Method for evaluating toughness of novel power system in extreme weather

Abstract

The invention relates to the technical field of electrical system evaluation, in particular to a toughness evaluation method of a novel power system in extreme weather, which comprises the steps of constructing a digital twin model, collecting data in real time, performing fusion processing on multi-source data, identifying potential risks of the power system in extreme weather, determining system vulnerability and constructing an extreme weather scene. The method comprises the following steps: simulating weather events possibly occurring in the future, predicting the occurrence probability and influence range of extreme weather, analyzing system vulnerability, identifying key nodes and vulnerable equipment, defining and calculating a toughness index, optimizing an operation strategy, executing an emergency plan when the extreme weather occurs, evaluating a plan execution effect, and continuously optimizing the plan through feedback; according to the method, the dynamic response capability and the toughness evaluation accuracy of the power system in extreme weather are remarkably improved, the actual operation state of the power system is reflected in real time, the model parameters can be dynamically adjusted through the digital twin technology, and the real response of the system under the changing condition is simulated.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical system assessment, and in particular to a method for assessing the resilience of a new type of power system under extreme weather conditions. Background Art

[0002] Existing technologies mainly evaluate and optimize the resilience of power systems through historical data analysis, static models, disaster emergency plans, data monitoring and alarm systems, optimization algorithms, and simulation tools. These methods combine statistical analysis, topological structure research, and real-time monitoring to identify weak links in the system, plan emergency responses, and optimize resource allocation to improve the stability and response capabilities of power systems under extreme weather conditions.

[0003] However, the inventors have discovered that this technical solution still has at least the following defects:

[0004] First, in the existing technology, although historical data and static models are used to evaluate and optimize the power system, these methods often have certain limitations. First, the evaluation methods based on historical data usually rely on past fault records and extreme weather events, and cannot fully cope with new or more complex weather conditions that may occur in the future. In addition, the static model assumes that the system operating conditions remain unchanged, which makes it difficult to reflect the dynamic changes of the power system in actual extreme weather, resulting in the evaluation results being inaccurate.

[0005] Second, although existing disaster emergency plans and data monitoring systems can provide certain emergency responses when facing extreme weather, they usually lack real-time adjustment and optimization capabilities. These plans and systems are often based on preset operating procedures and fixed emergency rules, and cannot quickly adapt to changes in emergencies, which may lead to poor implementation of emergency measures. In addition, traditional alarm systems can only identify known abnormal situations and lack sufficient response capabilities for new risks in complex and changeable extreme weather scenarios.

[0006] Third, although existing optimization algorithms and simulation technologies have made some progress in improving the operating efficiency of power systems, they are still insufficient in dealing with complex scenarios of extreme weather. Many optimization algorithms focus on the economy and efficiency of the system, while ignoring the resilience and long-term stability of the system. In addition, existing simulation tools are usually based on simplified models, which make it difficult to fully capture the comprehensive impact of extreme weather on the power system, thus having certain limitations in planning and decision support. Summary of the invention

[0007] Based on the above objectives, the present invention provides a method for assessing the resilience of a new power system under extreme weather conditions.

[0008] The resilience assessment method of the new power system under extreme weather conditions includes the following steps:

[0009] S1, digital twin model construction and data collection: Based on the actual operation data of the power system, historical fault records and extreme weather data, a digital twin model is constructed to collect data in real time, including weather, equipment status and load conditions, and input the data into the model to achieve real-time simulation and monitoring of the system;

[0010] S2, multi-dimensional risk perception and fusion assessment: using the digital twin model to fuse multi-source data, identify the potential risks of the power system under extreme weather conditions from the perspectives of meteorology, geology, and hydrology, and determine the system's vulnerability;

[0011] S3, extreme weather scenario construction and vulnerability analysis: Based on the digital twin model and risk assessment results, extreme weather scenarios are constructed to simulate possible weather events in the future. The probability and impact range of extreme weather are predicted through climate models, system vulnerabilities are analyzed, and key nodes and vulnerable equipment are identified.

[0012] S4, Resilience Index Definition and Calculation: Based on the vulnerability analysis results, define and calculate resilience indicators, including system recovery time, service interruption time and economic losses;

[0013] S5, real-time monitoring and dynamic adjustment: During system operation, the digital twin model and real-time monitoring data are used to continuously monitor and dynamically optimize the operation strategy;

[0014] S6, emergency plan execution and feedback optimization: When extreme weather occurs, execute the emergency plan, use the digital twin model to provide real-time feedback and optimize and adjust. After the event, evaluate the execution effect of the plan and continuously optimize the plan through feedback.

