Resilience assessment method for power systems under extreme weather conditions

Abstract

The present invention relates to the technical field of electrical system assessment, and specifically to a method for assessing the resilience of a new type of power system under extreme weather conditions, including constructing a digital twin model, collecting data in real time, fusing and processing multi-source data, identifying potential risks of the power system under extreme weather conditions, determining system vulnerability, constructing extreme weather scenarios, simulating possible weather events in the future, predicting the probability of occurrence and impact range of extreme weather conditions, analyzing system vulnerability, identifying key nodes and vulnerable equipment, defining and calculating resilience indicators, optimizing operating strategies, executing emergency plans when extreme weather conditions occur, evaluating the effectiveness of plan execution, and continuously optimizing plans through feedback. The present invention significantly improves the dynamic response capability of the power system under extreme weather conditions and the accuracy of resilience assessment, reflects the actual operating status of the power system in real time, and the digital twin technology can dynamically adjust model parameters to simulate the real response of the system under changing conditions.

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 an electric power system under extreme weather conditions. Background Art

[0002] Existing technologies primarily assess and optimize power system resilience through historical data analysis, static models, disaster response plans, data monitoring and alarm systems, optimization algorithms, and simulation tools. These methods, combined with statistical analysis, topological structure research, and real-time monitoring, enable identification of system vulnerabilities, emergency response planning, and optimized resource allocation, ultimately enhancing the power system's stability and resilience in extreme weather conditions.

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

[0004] First, while existing technologies utilize historical data and static models to evaluate and optimize power systems, these methods often have limitations. For one thing, historical data-based assessment methods often rely on past fault records and extreme weather events, failing to adequately address new or more complex weather conditions that may occur in the future. Furthermore, static models assume unchanging system operating conditions, making it difficult to reflect the dynamic changes in power systems under extreme weather conditions, potentially leading to inaccurate assessment results.

[0005] Second, while existing disaster emergency response plans and data monitoring systems can provide a certain level of emergency response in the face of extreme weather, they often lack the ability to adjust and optimize in real time. These plans and systems are often based on preset operational procedures and fixed emergency rules, making them unable to quickly adapt to changes in emergencies, potentially resulting in ineffective emergency response measures. Furthermore, traditional alarm systems can only identify known anomalies and lack the ability to adequately respond to the risks posed by complex and changing extreme weather scenarios.

[0006] Third, while existing optimization algorithms and simulation technologies have made some progress in improving power system operational efficiency, they remain insufficient for addressing the complexities of extreme weather scenarios. Many optimization algorithms focus primarily on system economics and efficiency, while neglecting resilience and long-term stability. Furthermore, existing simulation tools are often based on simplified models that struggle to fully capture the comprehensive impacts of extreme weather on power systems, resulting in limitations in planning and decision support. Summary of the Invention

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

[0008] The resilience assessment method for power systems 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. Real-time data collection, including weather, equipment status and load conditions, is input into the model to achieve real-time simulation and monitoring of the system.

[0010] S2, Multi-dimensional Risk Perception and Fusion Assessment: Using digital twin models, we fuse multi-source data to identify potential risks to the power system under extreme weather conditions from meteorological, geological, and hydrological perspectives, and determine system vulnerabilities.

[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 future weather events. The probability and impact range of extreme weather events are predicted using climate models. System vulnerabilities are analyzed to identify key nodes and vulnerable equipment.

[0012] S4, Resilience Indicator 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, digital twin models and real-time monitoring data are used to continuously monitor and dynamically optimize operation strategies;

[0014] S6, emergency plan execution and feedback optimization: When extreme weather occurs, the emergency plan is executed, and the digital twin model is used to provide real-time feedback and optimization adjustments. After the event is over, the execution effect of the plan is evaluated and the plan is continuously optimized through feedback.

[0015] Optionally, the S1 includes:

[0016] S11, Data Preprocessing: Collect actual power system operating data, including voltage, current, power, and frequency; historical fault records, including fault type, fault time, and fault location; and extreme weather-related data, including temperature, humidity, wind speed, and rainfall. Clean, format, and standardize this data.

[0017] S12, data fusion and feature extraction: using data fusion technology to integrate multi-source data and extract 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 Models: Based on preprocessed data, numerical methods and physical modeling techniques are used to construct a digital twin model of the power system. The model parameterization process includes modeling 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: Fusion processing will be carried out from multiple sources such as meteorological, geological, and hydrological data, using the weighted average method for data fusion processing;

[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 conditions and generate risk assessment reports.

