A new power system meteorological disaster prevention auxiliary decision method

Abstract

The application discloses a novel power system meteorological disaster defense auxiliary decision method, obtains a current whole network main equipment defect hidden danger set according to the geographical coverage range of meteorological disaster forecast and early warning information; screens a main equipment set located in a warning area, and obtains corresponding equipment reliability indexes in a production management information system; performs reliability secondary evaluation on a main equipment set associated with a current meteorological warning; generates an equipment operation and maintenance attention degree sorting table and a corresponding equipment side operation and maintenance decision according to the equipment reliability indexes and the reliability secondary evaluation results; reconstructs a fault set sorting table according to the power grid operation attention degree and the correlation between the equipment, and according to the 'N-X' principle; screens a fault set according to the most serious condition, and performs power system steady state analysis calculation for the whole network range; generates a whole network system control strategy suggestion for the current meteorological warning according to the calculation results, and forms a complete power grid operation decision in combination with the equipment side operation and maintenance decision.

Description

Technical Field

[0001] This invention relates to the field of power grid meteorological disaster prevention technology, and more specifically, to a novel auxiliary decision-making method for power system meteorological disaster prevention. Background Technology

[0002] Building a new power system with new energy sources as its core is a major driving force for achieving the "dual carbon" target. The new power system has undergone profound changes in its structure on the power source side, grid side, and load side. The large-scale application of new equipment and technologies poses greater challenges to the safe and stable operation of the system. Some intelligent technologies have already been applied in disaster prevention and mitigation for the new power system.

[0003] Power Grid Disaster Prevention and Mitigation System. Based on the power grid GIS platform and professional meteorological monitoring and early warning data, the system uses vector positioning and data definition of meteorological monitoring stations and various professional meteorological data from meteorological departments, as well as power grid facilities, on the power grid GIS platform. This enables professional meteorological monitoring, forecasting, and early warning of power grid facilities based on precise geographic information. It can monitor meteorological information such as temperature, wind direction, wind speed, relative humidity, precipitation intensity, surface air pressure, and visibility near substations and transmission corridors in real time, providing the power grid with intuitive real-time meteorological data.

[0004] Digital twin technology. This involves establishing digital twin models of power transmission and transformation equipment using digital twin methods, conducting multi-dimensional data fusion analysis and risk assessment based on digital twins, and intuitively reflecting the status of the equipment and the external environment before or during various disasters. This enables virtual-real interaction, collaborative linkage, and proactive early warning for equipment disaster prevention and emergency management, thereby improving the automation and intelligence level of power equipment in disaster prevention and mitigation.

[0005] Extreme weather disasters are "black swan" events, characterized by low probability and high loss, with severe consequences once they occur. Therefore, it is necessary to develop a new power system safety early warning platform to address extreme weather disasters. From the perspective of provincial power grids, this platform should enhance the prevention, resilience, emergency response, and recovery capabilities of the new power system under extreme weather disaster conditions, considering both equipment reliability and system reliability. This would achieve pre-event holistic perception of a digital "transparent power grid," intelligent early warning of grid operation risks, rapid coordination of the power grid and external resources during the event, and intelligent post-event analysis and decision support for grid operation. Currently, effective work on interactive and collaborative analysis of the safety and stability of the new power system under extreme weather disaster conditions, along with proactive early warning, based on both equipment reliability and system reliability dimensions, has not yet been effectively implemented in China.

[0006] The published patent CN202010514091.6 discloses a power grid fault prediction method and system based on numerical meteorological data and machine learning. The method includes: acquiring basic power grid information, historical operational fault data, and historical numerical meteorological data for the analyzed area; calculating historical macroscopic reliability index data for each component based on the historical fault data to establish a neural network; and inputting pairs of historical reliability data and historical meteorological data into the neural network for training. After parameter adjustment, a correlation model between reliability data and meteorological data is obtained. This method uses current or historical numerical meteorological data as input parameters for component reliability analysis and power grid fault prediction, but it does not perform correlation analysis between the equipment set and equipment reliability index and meteorological type.

[0007] The published patent CN201911259150.3 discloses a power system risk control method considering the probability of transmission line meteorological disaster failures. The method includes acquiring data parameters of the target power system; calculating the failure probability of each transmission line in the target power system experiencing a meteorological disaster trip; establishing an optimization objective function and optimization constraints for the power system considering the failure probability of transmission line meteorological disaster failures; solving the optimization objective function based on the optimization constraints to obtain the final power system risk control result considering the failure probability of transmission line meteorological disaster failures. This method uses the failure probability of transmission lines under meteorological conditions as the boundary condition for system analysis, but it does not perform reliability analysis on all transmission and transformation equipment within the meteorological impact area and rank them according to the reliability analysis results. Summary of the Invention

[0008] To address the shortcomings of existing technologies, the present invention aims to provide a novel auxiliary decision-making method for meteorological disaster prevention in power systems.

