Intelligent power distribution network anti-misoperation system applying electric power GIS

Abstract

The invention relates to the technical field of intelligent power distribution networks, and discloses an intelligent power distribution network anti-misoperation system applying an electric power GIS (Geographic Information System), which comprises a data acquisition module, a data integration module, a calculation module, a data analysis module, an execution module and a communication module. Compared with the traditional technical means, the system has the advantages that the data integration processing, the anomaly detection precision, the real-time response capability and the information display comprehensiveness are remarkably improved, and the system can be widely applied to the power distribution network by optimizing the application of the multi-source data and an intelligent analysis strategy. According to the system, the safety and stability of the power distribution network are remarkably improved, the risk of misoperation is reduced, and a solid technical guarantee is provided for efficient and stable operation of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent distribution network, and in particular to an anti-error operation system of an intelligent distribution network using an electric power GIS. Background Art

[0002] Power GIS (Geographic Information System) technology has become a key tool to improve power grid management and operational efficiency. Power GIS technology has a wide range of applications, from power grid design, planning, construction to daily operation and maintenance, and can achieve more efficient management by integrating geographic information and power system data.

[0003] Although the current intelligent distribution network management system has a certain level of automation and intelligence, it still has significant deficiencies in data integration, anomaly detection and real-time response. Traditional systems often have difficulty processing and integrating information from different data sources, resulting in limited data processing capabilities. In addition, the anomaly detection and response mechanisms of these systems are often not sensitive enough to identify and respond to potential problems in the distribution network in a timely manner. These defects limit the effectiveness of the system in ensuring the safety and stability of the power grid and affect the operating efficiency and safety of the distribution network.

[0004] Therefore, we proposed an intelligent distribution network anti-error operation system using power GIS to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent distribution network anti-error operation system using electric power GIS to solve the problem that traditional systems often find it difficult to process and integrate information from different data sources, resulting in limited data processing capabilities. The abnormal detection and response mechanisms of these systems are often not sensitive enough, which limits the effectiveness of the system in ensuring the safety and stability of the power grid and affects the operating efficiency and safety of the distribution network.

[0006] To achieve the above object, the present invention provides the following technical solutions: an intelligent distribution network error prevention operation system using electric power GIS, comprising a data acquisition module, a data integration module, a calculation module, a data analysis module, an execution module and a communication module;

[0007] The data acquisition module is used to collect multi-source data in power grid operation;

[0008] The data integration module is used to pre-process the multi-source data collected by the data collection module, and reorganize the pre-processed data to generate a first data set, a second data set, and a third data set;

[0009] The calculation module is used to integrate and calculate the first data set, the second data set and the third data set, so as to generate an abnormal reference coefficient YCX;

[0010] The data analysis module is used to compare the abnormal reference coefficient YCX with the preset first threshold value Y, thereby generating a first comparison result, and judging whether the current power grid has an erroneous operation through the first comparison result;

[0011] If the first comparison result is that an erroneous operation occurs, the abnormal reference coefficient YCX is integrated and calculated with the preset first threshold value Y to generate a magnitude division coefficient, and the magnitude division coefficient is compared with the preset second threshold value R to generate a second comparison result;

[0012] The execution module executes a corresponding protection operation according to the second comparison result;

[0013] The communication module is used to display multiple data and operations on the terminal.

[0014] Preferably, the data acquisition module includes a first acquisition unit, a second acquisition unit and a third acquisition unit;

[0015] The first acquisition unit is used to collect device status data, including real-time voltage, real-time current and real-time device temperature;

[0016] The second acquisition unit is used to collect equipment topology data, including real-time line resistance, line length and real-time ground resistance;

[0017] The third acquisition unit is used to collect comprehensive equipment data, including operation time interval, real-time ambient temperature, extreme equipment temperature value, extreme voltage and extreme current.

