8010 results about "Wind power generation" patented technology

The invention relates to the technical field of wind power generation equipment data processing. The invention provides a time sequence database data compression method, system and device and a storage medium. The method comprises the steps of dynamically partitioning time sequence data based on a preset time window; hybrid coding compression is performed on each data block, and a composite strategy of difference coding and run length coding is adopted for numerical data; a metadata index of the compression blocks is established, the time range, the data feature statistics and the compression parameters of each data block are recorded, and the metadata index comprises extreme value distribution, variance features and data fluctuation frequency indexes; and dynamically adjusting a compression strategy according to a historical data feature analysis result, predicting data fluctuation modes of different equipment sensors through a machine learning model, and automatically selecting an optimal coding combination and compression granularity for subsequent data blocks. The problems that when wind power plant time sequence data are processed through an existing method, time sequence characteristics are difficult to adapt, the storage cost is high and the query efficiency is low are solved.

The invention relates to the technical field of wind power generation, and discloses a wind turbine generator gearbox fault diagnosis method and device and computer equipment, and the method comprises the steps: collecting multi-source data of a wind turbine generator gearbox; respectively extracting a time domain feature, a frequency domain feature, a temperature feature and a working condition feature of the multi-source data; performing weighted feature fusion on the time domain feature, the frequency domain feature, the temperature feature and the working condition feature to obtain a real-time fusion feature; obtaining historical health data of the wind turbine generator gearbox, establishing a health degree reference of the wind turbine generator gearbox based on the historical health data, and constructing a health degree model based on the real-time fusion features and the health degree reference; and performing fault diagnosis on the wind turbine generator gearbox by adopting the health degree model. According to the method, multi-source data of the gearbox of the wind turbine generator is combined, and gearbox health state evaluation and fault type identification are realized through dynamic feature fusion and deep learning modeling.

The invention provides a service life prediction method based on structural fatigue analysis of floating type offshore wind power equipment, and relates to the technical field of wind power generation. Equipment structure data of the floating type offshore wind power equipment is acquired, a sensor network is connected to acquire environmental load information, and a multi-body coupling simulation model is established based on the equipment structure data; environmental load information is input into the multi-body coupling simulation model for stress amplitude and load change, fatigue damage accumulation data is calculated based on the multi-body fatigue load spectrum, and the residual life of the floating type offshore wind power equipment is predicted through the fatigue damage accumulation data. The technical problem that in the prior art, unique stress characteristics and dynamic response of floating type offshore wind power equipment are ignored, and consequently the service life of the wind power equipment is predicted inaccurately is solved. The technical effects that the fatigue accumulation process of the floating type offshore wind power equipment is accurately calculated, and the operation safety and the service life prediction precision of the floating type wind power equipment are improved are achieved.

The invention relates to the technical field of wind power generation, in particular to a long flexible blade monitoring method and system based on multi-source data fusion, which adopts a multi-source data fusion technology, collects optical fiber strain, vibration, environment and image data by arranging sensor nodes on a fan blade, and generates comprehensive feature data through preprocessing; and extracting potential feature vectors by a multi-modal auto-encoder, and mapping the potential feature vectors into predicted stress data by using a physical information neural network in combination with offline finite element simulation data and physical constraint conditions. Then, a control decision is generated through the multi-modal deep fusion network and hierarchical reinforcement learning, the blade state is adjusted in real time, and the model is optimized through a closed-loop feedback mechanism; according to the invention, precise monitoring and active regulation and control of special working conditions such as blade pollution and icing are realized.