[0025] Optionally, S3 includes:

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

[0027] S32, Impact Range Prediction: Use climate models to predict the geographic and temporal impact range of extreme weather events and calculate the probability distribution of events occurring within 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 this data, and identify the major extreme weather event types (such as hurricanes, floods, blizzards, etc.) and their characteristic parameters that have occurred in history;

[0032] S312, Climate Model Selection and Configuration: Select and configure a global climate model (GCM) and a regional climate model (RCM) appropriate 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 possible extreme weather events 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 using 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 indicators 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, is continuously collected and input into the digital twin model in real time for dynamic simulation;

[0043] S52, Status Monitoring and Anomaly Detection: Use digital twin models to monitor system status in real time and analyze 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 system's operation strategy is dynamically adjusted using the optimization algorithm to optimize the algorithm's objective function. Through dynamic adjustment, the system's response time and operation cost in extreme weather conditions are reduced.

[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 the alarm data for subsequent analysis.

[0049] Optionally, the S53 includes:

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

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

[0052] S533, Optimization Algorithm Selection and Execution: Select the particle swarm optimization algorithm to solve the objective function and dynamically adjust the system's operating strategy;

[0053] S534, Strategy Adjustment and Implementation: Adjust the system operation strategy based on the optimization results, implement the adjusted strategy in the system, and continuously evaluate its effectiveness through real-time monitoring data;

[0054] S535, dynamic feedback and re-optimization: Feedback the implemented strategy effects into the optimization algorithm, use 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 a pre-defined emergency plan, which includes priority power supply for critical equipment, load transfer plans, and backup power scheduling.

[0057] S62, Real-time feedback and dynamic adjustment: Use the digital twin model to simulate and monitor the implementation of the current emergency plan in real time, and calculate the difference between the actual implementation 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, conduct a comprehensive evaluation of the implementation of the emergency plan;

[0060] S64, Feedback Optimization and Plan Update: Based on the post-incident evaluation results, analyze the shortcomings of 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 dynamic response capability of the power system in extreme weather conditions and the accuracy of resilience assessment. 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 to simulate the system's true response under changing conditions, ensuring that the assessment results are closer to reality. This innovative approach enables the power system to identify potential risks in advance and formulate more scientific and reasonable response strategies.

[0064] This invention also uses multidimensional risk perception and fusion assessment technology to comprehensively assess the power system's vulnerability to extreme weather conditions, taking into account multiple sources of data, including meteorological, geological, and hydrological data. Compared to traditional single-dimensional analysis methods, multidimensional risk perception can more accurately identify system weaknesses and potential failure points, enabling targeted preventive measures before extreme weather events occur. Furthermore, fusion assessment technology combines information from multiple data sources, improving the reliability and accuracy of assessment results and providing strong support for early warning and optimization of power systems.

[0065] The present invention introduces a real-time feedback mechanism and a dynamic optimization algorithm in the execution and feedback optimization of emergency plans, making the power system more flexible and adaptable when responding to 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0067] Figure 1 Schematic diagram of a method for assessing the resilience of a 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 with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

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

[0071] In general, terms can be understood, at least in part, from their 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 Figure 1, the resilience assessment method for power systems 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. Real-time data collection, including weather, equipment status and load conditions, is input into the model to achieve real-time simulation and monitoring of the system.

[0074] S2, Multi-dimensional Risk Perception and Fusion Assessment: Using digital twin models, we fuse multi-source data to identify potential risks to the power system under extreme weather conditions from meteorological, geological, and hydrological perspectives, and determine system vulnerabilities.

[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 future weather events. The probability and impact range of extreme weather events are predicted using climate models. System vulnerabilities are analyzed to identify key nodes and vulnerable equipment.

[0076] S4, Resilience Indicator Definition and Calculation: Based on the vulnerability analysis results, 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, digital twin models and real-time monitoring data are used to continuously monitor and dynamically optimize operating strategies to improve system stability and response speed 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 implementation effect of the plan, continuously optimize the plan through feedback, and enhance future response capabilities.

[0079] S1 includes:

[0080] S11, Data Preprocessing: Collect actual power system operating data, including voltage, current, power, and frequency; historical fault records, including fault type, fault time, and fault location; and extreme weather-related data, including temperature, humidity, wind speed, and rainfall. Clean, format, and standardize this data to ensure consistency and accuracy.