[0009] The present invention adopts the following technical solution.

[0010] A novel auxiliary decision-making method for meteorological disaster prevention in power systems, the method comprising the following steps:

[0011] Step 1: Based on the geographical coverage of meteorological disaster forecast and early warning information, obtain the current set of defects and hidden dangers of the entire network's main equipment, D. q ;

[0012] Step 2, filter the set D of main devices located within the warning area. y And obtain the corresponding equipment reliability index R1 in the production management information system;

[0013] Step 3: For the set of main equipment defects and potential hazards in the entire network, identify the set D of main equipment related to the current weather warning. g A secondary reliability evaluation was performed, and the secondary reliability evaluation result R2 was obtained.

[0014] Step 4: Based on the equipment reliability index R1 and the secondary reliability evaluation result R2, generate the equipment operation and maintenance attention ranking table S1 and the corresponding equipment-side operation and maintenance decision P1.

[0015] Step 5: Based on the grid operation focus and the correlation between equipment, perform secondary sorting and filtering on the system side, and reconstruct the fault set sorting table S2 according to the "NX" principle;

[0016] Step 6: Select the fault set according to the most severe case, and perform power system steady-state analysis calculations for the entire network.

[0017] Step 7: Based on the calculation results, generate a system control strategy suggestion P2 for the entire network for the current weather warning, and combine it with the equipment-side operation and maintenance decision P1 to form a complete power grid operation decision P.

[0018] Furthermore, in step 1, meteorological disaster forecast and early warning information is divided into forecast information and early warning information, and the meteorological disaster forecast and early warning information is accompanied by geographic coordinate data;

[0019] Collection of defects and hidden dangers of main equipment across the entire network (D) q This data comes from the production management information system and is a statistical analysis of existing and unresolved defects and potential hazards in power transmission and transformation main equipment. It includes equipment type, equipment name, city / prefecture, station / line, voltage level, defect description, weather-related factors, and maintenance recommendations.

[0020] Furthermore, in step 2, based on the geographic coordinate data accompanying the meteorological disaster forecast and early warning information, the set D of main equipment located within the meteorological warning area is selected by querying the production management information system. y At the same time, the status evaluation results of the corresponding main power transmission and transformation equipment, namely the equipment reliability index R1, can be directly queried in the production management information system.

[0021] Furthermore, in step 3, the scope and conditions of the equipment targeted by the secondary evaluation include:

[0022] ① Equipment located within the warning area and known to have defects strongly correlated with the current weather warning, i.e., the set of defects and hidden dangers of the entire network's main equipment D. q With the main equipment set D within the warning area y In the intersection of these, the set of main equipment D that is strongly correlated with the current weather warning is... g ;

[0023] ② When a section of a transmission line defined as an important transmission channel or a dense transmission channel is located within the warning area;

[0024] ③ Main equipment strongly correlated with meteorological early warning was selected based on the power grid environmental disaster distribution map.

[0025] Furthermore, the secondary reliability evaluation will use the health index of station line equipment, dense transmission channels, important transmission channels, key crossings, lines erected on the same tower, a list of key equipment, and a power grid environmental disaster distribution map as boundary conditions for auxiliary analysis.

[0026] Furthermore, in step 4, based on the equipment reliability index R1 and the secondary reliability evaluation result R2, the equipment status evaluation scores within the set are sorted, and the equipment operation and maintenance attention is assigned from low to high, generating an equipment operation and maintenance attention ranking table S1; the equipment in the corresponding equipment operation and maintenance attention ranking table S1 will be assigned different equipment-side operation and maintenance decisions P1 according to their defect status and weather warning status.

[0027] Furthermore, in step 5, the fault set is reconstructed based on the NX principle. Based on the equipment operation and maintenance attention ranking table S1, the station line equipment with strong correlation is the main consideration. The station line equipment and power supply points associated with it are introduced, the station line equipment with weak correlation is removed, and the priority of the station line equipment retained above is changed to form the fault set ranking table S2 on the system analysis side.

[0028] Furthermore, in step 6, the system reliability analysis calculation uses a power system analysis and synthesis program to perform power system transient stability calculation. The calculation determines whether the generator units in the system can maintain synchronous operation after the system is subjected to a large disturbance. It analyzes various factors affecting the transient stability of the power system and, based on this, obtains control strategies to improve the transient stability of the power system.