The invention provides a new energy storage consumption capability index prediction method, and relates to the field of power system operation, and the method comprises the steps: building a nonlinear mapping relation between solar radiation intensity, photovoltaic module conversion efficiency, a temperature coefficient, an aging attenuation rate factor and power generation efficiency for photovoltaic power generation, so as to dynamically correct a photovoltaic power generation potential output prediction value; based on a potential output prediction result of photovoltaic and wind power generation, solving an energy storage capacity configuration and operation strategy through an intelligent optimization algorithm to maximize the benefit of an energy storage system, and obtaining a dynamic prediction value of an energy storage consumption capability index; and combining prediction results of the statistical model based on the historical data, the dynamic model based on the physical mechanism and the data-driven model based on machine learning by adopting an adaptive weighted fusion strategy. According to the method, accurate prediction of potential output of new energy power generation and dynamic evaluation of the energy storage absorption capability are realized, and effective technical support is provided for new energy grid connection and optimal configuration of an energy storage system.

The invention provides an equipment health management method and system for a wind generating set. The method belongs to the technical field of wind power generation, and comprises the following steps: acquiring multi-source data of a wind generating set, preprocessing the acquired multi-source data, and integrating the preprocessed multi-source data into a uniform data format by using a data fusion technology; based on domain knowledge and data characteristics, key features reflecting the health state of the equipment are extracted, and a feature subset with the highest prediction value for the health state of the equipment is screened out through a feature selection algorithm. Key components of the wind generating set are monitored in real time through a high-precision sensor network, and comprehensive collection of data is realized in combination with multi-source information such as a historical fault database and meteorological data.

A double-path ultra-short-term wind power prediction method based on numerical weather forecast and multi-order time sequence dynamic gating fusion comprises the following steps: acquiring wind power generation historical data and numerical weather forecast data of a wind power plant, and screening weather factors highly related to wind power by using an MIC; the CEEMDAN is adopted to decompose the power sequence into a plurality of intrinsic mode functions (IMF); a dual-path prediction architecture is constructed, one path adopts xLSTM to predict an intrinsic mode function (IMF), all subsequences are superposed, and a prediction result is obtained; in the other path, the XGBoost is combined with key meteorological characteristics of an intrinsic mode function (IMF) and a numerical weather forecast (NWP) for prediction, and all the subsequences are superposed to obtain a prediction result; the method comprises the following steps: designing an MT-DGFusion module through an enhanced attention and dynamic gating network; and fusing the dual-path prediction results through an MT-DGFusion module to obtain a final prediction result. According to the method, double breakthrough of prediction precision and stability is realized, and a new technical path is provided for a complex time sequence prediction task.

The invention relates to the technical field of wind power generation, and discloses a wind power plant energy management method based on laser radar wind measurement, comprising the following steps: S1, deploying laser radar equipment, and collecting real-time data of wind speed, wind direction and turbulence intensity in a wind power plant; s2, constructing a three-dimensional dynamic model of a wind field, and modeling wind speed, wind direction and turbulence distribution in combination with laser radar data and historical meteorological data; s3, performing short-term prediction on the future wind speed by using the deep learning model, and outputting minute-level to hour-level wind energy prediction data; s4, optimizing a dynamic scheduling strategy of the wind turbine generator based on the prediction data; and S5, docking an energy storage system, optimizing charging and discharging behaviors of energy storage equipment, and realizing smooth power output. According to the invention, by fusing the laser radar wind measurement data, the deep learning prediction model and the intelligent optimization algorithm, high-precision wind condition monitoring, dynamic scheduling optimization and power output stability improvement of the wind power plant are realized.

A method, system, and device for wind speed prediction and layout optimization in wind power generation are provided. The method includes: obtaining a basic wind resource dataset of a target region; constructing a physics-informed neural network model based on the basic wind resource dataset; obtaining wind speeds data at a specific location in a velocity field based on the physics-informed neural networks and constructing a training dataset; training the physics-informed neural network model based on the training dataset; reconstructing a wind speed distribution within the velocity field and predicting wind speeds for a next time period with a wind farm using the trained physics-informed neural network model; and optimizing a layout of a wind turbine cluster based on a reconstructed wind speed distribution within the velocity field. The present application reconstructs a two-dimensional velocity field of the wind farm by training the PINN and enables accurate ultra-short-term wind speed prediction.

