6635 results about "Wind power" patented technology

A wind energy plant with a nacelle, a rotor, which features at least one rotor blade adjustable in its blade pitch angle, a central control device for controlling the wind energy plant and a control unit disposed in the rotor for controlling the blade pitch angle of the at least one rotor blade, wherein the central control device and the control unit in the rotor can exchange data with each other via a data link, which comprises at least one first sending and receiving device at the nacelle side and at least one second sending and receiving device at the rotor side, wherein a wireless network connection with a safety-oriented communication protocol is provided between the at least one first sending and receiving device and the at least one second sending and receiving device, and that one monitoring- and communication device at a time is associated to the first and / or the second sending and receiving device, which can monitor the function of the sending and receiving device and initiate a predetermined action in the case of an error.

The invention provides a combined wind power prediction method suitable for a distributed wind power plant, and the method comprises the steps: collecting the real-time meteorological data and historical power data of a wind power plant cluster, carrying out the cross-wind-plant data collaborative cleaning, and generating a time-space aligned standardized data set. Constructing an adaptive spatio-temporal feature extractor, outputting a spatio-temporal feature matrix, and inputting the spatio-temporal feature matrix into the spatio-temporal adaptive neural network, the graph attention prediction model and the physical constraint decision tree model to generate three prediction sequences. And the sequences are fused through a space-time collaborative attention mechanism to generate a dynamic weighted combination prediction result. And performing physical constraint correction on the result by using a space-time residual error correction network to generate a final prediction sequence. And updating the neural network topological structure based on the prediction error distribution, and outputting a prediction result with uncertainty evaluation to a power grid dispatching system. According to the method, the precision and reliability of wind power prediction of the distributed wind power plant can be improved, and the stability and economy of power grid dispatching are improved.

The invention discloses a pole climbing robot for a wind power tower pole, which is characterized in that the robot mainly comprises a robot body, 4-8 pole climbing mechanisms, a snake-shaped swing arm and a movable studio, wherein the snake-shaped swing arm and the movable studio are mounted on one side of the robot body; the robot body is an annular body formed by combining two semicircular cambered bodies or three equal cambered bodies; the diameter of an internal ring of the circular body is matched with the diameter of the wind power tower pole; the pole climbing mechanisms mainly comprise upper foot wheels, lower foot wheels, connecting arms, driving gears, thrust screw rods, nut gears and supporting seats; the upper foot wheels are connected with the lower foot wheels by the connecting arms through wheel shafts; the connecting arms are connected with the thrust screw rods by connecting pin shafts on the connecting arms, and form movable hinges; the 4-8 pole climbing mechanisms are distributed in the circular robot body uniformly; the snake-shaped swing arm consists of three swing arm parts, namely a large arm, a middle arm and a small arm; and the movable studio comprises two paralleled linear guide rails, a walk driving device and an operation room.

The invention discloses a fan blade state monitoring method based on multi-sensor fusion, relates to the technical field of wind power, and is suitable for wind energy prime mover equipment manufacturing and blade state monitoring technologies of onshore and offshore wind generating sets. The method comprises the following steps: acquiring operation data, a vibration signal, an acoustic signal and a pulse signal of a fan; the current working condition state of the fan is recognized, common-mode fault verification, local damage positioning and transient stress damage analysis are carried out on the vibration signals and the acoustic signals, and a fault analysis result and a first damage analysis result are obtained; performing phase-locked amplification analysis on the vibration signal and the acoustic signal through active excitation to obtain a second damage analysis result; and finally, a comprehensive state monitoring report of the fan blade is generated, so that the problems of difficulty in identification of weak damage and high false alarm rate of blades of land and offshore wind generating sets in wind energy prime mover equipment manufacturing under a non-stable working condition are solved, and the equipment operation and maintenance intelligent level in the wind energy prime mover equipment manufacturing industry is effectively improved.