[0029] Furthermore, in step 6, before the system stability analysis calculation is executed, an online verification is first performed. The equipment operation and maintenance attention ranking table determined based on the latest meteorological data is compared with the equipment operation and maintenance attention ranking table corresponding to the previous meteorological update. Newly added station equipment is included in the online verification procedure, and the verification result determines whether a new system stability analysis calculation needs to be executed.

[0030] Furthermore, in step 7, based on the conclusions of the static and dynamic security and stability analysis of the power grid, a power grid system control strategy suggestion P2 will be automatically generated for the current weather warning, including prevention and control, defense strategies, emergency response, and rapid recovery.

[0031] Among them, prevention and control include power flow control at key sections, generator output and regional load control; defense strategies include energy storage operation control based on frequency rapid identification, pumped storage low-frequency pump tripping control, and hydropower low-frequency self-starting control; emergency response includes emergency load control, wide-area protection control, and active islanding operation; rapid recovery includes grid restoration recommendations, rapid load recovery recommendations, and grid-source coordinated recovery recommendations.

[0032] The beneficial effects of this invention are that, compared with the prior art, the information sources for weather forecasting and early warning in this invention include 72-hour numerical weather forecasts and meteorological disaster level early warning information. Its information sources are more comprehensive, have a wider coverage, higher accuracy, and are updated more promptly, making it more effective for rolling updates of power grid and equipment reliability analysis.

[0033] This invention considers boundary conditions such as the health index of station and line equipment, dense power transmission channels, important power transmission channels, key crossings, lines erected on the same tower, a list of key equipment, and a power grid environmental disaster distribution map for reliability analysis of station and line equipment in areas affected by meteorology. Its reliability analysis and early warning are more targeted, and it can update the equipment reliability index in real time according to changes in meteorological regions.

[0034] This invention ranks equipment based on its reliability index and then reconstructs the fault set using the NX method. This results in higher operational reliability for power grids in meteorologically affected areas, more credible decision-making recommendations, and more effective control strategies. For changes in the equipment reliability index caused by meteorological changes, online automatic verification can determine whether a new system stability analysis calculation is needed, leading to a higher degree of automation. Data interaction between equipment-side analysis and system-side analysis is achieved, and data is visualized in the form of a digital twin. The system decision-making recommendations provided integrate operation and maintenance suggestions from both the equipment and system sides, making them more instructive and actionable for operation management departments. Attached Figure Description

[0035] Figure 1 This is a flowchart of the novel power system meteorological disaster prevention auxiliary decision-making method described in this invention. Detailed Implementation

[0036] The present application will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and should not be construed as limiting the scope of protection of the present application.

[0037] like Figure 1 As shown, a novel auxiliary decision-making method for meteorological disaster prevention in power systems specifically includes the following steps:

[0038] Step 1: Based on the geographical coverage of meteorological disaster forecast and early warning information, obtain the current set of defects and hidden dangers of the entire network's main equipment, D. q ;

[0039] Meteorological disaster forecasting and early warning information is divided into forecast information and early warning information. Forecast information mainly comes from the 72-hour rolling numerical weather forecasts issued by provincial meteorological bureaus, and mainly includes basic information such as temperature, humidity, wind speed, wind direction, and precipitation, with an accuracy coverage area of ​​generally 3×3 kilometers. Early warning information mainly comes from the disaster prevention and mitigation center within the power grid company, and includes graded early warning information issued for extreme power grid meteorological disasters such as typhoons, lightning, galloping, and wildfires. In addition, it also includes emergency forecast information at the social level and key power supply guarantee tasks within the power grid company.

[0040] Meteorological disaster forecasts and early warnings are accompanied by geographic coordinate data, which can be displayed in a cloud-like manner on two-dimensional or three-dimensional maps based on a geographic information system. By applying meteorological early warning analysis algorithms covering different seasons and themes, predictive early warning analysis maps are generated hourly based on real-time meteorological warnings and numerical weather forecasts. Specifically, spring combines "wildfires + strong winds", summer combines "typhoons + thunderstorms + heavy rain + high temperatures", autumn combines "strong winds + fog and haze", and winter combines "cold waves + icing + strong winds + low temperatures", combining seasonal meteorological disasters with analysis algorithms to form comprehensive predictive early warning analysis maps. Based on numerical meteorological data, dynamic distribution maps of the environment and disasters are generated on a rolling basis, covering the distribution of all elements such as wind direction, temperature, and precipitation.

[0041] Meteorological disaster warning types include: high temperature, low temperature, cold wave, typhoon, rainstorm, wildfire, strong wind, etc. The numerical meteorological forecast data is updated once per hour.

[0042] The decision support system built based on the method provided by this invention is usually built in a dedicated power grid environment. Therefore, it is necessary to reserve an external network data interface for emergency information and major events at the social level.

