728 results about "Extreme weather" patented technology

The invention relates to a groundwater safety assessment method under an extreme climate event, which comprises the following steps: collecting multi-source heterogeneous data such as meteorological data, geological data, hydrological data and remote sensing data, and constructing a unified groundwater safety knowledge graph through standardized cleaning, semantic alignment and deletion completion; monitoring an extreme climate event in real time, and updating a node relation weight and sparsifying a transmission path based on knowledge graph dynamic evolution and a time sequence attention mechanism; performing risk propagation path reasoning on the dynamic knowledge graph in combination with an improved graph neural network, identifying key pollution nodes, and outputting a structured risk level and a coping suggestion; the system continuously optimizes atlas and model parameters based on evolution feedback, and high adaptability and reasoning precision of emergency response are achieved. According to the method, the intelligence, the real-time performance and the accuracy of underground water risk assessment are improved. The problems that the underground water pollution propagation path is difficult to dynamically identify and the decision adaptability is insufficient under extreme climate events are solved.

The invention discloses a low-altitude airway flow field sensitive area dynamic identification optimization method based on set simulation, and the method comprises the steps: building a low-altitude flow field preprocessing data base with consistent time and space based on Beidou subdivision grids and multi-source heterogeneous data fusion; constructing a low-altitude airspace digital twinning environment based on the data; based on the low-altitude airspace digital twin environment and the cellular automaton-fluid coupling model, generating a diversified flow field evolution scene covering extreme weather and equipment faults; based on a set simulation result, extracting a high-conflict probability region through a spatio-temporal clustering algorithm and quantifying region risk features; generating an air route planning scheme meeting security constraints through a multi-objective evolutionary algorithm based on the quantitative regional risk features; on the basis of a low-altitude airspace digital twin environment and an air route planning scheme, verifying the feasibility of the air route planning scheme through historical data playback and virtual-real fusion test; and according to a verification feedback result, carrying out dynamic feedback optimization on the low-altitude air route flow field sensitive area identification and air route planning scheme.

The invention relates to the technical field of environmental monitoring and atmospheric pollution early warning, in particular to a Yangtze River Delta composite extreme weather ozone pollution early warning model construction method, which comprises the following steps of S1, acquiring high-resolution meteorological data and pollutant concentration data of a Yangtze River Delta region to form an original data set; and S2, carrying out missing value interpolation and abnormal value elimination on the meteorological data and the pollutant data, and carrying out grid alignment according to time and space to generate a unified spatial-temporal characteristic matrix. According to the method, by collecting Yangtze Delta high-resolution weather and pollutant data, performing data cleaning, bimodal feature coding and joint representation modeling, predicting the ozone concentration and generating regional early warning through multi-layer Transform self-adaptive attention, the problems that traditional ozone early warning mostly depends on a single-modal prediction model, and the reliability of the ozone early warning is greatly improved are solved. And due to the lack of multi-modal space-time dependent capture, the problem of early warning information lag is caused.

Disclosed in the present invention are an optimization-based regulation and control method and apparatus for distributed energy storage, a device, and a storage medium. The method comprises: on the basis of current weather parameters, constructing an extreme weather model; determining a plurality of faulted and out-of-service elements in which faults will occur in a power distribution network; then on the basis of the faulted and out-of-service elements, system parameters of a distributed energy storage system, and an operation parameter of the power distribution network, constructing a dual-objective optimization model taking load loss minimization and power failure time minimization as objectives; and finally, solving for the dual-objective optimization model to generate an optimal regulation and control scheme applied to the distributed energy storage system. Therefore, when a fault has occurred in the power distribution network, the distributed energy storage system is regulated and controlled by means of the optimal regulation and control scheme, such that power supply of the power distribution network is restored as soon as possible in extreme typhoon weather, and the transient power deficit of the power distribution network is made up promptly. Therefore, the present invention can effectively guarantee the safe operation of power distribution networks under the condition of extreme typhoon weather.