The invention relates to the technical field of wind power generation. The invention provides a fan main shaft backward movement monitoring and evaluating method and system. The method comprises the following steps: acquiring an axial displacement signal, a vibration signal, a bearing temperature signal and a lubrication state parameter of a fan main shaft in real time; based on a multi-parameter fusion algorithm, performing coupling analysis on the axial displacement, the vibration amplitude, the bearing temperature and the lubrication state to generate a comprehensive risk index; performing dynamic diagnosis on the comprehensive risk index in combination with a main shaft backward movement mechanism model, and identifying at least one fault root cause of axial force imbalance, thermal stress abnormality or lubrication failure; an alarm threshold value is dynamically adjusted according to wind speed change, temperature fluctuation and load working conditions, and graded early warning is triggered based on the fault root cause; and based on historical operation data and a real-time monitoring result, evaluating a main shaft backward movement risk level, and generating a decision report containing a maintenance priority and a technical improvement suggestion. The problem that existing fan main shaft backward movement monitoring depends on manual inspection and offline detection technologies, and limitation exists is solved.

The invention relates to the field of new energy, and discloses a wind power generation power prediction method based on space-time diagram convolution and gating attention, and the method comprises the steps: obtaining the geographic position information, meteorological information and historical wind power generation power data of each fan in a wind power plant, and obtaining the normalized data; constructing a dynamic adjacency matrix based on the maximum information coefficient among the historical power data of each fan in the wind power plant, and generating a graph structure; a node set of the graph structure corresponds to each station in the wind power cluster, and an edge set is dynamically determined by a maximum information coefficient of historical power data between the stations; spatial feature extraction is carried out by using a graph convolutional network, and a graph structure learning module is introduced; and inputting the sequence output by the graph structure learning module into a gating circulation unit, introducing an Informer encoder based on a sparse attention mechanism, and generating a wind power prediction result of a future time step. According to the invention, high-precision prediction of the wind power generation power in a multi-fan scene is realized.

The invention relates to an intelligent charging method and system based on light and wind complementation and intelligent energy storage. According to the method, dynamic optimized charging is realized through multi-source data collaborative decision, and the method comprises the following steps: firstly, collecting real-time data such as light-wind power generation, energy storage SOC, charging load and commercial power supply, analyzing a wind-solar power generation complementary relationship and calculating a real-time complementary coefficient; a dynamic weight distribution scheme of light wind and commercial power is generated in combination with the commercial power state, and an energy storage charging and discharging instruction is generated by fusing peak and valley electricity prices and energy storage efficiency optimization; and based on the adjusted energy storage output power, comprehensively considering the charging demand and the vehicle battery state, and dynamically distributing the power of the charging pile through the charging priority. By adopting the method, energy complementation, economic scheduling and charging demand management can be combined, the utilization rate of renewable energy sources is effectively improved, and the charging cost is reduced.

The invention discloses a pitch angle dynamic compensation method based on multi-radar space-time synchronization, and relates to the technical field of wind power generation. In order to solve the problem that the pitch angle control precision is insufficient due to the fact that three-dimensional wind field space-time characteristics cannot be captured in traditional single-point wind speed measurement, a wind wheel plane three-dimensional space-time speed field is synchronously constructed through a horizontal-vertical radar array, and blade segmented aerodynamic load distribution is calculated in real time in combination with a flow field-structure coupling model; establishing a vorticity transport equation under a rotating coordinate system based on a vorticity-shearing force transfer function, mapping dynamic load deviation through a neural network to generate a reference variable pitch instruction, and fusing the tower shadow interference feature vector to perform pre-compensation; a quadratic programming algorithm is adopted to resist saturation optimization, so that the variable pitch rate strictly follows a dynamic constraint curve of a hydraulic system. According to the method, the problem of aerodynamic load phase delay is effectively solved, periodic load fluctuation caused by the tower shadow effect is reduced, and technical guarantee is provided for stable operation of a large wind turbine generator under the complex wind condition.