[0043] Collection of defects and hidden dangers of main equipment across the entire network (D) q The data mainly comes from the power grid company's production management information system. It is a statistical analysis of existing and unresolved defects and hidden dangers in the main transmission and transformation equipment of the province. Specifically, it includes key information such as equipment type, equipment name, city, station and line, voltage level, defect description, weather conditions associated with it, and operation and maintenance suggestions.

[0044] Step 2, filter the set D of main devices located within the warning area. y And obtain the corresponding equipment reliability index R1, i.e., the status evaluation result, in the production management information system;

[0045] The power grid company's production management information system generally has the geographical coordinates of all main transmission and transformation equipment in the province, including substations and transmission towers. Upon receiving weather warnings and forecasts, based on the geographical coordinate data accompanying the weather disaster warnings and forecasts, the production management information system can be used to filter out the set D of main equipment located within the weather warning area. y Meanwhile, the production management information system has conducted a status evaluation of all main power transmission and transformation equipment, and the evaluation result E1 is consistent with that of the main power transmission and transformation equipment D. y One-to-one correspondence, which can be directly queried and obtained within the system. The status evaluation results adopt a percentage-based deduction system, and the results have been normalized. The corresponding level results are divided into four statuses: "normal, attention, abnormal, and serious".

[0046] The system showcases the 500 kV grid structure from the provincial power grid perspective and the regional 220 kV grid structure from the municipal power grid perspective. Centered on primary grid equipment, it connects internal grid operation data, external sensing data, meteorological early warning data, and geospatial data, overlaying them across time and space. This four-dimensional display, combining primary equipment with environmental and geographic information data, constructs a panoramic visual monitoring system for the digital twin power grid. Under current numerical weather forecasting conditions, meteorological early warning areas are finely distributed according to severity levels. Simultaneously, the system correlates the status evaluation results and real-time reliability of 220 kV and above substations within the warning area, presenting this information in the form of cloud maps.

[0047] In addition to querying from the production management information system, the status evaluation results can also be obtained by using the system's online evaluation module and by evaluating through a real-time reliability analysis model.

[0048] Step 3: For the set of main equipment defects and potential hazards in the entire network, identify the set D of main equipment related to the current weather warning. g A secondary reliability evaluation was performed, and the secondary reliability evaluation result R2 was obtained.

[0049] Because power grids and equipment are likely to be greatly affected when extreme weather disasters occur, the reliability of power grid equipment located in the warning area usually decreases or the equipment status evaluation result decreases after receiving weather warning and forecast information. This is specifically manifested as "reduced reliability probability, increased vulnerability" or "reduced status evaluation score, downgraded status evaluation level", etc. Therefore, a secondary evaluation is required.

[0050] The scope and conditions for the secondary evaluation include: ① equipment located within the warning area that is known to have defects strongly correlated with the current weather warning, i.e., the set of defects and hidden dangers of the main equipment in the entire network, D. q With the main equipment set D within the warning area y In the intersection of these, the set of main equipment D that is strongly correlated with the current weather warning is... g② When the transmission line section defined as an important or densely populated transmission channel is located within the warning area; ③ When the main equipment strongly correlated with the meteorological warning is selected based on the power grid environmental disaster distribution map. For equipment under the above three conditions, a secondary reliability evaluation is required to obtain the secondary reliability evaluation result R2.

[0051] A provincial-level important transmission corridor is defined as a crucial power supply line connecting two provinces. For example, a certain line (Line 1 and Line 2) is a power supply line for a city hosting major events or important meetings. A simultaneous tripping of Line 1 and Line 2 would cause DC power outage at a converter station, shut down a power plant, affect power transmission between regions, and could potentially trigger political incidents during important events in the city. Furthermore, this line contains a section with a Level II wildfire risk; multiple unsuccessful attempts to close the line due to wildfires would result in transmission line load losses and significant economic losses, with serious consequences. Therefore, this line can be defined as a provincial-level important transmission corridor.

[0052] Provincial-level dense transmission corridors are defined as the cross-section of a corridor containing four or more 500 kV transmission lines within a 600-meter radius. These corridors are more likely to exist at the exits of important substations and in narrow, elongated peninsula regions.

[0053] The power grid environmental hazard distribution map includes seven provincial-level maps: ice zone map, wind zone map, galloping zone map, lightning zone map, bird damage zone map, pollution zone map, and wildfire zone map. The distribution map is constructed based on the ArcGIS platform, utilizing point, line, polygon, and raster layer data, as well as automatic interpolation calculations and color configuration schemes to achieve detailed power grid hazard distribution map display and analysis. Taking into account the geographic coordinate data accompanying the distribution map, the power grid environmental areas where transmission and transformation main equipment is located can be specifically determined. Under meteorological warning conditions strongly correlated with certain environmental areas, a secondary evaluation will be triggered. For example, the coastal area of ​​a certain province is classified as a pollution zone (Level D), which is prone to external insulation accidental deletion during the transition from winter to spring. Therefore, when encountering prolonged periods of no precipitation and strong winds in early spring, equipment in pollution zone Level D will be screened and a secondary evaluation will be triggered.