[0018] Preferably, the data integration module includes a data preprocessing unit and a data sorting unit;

[0019] The data preprocessing unit is used to preprocess the multi-source data collected by the data acquisition module and perform dimensionless conversion after preprocessing;

[0020] The data sorting unit is used to sort the dimensionless multi-source data and form a first data set, a second data set and a third data set;

[0021] The first data set includes real-time voltage, real-time current, and real-time device temperature;

[0022] The real-time voltages are recorded as SDY1, SDY2, SDY3, ..., SDY according to the timestamps. n ;

[0023] The real-time current is recorded as SDL1, SDL2, SDL3, ..., SDL according to the timestamp. n ;

[0024] The real-time device temperatures are recorded as SWD1, SWD2, SWD3, ..., SWD according to the timestamp. n ;

[0025] The second data set includes real-time line resistance, line length XCD, and real-time ground resistance;

[0026] The real-time line resistance is recorded as SXZ1, SXZ2, SXZ3, ..., SXZ according to the timestamp. n ;

[0027] The real-time ground resistance is recorded as SDZ1, SDZ2, SDZ3, ..., SDZ according to the timestamp. n ;

[0028] The third data set includes an operation time interval CJG, a real-time ambient temperature, a limit device temperature value JWD, a limit voltage JDY, and a limit current JDL;

[0029] The real-time ambient temperatures are recorded as SHW1, SHW2, SHW3, ..., SHW according to the timestamps. n .

[0030] Preferably, the data calculation unit includes a first calculation module and a second calculation module;

[0031] The first calculation module is used for integrating the first data set, the second data set and the third data set into a first reference value S1, a second reference value S2 and an intervention value K;

[0032] The second calculation module is used to integrate the first reference value S1, the second reference value S2 and the intervention value K to calculate the abnormal reference coefficient YCX.

[0033] Preferably, the first reference value S1, the second reference value S2 and the intervention value K are calculated and obtained by the following formulas respectively:

[0034]

[0035] Where: SDY n 、SDL n 、SWD n SXZ n ,SDZ n and SHW n They are the real-time voltage, real-time current, real-time device temperature, real-time line resistance, real-time ground resistance and real-time ambient temperature at timestamp n;

[0036] SDY n-1 、SDL n-1 、SWD n-1 SXZ n-1, SDZ n-1 and SHW n-1 are the real-time voltage, real-time current, real-time device temperature, real-time line resistance, real-time ground resistance, and real-time ambient temperature at timestamp n - 1 respectively;

[0037] XCD is the line length, CJC is the operation time interval, JWD is the limit device temperature value, JDY is the limit voltage value, and JDL is the limit current value.

[0038] Preferably, the abnormal reference coefficient YCX is calculated and obtained through the following formula;

[0039]

[0040] In the formula: S1 is the first reference value S1, S2 is the second reference value S2, a1 and a2 are weight values, and the values of a1 and a2 are adjusted and set by the user, and ln is the logarithmic function.

[0041] Preferably, the data analysis unit includes a first analysis unit and a second analysis unit;

[0042] Among them, the first analysis unit is used to generate a first comparison result, specifically as follows;

[0043] When AQX < Y, it means that there is no safety problem in the current distribution network;

[0044] When AQX ≥ Y, it means that there is a safety problem in the current distribution network

[0045] The second analysis unit is used to generate a second comparison result, specifically as follows;

[0046] When LJZ < R, it means that the distribution network is about to enter the first-level abnormal state;

[0047] When R ≤ LJZ < R * 110%, it means that the distribution network is about to enter the second-level abnormal state;

[0048] When LJZ ≥ R * 110%, it means that the distribution network is about to enter the third-level abnormal state.

[0049] Preferably, the magnitude division coefficient LJZ is calculated and obtained through the following formula;

[0050]

[0051] In the formula: YCX is the abnormal reference coefficient, and Y is the first threshold.