The invention discloses an optimal design method for a floating wind power-wave energy multi-energy complementary power generation platform, which comprises the following steps of: constructing an integrated and parameterized system model which is a fully-coupled and parameterized numerical model comprising all key components of the floating wind power-wave energy multi-energy complementary power generation platform; all key design parameters influencing the system performance are set as parameterized variables; establishing a multidisciplinary coupling dynamic simulation model; defining a multi-objective optimization problem including decision variables, objective functions and constraint conditions; the decision variable selects a group of core variables from the parameterized variables as optimization input; and combining the multi-objective optimization problem with a multidisciplinary coupling dynamic simulation model, executing a multi-objective optimization cycle, and generating and deciding a Pareto optimal solution set to obtain typical design schemes with different characteristics. According to the method, the global optimization design of the floating wind power-wave energy multi-energy complementary power generation platform can be realized.

The invention discloses a low-altitude wind field prediction method and system based on a space-time diagram convolutional network, and relates to the technical field of weather forecast and wind energy utilization, and the method comprises the steps: collecting wind field observation data and physical field data of all nodes of a target region, a dynamic space-time diagram is constructed based on a flow function-vorticity theory through a dynamic diagram construction module; extracting spatial information through a graph attention network to obtain a spatial feature tensor; the spatial feature tensor and the physical field data are processed by a PhysFusion-TransTCN encoder to obtain the deep spatial and temporal features of the wind field; performing hierarchical feature aggregation on the wind field deep spatial-temporal features through an output module to obtain a wind field prediction result of the target area; a wind field physical mechanism is deeply fused, multi-scale spatial-temporal feature fusion is realized, and prediction result precision and physical rationality are ensured.

A wind-photovoltaic ratio system optimization configuration method for a new energy hydrogen production park, the method comprising the steps of: S1) calculating an output time series of local wind power and photovoltaic power generation, and analyzing wind power and photovoltaic output characteristics and a wind-photovoltaic correlation; S2) establishing equivalent device models of a hydrogen energy storage unit and an electrochemical energy storage unit to form an electric bus power balance and hydrogen bus power balance relationship, and taking the minimization of a full life cycle cost as an objective function and taking into consideration constraint conditions such as the power balance, device operation characteristics and a land restriction to establish a system planning optimization configuration model for a new energy hydrogen production park, which model takes a wind-photovoltaic ratio into consideration; S3) integrating the objective function and the constraint conditions to obtain a system planning and optimization configuration scheme for the new energy hydrogen production park and the total system configuration cost; and S4) taking into consideration an electricity price factor and an energy storage operation characteristic constraint, performing annual hydrogen production timing operation simulation, and analyzing a park electricity purchase demand and a new energy consumption capacity under the present planning optimization configuration scheme.

The invention discloses a multi-modal data fusion and intelligent analysis platform and method for an unmanned aerial vehicle inspection system for a wind power plant. The platform comprises a multi-modal data acquisition module, a feature extraction and standardization module, a multi-modal information fusion module, a joint learning and optimization module, a domain knowledge injection module and an intelligent decision and application module. The system processes multi-source heterogeneous data through an integrated learning and deep learning fusion strategy, projects features to a shared semantic space by using joint training and comparative learning to enhance the anomaly discrimination ability, and performs verification and semantic enhancement on a supervised retrieval result in combination with a knowledge base in the wind power field. And finally, outputting a high-reliability diagnosis report and a maintenance suggestion. According to the invention, accurate identification and positioning of the fan fault are realized, and the inspection efficiency and the system decision reliability are significantly improved.

The invention discloses a new energy equipment troubleshooting method based on a knowledge graph and a large model. The method is used for solving the problems of fault positioning and troubleshooting of photovoltaic and wind power new energy equipment and the like. The method mainly comprises the following steps: performing data cleaning and preprocessing on a new energy equipment troubleshooting professional field document, and constructing a structured knowledge graph of equipment types, fault types, fault information and solution nodes in a graph database through Cypher; the big language model analyzes the user fault description, extracts and maps node attributes in the knowledge graph, and generates a graph query statement; the question-tracing type multi-round interaction is realized, the missing information is dynamically complemented, and the basic fault is accurately positioned in a step-by-step reasoning mode; according to the method, multi-modal resources such as texts, schematic diagrams and videos stored in the knowledge graph are combined, a step-by-step reasoning path and a solution with illustrative features are generated through a preset template, the whole reasoning process is visualized in a branch thinking graph form, and the accuracy, interpretability and user experience of troubleshooting are remarkably improved.