[0054] In the secondary reliability evaluation analysis, the health index of station line equipment, dense transmission channels, important transmission channels, key crossings, lines erected on the same tower, the list of key equipment, and the distribution map of power grid environmental disasters will be used as boundary conditions for auxiliary analysis.

[0055] Step 4: Based on the equipment reliability index R1 and the secondary reliability evaluation result R2, generate the equipment operation and maintenance attention ranking table S1 and the corresponding equipment-side operation and maintenance decision P1.

[0056] Based on the equipment reliability index R1 and the secondary reliability evaluation result R2, the equipment is sorted according to its condition evaluation score within the set, and its maintenance attention is assigned from low to high, generating an equipment maintenance attention ranking table S1. This process is equivalent to a sorting and filtering performed on the equipment side. The lower the equipment condition evaluation score, the higher its maintenance attention ranking. The equipment in the corresponding maintenance attention ranking table S1 will be assigned different equipment-side maintenance decisions P1 based on its defect status and weather warning status.

[0057] The equipment maintenance focus ranking has comprehensively considered lists such as station equipment health index, dense transmission channels, important transmission channels, key crossings, lines erected on the same tower, and key equipment ledgers, as well as real-time rolling meteorological warning ranges. Meanwhile, equipment-side maintenance decision P1 has also provided decision suggestions that meet the needs of equipment-side maintenance management. At this point, the equipment maintenance focus ranking table S1 can be directly analyzed and calculated according to the N-1 and N-2 principles in system analysis calculations aimed at power system risk assessment. However, considering the deep interactive coupling of multiple flows in cyber-physical systems, some equipment in the equipment maintenance focus ranking table S1 exhibits strong correlations between station equipment and between equipment and meteorological conditions. Therefore, the N-1 and N-2 principles may no longer meet the needs of system stability analysis calculations. It is necessary to preprocess the data in the equipment maintenance focus ranking table S1 from dimensions such as data quality and time scale to meet the requirements of risk assessment algorithms for equipment ledger data, equipment health data, grid structure data, system operation data, and external environment data, forming a dataset that meets risk assessment needs.

[0058] Step 5: Based on the grid operation focus and the correlation between equipment, perform secondary sorting and filtering on the system side, and reconstruct the extreme fault set sorting table S2 according to the "NX" principle;

[0059] The equipment operation and maintenance attention ranking table S1 is used as the initial set for equipment vulnerability analysis under extreme weather disasters. This set comprehensively considers the potential impact of extreme weather disasters on power grid equipment, and quantifies the potential impact on power grid equipment under different seasonal scenarios, which can provide guidance for the establishment of fault sets for power grid risk assessment.

[0060] The power grid critical link identification technology generates a power grid equipment importance assessment result. Based on the power grid operation focus and the correlation between equipment, the key equipment, networks, and systems that maintain the basic functions of the power system are identified. By integrating equipment physical status with power grid security information, the priority of key power grid components in the initial set of equipment vulnerability analysis is increased. An extreme fault set ranking table S2(NX) suitable for power grid risk assessment is reconstructed based on the principle of "prioritizing low-probability, high-loss events." This process is equivalent to a secondary sorting and filtering in the system-side analysis. This process is based on the primary sorting S1, following the logic of system stability analysis, primarily considering strongly related station lines and equipment, introducing associated station lines and power sources, removing weakly related station lines and equipment, and changing the priority of the retained station lines and equipment to form the extreme fault set ranking table S2 on the system analysis side. This part realizes the data interaction from equipment reliability analysis to system reliability analysis. Based on this, static and dynamic safety and stability analyses of the power grid are conducted according to the most severe scenarios. An overall control scheme for the power grid under extreme meteorological disasters, including "prevention and control, resistance strategies, emergency response, and rapid recovery," is proposed to enhance the resilience and toughness of the new power system and provide a scientific basis for power grid safety and stability analysis.

[0061] Step 6: Select the fault set according to the most severe case, and perform power system steady-state analysis calculations for the entire network.

