17258 results about "Wind power" patented technology

Disclosed is a microgrid optimal scheduling method taking into consideration a system operation risk and user satisfaction, comprising: generating a power prediction curve of wind power and photovoltaic new energy output in a microgrid, using a Monte Carlo simulation method to obtain a prediction error range to generate a typical scene set, and by means of a data fitting method, acquiring a wind-photovoltaic prediction error probability density distribution function; using value-at-risk and conditional value-at-risk theories to quantify a microgrid operation risk which is increased by slightly high or low output caused by wind power and photovoltaics; obtaining the scheduling cost of an adjustable load in a microgrid system; taking the minimum sum of conditional value-at-risk cost and microgrid system operation cost as an objective function and a user satisfaction level under a demand-side response as a constraint, establishing a microgrid optimal scheduling model taking into consideration the operation risk and the user satisfaction; and using a genetic algorithm to solve the model, and obtaining an optimal result of grid-connected microgrid system optimal scheduling. The safety, economic efficiency, stability and reliability of the microgrid are enhanced.

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 discloses a wind power bolt state detection method and system, and belongs to the technical field of mechanical structure testing and health monitoring, and the method comprises the steps: obtaining the real-time strain data of each monitoring point; acquiring operation condition data of the unit in real time; dynamically generating a finite element model of the tower drum structure according to the operation condition data; the finite element model and the real-time strain data are combined to generate a stress distribution map of the whole tower cylinder bolts; inputting the stress distribution atlas into a preset fatigue damage model, calculating a bolt life attenuation rate and positioning an external load abnormal source; and when it is detected that the life attenuation rate exceeds a preset threshold value, generating a grading early warning signal and outputting a maintenance strategy according to a positioning result. According to the method, a wireless strain sensor network, dynamic finite element model correction and multi-dimensional fatigue damage analysis technology are adopted, a whole tower barrel stress distribution map is generated in real time, external working condition data are associated, the bolt service life attenuation rate can be accurately evaluated, a load abnormal source can be positioned, and a graded maintenance strategy can be output.

The invention relates to the field of fault early warning, in particular to a wind power fault dynamic early warning method and system based on multi-source heterogeneous data fusion. According to the method, multi-source data such as SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator are collected in real time, standardization processing is carried out, and a multi-dimensional feature vector is constructed. And generating a fusion data set by using an adaptive weighted fusion algorithm, constructing a fault prediction model based on a deep convolutional neural network, and outputting a health state assessment value and a fault risk level in real time after historical fault sample supervised training. And when the risk level exceeds a threshold value, generating an early warning signal containing a fault type and a positioning and repairing suggestion, dynamically adjusting a monitoring parameter weight, iteratively updating a model, and realizing adaptive optimization of an early warning strategy. The problem that an existing method depends on single data source and multi-source data fusion is solved, and accurate dynamic early warning is achieved.

A power grid planning method and system adapted to multiple uncertainties and multi-objective requirements, which relate to the technical field of power grid planning. The method comprises: constructing a deterministic multi-objective coordinated planning model with the objectives of minimizing economic cost, reliability risks and environmental impact; on the basis of an IGDT, constructing a multi-uncertainty model considering wind power, photovoltaic power and load; converting a model into a second-order cone programming model by using variable substitution, absolute value linearization, second-order cone relaxation and McCormick envelopes relaxation methods; and solving the second-order cone programming model, in order to obtain a power grid planning scheme that adapts to uncertainties in wind and photovoltaic power output and load demand growth and meets the requirements for the economy, reliability and environmental friendliness of power grid operation. Thus, economic benefits of power grid planning are improved, carbon emissions from power grid operation are reduced, the reliability of a power grid is enhanced, and an obtained planning scheme has high robustness within expected objectives of planners.

