86 results about "Extreme events" patented technology

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

The invention belongs to the technical field of ecological risk prediction, and relates to an extreme weather event and ecological risk prediction method using a generative adversarial network, through a meteorological generation branch, spatial-temporal characteristics of a meteorological field are synchronously extracted by using a 3D residual network and Transform and a future meteorological state is predicted, and an ecological generation branch is based on a meteorological prediction result and topographic data. An ecological risk map is dynamically generated through hole convolution U-Net, and mismatch of resolution and dimension caused by cross-model interpolation is avoided; a discriminator introduces a physical conservation verification module and an ecological association module, physical rationality loss and ecological logic consistency loss of a generator are jointly optimized in adversarial training, in addition, a dynamic feedback mechanism corrects and generates errors through real-time observation data, and spectrum normalization is utilized to constrain model parameters, so that the reliability of the model is improved. The accuracy of extreme event and ecological risk coupling prediction is further improved; therefore, seamless space-time coupling of the weather and the ecological field is realized, and the generated result has both physical conservation and ecological relevance.

The invention relates to the technical field of meteorological prediction, in particular to an extreme meteorological prediction method based on a typical meteorological year, which comprises the following steps: firstly, constructing a data set of the typical meteorological year, and generating high-quality meteorological data through interpolation and fusion technologies in combination with ground, reanalysis and satellite data; the generative adversarial network is used for enhancing data, the problem that extreme event samples are insufficient is solved, and key meteorological variables are extracted through super-resolution and feature dimension reduction; based on a convolutional neural network and a long-short-term memory network, constructing a deep learning model to perform multi-task prediction; a physical constraint rule is introduced, such as energy and mass conservation, and it is ensured that prediction conforms to a meteorological rule; and through an uncertainty quantification technology, generating a confidence interval of a prediction result, and providing risk assessment and decision support information. According to the method, the precision and credibility of extreme meteorological event prediction are improved, and disaster prevention and reduction, resource scheduling, agricultural production and the like are supported.

The invention discloses an electricity-gas-heat integrated energy microgrid optimal configuration system considering an extreme event, which belongs to the technical field of integrated energy system optimization and comprises the steps of constructing a system model, setting an objective function, calculating, setting constraint conditions, setting extreme safety constraints, and establishing a planning model and a solving model. The system model comprises a gas turbine, a gas-fired boiler, photovoltaic equipment, wind power equipment, an electric heating pump and battery energy storage and heat energy storage equipment, and is used for meeting the requirements of electric energy, heat energy and gas loads in the system and ensuring the balance of the electric quantity and heat of the system. By constructing an optimization model of a comprehensive energy system, the uncertainty of renewable energy output is processed by utilizing a static robust optimization technology, and the uncertainty of power grid or gas grid faults caused by extreme natural disasters is combined, so that the operation safety and stability of the system in the disaster period are ensured.

The invention provides a method and a system for identifying a vulnerable part of a power system under an extreme event, and the method comprises the steps: determining an equivalent degree index according to the degree of a node in a power grid and the influence on the network connection level; determining a connectivity influence index according to the influence on the network connectivity after the channel is lost; identifying nodes with structural vulnerability and power transmission channels according to the equivalent degrees and connectivity influence indexes; determining a node equivalent power flow betweenness index according to the load carried by the node and the load for supplying power to the interconnected node; determining an equivalent power flow betweenness index of the power transmission channel according to the transmission power of the channel and the proportion of power vacancy caused by loss in the total load capacity of the receiving end system; identifying functional vulnerability nodes and power transmission channels according to the equivalent power flow betweenness indexes of the nodes and the power transmission channels; and determining the nodes and the power transmission channels which present the structure and function vulnerabilities at the same time as comprehensive vulnerable nodes or channels, thereby realizing quantitative identification of the vulnerability degrees of the power grid nodes and the power transmission channels under the extreme event.

The invention discloses an electrical integrated energy system toughness planning method considering an extreme event tail risk, and relates to the technical field of integrated energy system optimization, and the method comprises the steps: firstly, considering the massive load shedding risk caused by a small-probability extreme event, and constructing a pre-disaster defense-post-disaster emergency response electrical integrated energy system toughness planning frame; secondly, comprehensively considering toughness planning cost, emergency scheduling cost and tail risk loss in a small-probability extreme scene, and establishing an objective function of a planning model; then, for a disaster stage, establishing disaster prevention resource deployment related constraints of the toughness planning model; then, considering operation constraints of the post-disaster power distribution system and the natural gas system, and establishing post-disaster emergency response related constraints; and finally, performing linearization processing on the established planning model, and calling a commercial solver for solving.

