexible DC interconnection system can be identified in a timely manner, enabling timely optimization of the flexible DC interconnection system's operation and the implementation of corresponding preventative measures.

[0153] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0154] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A stability analysis system for flexible DC interconnection systems, characterized in that, include: Stability analysis unit; The anomaly analysis unit is used for: A large amount of historical data of the flexible DC interconnection system is collected, and the data is cleaned and preprocessed. An anomaly diagnosis model of the flexible DC interconnection system is established by collecting and organizing a large amount of historical data, and the characteristics and laws of the operation of the flexible DC interconnection system are extracted to realize the reliability analysis of the flexible DC interconnection system. Visual unit; The anomaly analysis unit includes: The data collection module is used for: Collect a large amount of historical data from the flexible DC interconnection system, including the system's operation data, status data, and fault data. The data processing module is used for: Data cleaning and preprocessing are performed on the large amount of historical data collected to improve the quality and reliability of the historical data and ensure the accuracy of subsequent modeling based on the historical data. The anomaly analysis module is used for: Data mining techniques are used to mine and analyze the processed historical data, extract the patterns and characteristics of the operation of the flexible DC interconnection system, and establish an anomaly diagnosis model to conduct reliability analysis, thereby discovering potential problems and risks in the flexible DC interconnection system. The data processing module includes: The target data cleaning module is used to clean the historical data and extract the target data for cleaning from the historical data; wherein, the data types of the target data for cleaning include incomplete data, erroneous data, duplicate data, and irrelevant data; The first data volume extraction module is used to extract the amount of incomplete and erroneous data contained in the cleaned target data; The initial quality judgment coefficient acquisition module is used to acquire an initial quality judgment coefficient based on the amount of incomplete and erroneous data contained in the cleaning target data. The initial quality judgment coefficient is acquired using the following formula: Where Q represents the initial quality determination coefficient; D 01 and D 02 These represent the amount of incomplete data and the amount of erroneous data, respectively; D z W represents the total amount of historical data; 01 and W 02 S represents the average weight coefficient of all key fields included in the incomplete data and the average weight coefficient of all key fields in the erroneous data, respectively; 01 and S 02 These represent the severity coefficients of incomplete data and erroneous data, respectively, and the severity coefficient of incomplete data is obtained using the following formula: Among them, S 01 k represents the severity coefficient of incomplete data. e01 Let represent the first adjustment coefficient, and the value of the first adjustment coefficient ranges from 0.35 to 0.78; n represents the number of key fields contained in the incomplete data; M represents the total number of key fields contained in the historical data; N qi N represents the number of missing characters in the i-th key field; fi This represents the total number of characters in the i-th key field; Meanwhile, the severity coefficient of the erroneous data is obtained using the following formula: Among them, S 02 k represents the severity coefficient of the erroneous data. e02 This represents the second adjustment coefficient, and the value range of the second adjustment coefficient is 0.46-0.81; m represents the number of key fields contained in the erroneous data; M represents the total number of key fields contained in the historical data; M qi N represents the number of erroneous characters in the i-th key field. fi N represents fi This represents the total number of characters in the i-th key field.

2. The stability analysis system for a flexible DC interconnection system according to claim 1, characterized in that: The stability analysis unit includes: The stability modeling module is used for: Based on the various components and operating environment of the flexible DC interconnection system, a digital analysis model of the flexible DC interconnection system is constructed using RT-LAB simulation software. The constructed digital analysis model is then debugged and optimized, and the optimized digital analysis model is exported for further analysis. The stability analysis module is used for: Based on the constructed digital analysis model, the operating status and behavior of the flexible DC interconnection system are predicted through simulation calculations of the digital analysis model, and the stability of the flexible DC interconnection system is analyzed based on the predicted operating status and behavior.