The invention discloses a new energy equipment intelligent operation and maintenance system and method based on digital twinning, and relates to the technical field of new energy equipment operation and maintenance management. The system comprises a data acquisition module, a digital twin construction module, a model adaptive module, an intelligent analysis module, a decision optimization module and a knowledge closed-loop module. The data acquisition module realizes multi-source heterogeneous data fusion and standardization; the digital twin construction module generates geometric, physical and behavior models and is linked with real-time data; the model adaptive module dynamically calibrates the key parameters; the intelligent analysis module completes anomaly detection, fault diagnosis and life prediction; the decision optimization module formulates a maintenance scheduling and operation strategy and realizes a control closed loop; and the knowledge closed-loop module constructs a structured fault graph through text analysis to continuously optimize the model. The method can be widely applied to wind power, photovoltaic, energy storage and other scenes, and efficient and intelligent operation and maintenance of new energy equipment are achieved.

The invention discloses a water electrolysis hydrogen production intelligent control system and method based on artificial intelligence. The system and method are suitable for a large-scale water electrolysis hydrogen production scene under the power supply condition of fluctuating renewable energy sources such as wind power and photovoltaic. The system comprises a data acquisition layer, an edge calculation layer, an intelligent control layer and an execution layer. Dynamic modeling of the running state of the hydrogen production system is achieved through multi-parameter real-time monitoring and feature extraction. The intelligent control layer fuses an LSTM prediction module and a reinforcement learning controller, the LSTM prediction module is used for predicting future renewable energy input and hydrogen demand trends, and the reinforcement learning controller calculates an optimal current density set value based on a prediction result so as to maximize hydrogen production efficiency per unit energy consumption. And meanwhile, a digital twinning technology and a safety protection mechanism are combined, thermoelectric dual regulation and control of the electrolysis process are achieved, and the control response speed and the system stability are improved. According to the invention, energy consumption can be effectively reduced, the service life of the stack is prolonged, and the operation efficiency and reliability of the hydrogen production system under complex load are improved.

The invention relates to the technical field of wind power tower monitoring, and discloses a wind power tower health monitoring method and system. The method comprises the following steps: acquiring structural vibration signals and environmental load data of a wind power tower drum, extracting dynamic response characteristics of the tower drum, and establishing a basic monitoring data set of the running state of the tower drum in combination with the environmental load data; performing multi-dimensional decomposition processing on the basic monitoring data set, separating an inherent frequency component, a damping characteristic parameter and an external excitation coupling component of the tower drum, and constructing a first evaluation index of tower drum structure health according to the relevance between the inherent frequency component and the damping characteristic parameter; historical damage records and material degradation data of the tower drum are obtained, the historical damage records and the external excitation coupling component are subjected to time sequence alignment analysis, and a second evaluation index of tower drum damage evolution is generated; and fusing the first evaluation index and the second evaluation index, establishing a comprehensive scoring model of the health state of the tower drum, dividing the health level of the tower drum, and outputting a real-time monitoring report.

The invention relates to the technical field of wind power, in particular to a dynamic response test method for a wind power variable pitch system. Comprising the steps of building a high-fidelity digital twinborn model, performing virtual testing and case optimization, performing physical self-adaptive testing execution, performing real-time analysis and decision-making driven by machine learning, dynamically adjusting testing stress, performing data feedback and model calibration and generating a comprehensive health report. Compared with the prior art which mainly depends on a fixed physical test process and artificial experience judgment and has the defects of low test efficiency, large resource consumption and difficulty in comprehensively covering extreme working conditions, the method innovatively introduces a high-fidelity digital twinning technology and a virtual test optimization process; according to the method, massive tests are executed in the virtual environment in a risk-free manner, and the enhanced test cases are intelligently generated, so that test forward movement and accurate optimization are realized, the pertinence and efficiency of physical tests are remarkably improved, and the test cost and period are greatly reduced.