The invention belongs to the technical field of cable connection, and particularly relates to a cable intermediate joint protection device applicable to extreme events, which comprises an upper shell, a lower shell, a secondary explosion-proof assembly and an arc extinguishing assembly, the upper shell and the lower shell are combined to form a main body shell structure, the secondary explosion-proof assembly and the arc extinguishing assembly are located in the main body shell structure, the secondary explosion-proof assembly is arranged in a central axis area of the main body shell structure, and the arc extinguishing assembly is located above the secondary explosion-proof assembly. Through the use of the secondary explosion-proof assembly, when deflagration occurs, quartz sand in a sealing rubber bag in the arc extinguishing assembly can be discharged to the outer side of the cable joint, so that the cable joint is not in contact with air, and the generation of explosion is hindered. The energy release hole upper cover sealing device plays a role in sealing the space and prevents liquid or sundries from entering the middle connector. The whole device achieves the effect of resisting extreme events.

The invention relates to a seismic landslide space scale prediction method and system based on graph deep learning, and the method comprises the steps: dividing a slope unit according to a digital elevation model of a research region, obtaining geographic elements related to a landslide disaster, integrating the data of each geographic element based on the slope unit, and carrying out the prediction of the landslide space scale. Constructing sub-graph data by utilizing spatial connection among the slope units in the grids, and forming a graph structure data set by all the sub-graph data; the slope unit in the subgraph is regarded as a node v, any two nodes are regarded as having a connection relationship, the connection relationship between the nodes is represented by using an edge matrix E, the element value of the edge matrix E is 1, and the landslide plane area in the slope unit without landslide occurrence is set as 0; and evaluating the plane area of the earthquake landslide in the area range by using the graph structure data set and a graph Transform model. And a practical framework and a technical path are provided for spatial distribution of landslide risks in an area range under the action of extreme events, especially earthquake landslide scale prediction.

The invention discloses a scene generation method and system for new energy extreme meteorological disturbance modeling, and relates to the technical field of power systems, and the method comprises the steps: recognizing a strong disturbance event under meteorological drive according to a constructed new energy effective output sample set, and constructing a generative adversarial neural network model embedded in a wind power photovoltaic physical boundary condition, acquiring a virtual disturbance section; and based on the virtual disturbance section, constructing a new energy annual extreme disturbance scene set by integrating a plurality of typical days embedded with disturbance, and applying the new energy annual extreme disturbance scene set to new energy extreme meteorological disturbance modeling and sample enhancement. The method has strong representation capability, good training stability and scene expansion capability, and can provide more representative and diversified input scenes for dispatching optimization and supporting power supply planning of the power system, so that the robustness and risk perception capability of the system for coping with extreme events are improved.

The invention relates to a flood and drought disaster risk assessment and deduction system and method based on slope multi-level regulation and storage, and belongs to the technical field of flood and drought disaster risk prevention and control and watershed hydrological regulation and control. The method comprises the following steps: acquiring multi-source data of a target drainage basin, performing water source refined analysis through coupling of water transfer coefficients (K1 and K2) based on a hierarchical synergistic effect of'upper closure-middle retention-lower dissipation 'of a slope, and calculating a controllable, effective and renewable water source amount; in combination with the regulation and storage capacity difference of each level, carrying out flood and drought disaster chain risk collaborative assessment, and outputting hierarchical risks and 0-10 level set risk indexes; a physical mechanism and data driving coupled scene deduction model is driven, and historical extreme event redisk and future climate scene prediction are completed; and after rendering by a visual engine, through hierarchical slope regulation and storage threshold comparison, outputting a regulation and storage measure layout optimization scheme. The system adopts a micro-service architecture and comprises a data management module, a multi-level regulation and storage collaborative analysis module, a water source analysis module, a risk evaluation module, a scene deduction module, a decision support module and a threshold comparison module. According to the method, high-resolution accurate positioning, low-error simulation and efficiency improvement of flood and drought disaster risks are realized, a decision closed loop from risk identification to optimization regulation is formed, and direct and quantitative technical support can be provided for flood and drought prevention and control engineering practice of drainage basins.