The invention relates to the technical field of wind power generation, and discloses a cross-scale wind power plant wind resource evaluation method based on a numerical mode, comprising the following steps: step 1, acquiring multi-source observation data of a target area; step 2, screening a WRF optimal configuration scheme adaptive to the regional terrain; step 3, through a WRF power downscaling method, generating annual scale wind resource data as initial wind resource data; 4, performing partition correction on the initial wind resource data to obtain wind measurement data; step 5, constructing a WRF-WFP model coupling the WRF mode and the wind power plant parameterized model; step 6, optimizing WRF lower boundary conditions; 7, simulating the atmospheric flow of the target area, and quantifying the wake flow evolution characteristics of the wind power plant; 8, constructing a wake flow analysis model; and step 9, formulating a collaborative productivity plan of the wind power plant group. The power generation capacity of the wind power plant to be developed can be accurately predicted, and a reliable basis is provided for scientific decision-making of resource distribution development and planning of the million-kilowatt wind power plant.

The invention relates to a wind power prediction method and device based on a pre-trained large language model, and belongs to the field of wind power generation, and the method comprises the steps: dividing a wind power data sequence into a plurality of time sequence patches; dividing the time sequence patch into a plurality of equal-length subsequences; extracting data features of the equal-length subsequences; converting the data features into semantic cue words; adding a text instruction to the semantic prompt word to obtain a semantic instruction; splicing the time sequence patch and the corresponding position code, time code and semantic instruction to obtain a fusion sample corresponding to the time sequence patch; inputting the plurality of fusion samples into a large language model for pre-training, and performing parameter adjustment on the large language model to obtain a wind power prediction model; and inputting the fusion sample of the new wind power data into the wind power prediction model, and outputting a wind power prediction value. According to the method, under the condition that the number of historical operation data samples is small, the large language model is pre-trained through the fusion samples, and high-precision wind power prediction is achieved.

The invention relates to a TCN-LSTM wind power generation capacity prediction method combining time-frequency conjoint analysis and a multi-head self-attention mechanism. The TCN-LSTM wind power generation capacity prediction method comprises the following steps: preprocessing original data of wind power generation; frequency domain features of the wind power generation data are extracted through wavelet transform, adaptive weighted fusion is carried out on the frequency domain features and time domain features, and a time-frequency conjoint analysis module is constructed; a TCN-LSTM combined network of a time convolution network TCN and a long short-term memory network LSTM is constructed; a multi-head self-attention mechanism is introduced into the TCN-LSTM combination network, and a TCN-LSTM wind energy generating capacity prediction model combining time-frequency analysis and the attention mechanism is generated; training the model by using the wind power generation data set; the trained prediction performance is evaluated; and performing wind power generation prediction by using the final wind power generation prediction model. According to the method, the accuracy of wind power generation prediction and the calculation efficiency of the model are remarkably improved, and the precision of wind power prediction is improved.

The invention belongs to the technical field of wind power generation, and discloses a wind driven generator multi-drive variable pitch control method based on big data, which comprises the following steps of: constructing a multi-source data acquisition network covering an environment, a unit, a power grid and a driver, and combining preprocessing means such as denoising and standardization and feature extraction methods such as principal component analysis; high-quality input is provided for a short-term prediction model; the model can accurately pre-judge wind conditions and power requirements in the future 10-30 minutes, and is matched with a global optimization algorithm to dynamically adjust a variable pitch strategy, so that the wind driven generator can still keep stable power output under complex working conditions of gust, power grid fluctuation and the like, and particularly has outstanding performance in areas with unstable wind resources. According to mechanical coupling characteristics of the multi-drive variable pitch system, mechanical parameters such as a gear transmission gap and shafting rigidity are fully included in global optimization, and power output, mechanical fatigue and load distribution are balanced through a multi-objective optimization algorithm.