The invention belongs to the technical field of power network inspection, and particularly relates to a power network security inspection system which comprises a multi-source sensing module, an edge calculation module, a zero-trust security verification module, a core analysis module, an execution module and a feedback optimization module. Through an edge-cloud collaborative architecture, sensors, network flow and video data are fused in real time, the detection efficiency is improved, a physical equipment state and zero-trust dynamic authority control are introduced, 'physical-digital 'dual verification is realized, real threats and false alarms are marked, algorithm parameter adjustment is guided, and a feedback optimization module continuously reduces the false alarm rate through a closed loop mechanism; full-dimension safety protection of the power network is realized through modular design, dynamic branch processing and a closed-loop optimization mechanism; the threat level and the risk assessment result are dynamically adjusted through the environmental risk correction factor, the risk misjudgment rate is reduced, and the fault prediction accuracy in extreme weather is improved.

The invention relates to the technical field of energy meteorology and intelligent power grids, in particular to a multi-extreme meteorological high-risk scene set generation method, system and equipment based on a joint training generative adversarial network. The method comprises the following steps: constructing a physical information generative adversarial network framework comprising a generator, a discriminator, a predictor and a physical constraint module; designing a multi-objective loss function fusing adversarial loss, prediction loss, physical consistency loss and task performance loss; adopting a training strategy combining meta-learning initialization and incremental learning to jointly optimize parameters of the generator and the discriminator in stages; extreme risk scene data of specified disaster types, seasons and intensity grades are generated through condition vector control, and the extreme risk scene data are stored in a high-risk scene library after physical consistency verification. Through the method, a multi-extreme-weather high-risk scene set with statistical authenticity, physical rationality and task correlation can be directly generated, and the risk identification, scheduling optimization and toughness evaluation capabilities of the clean energy base under extreme weather conditions are remarkably improved; the problems of sample scarcity, model overfitting and lack of physical constraints in scene generation in the prior art are solved, efficient and automatic generation of a high-risk scene is realized, and reliable data support is provided for power grid toughness evaluation and scheduling decision of a clean energy base.

The invention relates to a meteorological deduction method and device fusing physical constraints and a neural network, and the method comprises the steps: obtaining multi-source meteorological data, and constructing a spatial-temporal feature input tensor; the spatio-temporal feature input tensor is subjected to standardization processing and then input into a deep learning network model, and a future weather prediction result is obtained; the model extracts time sequence evolution features and space attention features through a neural network module and a space attention module respectively, and integrates the time sequence evolution features and the space attention features in a splicing form; for a forecast task of a future gamma day, a deep learning network model and a physical mode are adopted for prediction respectively, and a splicing time point is determined according to an error minimum principle, so that splicing of prediction results is carried out; when the physical mode is used for prediction, the improved regional numerical weather prediction model is used as a basis, atmospheric basic equation sets are integrated, and weather prediction at future moments is carried out. Compared with the prior art, the method has the advantages that the atmospheric physical law and data driving advantages are fused, and the extreme weather prediction precision and stability are improved.

The invention relates to the technical field of power system assessment, and discloses a power system vulnerability assessment method and system considering extreme weather influence, and the method comprises the steps: building a line fault probability model of a power system in extreme weather to determine the line time-varying fault probability under dynamic weather influence; generating a typical scene through a time sequence relativity matching generative adversarial network model with gradient penalty and Monte Carlo simulation; taking the generated typical scene as input, simulating cascading failure evolution through an alternating current cascading failure model, and outputting a cascading failure evolution path; and based on a fault evolution result, through a spectrogram theory and a Bayesian network, in combination with severity analysis, structural vulnerability evaluation and causal-probability pre-judgment, multi-level vulnerability quantification of the power system is realized, and the problems that single-level evaluation is insufficient and causal association is ignored in the prior art are solved. And the risk assessment precision and the disaster prevention capability of the power system in extreme weather are improved.