[0062] The fault set is selected based on the most severe scenario, and steady-state analysis calculations are performed on the entire power system. For static security and stability analysis, this part can be performed online or offline using background software. Based on real-time power grid operation data, static security and stability analysis of the power grid under extreme fault sets (NX) is conducted. First, the current power flow situation of the entire network needs to be obtained, which can be obtained in real time from D5000. D5000 is a basic platform for the next-generation smart grid dispatching technology support system. System reliability analysis calculations use the Power System Analysis Software Package (PSASP), a software package developed by the China Electric Power Research Institute for power system analysis calculations. The purpose of using PSASP for power system transient stability calculations is to: determine whether the generating units in the system can maintain synchronous operation after the system is subjected to large disturbances (such as short-circuit faults, large instantaneous load changes, disconnection of large-capacity generation, transmission, or substation equipment, etc.); analyze various factors affecting the transient stability of the power system; and, based on this, study measures to improve the transient stability of the power system.

[0063] This study leverages the special considerations of PSASP (Power Supply Utilization System) to enhance the transmission capacity of key system sections and lines through dynamic capacity expansion. Based on operational data from systems such as D5000, SCADA, and WAMS, it extracts real-time grid topology and operating modes, integrates equipment risk data, switch operation SOE data, weather and extreme event data, and stochastic prediction data such as power supply and load, and comprehensively considers current power flow characteristics, line losses, and the ultimate transmission capacity of key sections. Based on the key characteristics of the grid's static safe and stable operation using measured information, a grid operation adequacy assessment model is constructed to evaluate the adequacy of the grid's active and reactive power, achieve early warning of static operation risks, and realize a visual representation of the grid's operational reliability and risks.

[0064] In terms of dynamic safety and stability analysis, considering different fault types such as line short circuits and cross-line faults in the extreme fault set (NX) and different boundary conditions such as new energy power output, the dynamic change process of variables in the time domain and the evolution trend of characteristic root trajectories in the frequency domain are analyzed, and the conclusions of the dynamic safety and stability analysis of the system are given.

[0065] Regarding whether the system operation mode needs to be changed due to meteorological data updates, the verification method performed by the system-side reliability analysis is as follows: the equipment maintenance attention priority list corresponding to the meteorological update at the previous moment is denoted as S1, and the equipment maintenance attention priority list corresponding to the latest meteorological update is denoted as S2. ′ S1 ′ Newly added station equipment in S1 will be included in the verification process. The verification process performs a three-level data dictionary query based on "voltage level – equipment location – load condition." If the equipment is confirmed to be within the scope requiring verification, the system will issue a prompt "Current control strategy needs verification" and re-perform online or offline system stability calculations. If the equipment is confirmed not to be within the scope requiring verification, the current control strategy will remain unchanged. After implementing this rolling verification mode for the control strategy, system stability calculations are typically performed once a day.

[0066] Step 7: Based on the calculation results, generate a system control strategy suggestion P2 for the entire network for the current weather warning, and combine it with P1 to form a complete power grid operation decision P.

[0067] Based on the static and dynamic safety and stability analysis of the power grid, a system control strategy recommendation P2 for the current weather warning, encompassing "prevention and control, mitigation strategies, emergency response, and rapid recovery," will be automatically generated. This recommendation includes: prevention and control (critical section power flow control, generator output, and regional load control); mitigation strategies (frequency-based rapid identification of energy storage operation control, pumped storage low-frequency pump tripping control, and hydropower low-frequency self-starting control); emergency response (emergency load control, wide-area protection control, and active islanding operation); and rapid recovery (grid restoration recommendations, load rapid recovery recommendations, and grid-source coordinated recovery recommendations). By assessing the changes in key system characteristic variables from the potential safety and stability interval boundaries, an assessment of the safety and stability operation risks of the current operating mode will be provided. Furthermore, for different operational issues such as system active and reactive power adequacy, critical section congestion risk, transient voltage, power angle stability, and frequency stability, auxiliary decision-making methods, including risk operation optimization and control strategies, prevention and control, and emergency control, will be provided to guide the dispatching department.

[0068] Through the data interface, the above analysis conclusions, grid power flow prediction data, and overall control scheme are transmitted back to the equipment side. After integration with the equipment-side operation and maintenance recommendations P1, a complete power grid operation decision P is formed and visualized from a provincial or regional grid perspective. This part enables data interaction from system reliability analysis to equipment reliability analysis. Therefore, data interaction and integration of analysis results can be achieved between equipment reliability and system reliability analysis.