The invention relates to a power distribution network voltage partition control method and system based on a hierarchical K-means clustering algorithm, and belongs to the technical field of power distribution system voltage partition optimization. According to the technical scheme, power distribution network node parameters are collected in a self-adaptive partition mode, a node sensitivity coefficient and an eigenvector of an electrical distance are constructed, and then a K-means algorithm is used for fine region division; selecting a dominant node: solving a Jacobi matrix through load flow calculation, extracting a voltage sensitivity coefficient, and selecting a node with the maximum sensitivity as a dominant node in each partition; multi-objective optimization: constructing an optimization model with minimum network loss, minimum voltage deviation and highest voltage stability as objectives; and partition cooperative control: accessing wind power and photovoltaic power to the dominant node, adjusting reactive power output in real time according to an optimization result, and realizing partition autonomy and global cooperation. According to the invention, voltage fluctuation and out-of-limit are inhibited, system network loss is reduced, control efficiency and economy are improved, and the method is suitable for complex topology and high permeability scenes.

The invention discloses a wind power multi-scale decomposition prediction method. At present, single-point prediction is not comprehensive and accurate enough, and cannot adapt to quantitative accurate requirements of a wind power plant and a power grid dispatching mechanism in risk management. The method comprises the following steps of: forming an original wind power sequence from actually acquired wind power data, sequentially performing feature selection and data decomposition processing to form multi-scale modal data, and constructing a depth prediction model according to the multi-scale modal data; a probability prediction interval determination process is completed in the residual error distribution mode depth prediction model through adaptive bandwidth kernel density estimation; after actually obtained wind power data form an original wind power sequence, an initial model is established, feature selection processing is performed on the initial model, that is, weighted marginal contribution is calculated for each feature of the initial model according to all involved feature subsets by using an SHAP algorithm based on a Shapley value in a game theory, and the weighted marginal contribution of each feature of the initial model is calculated; and completing a feature data acquisition process of accurately quantifying interdependence and interaction effect between features.

The invention discloses an existing wind power blade electrothermal film deicing control method based on big data, and relates to the technical field of wind turbine generator operation control and ice prevention and removal. Frost ice / glaze ice probability output by an ice type discrimination model does not only give an alarm any more, but is directly converted into an increase / decrease coefficient constrained by boundary and monotonicity; energy can be fully put in a glaze ice scene, and overheating and over-consumption can be avoided in a frost ice scene; according to the method, the change trend of wind speed, temperature and relative humidity is introduced as modulation factors, the working condition of forming / turning glaze ice is responded in advance, and insufficient deicing caused by static threshold hysteresis is reduced; according to power deviation correction, a unit wind speed-power curve serves as a reference, a dead zone, segmented gain and accumulative saturation structure is adopted, on the premise of not depending on an external icing sensor, extrinsic performance of pneumatic degradation is closed into controlled quantity, sensitivity and stability are considered, and start-stop oscillation and duration drift are reduced.

The invention discloses a distributed new energy and energy storage primary frequency modulation multi-agent cooperative control method and system, and belongs to the technical field of power system frequency modulation. The method comprises the following steps: constructing a multi-agent power distribution network simulation environment based on power grid topology and integrated wind power / photovoltaic / energy storage models and constraints; distributing an independent intelligent agent for each unit, and defining an observation space containing information such as frequency deviation and an output adjustment action space; an MADDPG framework containing a shared Critic network and an independent Actor network is built, and cooperative training is achieved through experience playback; designing a comprehensive reward function fusing the frequency, the voltage, the energy storage SOC and the electricity abandoning rate; and outputting a real-time output instruction by using the trained model to realize primary frequency modulation cooperative control. The method can significantly improve the response speed and precision of the distributed energy participating in the primary frequency modulation, guarantees the system safety, reduces the power abandonment rate of the new energy, and meets the power grid safety requirements of the high-permeability new energy.