The invention belongs to the technical field of data analysis, and relates to a port ship berthing dynamic risk prediction and pilot intelligent matching method. According to the invention, by quantitatively calculating the comprehensive qualification score and dividing the grade, dynamic matching of the pilot and the task risk grade is realized, an evaluation mode dominated by artificial experience is broken, the accuracy of pilot qualification evaluation is improved, and the operation error rate caused by insufficient personnel qualification is reduced; by dynamically simulating the berthing process and calculating risk indexes such as the offset rate and the collision energy, the berthing risk is pre-judged and graded, the berthing risk identification accuracy is improved, equipment operation parameters, dangerous goods states and meteorological data are analyzed in real time, risks such as faults, leakage and extreme weather are accurately identified, corresponding measures are triggered, and the safety of berthing is improved. Targeted management of berthing risks is facilitated, and accident loss and port operation interruption risks are effectively reduced.

The invention discloses a new energy power prediction method and system based on a multi-scale state decomposition mechanism, and the method comprises the steps: collecting the multi-source historical data of a wind power station or a photovoltaic station, carrying out the preprocessing of the multi-source historical data, and constructing a training data set; constructing a new energy power prediction network, wherein the new energy power prediction network comprises a knowledge guide layer, a historical sequence encoder, a continuous dynamic system modeling layer and a physical perception decoding network; the knowledge guiding layer is connected with the historical sequence encoder in parallel, and the output of the knowledge guiding layer and the output of the historical sequence encoder are sequentially connected with the continuous dynamic system modeling layer and the physical perception decoding network; training the new energy power prediction network by using the training data set; and deploying the trained new energy power prediction network to a device end, and performing power prediction to obtain a prediction result. According to the method, the stability and the convergence speed of the prediction model in the early training stage and the robustness under the extreme meteorological condition are remarkably improved.

The invention discloses a partition load growth situation prediction method and system oriented to extreme weather, and the method comprises the steps: firstly obtaining and preprocessing multi-source historical data, and building an extreme weather event recognition mechanism, so as to generate an extreme weather event tag sequence; then, the load response characteristics of all the partitions are quantified based on the extreme weather time period, and the partitions are divided into a plurality of load increase situation categories with different response modes by adopting a clustering algorithm; thirdly, independently constructing and training an exclusive load prediction model for each load growth situation category; and finally, during prediction execution, selecting the sentry subareas in each category, and dynamically calibrating original prediction results of other follower subareas in the same category by monitoring prediction residual errors of the sentry subareas in real time and calculating a prospective correction amount according to a deviation propagation model. According to the invention, combination of classified exclusive modeling and real-time dynamic correction is realized, and the precision and reliability of load prediction in extreme weather are improved.

The invention relates to the technical field of meteorological early warning, and discloses a meteorological AI abnormity early warning method and system for extreme weather risk perception, and the method comprises the steps: collecting and preprocessing multi-source meteorological data, and obtaining standardized multi-source meteorological data; feature extraction and anomaly recognition are carried out on the standardized multi-source meteorological data in the continuous time period, and preliminary risk information and a first confidence value are obtained; and constructing a space-time diagram model and applying consistency constraint in the training and reasoning process of the space-time diagram model to obtain a risk identification result. And according to the risk identification result, determining a dynamic alarm threshold value to generate graded meteorological anomaly alarm information, and adjusting the sampling frequency. And sending the meteorological abnormity alarm information and the sampling adjustment instruction to a cloud platform and a terminal display device, and carrying out risk perception and early warning release of extreme weather. According to the invention, high-credibility, low-time-delay and full-period dynamic perception and hierarchical response of extreme weather risks are realized.

The invention discloses a load prediction method considering data enhancement in extreme weather, which relates to the technical field of power system load prediction and comprises the following steps: acquiring meteorological data and historical industry load data of a target area according to a fixed sampling interval; calculating the correlation between a temperature sequence and a load sequence in the multivariable feature matrix, and screening a temperature-sensitive target industry load set by combining the correlation strength and stability in an extreme scene; calculating daily extreme temperature of the temperature sequence by day, and judging and identifying different extreme weather days and corresponding similar day sets according to a temperature threshold value; performing sample expansion and elimination according to the similar day set and the representative test day, and establishing a basic training set and an enhanced training set; and constructing a reference model through double-stage training. Compared with a traditional method, all load data are simply used, redundant features are remarkably reduced, and model training efficiency and prediction accuracy are improved.