[0015] Optionally, the S1 includes:

[0016] S11, data preprocessing: collect actual operation data of the power system, including voltage, current, power, frequency; historical fault records, including fault type, fault time, fault location; and extreme weather related data, including temperature, humidity, wind speed, rainfall, and clean, format and standardize these data;

[0017] S12, data fusion and feature extraction: using data fusion technology, integrating multi-source data and extracting key features from them. The features extracted from the equipment status data may include the equipment health index (η);

[0018] S13, parametric modeling of digital twin model: Based on the preprocessed data, the digital twin model of the power system is constructed using numerical methods and physical modeling techniques. The model parameterization process includes modeling the key components in the system.

[0019] S14, real-time data input and simulation monitoring: Through the sensor network and communication system, the real-time collected data is input into the constructed digital twin model for simulation calculation and real-time monitoring.

[0020] Optionally, S2 includes:

[0021] S21, data fusion: the data from multiple sources such as meteorology, geology, and hydrology will be fused and processed using the weighted average method;

[0022] S22, multi-dimensional feature extraction: Extract multi-dimensional features from the fused data, including meteorological features (such as maximum wind speed V max ), geological characteristics (such as earthquake intensity L), and hydrological characteristics (such as rainfall R), and use these characteristics to assess the risk of the power system;

[0023] S23, vulnerability analysis: Based on the extracted multi-dimensional features, system vulnerability analysis is performed;

[0024] S24, Risk Assessment: Comprehensive Analysis of System Vulnerability Index V system , and combined with real-time monitoring data, identify the potential risks of the power system under extreme weather and generate a risk assessment report.

[0025] Optionally, S3 includes:

[0026] S31, extreme weather scenario construction: Based on the digital twin model and historical data, multiple extreme weather scenarios are generated using climate models GCM and RCM to simulate possible weather events and calculate the probability of extreme weather occurrence;

[0027] S32, Impact range prediction: Use climate models to predict the geographical and temporal impact range of extreme weather events and calculate the probability distribution of events occurring in a specific area;

[0028] S33, System Vulnerability Analysis: Based on the constructed extreme weather scenarios, analyze the vulnerability of the power system, identify key nodes and vulnerable equipment, and calculate the vulnerability index of the equipment;

[0029] S34, Critical Node Identification: Based on the vulnerability analysis results, identify the critical nodes and vulnerable devices in the system, give priority to the devices with the highest vulnerability index, and generate a detailed vulnerability analysis report.

[0030] Optionally, the S31 includes:

[0031] S311, Historical data analysis and preparation: Collect and organize historical meteorological data and records of extreme weather events in the area where the power system is located, conduct statistical analysis based on these data, and identify the main types of extreme weather events that have occurred in history (such as hurricanes, floods, blizzards, etc.) and their characteristic parameters;

[0032] S312, Climate model selection and configuration: Select and configure the global climate model (GCM) and regional climate model (RCM) suitable for the region, generate possible future climate change scenarios through these models, and calibrate model parameters in combination with historical data;

[0033] S313, extreme weather event simulation: Use calibrated global climate models (GCMs) and regional climate models (RCMs) to simulate extreme weather events that may occur in the future and calculate the probability of occurrence of different types of extreme weather events;

[0034] S314, scenario generation and verification: Generate multiple extreme weather scenarios based on the calculated probability of occurrence, and verify these scenarios through historical data;

[0035] S315, scenario visualization and output: Visualize the generated extreme weather scenarios and provide the probability of occurrence, time distribution, and geographical impact range.

[0036] Optionally, the S4 includes:

[0037] S41, system recovery time calculation: Based on the vulnerability analysis results, define and calculate the system recovery time T recovery ;

[0038] S42, service interruption time calculation: define and calculate the service interruption time T outage ;

[0039] S43, Economic loss assessment: Definition of economic loss C loss , evaluate it;

[0040] S44, calculation of comprehensive toughness index: The above three indexes are combined into a toughness index R system .

[0041] Optionally, S5 includes:

[0042] S51, real-time data collection and input: Through sensor networks and IoT devices, key operating data of the power system, including voltage, current, frequency, and temperature, are continuously collected, and these data are input into the digital twin model in real time for dynamic simulation;

[0043] S52, status monitoring and anomaly detection: Use the digital twin model to monitor the system status in real time and analyze the system operation data through anomaly detection algorithms;

[0044] S53, dynamic adjustment of operation strategy: Based on the real-time monitoring and anomaly detection results, the optimization algorithm is used to dynamically adjust the system's operation strategy, optimize the algorithm objective function, and reduce the system's response time and operation cost in extreme weather conditions through dynamic adjustment.

[0045] Optionally, the S52 includes:

[0046] S521, Simulation result generation and evaluation: Use the digital twin model to simulate the system's performance under different extreme weather scenarios, generate power supply paths and load distribution plans for each scenario, evaluate their impact on system resilience, and identify potential abnormal situations.