The invention relates to the technical field of wind power prediction. The invention provides a wind power prediction method and system. The method comprises the following steps: acquiring multi-dimensional meteorological time series data, three-dimensional elevation data and unit operation data of a target wind power plant; constructing a spatial-temporal feature fusion network, extracting time sequence dynamic features, and performing weighted fusion on the spatial correlation features and the time sequence dynamic features to obtain a fusion feature vector; establishing a hybrid prediction model, and taking the fusion feature vector as input to obtain a wind power initial prediction result; introducing a terrain correction factor, constructing a turbulence intensity compensation function, and performing micro-terrain disturbance correction on the wind power initial prediction result; and outputting a final power prediction curve and a confidence interval. The problems that in an existing wind power prediction method, a physical model is insufficient in complex terrain microclimate modeling precision, high in calculation complexity and difficult to meet the real-time requirement, a statistical learning method is limited in high-dimensional nonlinear time sequence feature expression capacity, and prediction errors are remarkably increased under the abnormal working condition are solved.

Disclosed in the present invention is an optimized dispatching method for a virtual power plant, which method is applied to a virtual power plant dispatching control center in a virtual power plant dispatching system. The method comprises: predicting next-day wind power and photovoltaic output values and power demand conditions of electric vehicles in a load aggregator on the day ahead; acquiring a next-day output scheme reported by each output unit in a virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit on the basis of a day-ahead electricity selling price, electric-vehicle charging and discharging strategies and the output characteristics and costs of other output units; and performing coordinated optimization on the basis of the next-day output schemes reported by the output units and a virtual power plant optimized dispatching model which takes demand responses and environmental costs into account, so as to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, such that each output unit performs execution on the basis of the final next-day output plan. The present invention makes load dispatching in a virtual power plant more flexible.

The invention discloses a wind power fault diagnosis operation and maintenance method based on multi-source data and knowledge retrieval enhancement. The method comprises the following steps: S1, collecting and preprocessing multi-modal data; s2, knowledge base construction: performing knowledge extraction, and constructing a corresponding wind turbine generator operation and maintenance knowledge graph database; s3, multi-granularity problem decomposition and retrieval optimization are carried out, and a sub-problem list is generated by deeply grading and splitting problems in combination with doubt degree and dependency analysis; s4, knowledge retrieval enhancement generation: combining knowledge graph sub-graph retrieval and SCADA real-time data dynamic weight adjustment to generate a final answer; and S5, result generation and feedback optimization are carried out, and the validity of the diagnosis result is verified through a real-time verification mechanism. According to the method, the accuracy, the reliability and the real-time performance of fault diagnosis can be remarkably improved in a limited fault data environment.

The invention provides a wind power plant topographic surveying and mapping method and system based on an unmanned aerial vehicle, and relates to the technical field of topographic surveying and mapping. Comprising the following steps: according to topographic characteristics of a wind power plant, adopting a multi-objective optimization model to respectively generate preliminary surveying and mapping routes for a plurality of unmanned aerial vehicles; the multi-objective optimization model takes coverage rate maximization as an optimization objective, and takes cross minimization and path length optimal solution among a plurality of unmanned aerial vehicle routes as constraint conditions; according to the real-time meteorological data, performing dynamic optimization on the preliminary surveying and mapping routes, and generating dynamically optimized surveying and mapping routes for the multiple unmanned aerial vehicles; the multiple unmanned aerial vehicles fly along the dynamically optimized surveying and mapping route, and surveying and mapping data are acquired through the data acquisition equipment. According to the wind power plant terrain complexity and the real-time meteorological data, the surveying and mapping route with the maximum coverage rate is generated for each unmanned aerial vehicle, and blind areas are eliminated through real-time supplementary measurement, so that the completeness of terrain data in the surveying and mapping area is ensured, and the missing of key terrain information is reduced.

The invention discloses a wind power converter I-GBT monitoring system, particularly relates to the field of wind power converter I-GBT module monitoring, is used for solving the problem of inaccurate fault prediction and early warning in long-term operation, and is cooperatively realized through period division, parameter screening, index evaluation, dynamic adjustment and multi-stage early warning. The cycle division is used for identifying aging characteristics of the IGBT module in different life cycles and screening out monitoring parameters highly related to the state of the bonding wire. And through a multi-dimensional dynamic fusion model, the uniqueness of monitoring parameters is evaluated, and the accuracy of fault prediction is ensured. And the dynamic adjustment module dynamically optimizes the weight of the monitoring parameter according to the periodic change and the environment variable, so that the adaptability and the real-time performance of the monitoring are enhanced. The multi-stage early warning mechanism generates early warning signals of different grades according to changes of key parameters in a period, and potential faults are early warned in advance. The operation reliability of the wind power converter is improved, the sudden failure risk is reduced, and safe and stable operation of the system is guaranteed.