The invention discloses a wind generating set transmission chain fatigue load prediction method and system considering sandstorm characteristics, and belongs to the technical field of wind power generation and structure fatigue prediction. According to the method, a wind speed time sequence capable of representing sandstorm evolution characteristics is obtained and serves as external physical excitation to be input into a wind-machine-electricity coupling state space model, and a dynamic torque time sequence of a transmission chain low-speed shaft is obtained through calculation; on the basis, a load spectrum is generated by adopting a rain flow counting method, and the accumulated fatigue damage degree corresponding to the sandstorm event is calculated in combination with the Miner linear accumulated damage criterion and the S-N curve of the low-speed shaft material; and further comparing the damage degree with a preset threshold value, and when the damage degree exceeds the threshold value, automatically generating a preventive active load reduction control strategy aiming at the sandstorm, thereby realizing a closed loop of prediction and control. According to the method, the influence of extreme non-stable wind conditions such as sandstorm and the like on the fatigue damage of the transmission chain can be quantitatively evaluated, and technical support is provided for safe operation and predictive maintenance of a wind turbine generator in extreme weather.

The invention discloses an electric power information physical system risk identification method and system in extreme weather, and the method comprises the steps: introducing the influence of extreme weather into an information-energy flow model of an electric power information physical system, and building an information-energy-extreme weather unified model; n-2 fault scanning is carried out on a power-communication coupling line of the power information physical system to obtain a fault scene set, risks of the power information physical system in all fault scenes in the fault scene set are analyzed and recognized based on an information-energy-extreme weather unified model, and a risk recognition result is obtained; therefore, the influence of extreme weather is considered in the information-energy flow model, the information-physical interaction process can be accurately described, traditional line / element N-1 fault scanning is expanded into power-communication coupling line N-2 fault scanning, a more robust and practical fault scene set can be obtained, the risk identification result is more accurate, the calculation speed is high, and the method is suitable for popularization and application. Therefore, the accuracy of risk identification is improved, and the lower complexity is ensured.

The invention discloses a multi-scene coupled extreme weather adaptive power system planning method and system, belongs to the technical field of power system planning, and aims to solve the problem that traditional planning does not fully consider extreme weather influence and does not integrate multi-energy complementarity. Comprising the steps of collecting installed capacity, historical weather and power load data of a power grid unit; a conventional weather scene set is generated through clustering by adopting a kmeans algorithm, and an extreme weather scene set is generated in combination with types of extreme high temperature, cold waves and the like; constructing operation models and policy constraints of the thermal power unit, the new energy unit and the electrochemical energy storage unit; based on the planning-operation coupling framework, establishing a two-stage stochastic programming model with the goal of minimizing the total investment and operation cost; and integrating the multi-scene solving model, and outputting an adjustment power supply planning scheme. Through multi-scene coupling and two-stage optimization, planning and operation collaboration is realized, the economy, safety and adaptability of the power system in extreme weather are improved, and technical support is provided for low-carbon transformation.

The invention relates to a heat supply prediction method and system based on spatial-temporal feature fusion deep learning, and the method comprises the following steps: S1, carrying out the collection and fusion of multi-source heterogeneous data, and constructing an integrated data set; s2, preprocessing the data; s3, constructing a graph structure model of the heat supply system, and constructing a weighted undirected graph; s4, constructing and executing forward calculation of the space-time double-flow deep network; s5, designing a composite loss function including mean square error loss and physical constraint loss, and performing joint optimization training on the space-time double-flow deep network; and S6, performing multi-step heat supply load prediction by using the trained model, outputting a heat supply load curve of each heat exchange station in a specified time period in the future, and integrating a prediction result with a heat supply scheduling system. The method has the advantages that the prediction precision is improved compared with that of a traditional machine learning model by capturing the spatial-temporal characteristics at the same time, and the advantages are more remarkable in extreme weather.