[0047] S522, anomaly detection algorithm execution: Analyze the difference between the real-time monitoring data and the digital twin model prediction data through the anomaly detection algorithm, and calculate the anomaly metric E(t).

[0048] S523, alarm response and adjustment suggestion generation: When the anomaly detection algorithm triggers an alarm, the system generates adjustment suggestions or automatic adjustment plans, giving priority to protecting key equipment and areas, and records alarm data for subsequent analysis.

[0049] Optionally, the S53 includes:

[0050] S531, objective function definition: Based on the real-time monitoring and anomaly detection results, define the system's optimization objective function, which includes: system response time T response , operating cost C operation and system stability index R stability ;

[0051] S532, constraint condition setting: setting constraint conditions in the optimization process;

[0052] S533, optimization algorithm selection and execution: select particle swarm optimization algorithm to solve the objective function and dynamically adjust the system operation strategy;

[0053] S534, Strategy adjustment and implementation: adjust the system operation strategy according to the optimization results, implement the adjusted strategy in the system, and continuously evaluate its effect through real-time monitoring data;

[0054] S535, dynamic feedback and re-optimization: Feedback the effect of the implemented strategy to the optimization algorithm, use the actual operation data to re-evaluate the objective function and constraints, and perform secondary optimization when necessary.

[0055] Optionally, S6 includes:

[0056] S61, emergency plan activation: When an extreme weather event occurs, the system automatically detects the anomaly and activates the pre-made emergency plan, which includes priority power supply for key equipment, load transfer plan, and backup power supply scheduling plan;

[0057] S62, real-time feedback and dynamic adjustment: Use the digital twin model to simulate and monitor the execution of the current emergency plan in real time, and calculate the difference between the actual execution effect and the expected effect;

[0058] When the difference exceeds the set tolerance range, the system automatically adjusts the emergency plan, including reallocating loads and adjusting power supply paths;

[0059] S63, Post-event evaluation: After the extreme weather event is over, conduct a comprehensive evaluation of the implementation of the emergency plan;

[0060] S64, Feedback Optimization and Plan Update: Based on the post-event evaluation results, analyze the deficiencies in the emergency plan and optimize and update the plan strategy;

[0061] S65, plan verification and re-optimization: simulate and verify the updated plan strategy, perform secondary optimization, and finally incorporate the optimized plan into the system's emergency response mechanism.

[0062] Beneficial effects of the present invention:

[0063] This invention, by introducing digital twin technology, significantly improves the accuracy of the dynamic response capability and resilience assessment of the power system under extreme weather conditions. The digital twin model not only reflects the actual operating status of the power system in real time, but also predicts the system performance under various extreme weather scenarios in simulation. Unlike existing static models, digital twin technology can dynamically adjust model parameters, simulate the real response of the system under changing conditions, and ensure that the evaluation results are closer to reality. This innovative method enables the power system to identify potential risks in advance and formulate more scientific and reasonable response strategies.

[0064] The present invention also uses multi-dimensional risk perception and fusion assessment technology to comprehensively consider multi-source data such as meteorology, geology and hydrology to comprehensively assess the vulnerability of the power system under extreme weather. Compared with the traditional single-dimensional analysis method, multi-dimensional risk perception can more accurately identify the weak links and potential failure points of the system, so as to take targeted preventive measures before the extreme weather arrives. In addition, fusion assessment technology can combine information from multiple data sources, improve the reliability and accuracy of the assessment results, and provide strong support for early warning and optimization of the power system.

[0065] The present invention introduces a real-time feedback mechanism and a dynamic optimization algorithm in the execution and feedback optimization of emergency plans, so that the power system has higher flexibility and adaptability when dealing with extreme weather. During the execution of the emergency plan, the execution strategy is continuously adjusted and optimized through real-time monitoring and feedback from the digital twin model to ensure the effectiveness of emergency measures. After the event is over, the system will evaluate the execution effect of the plan, and continuously improve the content of the plan through the feedback mechanism to enhance future response capabilities. This innovative method significantly enhances the emergency response capability of the power system, ensuring rapid recovery and maintaining stable operation of the system in extreme weather. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1 A schematic diagram of a method for evaluating the resilience of a new power system under extreme weather conditions according to an embodiment of the present invention;

[0068] Figure 2 Schematic diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0070] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0071] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0072] like Figure 1-Figure 2 As shown in the figure, the resilience assessment method of the new power system under extreme weather conditions includes the following steps:

[0073] S1, digital twin model construction and data collection: Based on the actual operation data of the power system, historical fault records and extreme weather data, a digital twin model is constructed to collect data in real time, including weather, equipment status and load conditions, and input the data into the model to achieve real-time simulation and monitoring of the system;

[0074] S2, multi-dimensional risk perception and fusion assessment: using the digital twin model to fuse multi-source data, identify the potential risks of the power system under extreme weather conditions from the perspectives of meteorology, geology, and hydrology, and determine the system's vulnerability;

[0075] S3, extreme weather scenario construction and vulnerability analysis: Based on the digital twin model and risk assessment results, extreme weather scenarios are constructed to simulate possible weather events in the future. The probability and impact range of extreme weather are predicted through climate models, system vulnerabilities are analyzed, and key nodes and vulnerable equipment are identified.