[0052] Preferably, the protection operation of the execution module is as follows;

[0053] When the distribution network is about to enter the first-level abnormal state, the self-check function of the equipment is activated to check the status of key equipment, including parameters such as voltage, current, and temperature, to determine whether there are hidden dangers;

[0054] When the distribution network is about to enter the secondary abnormal state, the self-check function of the equipment is activated, and the affected equipment or lines are partially isolated to prevent the abnormal state from spreading to other parts;

[0055] When the distribution network is about to enter the third-level abnormal state, the entire distribution network or part of the area will be isolated.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. This system realizes all-round and multi-level monitoring and protection of the distribution network by integrating six modules: data acquisition, processing, calculation, analysis, execution and communication. Compared with traditional technical means, the system has significantly improved data integration processing, anomaly detection accuracy, real-time response capability and comprehensiveness of information display. By optimizing the application of multi-source data and intelligent analysis strategies, the system significantly improves the safety and stability of the distribution network, reduces the risk of misoperation, and provides a solid technical guarantee for the efficient and stable operation of the power system.

[0058] 2. The data calculation unit integrates calculations and introduces the abnormal reference coefficient YCX, making the system's assessment of the power grid status more comprehensive and accurate. Compared with traditional technical means, the improved system not only improves the sensitivity of misoperation detection, but also enhances the ability to handle complex environments and multi-dimensional data, ultimately improving the safety and stability of the distribution network. Through these innovations, the system can better meet the needs of modern power systems for intelligent and efficient protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a system flow chart of the present invention.

[0060] In the figure: 1. Data acquisition module; 11. First acquisition unit; 12. Second acquisition unit; 13. Third acquisition unit; 2. Data integration module; 21. Data preprocessing unit; 22. Data sorting unit; 3. Calculation module; 31. First calculation unit; 32. Second calculation unit; 4. Data analysis module; 41. First analysis unit; 42. Second analysis unit; 5. Execution module; 6. Communication module. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Example 1: Please refer to Figure 1 , an intelligent distribution network error prevention operation system using electric power GIS, comprising a data acquisition module 1, a data integration module 2, a calculation module 3, a data analysis module 4, an execution module 5 and a communication module 6;

[0063] The data acquisition module 1 is used to collect multi-source data in the operation of the power grid;

[0064] The data integration module 2 is used to pre-process the multi-source data collected by the data collection module 1, and reorganize the pre-processed data to generate a first data set, a second data set, and a third data set;

[0065] The calculation module 3 is used to integrate and calculate the first data set, the second data set and the third data set, so as to generate an abnormal reference coefficient YCX;

[0066] The data analysis module 4 is used to compare the abnormal reference coefficient YCX with the preset first threshold value Y, thereby generating a first comparison result, and judging whether the current power grid has an erroneous operation according to the first comparison result;

[0067] If the first comparison result is that an erroneous operation occurs, the abnormal reference coefficient YCX is integrated and calculated with the preset first threshold value Y to generate a magnitude division coefficient, and the magnitude division coefficient is compared with the preset second threshold value R to generate a second comparison result;

[0068] The execution module 5 executes a corresponding protection operation according to the second comparison result;

[0069] The communication module 6 is used to display multiple data and operations on the terminal.

[0070] In this embodiment: Data acquisition module 1 is responsible for collecting multi-source data from various nodes and devices in the distribution network. These data include key information of power equipment. By deploying various sensors and monitoring equipment, the system can obtain the operating status of the power grid in real time and comprehensively. Through accurate data collection, the module ensures efficient monitoring and timely feedback of the power grid status, avoiding the risk of misoperation caused by data lag or loss in traditional systems. Real-time data collection can quickly identify potential operational risks, so as to intervene before problems occur.

[0071] The data integration module 2 preprocesses the multi-source data obtained by the data acquisition module 1. The processing steps include data cleaning, format conversion and denoising. After preprocessing, the data is reorganized into three data sets. By preprocessing and integrating multi-source data, this module improves the reliability and consistency of the data. Accurate data information not only reduces data redundancy and errors, but also provides a high-quality data foundation for subsequent analysis and calculation, and optimizes the overall data flow of the system.

[0072] The calculation module 3 integrates and calculates the first data set, the second data set and the third data set generated by the data integration module 2 to generate an abnormal reference coefficient YCX. This coefficient reflects the overall operating status of the current power grid and provides a basis for subsequent misoperation judgments. The module accurately quantifies the stability of the power grid operation by comprehensively calculating information from multiple data sources. The generated abnormal reference coefficient YCX can be used as a reliable indicator to determine whether the power grid is in an abnormal state. Compared with the traditional single data monitoring method, the multi-data fusion calculation of this module is more comprehensive and accurate.