The invention provides a LanMei basin multi-disaster risk early warning method, system and equipment and a medium, and relates to the technical field of disaster early warning. The method comprises the steps that a multi-disaster cooperative processing engine is planned to be constructed, and disaster coupling calculation and dynamic early warning with high temporal-spatial resolution are achieved. Forecasting and early warning of short-term and medium-term extreme events such as rainstorm, high temperature and typhoon and disasters such as derived flood, drought and geological landslide are achieved, and coupling modeling is achieved. And performing normalization and visualization processing on the multi-disaster forecasting result, outputting a risk early warning result, and displaying the multi-disaster risk in a visual map and chart form. According to the scheme provided by the invention, intelligent early warning support is provided for disaster prevention and reduction in the LanMei basin, and the accuracy and timeliness of disaster early warning are improved.

The application provides a power distribution network elasticity planning method, device and equipment considering extreme events, and relates to the technical field of power distribution network planning. The method comprises the following steps: fitting an influence expectation of extreme events in a region where a target power distribution network is located based on historical data of the extreme events; wherein the extreme events include meteorological extreme events, geological extreme events and human-caused extreme events; taking the influence expectation and the topological structure of the target power distribution network as inputs, taking reinforcement decision and elasticity resource allocation as outputs, constructing a target function based on node reinforcement decision cost, elasticity resource allocation cost, disaster loss cost and operation cost, constructing a constraint condition based on upper and lower limits of an expectation probability, a power distribution network resilience limit and extreme event measures, and obtaining an elasticity planning model of the target power distribution network; taking minimization of the target function as an optimization target, solving the elasticity planning model, and obtaining an elasticity planning result of the target power distribution network. The application can solve the problem of inaccurate extreme event evaluation in traditional planning.

The invention discloses an industrial park micro-grid toughness planning method and system based on opportunity constraint, and relates to the technical field of power grid planning, and the method comprises the steps: building a combined scene pool containing scene weight according to the extreme strength probability of risk factors in a historical scene based on the coupling and association failure of multiple risk factors; constructing a pre-disaster protection planning layer according to opportunity constraints and protection dimensions; constructing a post-disaster recovery planning layer by using a staged recovery mechanism and an emergency operation mode; constructing an optimization objective function by minimizing the protection cost and the recovery cost; constructing a dual-stage planning model by using the pre-disaster protection planning layer, the post-disaster recovery planning layer and the optimization objective function; and matching the current triggering scene and the occurrence probability according to the real-time industrial park micro-grid operation data and the combined scene pool, inputting the current triggering scene and the occurrence probability into the double-stage planning model, and generating a tough protection planning strategy. The method has the beneficial effects that the protection planning strategy gives consideration to the toughness requirement and disaster recovery requirement of the micro-grid under the extreme event.

The application relates to a key power transmission section optimization identification method under an extreme event N-K broken line fault scene, which comprises the following steps: S1, converting a power grid into a non-directed graph, searching and deleting a hanging node in the non-directed graph, and finding a cut point of the simplified non-directed graph through a Tarjan algorithm; S2, performing power flow calculation on a large power grid to obtain power flow of each line in the power grid, and establishing a network flow graph of each partition of the large power grid according to a power flow calculation result and a maximum transmission power rating of the line; S3, iteratively searching by excluding each edge in an initial cut set in turn, identifying and deleting redundant cut edges and invalid cut edges, until the search of all minimum cut sets in the network flow graph is completed, and a power transmission section is obtained; and S4, obtaining a key power transmission section with an overload tripping risk when N-K lines are broken. The application can quickly and effectively identify the key power transmission section of the large power grid under an extreme event such as a natural disaster, and ensures safe and reliable operation of the large power grid.

The invention relates to a power system control method and device based on extreme event uncertainty, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring historical data of an extreme event, and determining an initial uncertainty set according to the historical data; in the ith round, obtaining a sample generation amount, and selecting a predicted value corresponding to the uncertainty variable from the initial uncertainty set according to the sample generation amount to obtain a plurality of sample points; selecting a plurality of target sample points from the plurality of sample points based on a preset percentage threshold, and obtaining an intermediate uncertainty set based on predicted values corresponding to the plurality of target sample points; performing performance evaluation on the intermediate uncertainty set, and if a first preset condition is met, taking the intermediate uncertainty set as an uncertainty set; and using the uncertainty set to control the operation of the power system. By adopting the method, load uncertainty and unit operation uncertainty caused by extreme events in a power system can be solved.