[0076] S4, Definition and calculation of resilience indicators: Based on the results of vulnerability analysis, define and calculate resilience indicators, including system recovery time, service interruption time and economic losses, to comprehensively evaluate the performance of the power system under extreme weather conditions;

[0077] S5, real-time monitoring and dynamic adjustment: During system operation, the digital twin model and real-time monitoring data are used to continuously monitor and dynamically optimize the operation strategy to improve the stability and response speed of the system in extreme weather conditions;

[0078] S6, emergency plan execution and feedback optimization: When extreme weather occurs, execute the emergency plan, use the digital twin model to provide real-time feedback and optimize and adjust. After the event, evaluate the execution effect of the plan, continuously optimize the plan through feedback, and enhance future response capabilities.

[0079] S1 includes:

[0080] S11, data preprocessing: collect actual operation data of the power system, including voltage, current, power, frequency; historical fault records, including fault type, fault time, fault location; and extreme weather related data, including temperature, humidity, wind speed, rainfall, and clean, format and standardize these data to ensure data consistency and accuracy;

[0081] S12, data fusion and feature extraction: using data fusion technology, multi-source data are integrated and key features are extracted from them. The features extracted from the equipment status data may include the equipment health index (η), which is expressed as:

[0082]

[0083] Among them, C current is the current operating status of the device, C max and C min are the maximum and minimum operating capacities of the equipment, respectively;

[0084] S13, parametric modeling of digital twin model: Based on the preprocessed data, the digital twin model of the power system is constructed using numerical methods and physical modeling techniques. The model parameterization process includes modeling the key components in the system (such as transformers, generators, transmission lines, etc.), which is expressed as:

[0085] P(t)=P 0 ×(1-e -λt );

[0086] Where P(t) is the power output of the system at time t, P 0 is the initial power, λ is the attenuation coefficient of the system;

[0087] S14, real-time data input and simulation monitoring: Through the sensor network and communication system, the real-time collected data (such as meteorological data, equipment status data, load conditions) is input into the constructed digital twin model for simulation calculation and real-time monitoring, which is expressed as:

[0088]

[0089] Among them, E res is the system toughness error, P i is the power output of the ith simulation, P target is the target power, and N is the number of simulations.

[0090] The digital twin model of the power system is systematically constructed and optimized in a step-by-step manner. Data preprocessing ensures the accuracy and consistency of input data, data fusion and feature extraction ensure that key features are effectively utilized, parametric modeling provides accurate simulation of the performance of key equipment, and real-time data input and simulation monitoring achieve accurate real-time prediction and optimization of the system under extreme weather conditions, providing strong support for the resilience assessment of the power system.

[0091] S2 includes:

[0092] S21, data fusion: The data from multiple sources such as meteorology, geology, and hydrology will be fused and processed using the weighted average method, which can be expressed as:

[0093]

[0094] Among them, D fused is the fused data, D i is the i-th source data, w i is the corresponding weight coefficient, n is the number of data sources, and the weight coefficient is set according to the importance and reliability of the data;

[0095] S22, multi-dimensional feature extraction: Extract multi-dimensional features from the fused data, including meteorological features (such as maximum wind speed V max ), geological characteristics (such as earthquake intensity L), and hydrological characteristics (such as rainfall R), and use these characteristics to assess the risk of the power system;

[0096] S23, vulnerability analysis: Based on the extracted multi-dimensional features, system vulnerability analysis is performed, expressed as:

[0097]

[0098] Among them, V system is the system vulnerability index, F j is the vulnerability impact factor corresponding to the jth feature, S j is the importance weight of the feature, m is the number of features, and the index is used to quantify the vulnerability of the power system under multi-dimensional conditions;

[0099] S24, Risk Assessment: Comprehensive Analysis of System Vulnerability Index V system , and combined with real-time monitoring data, identify the potential risks of the power system under extreme weather conditions, generate risk assessment reports, and provide a basis for subsequent decision-making and optimization;

[0100] Through systematic multi-source data fusion and multi-dimensional feature extraction, the vulnerability of the power system under extreme weather conditions can be accurately identified. Through weighted average and vulnerability index calculation, the potential risks of the system are quantified, providing a scientific basis for real-time monitoring and optimized decision-making, and improving the resilience and reliability of the power system.