The invention relates to the technical field of power dispatching management and control, and discloses a power distribution network power dispatching method based on a virtual power plant AI large model and demand response, and the method comprises the steps: collecting the operation data of a power distribution network, and constructing a feature vector; an AI large model is adopted to calculate and predict load output, and joint uncertainty information is output; calculating a system power unbalance amount, and generating a scheduling strategy; issuing a scheduling instruction corresponding to the scheduling strategy and executing the scheduling instruction; comprehensive performance evaluation indexes are calculated, and whether a performance reduction reason diagnosis mechanism is started or not is judged; according to the method, the AI large model is adopted, the load power, the photovoltaic output and the wind power output are predicted at the same time through a multi-task learning strategy, and the correlation among multiple variables is fully utilized; by constructing a multi-objective optimization model, comprehensively considering economy, safety and reliability and adopting an improved particle swarm optimization algorithm for solving, coordinated optimization configuration of demand response resources is realized, power grid fluctuation is effectively reduced, and power supply reliability is improved.

The invention belongs to the technical field of wind power weather forecasting, and provides an all-region three-dimensional wind speed correction method and system, and the method comprises the steps: constructing a weather numerical forecasting model, and obtaining wind field forecasting data; fusing the preprocessed multi-source data by adopting an optimal interpolation method to obtain three-dimensional space-time continuous wind field analysis data; based on the wind field forecast data and the wind field analysis data, features are extracted and fused, then a historical forecast error sample set is constructed, a wind speed correction model is constructed, and the historical forecast error sample set is utilized to train the wind speed correction model; introducing an initial value, a physical parameter and boundary condition disturbance, calculating a mean value and a standard deviation of each set result, extracting a probability distribution feature of a wind speed, constructing a confidence interval, estimating a probability density function, and quantifying an occurrence probability of an extreme wind speed event; and the prediction result of the wind speed correction model and the multi-source wind field observation data are fused to generate final three-dimensional wind field data, so that the actual requirements of wind power prediction and power grid dispatching can be met.

The invention relates to the technical field of new energy power data governance, in particular to a multi-source fusion cleaning and closed-loop quality control method and device for wind power data and a medium, and the method comprises the steps: generating an initial data set through collecting SCADA time sequence data and at least one type of external meteorological data of a wind turbine generator; based on the initial data set, executing physical consistency check, grouping adaptive statistical detection, unsupervised machine learning detection and time sequence residual analysis in parallel, and generating four independent exception judgment vectors; inputting the four abnormal judgment vectors into a preset fusion decision model, and outputting a comprehensive abnormal label; obtaining a repaired data set according to the comprehensive abnormal label; and calculating a multi-dimensional quality score representing data quality based on the repaired data set, and triggering a quality control closed-loop operation according to the multi-dimensional quality score and a comparison test result. Through the arrangement, multi-dimensional fusion detection and self-adaptive intelligent repair can be realized, and integrated cleaning of quality quantitative evaluation and closed-loop verification can be realized.

The invention discloses a wind power converter oscillation characteristic suppression method, system and device based on broadband impedance scanning and a storage medium, and relates to the technical field of wind power grid-connected control, and the method comprises the steps: based on grid-connected steady-state small signal voltage disturbance injection, respectively extracting broadband response data of positive sequence impedance and negative sequence impedance, and constructing a machine end broadband impedance model; constructing an impedance phase and amplitude matching criterion based on the broadband impedance model in combination with the power grid side equivalent impedance, identifying risk frequency bands of subsynchronous and super-synchronous oscillation, and determining a dynamic influence interval related to the phase-locked loop bandwidth; adjusting a phase-locked loop bandwidth and a filtering parameter based on a risk frequency band and a dynamic influence interval, and executing d / q control link collaborative constraint in combination with a leakage matrix parameter; according to the method, accurate modeling of positive and negative sequence impedance of the wind power converter and bandwidth adaptive vibration suppression control are realized, and the stability and the anti-oscillation capability of the system are improved.

The invention discloses a water-wind-light combined intelligent control system based on multi-energy coordinated control, and belongs to the technical field of multi-energy coordinated control, and the system comprises the steps: firstly collecting environment and operation parameters of wind power, photovoltaic and hydroelectric equipment, and carrying out the preprocessing of the environment and operation parameters to form a structured data set with a space-time label and a dynamic sampling identifier; performing space-time correlation analysis on the data set, constructing a wind power fluctuation and hydropower regulation margin real-time response matrix and a photovoltaic output attenuation and hydropower storage capacity compensation time sequence correlation model, and quantifying the multi-energy dynamic complementation degree and grading the data credibility through a two-dimensional coupling algorithm; then combining with a historical meteorological scene association rule base, constructing a water-wind-light combined power supply capability prediction model containing an energy characteristic mutual compensation coefficient, and generating a hierarchical regulation and control strategy with a space-time priority; and finally, executing a regulation and control strategy to complete parameter adjustment, continuously monitoring through closed-loop feedback, and triggering multi-energy collaborative complementary regulation when the single energy regulation capability is saturated, thereby realizing water-wind-light efficient collaborative control.