This invention discloses an extreme wind field discrimination method based on non-equilibrium Wasserstein distance, belonging to the field of meteorological wind energy prediction technology. The method includes the following steps: obtaining a set of wind field samples in the study area during the target period to construct a reference field; calculating the non-equilibrium Wasserstein distance between each wind field sample and the reference field as an extremity index; fitting the statistical distribution of the extremity index; calculating an extreme discrimination threshold based on the fitted GEV distribution model; and comparing the extreme index with the extreme discrimination threshold to determine whether the wind field sample is an extreme event or a non-extreme event. This invention, employing the aforementioned extreme wind field discrimination method based on non-equilibrium Wasserstein distance, can provide a high-precision foundation for identifying typical extreme wind fields and constructing scenarios for research such as climate change scenario analysis, extreme scenario risk assessment, renewable energy uncertainty quantification, and energy system resilience optimization.

The present invention discloses a method and system for predicting rapid dry-heat-to-humid composite extreme weather, which relates to the field of meteorological and hydrological technology. An objective multi-fractal detrended fluctuation analysis method is used to determine event threshold data, historical composite extreme weather data, and future composite extreme weather data, which is not affected by people's subjective consciousness. Based on the event threshold data, historical event characteristic change data, future event characteristic change data, dependency assessment data, and future potential risk assessment data corresponding to the historical composite extreme weather data and the future composite extreme weather data are calculated. The extreme weather prediction data of the detection area is constructed using the historical event characteristic change data, the future event characteristic change data, the dependency assessment data, and the future potential risk assessment data. The present invention fully considers the interaction and dependency relationship between the values of various meteorological elements in the composite extreme event, and considers the occurrence characteristics and potential risks of the event under the future scenario mode, thereby improving the accuracy of prediction for high-risk rapid dry-heat-to-humid composite extreme weather.

The invention discloses a composite high-temperature drought prediction method considering inter-annual increment of inter-monthly difference and convolutional neural network fusion, and the method comprises the following steps: based on numerical mode data and a target region, considering that the regional summer composite high-temperature drought has significant monthly difference in spatial distribution and causes; screening key physical influence factors of monthly composite high-temperature drought events by using an inter-annual increment method, and constructing a normalized data set; constructing a monthly prediction model fusing an inter-annual increment method and a convolutional neural network (CNN); using the training set and the verification set to train the model in batches; inputting the test set into the trained model, and outputting a prediction result of the monthly composite high-temperature drought event; the prediction capability of the composite high-temperature drought extreme event can be effectively improved, and disaster prevention and reduction are facilitated.

The invention provides an integrated energy multi-agent collaborative optimization scheduling method and system under an extreme event, and belongs to the technical field of integrated energy system scheduling. The method comprises the following steps: extracting spatial-temporal characteristics of multiple agents of the comprehensive energy system, performing knowledge distillation lightweight, setting the spatial-temporal characteristics to be in a global system state, constructing an agent lightweight general model in combination with Nash equilibrium, and solving the model; obtaining a global system state and an optimal action strategy of each agent in the global system state; combining the optimal action strategy with a built power failure probability model of each extreme event, a built extreme event driven electricity-gas-heat-storage multi-energy disturbance propagation model solving model and a generated adjustment demand matrix; adopting a deep reinforcement learning method to construct a multi-agent collaborative strategy model; and inputting the real-time monitoring data of the comprehensive energy multi-agent into the multi-agent collaborative strategy model to obtain a comprehensive energy multi-agent collaborative optimization scheduling strategy under the extreme event. According to the invention, the regulation and control precision and safety of the integrated energy system under extreme events are improved.

The embodiment of the invention discloses a method and a system for identifying an asymmetric meteorological flood and drought sharp turn event. The method comprises the following steps: processing first rainfall data of a target area in a preset time on a time scale to obtain second rainfall data of a ten-day scale time sequence; according to the second rainfall data, generating a first standardized rainfall index of the target area in a single-ten-day-scale time sequence and a second standardized rainfall index of the target area in a multi-ten-day-scale time sequence, then, according to the first standardized rainfall index and the second standardized rainfall index, respectively determining a flood event and a drought event of the target area on the time sequence within the preset time, and finally, according to the flood event and the drought event of the target area on the time sequence within the preset time, determining the drought event. Therefore, the method can accurately recognize the flood and drought sudden turning event of the target area in the preset time, and has important theoretical significance and application value for risk assessment of extreme events and formulation of disaster prevention and reduction measures.