3. The stability analysis system for a flexible DC interconnection system according to claim 2, characterized in that: The stability modeling module includes the following process: Define the purpose and objectives of the modeling, and determine the specific components and operating environment of the flexible DC interconnect system to be simulated; A hardware-in-the-loop simulation platform for a flexible DC interconnection system was built based on RT-LAB simulation software. Based on the actual parameters of the flexible DC interconnection system, the modular equipment in the flexible DC interconnection system is modeled, a digital analysis model is constructed, and the model is exported for stability analysis.

4. The stability analysis system for a flexible DC interconnection system according to claim 3, characterized in that: The completed digital analysis model is debugged and optimized, specifically as follows: The constructed digital analysis model is simulated to check its operation and debug it. The simulation results are used to verify the correctness and feasibility of the digital analysis model. Identify and optimize the problems in the digital analysis model based on the simulation results; The optimized digital analysis model is exported and used by the analysis module to perform stability analysis on the flexible DC interconnection system.

5. The stability analysis system for a flexible DC interconnection system according to claim 3, characterized in that: The modular equipment includes a battery energy storage module, an inverter module, a DC-DC converter module, and a capacitor filter module, specifically: Based on the specific requirements of the flexible DC interconnection system, add the corresponding modules and set detailed parameters. By connecting the various modules, a complete digital analysis model of the flexible DC interconnection system is constructed.

6. The stability analysis system for a flexible DC interconnection system according to claim 1, characterized in that: The secondary quality assessment module includes: The second data extraction module is used to extract duplicate and irrelevant data from the cleaning target data when the initial quality judgment coefficient is lower than the preset coefficient threshold. The secondary quality judgment coefficient acquisition module is used to obtain a secondary quality judgment coefficient using duplicate and irrelevant data in the cleaned target data. The secondary quality judgment coefficient is obtained using the following formula: Where E represents the secondary quality determination coefficient; D 03 and D 04 D represents the amount of duplicate data and the amount of irrelevant data, respectively; z W represents the total amount of historical data; 03 and W 04 These represent the average weight coefficients of all key fields in duplicate data and the average weight coefficients of all key fields in irrelevant data, respectively; D 01 and D 02 W represents the amount of incomplete data and the amount of erroneous data, respectively; 01 and W 02 These represent the average weight coefficients of all key fields included in the incomplete data and the average weight coefficients of all key fields in the erroneous data, respectively. The second data quality determination module is used to determine that the data quality of the historical data is poor and to issue a data quality alarm when the secondary quality determination coefficient is lower than a preset secondary determination coefficient threshold.

7. The stability analysis system for a flexible DC interconnection system according to claim 1, characterized in that: The visualization unit includes: The acquisition module is used for: Obtain the stability analysis results of the stability analysis unit and the reliability analysis results of the anomaly analysis unit; Analysis module, used for: The obtained stability analysis results and reliability analysis results are analyzed separately. Based on the stability analysis results, corresponding stability control strategies are formulated and the operation of the flexible DC interconnection system is optimized through the stability control strategies. Based on the reliability analysis results, potential problems and risks in the flexible DC interconnection system are identified, and corresponding preventive measures are taken for the flexible DC interconnection system. The display module is used for: The obtained stability and reliability analysis results are visualized, and the stability control strategies and preventive measures formulated by the analysis module are also visualized.

8. An analysis method for a stability analysis system for flexible DC interconnection systems as described in any one of claims 1-7, characterized in that: Includes the following steps: A detailed model of the flexible DC interconnection system is constructed, and a digital analysis model of the flexible DC interconnection system is built. The constructed digital analysis model is debugged and optimized, and the stability analysis of the flexible DC interconnection system is performed based on the optimized digital analysis model to obtain the final stability analysis results. Collect a large amount of historical data from the flexible DC interconnection system, and perform data cleaning and preprocessing; By collecting and organizing a large amount of historical data, an anomaly diagnosis model for the flexible DC interconnection system is established, enabling reliability analysis of the flexible DC interconnection system and ultimately obtaining the reliability analysis results. The stability analysis results and reliability analysis results are obtained and visualized. Based on the stability analysis results, the operation of the flexible DC interconnection system is optimized, and based on the reliability analysis results, preventive measures are taken for the flexible DC interconnection system.

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