The invention relates to the technical field of artificial intelligence and electric power system prediction, and discloses a wind power short-term output prediction method based on multi-modal data, and the method comprises the steps: obtaining the multi-modal data, such as historical output, numerical weather forecast, actually measured weather of an anemometer tower, landform and fan operation state; performing sliding window segmentation on the output sequence and identifying a mutation interval; calculating a local optimal alignment path of each mode in the mutation interval based on a dynamic time warping algorithm; non-uniform resampling is carried out in this way, and a time-synchronized multi-modal alignment feature sequence is generated; and inputting a hybrid neural network formed by a gating circulation unit and an attention mechanism, and outputting a high-precision output prediction value in the next 15 minutes. The system comprises corresponding function modules. According to the method, through dynamic time alignment and cross-modal feature fusion, the wind power short-term prediction precision is remarkably improved, the root-mean-square error in a sudden change scene is reduced by 23.7%, and reliable support is provided for power grid dispatching.

The invention discloses a multi-objective optimization method and system for a regional integrated energy system, and the method comprises the steps: constructing a multi-source input sequence sample; inputting a multi-source input sequence sample into the wind power and photovoltaic prediction model for processing, obtaining a current wind power and photovoltaic output optimization prediction result, calculating the current wind power and photovoltaic output optimization prediction result and a really constructed sample, obtaining a combined loss function value to train the model, obtaining the trained wind power and photovoltaic prediction model, processing the sample collected in real time, and obtaining a wind power and photovoltaic output prediction result. Outputting current wind power and photovoltaic output optimal values; and constructing an online optimization model of the integrated energy system, performing online optimization solution on the constructed model by an accelerated particle swarm optimization algorithm to obtain a control strategy of the energy system, issuing the control strategy to the energy equipment, and performing multi-target optimization scheduling. According to the method, the wind power and photovoltaic prediction model is combined with the accelerated particle swarm optimization algorithm, prediction errors are fully considered, and the stability and flexibility of the regional integrated energy system are enhanced.

The invention discloses a wind power operation and maintenance ship embarkation system compensation control method based on active disturbance rejection control and related equipment. The method comprises the following steps: predicting a system external wave disturbance variable of a six-degree-of-freedom embarkation compensation platform according to environmental parameters through a wave disturbance prediction model; the total disturbance quantity in the system is predicted through the extended state observer according to the real-time attitude information and the electric cylinder control signal, and then the total disturbance of the system is obtained; a nonlinear control quantity is calculated through an active disturbance rejection controller according to real-time attitude information, expected attitude information and a preset decoupling matrix, a feed-forward compensation quantity is calculated according to total disturbance of a system, then an output instruction of the controller is calculated, and compensation control is achieved. According to the embodiment of the invention, compensation control can be realized through the wave interference prediction model, the state expansion observer and the active disturbance rejection controller, and high precision and response speed are achieved. The method can be widely applied to the technical field of automation control.

The invention discloses a wind turbine generator blade damage judgment method and detection system based on voiceprint detection, and belongs to the technical field of wind turbine generator blade detection. The damage judgment method comprises the following steps: acquiring environmental parameters of a wind power plant, and establishing a sound velocity correction model based on the environmental parameters; acquiring a blade resonance sound signal, and correcting the blade resonance sound signal based on the sound velocity correction model to obtain a blade resonance sound signal with a high signal-to-noise ratio; performing FFT spectrum analysis on the blade resonance sound signal with the high signal-to-noise ratio, identifying the peak position of the first three-order natural resonance frequency of the blade, and performing analysis to obtain the current modal frequency, damping ratio and sub-band energy distribution entropy value of the blade; historical baselines of the modal frequency, the damping ratio and the sub-band energy distribution entropy of the blade in the healthy state are obtained, and the modal frequency offset, the damping ratio change rate and the acoustic impedance spectrum entropy of the blade are obtained; and wind turbine generator blade damage determination is carried out by using a high-precision time difference detection technology.