[0101] S3 includes:

[0102] S31, extreme weather scenario construction: Based on the digital twin model and historical data, multiple extreme weather scenarios are generated using the climate models GCM and RCM to simulate possible weather events and calculate the probability of extreme weather occurrence, expressed as:

[0103]

[0104] Among them, P weather is the probability of occurrence of extreme weather events;

[0105] S32, Impact range prediction: Use climate models to predict the geographical and temporal impact range of extreme weather events and calculate the probability distribution of events occurring in a specific area, expressed as:

[0106] R(x,y)=P weather ×G(x,y);

[0107] Among them, R(x,y) is the probability of an event occurring at location (x,y), and G(x,y) is the influencing factor of the geographical location;

[0108] S33, System Vulnerability Analysis: Based on the constructed extreme weather scenarios, analyze the vulnerability of the power system, identify key nodes and vulnerable equipment, and calculate the vulnerability index of the equipment, expressed as:

[0109]

[0110] Among them, V device is the equipment vulnerability index, F impact is the impact factor of extreme weather, S device is the importance weight of the device, R device The recovery capability of the equipment;

[0111] S34, key node identification: Based on the vulnerability analysis results, identify the key nodes and vulnerable devices in the system, give priority to the devices with the highest vulnerability index, and generate a detailed vulnerability analysis report to provide data support for subsequent optimization.

[0112] By systematically constructing extreme weather scenarios and accurately predicting their impact range, combined with vulnerability index calculation, key nodes and vulnerable equipment in the power system can be effectively identified. Through probability analysis and vulnerability assessment, a scientific basis is provided for optimizing decision-making, improving the system's defense and recovery capabilities under extreme weather conditions, and enhancing the system's overall resilience.

[0113] S31 includes:

[0114] S311, Historical data analysis and preparation: Collect and organize historical meteorological data and records of extreme weather events in the area where the power system is located, conduct statistical analysis based on these data, and identify the main types of extreme weather events that have occurred in history (such as hurricanes, floods, blizzards, etc.) and their characteristic parameters;

[0115] S312, Climate model selection and configuration: Select and configure the global climate model (GCM) and regional climate model (RCM) suitable for the region, generate possible future climate change scenarios through these models, and combine historical data to calibrate model parameters to improve prediction accuracy;

[0116] S313, extreme weather event simulation: Use the calibrated global climate model GCM and regional climate model RCM to simulate extreme weather events that may occur in the future and calculate the probability of occurrence of different types of extreme weather events, expressed as:

[0117]

[0118] Among them, P extreme is the probability of occurrence of extreme weather events, N extreme is the number of occurrences of a specific extreme weather event, N total is the total number of all possible weather events;

[0119] S314, scenario generation and verification: Generate multiple extreme weather scenarios based on the calculated probability of occurrence, and verify these scenarios through historical data to ensure the comprehensiveness and representativeness of the scenarios;

[0120] S315, Scenario Visualization and Output: Visualize the generated extreme weather scenarios, provide occurrence probability, time distribution, and geographical impact range, and output for subsequent vulnerability analysis and decision support

[0121] By combining historical data and accurately calibrated climate models (GCM and RCM), a variety of extreme weather scenarios are generated to ensure the accuracy and representativeness of the simulation results. By calculating the probability of extreme weather events and using Monte Carlo simulation and other techniques to verify the comprehensiveness of the scenarios, scientific predictions of future extreme weather risks are provided. Visual output makes these scenarios intuitive and easy to understand, providing strong support for vulnerability analysis and emergency decision-making of power systems.

[0122] S4 includes:

[0123] S41, system recovery time calculation: Based on the vulnerability analysis results, define and calculate the system recovery time T recovery , expressed as:

[0124]

[0125] Among them, R i is the recovery time of the ith device, α i is the importance weight of the device in the system, and n is the number of affected devices in the system;

[0126] S42, service interruption time calculation: define and calculate the service interruption time T outage , expressed as:

[0127]

[0128] Among them, L j is the load of the jth region, T j is the power outage time of the area, and m is the number of affected areas;

[0129] S43, Economic loss assessment: Definition of economic loss C loss , and evaluate it, expressed as:

[0130]

[0131] Among them, C k is the repair cost of the kth fault, D k is the duration of the power outage caused by the fault, and p is the number of faults occurring in the system;

[0132] S44, calculation of comprehensive toughness index: The above three indexes are combined into a toughness index R system , expressed as:

[0133]

[0134] Among them, β 1 , β 2 and β 3 The weight coefficients of restoration time, service interruption time and economic loss are used to comprehensively evaluate the performance of the power system under extreme weather conditions.