The invention provides a multi-platform cooperative unmanned inspection task planning method and system for a wind power scene, and relates to the technical field of wind power plant inspection, and the method comprises the steps: based on a wind power plant three-dimensional scene model, adaptively dividing an inspection region, constructing a reachability path network, calculating an inspection difficulty coefficient according to target detection point information, and calculating a target inspection point; and generating an unmanned aerial vehicle and unmanned vehicle inspection area. A sensor is deployed to collect environment and equipment state data, feature vectors are extracted to establish an inspection constraint model, and main and branch inspection paths are planned on the reachability path network. And performing multi-platform relative positioning by using ultra-wideband radio waves, and dynamically optimizing an inspection path according to the equipment health score and the inspection constraint model. Through multi-platform cooperation, adaptive path planning and intelligent state identification, the wind power plant inspection efficiency and accuracy are improved, and the inspection cost is reduced.

The invention relates to the technical field of wind turbine generator control, and discloses a wind turbine generator control optimization method based on dynamic changes of wind speed and wind direction. According to the method, real-time wind speed time sequence data and three-dimensional wind direction vector field data of a target wind field are received, a wind field dynamic analysis model is constructed by using a space-time convolutional neural network, and a wind field energy density distribution matrix and a turbulence intensity probability graph are generated. And constructing a multi-target adaptive optimization model on the basis, generating a unit control parameter instruction set, optimizing a cooperative adjustment coefficient according to a preset unit load-power generation efficiency balance equation, and outputting an optimal control action sequence to a wind turbine generator master control system through Bayesian optimization framework iterative updating. The method can accurately sense the wind field change, effectively balance the unit load and power generation efficiency, realize multi-unit cooperative control, improve the wind energy capture efficiency, and improve the operation stability and economic benefits of the wind turbine generator.

The invention relates to the technical field of participation of a wind storage system in power grid frequency modulation, and particularly provides an active regulation power quantitative evaluation method and device for self-adaptive frequency modulation of a wind storage system, and the method comprises the steps: constructing an energy storage system frequency response model, and calculating the active power of energy storage participating in frequency modulation; constructing a wind turbine generator wind speed fluctuation model, simulating the wind speed fluctuation condition in the same wind power plant according to the wind speed probability distribution, and generating a section of random wind speed disturbance; constructing a frequency response model of the wind turbine generator, and calculating active power participating in frequency modulation; a wind storage system adaptive distribution model is constructed based on a depth deterministic strategy gradient algorithm, and optimal distribution of a virtual droop control coefficient and a virtual inertia control coefficient of the wind storage system is realized; and calculating the active regulation power of the wind storage system after self-adaptive frequency modulation. And calculating the adjustable active power of the wind turbine generator and the energy storage equipment participating in the frequency modulation strategy when the optimal frequency modulation effect is achieved and the service life of the equipment is balanced.

The present invention provides a wind power off-grid hydrogen production power supply topology and control method without a step-down transformer, including: obtaining wind speed data, using a wavelet threshold algorithm for denoising, predicting power based on an LSTM-GRU hybrid neural network, and extracting power fluctuation characteristics through multi-scale entropy analysis; obtaining load data, using Kalman filtering and recursive least squares to establish an impedance model, and extracting dynamic characteristics in combination with Hilbert transform; using a differential evolution algorithm for multi-objective optimization to generate an optimal switching sequence and power allocation strategy; achieving system control through space vector modulation, balanced control, soft switch control and harmonic compensation; achieving state estimation and disturbance suppression based on a sliding mode observer and a nonlinear observer; and achieving fault diagnosis and protection using a deep belief network. The present invention improves system efficiency and reliability and improves dynamic performance.