[0073] The data analysis module 4 first compares the abnormal reference coefficient YCX generated by the calculation module 3 with the preset first threshold value Y to determine whether the current power grid has any misoperation. If a misoperation is detected, the magnitude division coefficient is further calculated and compared with the preset second threshold value R to evaluate the severity and level of the misoperation. This module can analyze the abnormal state of the power grid in a hierarchical manner, not only detecting misoperation, but also classifying it according to the severity of the anomaly. Compared with the traditional single anomaly detection method, this module is more accurate in the determination and level division of misoperation, and can provide a more detailed protection strategy for the system.

[0074] The execution module 5 performs corresponding protection operations according to the second comparison result generated by the data analysis module 4. When the power grid is judged to have entered different levels of abnormal states, the system will take corresponding countermeasures, such as early warning prompts, load diversion, emergency power off, etc., and take flexible countermeasures through analysis results to minimize the risks and losses caused by misoperation of the power grid. Through real-time automated response, the efficiency of responding to abnormal situations is improved, avoiding the expansion of faults caused by untimely manual intervention in traditional systems.

[0075] The communication module 6 is used to display the collected data, calculation results, analysis results and executed operations on the terminal for real-time viewing and monitoring by operation and maintenance personnel. This module ensures the efficient transmission of information inside and outside the system, and provides an intuitive interface support for decision-making. Through real-time information display and transmission, the transparency and operability of the system are improved. Operation and maintenance personnel can obtain the latest power grid status information at any time to ensure that timely and correct decisions can be made. Compared with the traditional single data display method, the multi-dimensional information display of this module greatly improves the user experience of the system.

[0076] This system realizes all-round and multi-level monitoring and protection of the distribution network by integrating six modules: data acquisition, processing, calculation, analysis, execution and communication. Compared with traditional technical means, the system has significantly improved data integration processing, anomaly detection accuracy, real-time response capability and comprehensiveness of information display. By optimizing the application of multi-source data and intelligent analysis strategies, the system significantly improves the safety and stability of the distribution network, reduces the risk of misoperation, and provides a solid technical guarantee for the efficient and stable operation of the power system.

[0077] Example 2: Please refer to Figure 1 , the data acquisition module 1 includes a first acquisition unit 11, a second acquisition unit 12 and a third acquisition unit 13;

[0078] The first acquisition unit 11 is used to collect device status data, including real-time voltage, real-time current and real-time device temperature;

[0079] The second acquisition unit 12 is used to collect device topology data, including real-time line resistance, line length and real-time ground resistance;

[0080] The third acquisition unit 13 is used to acquire comprehensive equipment data, including operation time interval, real-time ambient temperature, extreme equipment temperature value, extreme voltage and extreme current.

[0081] In this embodiment: by collecting real-time voltage and current data of the equipment, the system can accurately monitor the operating status of the equipment, detect abnormal conditions in a timely manner, and avoid equipment damage or abnormal operation due to excessively high or low voltage or current. Real-time equipment temperature monitoring can help the system take corresponding protective measures when the equipment temperature is too high to prevent the equipment from malfunctioning or being damaged due to overheating.

[0082] By collecting real-time line resistance and ground resistance data, the system can evaluate the operating status of the line and prevent line overload or failure caused by abnormal resistance. The real-time data of line length and resistance enables the system to more accurately analyze and optimize the topology of the power grid and ensure the stability and efficiency of the distribution network.

[0083] By monitoring the operation time interval, the system can analyze the operation frequency, avoid equipment loss or misoperation caused by frequent operation, and improve the safety of operation. The real-time ambient temperature collection enables the system to adapt to the equipment operation status under different environmental conditions, adjust the operating parameters in time, and prevent equipment failures caused by changes in ambient temperature. By monitoring the equipment's extreme temperature, extreme voltage, and extreme current, the system can ensure that the equipment always operates within a safe range. Once it approaches or exceeds the limit value, the system can quickly take protective measures to prevent equipment damage.