The invention relates to the technical field of intelligent scheduling of water supply resources, in particular to an intelligent scheduling system and method for water supply resources in extreme weather, in which a time sequence coupling analysis unit collects pipe network pressure, turbidity and meteorological early warning data in real time, identifies the peak time period of the turbidity abrupt change rate through dual-channel verification, and sends the peak time period of the turbidity abrupt change rate to a cloud server; the dynamic strategy generation unit divides scenes according to the coefficients, starts staged pressure relief in a high-contact-ratio scene, controls total duration and single-stage pressure drop, integrates real-time compensation of flow velocity, generates a pre-pressure-relief instruction in a low-contact-ratio scene, and generates a pre-pressure-relief instruction according to the pre-pressure-relief instruction, and the dynamic strategy generation unit generates a pre-pressure-relief instruction according to the pre-pressure-relief instruction and the single-stage pressure drop in the high-contact-ratio scene. And the pipe network topology compensation correction triggering time is fused, and the execution intensity of two types of instructions is dynamically allocated in a mixed scene, so that the pipe network pressure and water quality collaborative guarantee in extreme weather is realized, and the safety and stability of a water supply system are maintained.

The invention relates to the technical field of urban inland inundation rapid prediction, and discloses an urban inland inundation rapid prediction method based on deep learning, and the method comprises the following steps: S1, collecting historical meteorological data, landform data, urban drainage system data and historical inland inundation event data; s2, performing data cleaning on the collected data, removing noise, filling missing values, and processing abnormal values; s3, constructing a deep learning model architecture; when urban inland inundation risk prediction is carried out, multi-source heterogeneous data are integrated and standardized, and a unified spatial-temporal characteristic analysis framework is constructed, so that the system can eliminate magnitude differences of weather, terrain and drainage system data, and data comparability of different regions is ensured; and meanwhile, dynamic feature extraction is performed on real-time rainfall data by using a deep learning model, an abnormal fluctuation rule of meteorological elements is identified, the waterlogging risk pre-judgment capability of extreme weather events is improved, and the stability and credibility of a prediction result are enhanced.

The invention relates to the technical field of air conditioner load prediction, and provides an air conditioner load prediction method and system, and the method comprises the steps: extracting intra-day meteorological data features and intra-day air conditioner load data features, and forming multi-dimensional data features; performing dimension reduction processing on the multi-dimensional data features to form a comprehensive feature curve; clustering the comprehensive characteristic curve, dividing the air conditioner load data in the historical day into a data set according to a clustering result, and dividing the data set into a training set and a test set; a plurality of prediction models corresponding to different meteorological scenes are constructed, the prediction models are trained through the corresponding training sets, hyper-parameter tuning is conducted on the prediction models through an improved sodat swarm optimization algorithm, and a plurality of air conditioner load prediction models are formed; and inputting the test set corresponding to various meteorological scenes into the corresponding air conditioner load prediction model, and outputting an air conditioner load prediction result. According to the invention, air conditioner load curves with obvious boundaries in different meteorological scenes can be effectively separated, and air conditioner power load prediction errors in extreme weather are reduced.

The invention belongs to the technical field of power system load prediction, and discloses a load prediction method and system based on similar day screening and time sequence alignment decomposition. The method comprises the following steps: acquiring a date feature vector and a meteorological feature vector of a to-be-predicted day, and a historical comprehensive feature matrix; screening a plurality of historical days with the highest comprehensive similarity from the historical comprehensive feature matrix as similar days; performing time sequence alignment on the load sequences of the screened similar days; extracting a trend component, a periodic component and a residual component of the aligned load sequence; performing trend component prediction, periodic component prediction and residual component prediction; and performing fusion based on the trend component prediction value, the periodic component prediction value and the residual component prediction value to obtain a load prediction result of the to-be-predicted day. According to the method, a high-precision load prediction model is constructed through multi-feature weighted similar day screening, dynamic time warping time sequence alignment and a multi-resolution time sequence decomposition technology, and the load prediction precision in extreme weather is improved.