The invention provides a three-dimensional wind field prediction method and system based on multi-modal complementary fusion learning. The method comprises the following steps: S1, acquiring remote sensing observation data and numerical simulation data; s2, obtaining standardized remote sensing features and simulation features; s3, obtaining a unified scene representation; s4, splicing the unified scene representation with the to-be-predicted space-time coordinates, inputting the spliced scene representation and the to-be-predicted space-time coordinates into a physical enhancement decoder, and outputting three-dimensional wind speed vectors at the corresponding space-time coordinates; and S5, iteratively optimizing parameters of the bimodal encoder, the cross-modal attention fusion module and the physical enhancement decoder to form a closed-loop prediction model. According to the method, multi-modal data complementation and physical information deep fusion are realized, through innovating a neural network architecture and a constraint mechanism, the prediction precision under a sparse data condition is remarkably improved, the physical credibility of a result is enhanced, and a technical support is provided for intelligent development of the wind power industry.

The invention discloses a large-scale regional sea wave rapid forecasting method and device based on data driving, and the method comprises the steps: obtaining multi-source marine physical environment data of a target sea area, and generating a standard physical field data flow with unified temporal-spatial resolution; constructing a multi-channel space-time input tensor containing wind field driving information, terrain boundary information and historical wave state information; the difference between the predicted wave height and the real wave height is minimized through a back propagation mechanism, so that a trained wave height prediction model is obtained; receiving latest wind speed field data output by a real-time observation or numerical forecasting mode, and outputting a sea wave significant wave height prediction field at a future target moment; the device is used for implementing the method. According to the technical scheme of the method provided by the invention, through a physical lag alignment mechanism, the time delay of transmitting wind energy to wave energy is accurately captured, and the modeling precision is improved; and meanwhile, rapid deduction of a large-scale sea wave field is realized based on deep learning, and high accuracy and high timeliness are achieved.

The invention provides a reinforcing beam reinforced by adopting an out-of-service wind power blade and a method for determining the bending rigidity. According to the reinforcing beam, a plurality of grooves are formed in at least one surface of a reinforced concrete beam; the reinforcing layer comprises reinforcing ribs and a cement-based composite matrix, a plurality of protrusions are arranged on the surface of the cement-based composite matrix, the protrusions are arranged at intervals in the length direction of the cement-based composite matrix, and the protrusions are correspondingly embedded into the grooves, so that the reinforcing layer is connected with the reinforced concrete beam to obtain a reinforced concrete reinforced beam; wherein the reinforcing ribs are obtained by disassembling and cutting a main beam of the retired wind power blade. According to the reinforcing beam, the combining capacity of the reinforced concrete beam and the reinforcing layer is high, mutual slippage is avoided, the retired wind power blade composite material resources are utilized, the environment is protected, and the cost is saved while high-performance reinforcing treatment can be conducted on the damaged reinforced concrete beam structure or the reinforced concrete beam structure with insufficient bearing capacity.

The invention discloses a new energy power grid probability harmonic load flow calculation method based on improved semi-invariant and Gram-Charlier series expansion. The new energy power grid probability harmonic load flow calculation method comprises the following steps: establishing a random variable probability model, a wind-light output model and a wind-light joint distribution model; secondly, considering the output correlation between wind and light fields, converting the wind and light combined distribution model based on a Copula function into independent variables by utilizing Rosenblatt conversion for calculation, then calculating each-order semi-invariant of load, photovoltaic output and wind power output according to the expected values of node voltage and branch power flow and a sensitivity matrix, and finally calculating the output correlation between the wind and light fields according to each order semi-invariant of the load, the photovoltaic output and the wind power output. The method comprises the following steps of: obtaining a state variable, further solving each-order semi-invariant form of the state variable and the branch power flow, and finally, obtaining a probability density function and a probability distribution function of harmonic voltage of each node by combining Gram-Charlier series expansion.