[0135] S5 includes:

[0136] S51, real-time data collection and input: Through sensor networks and IoT devices, key operating data of the power system, including voltage, current, frequency, and temperature, are continuously collected, and these data are input into the digital twin model in real time for dynamic simulation;

[0137] S52, state monitoring and anomaly detection: Use the digital twin model to monitor the system state in real time, and analyze the system operation data through the anomaly detection algorithm, expressed as:

[0138]

[0139] Among them, E(t) is the abnormality measure at time t, D actual (t) is the actual monitoring data, D model (t) is the data predicted by the digital twin model. When E(t) exceeds the preset threshold, an abnormal alarm is triggered;

[0140] S53, dynamic adjustment of operation strategy: according to the real-time monitoring and anomaly detection results, the system operation strategy is dynamically adjusted using the optimization algorithm, and the optimization algorithm objective function is expressed as:

[0141] minF(x)=α 1 T response +α 2 C operation +α 3 R stability ;

[0142] Among them, T response is the system response time, C operation is the operating cost, R stability is the system stability index, α 1 ,α 2 , α 3 By dynamically adjusting the corresponding weight coefficients, the response time and operating cost of the system in extreme weather conditions can be reduced, while the stability of the system can be improved;

[0143] Through real-time data collection, status monitoring, dynamic adjustment and continuous optimization, the efficient operation of the power system in extreme weather is ensured. The digital twin model and anomaly detection algorithm are used to monitor the system status in real time, dynamically adjust the operation strategy, and optimize the response time, operating cost and system stability. The feedback mechanism and continuous optimization further enhance the prediction accuracy and overall resilience of the system, enabling the power system to respond to extreme weather challenges more quickly and reliably.

[0144] S52 includes:

[0145] S521, Simulation result generation and evaluation: Use the digital twin model to simulate the system's performance under different extreme weather scenarios, generate power supply paths and load distribution plans for each scenario, evaluate their impact on system resilience, and identify potential abnormal situations.

[0146] S522, anomaly detection algorithm execution: The anomaly detection algorithm is used to analyze the difference between the real-time monitoring data and the digital twin model prediction data, and the anomaly metric E(t) is calculated, which is expressed as:

[0147]

[0148] Among them, E(t) is the abnormal measurement at time t. When E(t) exceeds the preset threshold, an abnormal alarm is triggered.

[0149] S523, alarm response and adjustment suggestion generation: When the anomaly detection algorithm triggers an alarm, the system generates adjustment suggestions or automatic adjustment plans, giving priority to protecting key equipment and areas, and records alarm data for subsequent analysis.

[0150] S53 includes:

[0151] S531, objective function definition: Based on the real-time monitoring and anomaly detection results, define the system's optimization objective function, which includes: system response time T response , operating cost C operation and system stability index R stability , expressed as:

[0152] minF(x)=α 1 T response +α 2 C operation +α 3 R stability

[0153] Among them, α 1 ,α 2 ,α 3 are the weight coefficients of the corresponding parameters, indicating the degree of concern for response time, operating cost and stability respectively;

[0154] S532, constraint setting: setting the constraint conditions in the optimization process to ensure that the adjusted strategy meets the system security and performance requirements, expressed as:

[0155] T response ≤T max ;

[0156] C operation ≤C budget ;

[0157] Rstability ≥R min ;

[0158] Among them, T max is the maximum allowed response time, C budget is the maximum operating cost within the budget, R min It is the minimum stability index required by the system;

[0159] S533, optimization algorithm selection and execution: select the particle swarm optimization algorithm to solve the objective function and dynamically adjust the system operation strategy, which is expressed as:

[0160]

[0161] Among them, x k is the strategy parameter after the kth iteration, η is the learning rate, is the gradient of the current strategy;

[0162] S534, Strategy Adjustment and Implementation: Adjust the system operation strategy according to the optimization results to ensure that the response time in extreme weather is minimized, the operation cost is optimized, and the stability of the system is maintained. The adjusted strategy is implemented in the system and its effect is continuously evaluated through real-time monitoring data;

[0163] S535, dynamic feedback and re-optimization: Feedback the effect of the implemented strategy to the optimization algorithm, use the actual operation data to re-evaluate the objective function and constraints, and perform secondary optimization when necessary to ensure that the system is always in the best operating state.

[0164] S6 includes:

[0165] S61, emergency plan activation: When an extreme weather event occurs, the system automatically detects the anomaly and activates the pre-made emergency plan, which includes priority power supply for key equipment, load transfer plan, and backup power supply scheduling plan;

[0166] S62, real-time feedback and dynamic adjustment: Use the digital twin model to simulate and monitor the execution of the current emergency plan in real time, and calculate the difference between the actual execution effect and the expected effect, expressed as:

[0167]

[0168] Among them, ΔE(t) is the difference measure of execution effect at time t, P actual (t) is the actual execution result, P expected (t) is the expected result;

[0169] When the difference exceeds the set tolerance range, the system automatically adjusts the emergency plan, including reallocating loads and adjusting power supply paths;

[0170] S63, post-event evaluation: After the extreme weather event is over, a comprehensive evaluation of the implementation effect of the emergency plan is conducted, expressed as:

[0171]

[0172] Among them, E overall is the overall execution effect evaluation value, T is the total duration of the event, N is the number of key indicators involved in the execution process, ΔE i (t) is the difference measure of the execution effect of the i-th indicator at time t;

[0173] S64, Feedback Optimization and Plan Update: Based on the post-event evaluation results, analyze the deficiencies in the emergency plan, optimize and update the plan strategy, expressed as:

[0174]

[0175] Among them, P new is the updated emergency plan strategy, P current is the current plan strategy, γ is the learning rate, Gradient for overall effect assessment;

[0176] S65, Plan Verification and Re-Optimization: Conduct simulation verification on the updated plan strategy to ensure its effectiveness in future extreme weather events, conduct secondary optimization, and finally incorporate the optimized plan into the system's emergency response mechanism.

[0177] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0178] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A new method for assessing the resilience of a new power system under extreme weather conditions, characterized in that: The following steps are involved: S1, digital twin model construction and data collection: Based on the actual operation data of the power system, historical fault records and extreme weather data, a digital twin model is constructed to collect data in real time, including weather, equipment status and load conditions, and input the data into the model to achieve real-time simulation and monitoring of the system; S2, multi-dimensional risk perception and fusion assessment: using the digital twin model to fuse multi-source data, identify the potential risks of the power system under extreme weather conditions from the perspectives of meteorology, geology, and hydrology, and determine the system's vulnerability; S3, extreme weather scenario construction and vulnerability analysis: Based on the digital twin model and risk assessment results, extreme weather scenarios are constructed, various weather events are simulated, the probability of occurrence and impact range of extreme weather are predicted through climate models, system vulnerabilities are analyzed, and key nodes and vulnerable equipment are identified; S4, Definition and calculation of resilience indicators: Based on the results of vulnerability analysis, define and calculate resilience indicators, including system recovery time, service interruption time and economic losses; S5, real-time monitoring and dynamic adjustment: During system operation, the digital twin model and real-time monitoring data are used to continuously monitor and dynamically optimize the operation strategy; S6, emergency plan execution and feedback optimization: When extreme weather occurs, execute the emergency plan, use the digital twin model to provide real-time feedback and optimize and adjust. After the event, evaluate the execution effect of the plan and continuously optimize the plan through feedback.

2. The method for evaluating the resilience of a new power system under extreme weather conditions according to claim 1 is characterized in that: The S1 includes: S11, data preprocessing: collect actual operation data of the power system, including voltage, current, power, frequency; historical fault records, including fault type, fault time, fault location; and extreme weather related data, including temperature, humidity, wind speed, rainfall, and clean, format and standardize these data; S12, data fusion and feature extraction: using data fusion technology, integrating multi-source data and extracting key features from them. The features extracted from the equipment status data may include the equipment health index (η); S13, parametric modeling of digital twin model: Based on the preprocessed data, the digital twin model of the power system is constructed using numerical methods and physical modeling techniques. The model parameterization process includes modeling the key components in the system. S14, real-time data input and simulation monitoring: Through the sensor network and communication system, the real-time collected data is input into the constructed digital twin model for simulation calculation and real-time monitoring.

3. The method for evaluating the resilience of a new power system under extreme weather conditions according to claim 1 is characterized in that: The S2 includes: S21, data fusion: the data from multiple sources such as meteorology, geology, and hydrology will be fused and processed using the weighted average method; S22, multi-dimensional feature extraction: extract multi-dimensional features from the fused data, including meteorological features, geological features, and hydrological features, and use these features to assess the risks of the power system; S23, vulnerability analysis: Based on the extracted multi-dimensional features, system vulnerability analysis is performed; S24, Risk Assessment: Comprehensive Analysis of System Vulnerability Index V system , and combined with real-time monitoring data, identify the potential risks of the power system under extreme weather and generate a risk assessment report.

4. The method for evaluating the resilience of a new power system under extreme weather conditions according to claim 1 is characterized in that: The S3 includes: S31, extreme weather scenario construction: Based on the digital twin model and historical data, multiple extreme weather scenarios are generated using climate models GCM and RCM to calculate the probability of extreme weather occurrence; S32, Impact range prediction: Use climate models to predict the geographical and temporal impact range of extreme weather events and calculate the probability distribution of events occurring in a specific area; S33, System Vulnerability Analysis: Based on the constructed extreme weather scenarios, analyze the vulnerability of the power system, identify key nodes and vulnerable equipment, and calculate the vulnerability index of the equipment; S34, Critical Node Identification: Based on the vulnerability analysis results, identify the critical nodes and vulnerable devices in the system, give priority to the devices with the highest vulnerability index, and generate a detailed vulnerability analysis report.