A method for risk assessment of a power system in extreme weather conditions considering deep feature mining for risk assessment of a power system. The risk assessment method includes: dividing the entire power system into regions based on geographical locations; combining historical wind speed data to construct a correlation model for strong wind extreme weather in multiple regions of the power system; constructing a strong wind scenario sample set for each region based on the correlation model; obtaining the probability of transmission line failure in the corresponding region under each strong wind scenario in the strong wind scenario sample set; randomly assigning a power system operating condition to each strong wind scenario in each region, and obtaining the operating risk value of the power system under the corresponding operating condition; constructing and training a risk assessment model; and using the model to provide a power system operating risk score assessment.

The invention relates to the technical field of power distribution networks, in particular to a disaster weather power distribution network grid power failure prediction method, system and device and a medium, a panoramic disaster sensing system is constructed by fusing meteorological satellites, sensors and power grid operation multi-source heterogeneous data, and a topology-electrical coupling complex network model is established to quantify the node-level fault probability; a multi-level risk conduction rule from meteorological factors to a user level is analyzed based on machine learning and a knowledge graph technology, and accurate prediction of the grid power failure probability is realized; a multi-dimensional early warning information driving dynamic response mechanism is synchronously generated, flexible load regulation and control, energy storage cooperative scheduling and network reconstruction strategies are integrated, rapid fault suppression is realized through linkage of first-aid repair resources, prediction-defense-recovery closed-loop management is formed, and the fault handling efficiency and disaster prevention toughness of the power distribution network in extreme weather are remarkably improved.

The invention relates to the technical field of numerical weather forecast, in particular to a meteorological large model prediction method based on a data correction model, which comprises the following steps of: firstly acquiring multi-source atmospheric observation, screening observation by topology-optimal transmission quantum annealing, and constructing a weighted error covariance; applying mass, energy and earth rotation gradient, and generating a conservation assimilation field through diffusion implicit sampling; calculating a mutual information mask and coupling a cloud top optical flow fine tuning phase; cloud motion consistent field pulse codes are sent to the symplectic decomposition pulse neural network for neural form hardware reasoning, a pulse threshold is adjusted in a closed loop to control energy drift, and an uncertainty field is output through parallel disturbance reasoning. The method has the advantages of high resolution, low power consumption and probability prediction capability, and the extreme weather path and intensity prediction precision is obviously improved.

The invention belongs to the technical field of wind power climbing event prediction, and particularly relates to a wind power climbing event prediction method considering extreme weather and time-space correlation information. The method comprises the following steps: acquiring historical actually measured meteorological data of each wind power station of a cluster; carrying out cold-wave weather event identification on historical actually measured meteorological data, generating an antagonistic network based on a time sequence, and carrying out cold-wave event sample expansion; an extreme learning machine is constructed, and cold-wave weather prediction is carried out; performing historical climbing event detection on historical power output results of each station of the cluster; dividing the climbing events into various climbing conditions with different severity degrees by using a K-Means clustering algorithm; and carrying out climbing event prediction. According to the method, sample support is provided for training of the climbing prediction model, the climbing events are clustered and divided by fusing the fan operation state and the climbing characteristics, and the harm degrees of different climbing events, especially the climbing events in extreme weather, are finely measured.

The invention relates to the technical field of intelligent heat supply, and discloses a load prediction energy-saving regulation and control method for secondary network heat supply. Extreme weather is identified through the multi-source meteorological data, and graded early warning signals are generated; collecting and processing data flow of a heat supply pipe network and an indoor sensor; fusing the data flow and the early warning signal, and generating a thermal load prediction value subjected to physical constraint and dynamic compensation by adopting a hybrid prediction model; based on the early warning level and the predicted value, a multi-objective optimization problem is solved in a rolling mode through model prediction control, and a water supply temperature and water pump frequency setting curve is generated; and finally, after instruction smoothing and safety verification, an execution mechanism is driven to complete accurate regulation and control. According to the invention, by constructing a sensing, predicting, optimizing and executing full-link intelligent regulation and control system, the industrial problem that the rapid mutability of extreme weather is not matched with the slow dynamic response of the system is solved, and the regulation and control quality and the energy efficiency level of the heat supply system are remarkably improved.