[0081] S12, data fusion and feature extraction: Using data fusion technology, multi-source data is 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 models: Based on preprocessed data, numerical methods and physical modeling techniques are used to construct a digital twin model of the power system. The model parameterization process includes modeling key components in the system (such as transformers, generators, transmission lines, etc.), which can be expressed as:

[0085] P(t)=P0×(1-e -λt );

[0086] Where P(t) is the power output of the system at time t, P0 is the initial power, and 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] A 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 the effective utilization of key features, parametric modeling provides accurate simulation of the performance of key equipment, and real-time data input and simulation monitoring enable accurate real-time prediction and optimization of the system under extreme weather conditions, providing strong support for power system resilience assessment.

[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, which is 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 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 to extreme weather events is accurately identified. Through weighted averaging and vulnerability index calculation, the system's potential risks are quantified, providing a scientific basis for real-time monitoring and optimized decision-making, thereby 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, possible weather events are simulated, and the probability of extreme weather occurrence is calculated, 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 For 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, combined with vulnerability index calculations, we can effectively identify key nodes and vulnerable equipment in the power system. Through probabilistic analysis and vulnerability assessment, we provide a scientific basis for optimized decision-making, improve the system's defense and recovery capabilities in extreme weather, and enhance its 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 this data, and identify the major extreme weather event types (such as hurricanes, floods, blizzards, etc.) and their characteristic parameters that have occurred in history;

[0115] S312, Climate Model Selection and Configuration: Select and configure a global climate model (GCM) and a regional climate model (RCM) appropriate for the region, generate possible future climate change scenarios through these models, and calibrate model parameters in combination with historical data to improve prediction accuracy;

[0116] 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, 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 using historical data to ensure their comprehensiveness and representativeness;

[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 with precisely calibrated climate models (GCM and RCM), we generate a variety of extreme weather scenarios, ensuring the accuracy and representativeness of the simulation results. By calculating the probability of extreme weather events and verifying the comprehensiveness of the scenarios using techniques such as Monte Carlo simulation, we provide scientific forecasts of future extreme weather risks. Visual output makes these scenarios intuitive and easy to understand, providing strong support for power system vulnerability analysis and emergency response decision-making.

[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 i-th 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 indicators are combined into a toughness index R system , expressed as:

[0133]

[0134] Among them, β1, β2 and β3 are the weight coefficients of recovery time, service interruption time and economic loss, respectively, which 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, is continuously collected and input into the digital twin model in real time for dynamic simulation;

[0137] 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 the anomaly detection algorithm, which is 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: Based on the real-time monitoring and anomaly detection results, the system operation strategy is dynamically adjusted using the optimization algorithm. The optimization algorithm objective function is expressed as:

[0141] minF(x)=α1T response +α2C operation +α3R stability ;

[0142] Among them, T response is the system response time, C operation is the operating cost, R stability is the system stability index, α1, α2, and α3 are the corresponding weight coefficients. Through dynamic adjustment, the system's response time and operating cost in extreme weather conditions are reduced, while improving system stability.

[0143] Through real-time data collection, status monitoring, dynamic adjustments, and continuous optimization, we ensure the efficient operation of the power system in extreme weather conditions. We leverage digital twin models and anomaly detection algorithms to monitor system status in real time and dynamically adjust operational strategies to optimize response time, operating costs, and system stability. This feedback mechanism and continuous optimization further enhance the system's forecast accuracy and overall resilience, enabling the power system to respond more quickly and reliably to extreme weather challenges.

[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 analyzes the difference between the real-time monitoring data and the digital twin model prediction data, and calculates the anomaly metric E(t), which is expressed as:

[0147]

[0148] Among them, E(t) is the abnormality measurement at time t. When E(t) exceeds the preset threshold, an abnormality 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 the alarm data for subsequent analysis.

[0150] S53 includes:

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

[0152] minF(x)=α1T response +α2C operation +α3R stability

[0153] Among them, α1, α2, and α3 are the weight coefficients of the corresponding parameters, which respectively indicate the degree of concern for response time, operating cost, and stability;

[0154] S532, constraint setting: Set the constraints 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] R stability ≥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 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's operating strategy, expressed as:

[0160]

[0161] Among them, x kis the policy 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 based on the optimization results to ensure that response time is minimized, operating costs are optimized, and system stability is maintained in extreme weather conditions. The adjusted strategy is implemented in the system and its effectiveness is continuously evaluated through real-time monitoring data.

[0163] S535, dynamic feedback and re-optimization: Feedback the implemented strategy effects into the optimization algorithm, use 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 a pre-defined emergency plan, which includes priority power supply for critical equipment, load transfer plans, and backup power scheduling.