The invention relates to the technical field of wind power generation. The impeller imbalance detection method based on multi-source data fusion comprises the steps that operation state data of a draught fan unit and environment data of corresponding time are obtained, and a multi-source data set is obtained; preprocessing the multi-source data set to obtain a preprocessed data set; feature parameters in the preprocessed data set are extracted, data fusion is carried out, and a multi-source feature fusion vector is constructed; according to the multi-source feature fusion vector, constructing a time sequence prediction model based on a gating circulation unit, and predicting a theoretical vibration baseline value under the current working condition in real time; and calculating a deviation degree between an actual vibration value and a predicted baseline through residual analysis, dynamically adjusting a detection threshold, and triggering imbalance early warning when the deviation degree exceeds the threshold. The problems that the accuracy of a detection result of a traditional impeller imbalance detection method is affected by various factors, misjudgment and missed judgment are prone to occurring, and the requirement of a modern wind power generation system for high-precision fault detection cannot be met are solved.

The invention belongs to the technical field of composite coatings, and particularly relates to a corrosion-resistant and ultraviolet-resistant protective coating for wind power generation equipment and a preparation method thereof.The corrosion-resistant and ultraviolet-resistant protective coating for the wind power generation equipment is prepared from, by mass, 40-60 parts of gradient fluorosilicone modified polyurethane resin, 5-12 parts of core-shell type ZnO-coated MOFs composite particles, 1-3 parts of a curing agent, 1-3 parts of a curing agent and 1-3 parts of a curing agent. According to the invention, the problems of poor ultraviolet resistance, poor weather resistance, poor corrosion resistance, poor corrosion resistance, poor corrosion resistance, poor corrosion resistance, poor corrosion resistance and the like in the existing coating and the preparation process thereof are well solved; the shielding layer is single and incapable and has a poor self-repairing effect; and the coating toughness of an epoxy resin system is insufficient and cannot adapt to fatigue deformation of equipment under a vibration working condition.

The invention discloses a wind power generation system operation optimization method based on intelligent control, and the method comprises the following steps: S1, collecting the real-time data during the operation of a wind power generation system, including the wind speed, the wind direction, the rotating speed of a generator, the output power, and the environment temperature; s2, constructing a performance model of the wind power generation system according to the real-time data; s3, based on the performance model, adjusting the operation parameters of the generator in real time to optimize the power generation efficiency of the system; and S4, when detecting that the system is abnormal, automatically triggering a fault diagnosis module, generating a fault processing suggestion and executing an optimization strategy. Through real-time data acquisition, performance model construction, operation parameter dynamic adjustment and intelligent fault diagnosis, the problems that a traditional wind power generation system is low in efficiency and not timely in fault response are solved, and the system has the advantages of dynamically optimizing power generation efficiency, timely processing system abnormity and improving operation stability.

The invention discloses a wind power equipment coating corrosion state monitoring method and device, equipment, a storage medium and a program product, and relates to the technical field of wind power generation, and the wind power equipment coating corrosion state monitoring method comprises the steps: obtaining equipment parameters of offshore wind power equipment and environment parameters collected by a sensor on the offshore wind power equipment; based on the equipment parameters and the environment parameters, establishing a digital twinborn model corresponding to the offshore wind power equipment; according to the digital twinborn model, the change condition of the coating state of the offshore wind power equipment is simulated, predicted coating state parameters are output, and the predicted coating state parameters at least comprise the coating corrosion state and the coating thickness of the offshore wind power equipment at the current time point. The technical problems that an existing wind power equipment coating corrosion state monitoring scheme is greatly limited by the environment, the workload is high, and the detection cost is high are solved.

The invention relates to the technical field of wind power generation control, in particular to a wind turbine generator wake flow optimization cooperative control system and method. According to the technical scheme, the wind turbine generator wake flow optimization cooperative control system comprises a feedforward LiDAR array which is deployed at the upstream 1-2 km of the prevailing wind direction of a wind power plant and used for collecting upstream three-dimensional wind field data in real time; the unit embedded sensor group comprises an ultrasonic anemograph and an inertial measurement unit IMU which are arranged in a cabin of each wind turbine unit, and the sampling frequency is not lower than 20Hz; the data fusion module is used for performing space-time alignment and noise filtering on the data of the LiDAR array and the unit embedded sensor through a convolutional neural network (CNN) and a Kalman filtering algorithm to generate a dynamic wind field digital twinborn model; and the LSTM wind field predictor predicts the wind speed and wind direction change trend in the future 30 seconds based on the dynamic wind field digital twinborn model. Through multi-source sensing fusion and dynamic game optimization, the operation efficiency and safety of the wind power plant under the dynamic wind condition are remarkably improved.