[0069] Furthermore, the auxiliary decision-making system built based on the method provided by this invention is constructed using a digital twin approach. Taking the entire provincial power grid as the primary perspective, real-time rolling meteorological information and power grid environment distribution information are overlaid on a two- or three-dimensional map. This dynamically displays the reliability analysis results of station and line equipment affected by weather conditions, as well as key station and line equipment, while simultaneously providing real-time equipment-side operation and maintenance suggestions. Based on the equipment operation and maintenance priority list provided by the equipment side, data is exchanged with the system side to construct an NX fault set for the system side. The system backend calculates the system stability analysis results under the most severe fault conditions, and the calculation results and control strategies are displayed in real-time on the system front-end interface under provincial and municipal power grid topologies. After data exchange with the equipment side, the analysis results are combined with the equipment-side operation and maintenance suggestions to form the provincial system's operation and maintenance decision-making suggestions. The system's calculation and analysis program acquires and calculates real-time power flow data according to the rolling update frequency of meteorological forecast and early warning information. After the calculation results are verified by the control strategy, if an update is needed, a new control strategy is provided; otherwise, the original control strategy remains unchanged.

[0070] This analysis uses Typhoon Bavi (No. 8 of 2020) as a case study. Based on typhoon path prediction and environmental monitoring and early warning, the typhoon was expected to make landfall at the border of Liaoning and North Korea. Forty-eight hours prior to landfall, early warning information was issued to four 500 kV substations and four 500 kV lines (each with double-circuit configuration, involving two transmission channels) potentially within the typhoon's path. The warning time was 21:00 on August 26, 2020, and a ranking table of equipment maintenance priorities and corresponding equipment-side maintenance decisions were generated. The warning involved a total of eight 500 kV transformers and four 500 kV lines (involving two transmission channels). The maintenance decision was: it was recommended that all equipment at the above-mentioned stations and lines undergo special inspections immediately. Through system analysis and NX-based fault set reconstruction, six strongly correlated 500 kV transformers and four 500 kV lines (two transmission channels) were retained, important power sources from nearby power plants were added, and two weakly correlated 500 kV transformers were removed. Based on this condition, a power grid stability analysis was conducted, and the following control strategies were derived: A certain 500 kV substation is expected to be most severely affected by a typhoon. Assuming that the 500 kV system of this substation is completely blacked out, ① under low load and high water conditions, the total output of a certain thermal power plant and a certain power plant should not exceed 650 MW to prevent overload of line ××; ② under high load and low water conditions, the total output of a certain power plant and a certain thermal power plant should not be less than 1050 MW to prevent overload of line ××.

[0071] The beneficial effects of this invention are that, compared with the prior art, the information sources for weather forecasting and early warning in this invention include 72-hour numerical weather forecasts and meteorological disaster level early warning information. Its information sources are more comprehensive, have a wider coverage, higher accuracy, and are updated more promptly, making it more effective for rolling updates of power grid and equipment reliability analysis.

[0072] This invention considers boundary conditions such as the health index of station and line equipment, dense power transmission channels, important power transmission channels, key crossings, lines erected on the same tower, a list of key equipment, and a power grid environmental disaster distribution map for reliability analysis of station and line equipment in areas affected by meteorology. Its reliability analysis and early warning are more targeted, and it can update the equipment reliability index in real time according to changes in meteorological regions.

[0073] This invention ranks equipment based on its reliability index and then reconstructs the fault set using the NX method. This results in higher operational reliability for power grids in meteorologically affected areas, more credible decision-making recommendations, and more effective control strategies. For changes in the equipment reliability index caused by meteorological changes, online automatic verification can determine whether a new system stability analysis calculation is needed, leading to a higher degree of automation. Data interaction between equipment-side analysis and system-side analysis is achieved, and data is visualized in the form of a digital twin. The system decision-making recommendations provided integrate operation and maintenance suggestions from both the equipment and system sides, making them more instructive and actionable for operation management departments.

[0074] The applicant of this invention has provided a detailed description of the embodiments of the invention in conjunction with the accompanying drawings. However, those skilled in the art should understand that the above embodiments are merely preferred embodiments of the invention. The detailed description is only intended to help readers better understand the spirit of the invention and is not intended to limit the scope of protection of the invention. On the contrary, any improvements or modifications made based on the inventive spirit of the invention should fall within the scope of protection of the invention.