5. The method for evaluating the resilience of a new power system under extreme weather conditions according to claim 4 is characterized in that: The S31 includes: S311, Historical data analysis and preparation: Collect and organize historical meteorological data and records of extreme weather events in the area where the power system is located, conduct statistical analysis based on these data, and identify the main types of extreme weather events that have occurred in history and their characteristic parameters; S312, Climate model selection and configuration: Select and configure global climate models and regional climate models suitable for the region, generate possible future climate change scenarios through these models, and calibrate model parameters in combination with historical data; S313, Extreme Weather Event Simulation: Use calibrated global climate models and regional climate models to simulate extreme weather events that may occur in the future and calculate the probability of occurrence of different types of extreme weather events; S314, scenario generation and verification: Generate multiple extreme weather scenarios based on the calculated probability of occurrence, and verify these scenarios through historical data; S315, Scenario Visualization and Output: Visualize the generated extreme weather scenarios and provide the probability of occurrence, time distribution, and geographical impact range.

6. The method for evaluating the resilience of a new power system under extreme weather conditions according to claim 1 is characterized in that: The S4 includes: S41, system recovery time calculation: Based on the vulnerability analysis results, define and calculate the system recovery time T recovery ; S42, service interruption time calculation: define and calculate the service interruption time T outage ; S43, Economic loss assessment: Definition of economic loss C loss , evaluate it; S44, calculation of comprehensive toughness index: The above three indexes are combined into a toughness index R system .

7. The method for evaluating the resilience of a new power system under extreme weather conditions according to claim 1, characterized in that: The S5 includes: S51, real-time data collection and input: Through sensor networks and IoT devices, key operating data of the power system, including voltage, current, frequency, and temperature, are continuously collected, and these data are input into the digital twin model in real time for dynamic simulation; S52, status monitoring and anomaly detection: Use the digital twin model to monitor the system status in real time and analyze the system operation data through anomaly detection algorithms; S53, dynamic adjustment of operation strategy: Based on the real-time monitoring and anomaly detection results, the optimization algorithm is used to dynamically adjust the system's operation strategy, optimize the algorithm objective function, and reduce the system's response time and operation cost in extreme weather conditions through dynamic adjustment.

8. The method for evaluating the resilience of a new power system under extreme weather conditions according to claim 7 is characterized in that: The S52 includes: S521, Simulation result generation and evaluation: Use the digital twin model to simulate the system's performance under different extreme weather scenarios, generate power supply paths and load distribution plans for each scenario, evaluate their impact on system resilience, and identify potential abnormal situations. S522, anomaly detection algorithm execution: Analyze the difference between the real-time monitoring data and the digital twin model prediction data through the anomaly detection algorithm, and calculate the anomaly metric E(t). S523, alarm response and adjustment suggestion generation: When the anomaly detection algorithm triggers an alarm, the system generates adjustment suggestions or automatic adjustment plans, giving priority to protecting key equipment and areas, and records alarm data for subsequent analysis.

9. The method for evaluating the resilience of a new power system under extreme weather conditions according to claim 7, characterized in that: The S53 includes: S531, objective function definition: Based on the real-time monitoring and anomaly detection results, define the system's optimization objective function, which includes: system response time T response , operating cost C operation and system stability index R stability ; S532, constraint condition setting: setting constraint conditions in the optimization process; S533, optimization algorithm selection and execution: select particle swarm optimization algorithm to solve the objective function and dynamically adjust the system operation strategy; S534, Strategy adjustment and implementation: adjust the system operation strategy according to the optimization results, implement the adjusted strategy in the system, and continuously evaluate its effect through real-time monitoring data; S535, dynamic feedback and re-optimization: Feedback the effect of the implemented strategy to the optimization algorithm, use the actual operation data to re-evaluate the objective function and constraints, and perform secondary optimization when necessary.

10. The method for evaluating the resilience of a new power system under extreme weather conditions according to claim 1, characterized in that: The S6 includes: S61, emergency plan activation: When an extreme weather event occurs, the system automatically detects the anomaly and activates the pre-made emergency plan, which includes priority power supply for key equipment, load transfer plan, and backup power supply scheduling plan; S62, real-time feedback and dynamic adjustment: Use the digital twin model to simulate and monitor the execution of the current emergency plan in real time, and calculate the difference between the actual execution effect and the expected effect; When the difference exceeds the set tolerance range, the system automatically adjusts the emergency plan, including reallocating loads and adjusting power supply paths; S63, Post-event evaluation: After the extreme weather event is over, conduct a comprehensive evaluation of the implementation of the emergency plan; S64, Feedback Optimization and Plan Update: Based on the post-event evaluation results, analyze the deficiencies in the emergency plan and optimize and update the plan strategy; S65, plan verification and re-optimization: simulate and verify the updated plan strategy, perform secondary optimization, and finally incorporate the optimized plan into the system's emergency response mechanism.

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