The invention relates to the technical field of wind power generation. The invention provides a wind field unit comparison type fault diagnosis method, system and device and a storage medium. The method comprises the following steps: synchronously acquiring operation and corresponding environment data of units of the same type in the same wind field, and establishing a multi-dimensional data set; building a normal working condition multi-dimensional parameter reference model by using clustering analysis and updating in real time; performing multi-scale comparison on a target and a reference unit, and extracting feature fusion to generate a comprehensive health index; establishing a double-layer diagnosis model, constructing a virtual unit based on digital twinning in the first layer, comparing residual errors and positioning a potential fault source, detecting outliers by using an improved isolated forest algorithm in the second layer, determining a fault propagation path in combination with an association rule, and establishing a fault mode knowledge base; and triggering an early warning mechanism according to the comprehensive health index and the fault mode classification. The problems that a traditional operation and maintenance mode is difficult in fault prediction, high in cost and lack of a reliable evaluation system, state monitoring is limited to a single parameter, multi-unit comparative analysis means are insufficient, and fault diagnosis is lagged are solved.

The invention discloses a cooling system, method and device of a wind generating set, and relates to the technical field of wind power generation. The cooling system comprises a cooling loop, the cooling loop comprises a cooling pump station, a cooling medium, a connecting assembly and a plurality of cooling branches connected in parallel, the connecting assembly communicates between the cooling pump station and the cooling branches, the cooling pump station drives the cooling medium to circularly flow in the cooling loop, and each cooling branch comprises N heat exchange assemblies connected in series, n is an integer greater than or equal to 2; wherein the outlet of the cooling pump station is respectively connected with the inlet of each cooling branch, and the inlet of the cooling pump station is respectively connected with the outlet of each cooling branch. In other words, when the cooling system is designed, the heat exchange assemblies are connected in series, so that the number of parts can be reduced, the cost is reduced, and the economical efficiency is improved.

The invention discloses a wind power generation ring forging three-dimensional design and quality management system based on 5G communication, and belongs to the technical field of wind power equipment manufacturing. The system comprises a modeling module used for constructing a parameterized three-dimensional design model and outputting design parameters used for quality evaluation and defect identification; the process data acquisition module acquires process data such as temperature, pressure and vibration of the key structure area in real time; the defect identification module fuses the image and the signal data, and outputs defect types, positions and sensitivity influences thereof; the quality evaluation module performs quantitative scoring on the product quality based on the scoring function and the disturbance response parameters; the feedback regulation and control module identifies the dominant deviation and generates a regulation and control instruction when the score is abnormal; and the 5G communication module realizes high-speed data interaction between the modules. The method realizes closed-loop control of design, manufacturing, detection and evaluation, improves the manufacturing quality and efficiency of ring forgings, and is suitable for manufacturing scenes of large-size and high-reliability wind power structural members.

The invention discloses a regional power grid wind power generation power prediction optimization method and system, and particularly relates to the technical field of power grid wind power generation. Multi-source environment data is collected, wind speed change characteristics are extracted, a time series data set is constructed, a hybrid prediction model is constructed in combination with a long-short term memory network and a gradient boosting decision tree, a time series trend and a nonlinear wind speed-power mapping relation are captured respectively, and prediction weights of the time series trend and the nonlinear wind speed-power mapping relation are adaptively adjusted through an attention mechanism. According to the method, the model hyper-parameters are adaptively adjusted by further combining Bayesian optimization, short-term prediction errors are corrected by using Kalman filtering, the real-time adaptability and stability of prediction are enhanced, the problems of prediction misalignment and the like caused by high nonlinearity and data scarcity of a wind speed mode in a complex terrain environment are effectively solved, and the prediction accuracy is improved. The wind power prediction precision is improved, a more stable and reliable scheduling reference is provided for a regional power grid, and the standby capacity demand and the operation cost are reduced.