The invention discloses a drainage basin extreme hydrological event test and hydrodynamic condition change determination method, and belongs to the technical field of hydraulic engineering, and the method specifically comprises the steps: S1, calculating a runoff annual distribution difference coefficient and a characteristic flow process; s2, extreme flood and extreme dry water events are screened, namely, according to the extreme flood condition, the first 1% quantile of the annual maximum flow Qmax in the historical sequence is selected; an extreme low water condition: selecting a minimum value of a runoff distribution difference coefficient lambda to represent that the water quantity difference between the flood season and the non-flood season is extremely small; s3, a typical hydrological event is determined by analyzing the relation between the characteristic flow and the annual average flow; s4, analyzing a water depth-flow relationship and dividing extreme flood types; s5, deducing a water depth-flow relation physical formula; and S6, analyzing and testing the hydrodynamic condition difference of the extreme flood. By adopting the method, the problems that a traditional method is insufficient in sensitivity to short-duration extreme hydrological events and lacks quantitative analysis on a hydrodynamic mechanism under the extreme events can be solved.

The application provides a method and system for classifying spatial heterogeneity extreme precipitation events, and the method comprises the following steps: obtaining original precipitation data in a preset time scale and spatial range, calculating a standardized precipitation anomaly index representing the abnormal degree of precipitation based on the original precipitation data; for the standardized precipitation anomaly index spatial field corresponding to each time point, calculating a statistical index for representing the overall spatial heterogeneity degree of the spatial field; based on the time series of the statistical index, screening out spatial heterogeneity extreme events according to a preset high quantile threshold; taking the spatial distribution of the standardized precipitation anomaly index corresponding to the spatial heterogeneity extreme events as an input sample, and performing unsupervised training based on a network model with a preset topological structure to cluster and generate typical spatial distribution modes of the spatial heterogeneity extreme events. Through the application, the extreme events with significant spatial differences can be identified, and the classification results are objective and stable, which provides a scientific basis for refined risk assessment.

The invention discloses a water-wind-solar complementary optimization scheduling method and system considering new energy extreme output events. The method comprises the following steps: constructing a new energy extreme output event library; a cascade hydropower dispatching risk assessment model oriented to the new energy extreme output event is constructed, the economy and reliability of cascade hydropower dispatching under different extreme events and water level states are quantitatively assessed, and weak links of system dispatching operation under the new energy extreme output event are identified; the method comprises the following steps: establishing a cascade hydropower multi-target optimization scheduling model giving consideration to the generating capacity and the scheduling risk, solving a non-inferior scheduling scheme set, selecting a compromise scheduling scheme giving consideration to economy and robustness by adopting a weight method, and adjusting a scheduling plan in a rolling manner according to real-time hydrological forecast and extreme event early warning. And the adjustment capability of cascade hydropower on the new energy extreme output event is improved. According to the method, the reliability of the water-wind-light complementary system under the new energy extreme event is improved, and technical support can be provided for formulating a system scheduling scheme for defending continuous multi-day extreme output of new energy.

The present invention relates to the field of deep learning artificial intelligence technology, and in particular to a post-disaster restorative maintenance decision-making method based on deep reinforcement learning. A post-disaster restorative maintenance decision-making method based on deep reinforcement learning provides an efficient and intelligent solution for post-disaster restoration of large-scale distributed cluster systems through the combination of complex network models and deep reinforcement learning algorithms. After an extreme event, the method can dynamically adjust the repair strategies of multiple maintenance teams to minimize the elasticity loss in the system recovery process, thereby achieving rapid and efficient recovery of the system and ensuring that the cluster system can resume normal operation as soon as possible after a disaster.

To provide an estimation and control method for frequency errors of extreme events.SOLUTION: The present invention proposes a frequency shift functional and a frequency shift functional estimation formula based on probability estimation, and constructs a method for reversely solving a confidence frequency shift value epsilon according to the frequency shift functional to improve a frequency distribution function. The present invention is suitable for estimating and controlling errors of occurrence frequency prediction of extreme events in various frequency statistical models by using hydrological observation data under probability statistical significance, so that the prediction accuracy and reliability of hydrological extreme events such as storm surge, rainstorm flood, tsunami, and the like are improved, risks caused by extreme natural disasters are reduced, and loss of life and property that may be caused is reduced.SELECTED DRAWING: Figure 1

The invention relates to the technical field of hydro meteorology, in particular to a method and a system for predicting and tracing extreme hydrological events in a reservoir area. The method comprises the following steps of S1, collecting meteorological and hydrological data, a global climate mode data set, geographic space data and stable isotope data of a reservoir area, and unifying a multi-source data scale through a time sequence fusion algorithm; according to the method, isotope data is adopted as an independent evidence constraint model, and compared with a traditional single model method, the method can more comprehensively capture a complex mechanism of occurrence of an extreme event, so that a simulation result is closer to an actual situation, and countermeasures can be taken in the face of an extreme climate event; the climate prediction and the isotope traceability technology are coupled for the first time, how causes of the extreme events change under different future climate scenes can be pre-judged in advance, and the research method and the model framework are not only suitable for specific areas, but also can be widely applied to research of the extreme events of large reservoirs and drainage basins.