Claims

1. A novel auxiliary decision-making method for meteorological disaster prevention in power systems, characterized in that, The method includes the following steps: Step 1: Based on the geographical coverage of meteorological disaster forecast and early warning information, obtain the current set of defects and hidden dangers in the main equipment of the entire network. ; Step 2: Filter the set of main devices located within the warning area. And obtain the corresponding equipment reliability index in the production management information system. ; Step 3, set up the main equipment cluster A secondary reliability evaluation was conducted to obtain the results of the secondary reliability evaluation. ; In step 3, the set of main equipment targeted by the secondary evaluation The scope and conditions include: ① Main equipment located within the warning area and known to have defects strongly correlated with the current weather warning, i.e., the set of defects and potential hazards in the entire network's main equipment. Collection of main equipment within the warning area The main equipment that is strongly correlated with the current weather warning at the intersection; ② When a section of a transmission line defined as an important transmission channel or a dense transmission channel is located within the warning area; ③ Main equipment strongly correlated with meteorological early warning was selected based on the power grid environmental disaster distribution map; Step 4, based on the equipment reliability index and reliability secondary evaluation results Generate a ranking table of equipment operation and maintenance priorities. and corresponding equipment-side operation and maintenance decisions ; Step 5: Based on the grid operation focus and the correlation between equipment, perform secondary sorting and filtering on the system side, and reconstruct the fault set sorting table according to the "NX" principle. ; In step 5, the fault set is reconstructed based on the NX principle, and sorted by equipment operation and maintenance focus table. Based on this, strongly correlated master equipment is retained, weakly correlated master equipment is removed, station line equipment and power points associated with the retained strongly correlated master equipment are introduced, and the priority of the retained master equipment is changed to form a fault set ranking table on the system analysis side. ; Step 6: Select the fault set according to the most severe case, and perform power system steady-state analysis calculations for the entire network. Step 7: Based on the calculation results, generate system control strategy recommendations for the entire network for the current weather warning. And combined with equipment-side operation and maintenance decisions Forming a complete power grid operation decision .

2. The novel power system meteorological disaster prevention auxiliary decision-making method according to claim 1, characterized in that, In step 1, meteorological disaster forecast and early warning information is divided into forecast information and early warning information, and the meteorological disaster forecast and early warning information is accompanied by geographic coordinate data; Collection of defects and hidden dangers in main equipment across the entire network This data comes from the production management information system and is a statistical analysis of existing and unresolved defects and potential hazards in power transmission and transformation main equipment. It includes equipment type, equipment name, city / prefecture, station / line, voltage level, defect description, weather-related factors, and maintenance recommendations.

3. The novel power system meteorological disaster prevention auxiliary decision-making method according to claim 2, characterized in that, In step 2, based on the geographic coordinate data accompanying the meteorological disaster forecast and early warning information, the main equipment set located within the meteorological warning area is selected by querying the production management information system. Simultaneously, the status evaluation results of the corresponding main power transmission and transformation equipment, i.e., the equipment reliability index, can be directly queried in the production management information system. .

4. The novel power system meteorological disaster prevention auxiliary decision-making method according to claim 1, characterized in that, The secondary reliability evaluation will use the main equipment health index, dense transmission channels, important transmission channels, key crossings, lines erected on the same tower, a list of key equipment, and a power grid environmental disaster distribution map as boundary conditions for auxiliary analysis.

5. The novel power system meteorological disaster prevention auxiliary decision-making method according to claim 1, characterized in that, In step 4, based on the equipment reliability index and reliability secondary evaluation results The equipment is sorted according to its status evaluation score within the set, and then assigned a value of "equipment maintenance attention" from low to high to generate an equipment maintenance attention ranking table. ; Ranking of corresponding equipment maintenance attention The equipment will be assigned different equipment-side operation and maintenance decisions based on its defect status and weather warnings. .

6. The novel power system meteorological disaster prevention auxiliary decision-making method according to claim 1, characterized in that, In step 6, the system steady-state analysis calculation uses a power system analysis and synthesis program to perform transient stability calculations of the power system. The calculation determines whether the generator units of the system can maintain synchronous operation after the system is subjected to a large disturbance. It analyzes various factors that affect the transient stability of the power system and, based on this, obtains control strategies to improve the transient stability of the power system.

7. The novel power system meteorological disaster prevention auxiliary decision-making method according to claim 6, characterized in that, In step 6, an online verification is performed before the system steady-state analysis calculation is executed. The equipment operation and maintenance attention priority list determined based on the latest meteorological data is compared with the equipment operation and maintenance attention priority list corresponding to the meteorological update at the previous moment. Newly added main equipment is included in the online verification program, and the verification results determine whether a new system steady-state analysis calculation needs to be executed.

8. The novel power system meteorological disaster prevention auxiliary decision-making method according to claim 1, characterized in that, In step 7, based on the system steady-state analysis results, a power grid system control strategy recommendation will be automatically generated for the current weather warning. This includes prevention and control, defense strategies, emergency response, and rapid recovery; Among them, prevention and control include power flow control at key sections, generator output and regional load control; defense strategies include energy storage operation control based on frequency rapid identification, pumped storage low-frequency pump tripping control, and hydropower low-frequency self-starting control; emergency response includes emergency load control, wide-area protection control, and active islanding operation; rapid recovery includes grid restoration recommendations, rapid load recovery recommendations, and grid-source coordinated recovery recommendations.

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