The invention discloses a wind power plant equipment fault intelligent diagnosis method and system. The method comprises the following steps: acquiring multi-dimensional sensor data of wind power plant equipment; carrying out preliminary screening on the data by utilizing an edge analysis algorithm, and determining data needing to be further diagnosed; the screened data are input into a pre-constructed fault diagnosis model, the data are fused through a multi-modal deep learning model in the pre-constructed fault diagnosis model, and then a fault occurrence area is determined through transfer learning, domain adaptation and a multi-agent reinforcement learning framework; meanwhile, the causal inference engine and the knowledge graph enhanced neural symbol inference module are responsible for deep processing and fault diagnosis. And finally, inputting the determined fault area and the diagnosis result into a dynamic system health index calculation module to generate a final fault diagnosis result. According to the method, the diagnosis efficiency and accuracy of the equipment fault of the wind power plant are remarkably improved, and a powerful guarantee is provided for stable operation of the wind power plant.

The invention provides a wind power booster station equipment fault prediction and diagnosis method and system, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: constructing an equipment topological relation through a knowledge graph, employing a double-flow heterogeneous graph neural network to extract space-time cooperation features, generating a candidate path based on multi-hop reasoning, extracting a key evidence chain, and calculating a credibility score. And combining multi-scale fault feature reconstruction and Tsallis entropy calculation to obtain a diagnosis result. According to the invention, the fault root cause can be accurately identified, the diagnosis accuracy is improved, the false alarm rate is reduced, and decision support is provided for wind power plant equipment maintenance.

The invention discloses an electromagnetic heating and heat preservation system for a wind power blade girder plate pultrusion die, and relates to the technical field of thermal field regulation and control, comprising the following steps: activating an electromagnetic coil array to generate an alternating magnetic field, synchronously starting a heat preservation layer device, and triggering a gradient heating mode when the temperature of the die reaches a preset threshold value; a mold temperature field is monitored in real time, the power of an electromagnetic coil and parameters of a heat preservation layer are dynamically adjusted, a target temperature gradient is established, an electromagnetic heating device and a mechanical traction device are linked in the pultrusion process, and heat dissipation control is triggered according to the material flowing state; and after heat dissipation control is completed, the main power source is cut off, the mode is switched to the waste heat recovery mode, mold waste heat is guided into the energy storage device, and the cooling rate is controlled. According to the method, accurate mode switching of electromagnetic heating from uniform temperature rise to gradient heating is achieved through cooperation of magnetic field activation and a gradient triggering mechanism, and the temperature gradient establishment speed and the morphological stability are effectively improved in cooperation with power regulation and control partition differentiation power instruction and thermal insulation layer parameter dynamic matching.

The invention relates to the technical field of visual inspection, in particular to a multi-mode industrial product dynamic defect detection system based on edge calculation. According to the method, by introducing a multi-modal image construction and enhancement mode, three-dimensional acquisition and enhanced expression of detail information of the surface of the wind power blade are realized, and by means of inter-modal image synchronization and a space registration mechanism, the consistency extraction capability of defect information under different sensor view angles is improved; through combination of density change trend analysis and direction mutation identification means, surface micro cracks, edge damages and other structural changes can be accurately identified in continuous frames, and further through multi-source discrimination and mutual elimination comparison of abnormal indexes, environmental interference and misjudgment risks are effectively eliminated, and the accuracy of the detection result is improved. Therefore, a stable defect track area with direction consistency and distribution continuity is screened out, accurate detection and partition identification of the surface defects of the wind power blade under the dynamic condition are achieved, and the reliability and precision of defect positioning are remarkably improved.

The invention relates to a multi-machine cooperative yaw control method and device considering a dynamic wind direction and a medium. The multi-machine cooperative yaw control method comprises the steps that S1, the dynamic relation between the wind direction and a wake flow area is established based on a Gaussian-curl hybrid model; s2, constructing a single-machine yaw optimization model based on model predictive control, optimizing single-machine yaw by taking maximum generating capacity of a wind power plant and reduction of fatigue load as optimization objectives, and obtaining a single-machine optimal yaw angle through rolling optimization and feedback correction; and S3, a serial optimization method is adopted to carry out multi-round iterative optimization on one fan by one fan, and an optimal multi-fan cooperative yaw control result is obtained. Compared with the prior art, the method has the advantages that higher total generating capacity and more balanced fatigue load can be realized, so that the whole wind power plant reaches the optimal state of power and load in a large range.