[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, conduct a comprehensive evaluation of the implementation of the emergency plan, expressed as:

[0171]

[0172] Among them, E overall is the overall execution effect evaluation value, T is the total duration of the event, and N is the number of key indicators involved in the execution process. 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-incident evaluation results, analyze the shortcomings of the emergency plan and optimize and update the plan strategy, expressed as:

[0174]

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

[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 encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been 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 method for assessing the resilience of power systems 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. Real-time data collection, including weather, equipment status and load conditions, is input into the model to achieve real-time simulation and monitoring of the system. S2, Multi-dimensional Risk Perception and Integrated Assessment: Using digital twin models, we integrate and process multi-source data to identify potential risks to the power system under extreme weather conditions from meteorological, geological, and hydrological perspectives, and determine system vulnerabilities. Specifically, we will: S21, data fusion: the data from meteorological, geological and hydrological sources will be integrated 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 risk 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 potential risks of the power system under extreme weather conditions and generate risk assessment reports; 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, and the probability and impact range of extreme weather events are predicted through climate models. System vulnerabilities are analyzed, and key nodes and vulnerable equipment are identified. S4, Resilience Indicator Definition and Calculation: Based on the vulnerability analysis results, 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, digital twin models and real-time monitoring data are used to continuously monitor and dynamically optimize operation strategies; S6, Emergency Plan Execution and Feedback Optimization: When extreme weather occurs, the emergency plan is executed, and the digital twin model is used to provide real-time feedback and optimization adjustments. After the event, the execution of the plan is evaluated and the plan is continuously optimized through feedback. Specifically, it includes: S61, Emergency Plan Activation: When an extreme weather event occurs, the system automatically detects the anomaly and activates a pre-defined emergency plan, which includes priority power supply for critical equipment, load transfer plans, and backup power scheduling. S62, Real-time feedback and dynamic adjustment: Use the digital twin model to simulate and monitor the implementation of the current emergency plan in real time, and calculate the difference between the actual implementation 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, conduct a comprehensive evaluation of the implementation of the emergency plan; S64, Feedback Optimization and Plan Update: Based on the post-incident evaluation results, analyze the shortcomings of 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.

2. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 1, wherein: Said S1 comprises: S11, Data Preprocessing: Collect actual power system operating data, including voltage, current, power, and frequency; historical fault records, including fault type, fault time, and fault location; and extreme weather-related data, including temperature, humidity, wind speed, and rainfall. Clean, format, and standardize this data. S12, data fusion and feature extraction: using data fusion technology to integrate multi-source data and extract 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 Models: Based on preprocessed data, numerical methods and physical modeling techniques are used to construct a digital twin model of the power system. The model parameterization process includes modeling 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 power system under extreme weather conditions according to claim 1, wherein: Said 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, and the probability of extreme weather occurrence is calculated; S32, Impact Range Prediction: Use climate models to predict the geographic and temporal impact range of extreme weather events and calculate the probability distribution of events occurring within 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.

4. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 3, wherein: 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, perform 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 and regional climate models appropriate 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 and regional climate models to simulate possible extreme weather events 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 using 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.

5. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 1, wherein: 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 indicators are combined into a toughness index R system .

6. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 1, wherein: 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, is continuously collected and input into the digital twin model in real time for dynamic simulation; S52, Status Monitoring and Anomaly Detection: Use digital twin models to monitor system status in real time and analyze system operation data through anomaly detection algorithms; S53, dynamic adjustment of operation strategy: Based on the real-time monitoring and anomaly detection results, the system's operation strategy is dynamically adjusted using the optimization algorithm to optimize the algorithm's objective function. Through dynamic adjustment, the system's response time and operation cost in extreme weather conditions are reduced.

7. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 6, wherein: 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 anomalies; 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; 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 the alarm data for subsequent analysis.

8. The method for evaluating the resilience of a power system under extreme weather conditions according to claim 6, wherein: The S53 includes: S531, objective function definition: Based on real-time monitoring and anomaly detection results, define the system's optimization objective function, which includes: system response time T response , operating costs C operation and system stability index R stability ; S532, constraint condition setting: setting the constraint conditions in the optimization process; S533, Optimization Algorithm Selection and Execution: Select the particle swarm optimization algorithm to solve the objective function and dynamically adjust the system's operating strategy; S534, Strategy Adjustment and Implementation: Adjust the system operation strategy based on the optimization results, implement the adjusted strategy in the system, and continuously evaluate its effectiveness through real-time monitoring data; S535, dynamic feedback and re-optimization: Feedback the implemented strategy effects into the optimization algorithm, use actual operation data to re-evaluate the objective function and constraints, and perform secondary optimization.

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