The invention relates to the technical field of wind power generation. The invention provides a yaw static and dynamic error adaptive method based on big data backtesting, which comprises the following steps of: constructing a multi-dimensional feature vector through multi-source data fusion data, and establishing a dynamic feature data set; based on the dynamic characteristic data set, a space-time diagram convolutional network is adopted to establish a wind power plant dynamic error prediction model, and space-time evolution rules of wind shear, turbulent flow and wake flow effects are captured; constructing a variational self-coding reference model based on historical full wind speed section data, calculating a residual error between a current working condition and the variational self-coding reference model in real time, and taking the residual error as a static error prediction value; performing adaptive weight fusion on the static error prediction value and the dynamic error prediction value to obtain a fusion error; and the fusion error is converted into the yaw angle correction amount, and the wind facing action is executed. The problems that an existing yaw error recognition technology is high in data dependence, lack of dynamic analysis, poor in model adaptability and difficult in complex wind field processing are solved.

The invention discloses a fan blade icing monitoring system and method, and relates to the technical field of wind power generation, and the system collects temperature, humidity and vibration signals in real time through a multi-sensor module installed on a fan blade, fuses multi-parameter data through an edge calculation unit, and calculates a blade icing risk factor # imgabs0 #. The risk factors are calculated by adopting a dynamic weighting model, and the icing state of the blade is accurately evaluated in combination with normalized characteristic values # imgabs1 #, # imgabs2 # and # imgabs3 #. And when the risk factor exceeds a set threshold value, the system triggers an alarm and transmits data to the cloud through the wireless communication module. The cloud platform predicts the future icing risk based on a long-short term memory (LSTM) neural network model in combination with historical data and real-time monitoring data, the accuracy and real-time performance of icing monitoring are improved, the method is suitable for operation in cold regions and high-altitude wind fields, and reliable guarantee is provided for safe and efficient operation of fans.

The invention discloses a design method and system for a wind turbine blade, and belongs to the field of wind power generation. Adopting a Latin hypercube design method to generate initial sample points of a design scheme in a design space of the wind turbine blade model; according to the initial sample points, constructing a Kriging agent model; an improved multi-target mantis algorithm is adopted to carry out iterative solution on the Kriging proxy model, and a Pareto leading edge solution set is obtained; and selecting an optimal solution as a design scheme of the wind turbine blade from the Pareto leading edge solution set according to constraint conditions of the wind turbine blade. Aiming at low wind turbine blade design optimization efficiency in the prior art, a wind turbine blade mathematical optimization model is constructed, then a Kriging proxy model is constructed according to sampling points, hyper-parameters of the Kriging proxy model are optimized, and on this basis, an improved multi-target mantis optimization algorithm and a multi-dimensional point adding strategy are introduced to carry out adaptive updating optimization solution and the like on the proxy model. And the optimization efficiency is improved.

The invention relates to the technical field of wind power generation, discloses an intelligent safety early warning method for wind power hoisting operation, and aims to solve the technical problems of insufficient perception and lack of data fusion and intelligent analysis decision in existing operation safety management. The method is characterized by comprising the following steps: constructing an intelligent terminal integrated with a UWB / IMU / safety belt / environment sensor; deploying a positioning base station network and a signal relay system; a multi-source data fusion intelligent analysis platform is established, and high-precision positioning, behavior recognition and deep learning risk prediction are achieved; and a closed-loop intelligent decision-making and execution system is constructed, and graded early warning, electronic fence, environment linkage and one-key help calling are realized. According to the method, a traditional experience driving mode is innovated into a data driving mode, the safety level and the operation efficiency of wind power hoisting operation are remarkably improved, and the defects of insufficient perception, information isolation and the like are overcome.