[0084] Example 3: Please refer to Figure 1 , the data integration module 2 includes a data preprocessing unit 21 and a data sorting unit 22;

[0085] The data preprocessing unit 21 is used to preprocess the multi-source data collected by the data collection module 1 and perform dimensionless conversion after preprocessing;

[0086] The data sorting unit 22 is used to sort the dimensionless multi-source data and form a first data set, a second data set and a third data set;

[0087] The first data set includes real-time voltage, real-time current, and real-time device temperature;

[0088] The real-time voltages are recorded as SDY1, SDY2, SDY3, ..., SDY according to the timestamps. n ;

[0089] The real-time current is recorded as SDL1, SDL2, SDL3, ..., SDL according to the timestamp. n ;

[0090] The real-time device temperatures are recorded as SWD1, SWD2, SWD3, ..., SWD according to the timestamp. n ;

[0091] The second data set includes real-time line resistance, line length XCD, and real-time ground resistance;

[0092] The real-time line resistance is recorded as SXZ1, SXZ2, SXZ3, ..., SXZ according to the timestamp. n ;

[0093] The real-time ground resistance is recorded as SDZ1, SDZ2, SDZ3, ..., SDZ according to the timestamp. n ;

[0094] The third data set includes an operation time interval CJG, a real-time ambient temperature, a limit device temperature value JWD, a limit voltage JDY, and a limit current JDL;

[0095] The real-time ambient temperatures are recorded as SHW1, SHW2, SHW3, ..., SHW according to the timestamps. n .

[0096] In this embodiment: the collected multi-source data is dimensionally processed by the data preprocessing unit 21, so that different data can be compared and calculated under the same dimension, avoiding calculation deviations caused by unit differences, effectively improving the accuracy of data processing, and making subsequent analysis and calculations more precise, thereby improving the reliability of the system in detecting and preventing misoperations.

[0097] The dimensionless multi-source data is structured and organized by the data organization unit 22, and divided into a first data set, a second data set, and a third data set. Each data set is recorded in detail according to the timestamp, so that the data at different time points can be clearly corresponding, which is helpful for time series analysis. The structured processing method not only makes the data easier to manage and store, but also lays a solid foundation for subsequent calculations and analysis, ensuring that the system's data comparison and analysis at different time points is more scientific and reasonable.

[0098] Through the optimized design of data integration module 2, the system has achieved efficient preprocessing, dimensionless and structured organization of multi-source data, making data analysis and calculation more accurate and scientific. This improvement not only improves the system's ability to detect misoperations, but also enhances the real-time evaluation and early warning capabilities of the power grid's operating status. Compared with traditional technical means, it significantly improves the safety and stability of the distribution network, and ultimately achieves the effect of preventing misoperations and ensuring power supply.

[0099] Example 4: Please refer to Figure 1 , the data calculation unit 3 includes a first calculation module 31 and a second calculation module 32;

[0100] The first calculation module 31 is used to integrate the first data set, the second data set and the third data set into a first reference value S1, a second reference value S2 and an intervention value K;

[0101] The second calculation module 32 is used to integrate the first reference value S1 , the second reference value S2 , and the intervention value K to calculate the abnormal reference coefficient YCX.

[0102] The first reference value S1, the second reference value S2 and the intervention value K are calculated and obtained by the following formulas respectively;

[0103]

[0104] Where: SDY n 、SDL n 、SWD n SXZ n ,SDZn and SHW n They are the real-time voltage, real-time current, real-time device temperature, real-time line resistance, real-time ground resistance and real-time ambient temperature at timestamp n;

[0105] SDY n-1 、SDL n-1 、SWD n-1 SXZ n-1 ,SDZ n-1 and SHW n-1 They are the real-time voltage, real-time current, real-time device temperature, real-time line resistance, real-time ground resistance and real-time ambient temperature at timestamp n-1;

[0106] XCD is the line length, CJC is the operation time interval, JWD is the limit equipment temperature value, JDY is the limit voltage value, and JDL is the limit current value.

[0107] The abnormal reference coefficient YCX is calculated by the following formula:

[0108]

[0109] In the formula: S1 is the first reference value S1, S2 is the second reference value S2, a1 and a2 are weight values, and the values ​​of a1 and a2 are adjusted and set by the user, and ln is a logarithmic function.