The invention discloses an electric power system flexible resource planning method, system and device considering multi-time scale extreme weather and a storage medium, and relates to the technical field of electric power system planning, and the method comprises the steps: generating a multi-time scale extreme weather scene based on a conditional diffusion model; constructing a flexible resource planning model of the mixed time scale power system; and calling a Gurobi solver by using a Yalmip toolkit of Matlab to solve the flexible resource planning model of the mixed time scale power system, and outputting an optimal planning scheme of flexible resources of the power system. According to the method, the multi-day extreme weather scene is generated based on the conditional diffusion model, spatial-temporal characteristics of multi-day extreme weather such as typhoon and high temperature can be accurately described, and compared with other methods, a diversified scene set with more statistical coverage can be reconstructed under limited historical extreme data; and a multi-time-scale operation scene basis with typicality and extreme property is provided for power system planning.

The invention relates to the technical field of power system optimization scheduling, and discloses a source network load storage optimization scheduling method suitable for flexible supply and demand balance in extreme weather. The method comprises the following steps: firstly, acquiring new energy power interval prediction data which aims at typical extreme weather scenes and is subjected to error correction; on the basis, the flexibility requirement of the system and the supply capability of multi-type resources are quantified; and furthermore, with maximization of the comprehensive benefit of the system as a target, a flexible vacancy virtual penalty term for stabilizing fluctuation risks caused by extreme weather is particularly introduced, an optimal scheduling model is constructed and solved, and a scheduling plan is generated. According to the method, by fusing extreme weather exclusive prediction information and an operation risk hedging mechanism, efficient coordination of source network load storage resources in extreme weather is realized, the new energy power abandoning rate is effectively reduced, the dependence on traditional thermal power is reduced, and the comprehensive operation benefit and safety margin of the system are improved.

The invention relates to the technical field of new energy power prediction, in particular to an extreme weather offshore wind power generation situation prediction method based on artificial intelligence, and the method comprises the steps: obtaining data under historical extreme weather, dividing the data into a training set and an optimization set, removing noise, extracting environment data, and carrying out the feature data fusion. The method comprises the following steps: determining a freezing proportion according to an extreme weather disaster grade, freezing partial layer parameters of a pre-trained conventional power generation prediction model, training an unfrozen layer, constructing an extreme weather power generation prediction model, periodically obtaining data in an optimization set through constructing a simulation time axis, carrying out automatic learning, and finally obtaining environmental data in real time for prediction. And judging the prediction accuracy according to the similarity between the prediction result and the optimization set data, and if the prediction accuracy is not accurate, analyzing an abnormal reason and correcting related parameters. The method provided by the invention effectively overcomes the difficulty of inaccurate offshore wind power generation prediction in extreme weather, and significantly improves the accuracy and reliability of wind power generation prediction under extreme weather conditions.

The invention discloses an extreme weather electrical control method based on multi-source sensing fusion, and the method comprises the following steps: S1, collecting the data of a multi-source sensor disposed in a power system, and constructing an original sensing data set; s2, performing time synchronization and frequency unification processing on the original sensing data set to generate a multi-source alignment data sequence; s3, extracting a fusion feature vector; s4, inputting the fusion feature vector into an Autoformer model, capturing long-term time sequence dependence among multi-source features, and outputting an extreme weather grade evaluation result and an electrical operation risk scoring vector; s5, inputting the risk score into an improved Gumbel-Softmax strategy selection network, and outputting a control action sequence and parameter configuration; and S6, finally converting the control strategy into an equipment control instruction and issuing the equipment control instruction in real time. And risk prediction and adaptive control of the electrical system under extreme conditions are realized.