The invention discloses a visualization method and system for an extreme event scene of a distribution network and a storage medium, and belongs to the field of power systems. The method comprises the following steps: extracting spatio-temporal features and event intensity features as extreme event features; extracting distribution network disaster damage characteristics according to the distribution network disaster damage data; obtaining a disaster damage prediction result according to the contribution degree of the extreme event feature to the distribution network disaster damage feature, the extreme event feature, the distribution network disaster damage feature, the meteorological data and a preset feature nonlinear mapping model; obtaining a distribution network risk level according to the disaster damage prediction result, the extreme event features and the distribution network disaster damage features; and constructing a spatial attenuation effect annular graph according to the spatial-temporal features and the event intensity features, constructing a feature importance waterfall graph according to the disaster damage prediction result and the contribution degree, constructing a spatial-temporal correlation thermodynamic graph according to the distribution network risk level and the distribution network disaster damage features, and obtaining a visual image of the distribution network extreme event. By implementing the invention, the problem that complicated information of the distribution network in an extreme event scene is difficult to present in the prior art is solved.

This invention discloses a method for generating extreme power load samples, belonging to the technical field of extreme power load sample generation. The method includes preprocessing the original load sequence; separating the base load sequence and industrial disturbance components using an industrial load disturbance identification model; slicing the base load daily to form a daily base load sequence set; decomposing the daily base load sequence set to obtain multi-scale detail sequences; constructing multi-scale time-frequency feature vectors; performing density peak clustering based on the multi-scale time-frequency feature vectors to obtain extreme load pattern samples; performing fine-grained clustering of the extreme load pattern samples using a dynamic density clustering algorithm, followed by Markov random field optimization to obtain a spatiotemporal template for extreme events; designing a diffusion generation model based on the spatiotemporal template; training the model with the extreme load pattern samples; generating load sequences and superimposing them with the industrial disturbance components to obtain extreme load samples. This invention solves the technical problem that existing technologies cannot generate a large number of reasonable extreme load samples.

The application discloses an extreme event-oriented main network key element identification method and system, and mainly relates to the technical field of extreme event prediction. The method comprises the following steps: based on the importance of load and the remedial measures of the power system to cope with extreme events, taking the maximum value of the system loss caused by the power cut of the extreme event as a first objective function; using a direct current flow model to model power system operation and establish element constraints; based on the first objective function, power system operation modeling and element constraints, establishing upper and lower two-layer robust models and converting the two-layer robust models into a main problem and a sub-problem; solving the main problem and the sub-problem and outputting the key element combination in the power system; and determining the importance degree of each element in the element combination through the repair sequence of the element. The application has the beneficial effects that it solves the problem that the Monte Carlo simulation method cannot generate the corresponding scene due to the small event occurrence probability, and identifies the key element combination in the power transmission network.

The invention relates to the technical field of wind power extreme event identification, in particular to an offshore wind power cluster output steep rise and steep drop extreme event identification method and system, and the method comprises the steps: segmenting a wind power original signal long sequence of an offshore wind power cluster into non-overlapping windows, carrying out the preprocessing of each window through employing a residual estimation method, and carrying out the recognition of the output steep rise and steep drop extreme event of the offshore wind power cluster; calculating regularization parameters, applying the regularization parameters to corresponding windows, respectively performing L1 trend filtering, and finally combining results; reducing an edge effect occurring in a merging result through cosine weighted fusion, and outputting a smoothed sequence; performing dynamic window wind power abrupt rising and abrupt falling event detection on the sequence, firstly setting a minimum window, setting an initial window length, and judging whether a time interval accords with a continuous rising or continuous falling constraint; if yes, the window length is enlarged until the maximum window is broken through or trend continuity is lost, and if not, whether a sudden rising and sudden falling event occurs or not is judged through a set power change amplitude threshold value and a set ramp rate threshold value.