[0110] In this embodiment: the first calculation module 31 integrates the first data set, the second data set and the third data set into a first reference value S1, a second reference value S2 and an intervention value K through a formula. This integrated calculation method effectively combines key parameters such as voltage, current, equipment temperature, line resistance, ground resistance, and ambient temperature to form a comprehensive reference index. In particular, by introducing the difference calculation between timestamps n and n-1 into the formula, the system's sensitivity and response speed to real-time changes are ensured. This integrated calculation method of multi-dimensional data enables the system to more accurately capture potential abnormal conditions in the power grid, improve the accuracy of misoperation detection, and thus enhance the safety protection capabilities of the power grid.

[0111] The second calculation module 32 further calculates the abnormal reference coefficient YCXYCX by integrating S1, S2 and K. The calculation of YCX adopts a logarithmic function and a weight adjustment mechanism. The user can adjust the weight values ​​of a1 and a2 according to actual needs, and flexibly customize the system's sensitivity to different abnormal conditions. In this way, the system can more scientifically evaluate the impact of different parameters on power grid anomalies. The introduction of the abnormal reference coefficient YCX makes the system's assessment of abnormal conditions more refined. The weight adjustment mechanism ensures the flexibility and adaptability of the system in different application scenarios, and significantly improves the system's anomaly detection capability and operating accuracy in complex environments.

[0112] Through the integration of calculations by the data calculation unit 3 and the introduction of the abnormal reference coefficient YCX, the system's assessment of the power grid status becomes more comprehensive and accurate. Compared with traditional technical means, the improved system not only enhances the sensitivity of misoperation detection but also strengthens the processing ability for complex environments and multi-dimensional data, ultimately improving the safety and stability of the distribution network. Through these innovations, the system can better meet the requirements of modern power systems for intelligence and high-efficiency protection.

[0113] Example Five: Please refer to Figure 1 , the data analysis unit 4 includes a first analysis unit 41 and a second analysis unit 42;

[0114] Among them, the first analysis unit 41 is used to generate a first comparison result, specifically as follows;

[0115] When AQX < Y, it means that there is no safety problem in the current distribution network;

[0116] When AQX ≥ Y, it means that there is a safety problem in the current distribution network

[0117] The second analysis unit 42 is used to generate a second comparison result, specifically as follows;

[0118] When LJZ < R, it means that the distribution network is about to enter the first-level abnormal state;

[0119] When R ≤ LJZ < R * 110%, it means that the distribution network is about to enter the second-level abnormal state;

[0120] When LJZ ≥ R * 110%, it means that the distribution network is about to enter the third-level abnormal state.

[0121] The magnitude division coefficient LJZ is obtained through the following formula calculation;

[0122]

[0123] In the formula: YCX is the abnormal reference coefficient, and Y is the first threshold.

[0124] The protection operation of the execution module 5 is specifically as follows;

[0125] When the distribution network is about to enter the first-level abnormal state, start the self-check function of the equipment to check the status of key equipment, including parameters such as voltage, current, and temperature, to determine whether there are potential hazards;

[0126] When the distribution network is about to enter the second-level abnormal state, start the self-check function of the equipment and at the same time perform local isolation on the affected equipment or lines to prevent the abnormal state from spreading to other parts;

[0127] When the distribution network is about to enter the third-level abnormal state, the entire distribution network or part of the area will be isolated.

[0128] In this embodiment: the first analysis unit 41 and the second analysis unit 42 in the data analysis unit 4 respectively perform graded judgments on the safety status of the distribution network. By calculating the abnormal reference coefficient YCXYCX and the magnitude division coefficient LJZ, the system can dynamically judge the safety status of the current distribution network and clearly distinguish between no safety issues, primary, secondary and tertiary abnormal states. The calculation formula of the magnitude division coefficient is introduced so that the system can quickly identify and warn of potential safety issues in the distribution network, thereby providing a clear basis for subsequent protection operations. Through accurate state division, the system can take corresponding measures more specifically to improve the safety and operation efficiency of the distribution network.

[0129] According to the comparison result generated by the second analysis unit 42, the execution module 5 can implement progressive protection operations according to different levels of abnormal conditions. In the first level abnormal condition, the system first starts the equipment self-check function to detect and eliminate potential hidden dangers as early as possible; in the second level abnormal condition, the system not only performs equipment self-check, but also isolates the affected equipment or lines locally to prevent the spread of abnormal conditions; in the third level abnormal condition, the system will isolate the entire distribution network or part of the area to ensure that the abnormality will not cause a larger range of safety accidents.

[0130] The progressive processing strategy enables the system to take the most appropriate response measures at different times and under different abnormal conditions, thereby reducing the impact on the operation of the distribution network. When potential hidden dangers are detected at an early stage, the expansion of the problem can be effectively prevented through equipment self-inspection and local isolation operations to ensure the stable operation of the distribution network. When the distribution network enters the third-level abnormal state, the execution module 5 can quickly implement full network isolation operations on the entire distribution network or part of the area. This design ensures that in the most serious cases, the system can cut off the fault source in time to prevent the abnormality from spreading to the entire power grid and ensure the safety of the overall power system.

[0131] The design of the data analysis unit 4 and the execution module 5 significantly improves the safety and reliability of the intelligent distribution network anti-error operation system through accurate judgment and graded response to abnormal conditions. The multi-level safety state judgment based on the abnormal reference coefficient YCX and the progressive processing strategy enable the system to take the most appropriate measures to ensure the safe operation of the power grid when facing different degrees of abnormalities. In particular, the full network isolation operation under the third-level abnormal state ensures the safety of the distribution network under the most severe conditions and further enhances the stability and emergency response capabilities of the system in power operation.

[0132] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0133] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent distribution network error-proof operation system using power GIS, characterized by: It comprises a data acquisition module (1), a data integration module (2), a calculation module (3), a data analysis module (4), an execution module (5) and a communication module (6); The data acquisition module (1) is used to collect multi-source data in power grid operation; The data integration module (2) is used to pre-process the multi-source data collected by the data collection module (1), and to reorganize the pre-processed data, thereby generating a first data set, a second data set, and a third data set; The calculation module (3) is used to integrate and calculate the first data set, the second data set and the third data set, so as to generate an abnormal reference coefficient YCX; The data analysis module (4) is used to compare the abnormal reference coefficient YCX with a preset first threshold value Y, thereby generating a first comparison result, and judging whether the current power grid has an erroneous operation through the first comparison result; If the first comparison result is that an erroneous operation occurs, the abnormal reference coefficient YCX is integrated and calculated with the preset first threshold value Y to generate a magnitude division coefficient, and the magnitude division coefficient is compared with the preset second threshold value R to generate a second comparison result; The execution module (5) executes a corresponding protection operation according to the second comparison result; The communication module (6) is used to display multiple data and operations on the terminal.

2. According to claim 1, an intelligent distribution network error-proof operation system using electric power GIS is characterized by: The data acquisition module (1) comprises a first acquisition unit (11), a second acquisition unit (12) and a third acquisition unit (13); The first acquisition unit (11) is used to acquire device status data, including real-time voltage, real-time current and real-time device temperature; The second acquisition unit (12) is used to acquire device topology data, including real-time line resistance, line length and real-time ground resistance; The third collection unit (13) is used to collect comprehensive equipment data, including operation time interval, real-time ambient temperature, extreme equipment temperature value, extreme voltage and extreme current.

3. According to claim 2, an intelligent distribution network error-proof operation system using electric power GIS is characterized by: The data integration module (2) includes a data preprocessing unit (21) and a data sorting unit (22); The data preprocessing unit (21) is used to preprocess the multi-source data collected by the data collection module (1), and perform dimensionless conversion after preprocessing; The data sorting unit (22) is used to sort the dimensionless multi-source data and form a first data set, a second data set and a third data set; The first data set includes real-time voltage, real-time current, and real-time device temperature; The real-time voltages are recorded as SDY1, SDY2, SDY3, ..., SDY according to the timestamps. n ; The real-time current is recorded as SDL1, SDL2, SDL3, ..., SDL according to the timestamp. n ; The real-time device temperatures are recorded as SWD1, SWD2, SWD3, ..., SWD according to the timestamp. n ; The second data set includes real-time line resistance, line length XCD, and real-time ground resistance; The real-time line resistance is recorded as SXZ1, SXZ2, SXZ3, ..., SXZ according to the timestamp. n ; The real-time ground resistance is recorded as SDZ1, SDZ2, SDZ3, ..., SDZ according to the timestamp. n ; The third data set includes an operation time interval CJG, a real-time ambient temperature, a limit device temperature value JWD, a limit voltage JDY, and a limit current JDL; The real-time ambient temperatures are recorded as SHW1, SHW2, SHW3, ..., SHW according to the timestamps. n .

4. According to claim 3, an intelligent distribution network error-proof operation system using electric power GIS is characterized by: The data calculation unit (3) comprises a first calculation module (31) and a second calculation module (32); The first calculation module (31) is used to integrate the first data set, the second data set and the third data set to calculate a first reference value S1, a second reference value S2 and an intervention value K; The second calculation module (32) is used for integrating the first reference value S1, the second reference value S2 and the intervention value K to calculate an abnormal reference coefficient YCX.

5. According to claim 4, an intelligent distribution network error-proof operation system using electric power GIS is characterized by: The first reference value S1, the second reference value S2, and the intervention value K are respectively obtained by calculating through the following formulas; Where: SDY n 、SDL n 、SWD n SXZ n ,SDZ n and SHW n They are the real-time voltage, real-time current, real-time device temperature, real-time line resistance, real-time ground resistance and real-time ambient temperature at timestamp n; SDY n-1 、SDL n-1 、SWD n-1 SXZ n-1 ,SDZ n-1 and SHW n-1 They are the real-time voltage, real-time current, real-time device temperature, real-time line resistance, real-time ground resistance and real-time ambient temperature at timestamp n-1; XCD is the line length, CJC is the operation time interval, JWD is the limit device temperature value, JDY is the limit voltage value, JDL is the limit current value, and e is the base function.

6. According to claim 5, an intelligent distribution network error-proof operation system using electric power GIS is characterized by: The abnormal reference coefficient YCX is obtained by calculating through the following formula; In the formula: S1 is the first reference value S1, S2 is the second reference value S2, a1 and a2 are weight values, and the values of a1 and a2 are adjusted and set by the user, and ln is the logarithmic function.

7. According to claim 6, an intelligent distribution network error-proof operation system using electric power GIS is characterized by: The data analysis unit (4) includes a first analysis unit (41) and a second analysis unit (42); Among them, the first analysis unit (41) is used to generate a first comparison result, specifically as follows; When AQX < Y, it means that there is no safety problem in the current distribution network; When AQX ≥ Y, it means that there is a safety problem in the current distribution network The second analysis unit (42) is used to generate a second comparison result, specifically as follows; When LJZ < R, it means that the distribution network is about to enter the first-level abnormal state; When R ≤ LJZ < R * 110%, it means that the distribution network is about to enter the second-level abnormal state; When LJZ ≥ R * 110%, it means that the distribution network is about to enter the third-level abnormal state.

8. According to claim 7, an intelligent distribution network error-proof operation system using electric power GIS is characterized by: The magnitude division coefficient LJZ is obtained by calculating through the following formula; In the formula: YCX is the abnormal reference coefficient, and Y is the first threshold.

9. An intelligent distribution network error-proof operation system using electric power GIS according to claim 8, characterized in that: The protection operation of the execution module (5) is specifically as follows; When the distribution network is about to enter the first-level abnormal state, start the self-check function of the equipment, check the status of key equipment, including parameters such as voltage, current, and temperature, to determine whether there are potential hazards; When the distribution network is about to enter the second-level abnormal state, start the self-check function of the equipment, and at the same time perform local isolation on the affected equipment or lines to prevent the abnormal state from spreading to other parts; When the distribution network is about to enter the third-level abnormal state, perform a full-network isolation operation on the entire distribution network or part of the area.

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