The invention discloses a wind power blade defect data automatic acquisition and analysis method based on an unmanned aerial vehicle, and particularly relates to the technical field of data acquisition and analysis. The method comprises the following steps: collecting blade surface heat distribution data, locally applying a heat source to a blade, collecting a temperature attenuation curve, calculating a heat diffusivity gradient difference value and a temperature attenuation time index, integrating a multi-parameter space, and classifying a heat distribution abnormal region into a normal region and a real defect region in combination with a deep learning model; cross validation is carried out through optical images and ultrasonic data, so that the detection accuracy is improved; dynamically adjusting a sensitivity threshold value and model parameters of the thermal imager according to a classification result, and generating a detection report; through data feedback iterative optimization model and task planning, the problems of misjudgment and missed judgment of the thermal infrared imager in complex material distribution are effectively solved, the accuracy, reliability and automation level of wind power blade defect detection are improved, and important support is provided for efficient wind power operation and maintenance.

The invention discloses a virtual power plant scheduling method based on a large language model and deep reinforcement learning, and belongs to the technical field of virtual power plant scheduling. Comprising the following steps: constructing a virtual power plant multi-agent cloud edge collaborative scheduling framework based on large language model driving; predicting wind power, photovoltaic power and load power based on a large language model; constructing a mathematical model of virtual power plant optimization scheduling; converting the virtual power plant optimization scheduling model into a Markov game process in combination with a large language model; performing initialization training on the strategy network of the edge layer intelligent agent by adopting imitation learning to obtain a pre-trained edge layer intelligent agent strategy network; and based on the pre-training strategy network of the boundary layer intelligent agent, combining with a large language model and adopting an improved multi-agent near-end strategy optimization algorithm to solve a scheduling strategy.

The invention discloses a coordinated optimization method for a wind power photovoltaic energy storage ratio, and belongs to the field of power distribution network optimization, and the method comprises the following steps: S1, multi-dimensional data collection and preprocessing; s2, constructing a robustness uncertainty set; s3, constructing a multi-time-scale collaborative distribution model: constructing the multi-time-scale collaborative distribution model, and determining a target power distribution scheme and an energy storage life loss evaluation index of the energy storage equipment; s4, performing dynamic multi-objective optimization solution: constructing a multi-objective function, and performing solution by adopting an improved multi-objective African eagle optimization algorithm; and S5, real-time feedback and dynamic correction: monitoring a system state through real-time acquisition of data, dynamically correcting the multi-time scale collaborative distribution model based on a prediction error, and adjusting an energy storage equipment power distribution strategy to form closed-loop optimization. By adopting the coordinated optimization method for the wind power and photovoltaic energy storage ratio, the renewable energy consumption capability and the system economy are remarkably improved, and the method has great engineering application value.

The invention provides a new energy output prediction method and system based on Monte Carlo Dropout, and the method comprises the steps: carrying out the probabilistic prediction of new energy output through a Monte Carlo Dropout technology, generating a dynamic prediction result containing a confidence interval, and quantifying the uncertainty of meteorological sudden change and equipment state; secondly, constructing a multi-stage random dynamic programming model, discretizing a prediction interval into multi-scene input, designing a non-linear objective function based on the discharge depth, and synchronously optimizing the electricity purchase cost and the energy storage aging cost; and finally, realizing rolling optimization of the system in combination with a model prediction control framework, and dynamically adjusting an energy storage aging cost weight by updating prediction data and a scheduling instruction on line and embedding an energy storage health state real-time feedback mechanism. The photovoltaic and wind power consumption rate can be remarkably improved, the full life cycle cost of an energy storage system is reduced, and meanwhile, the robustness of a scheduling strategy in extreme weather is ensured.

According to the body drying machine, a plurality of air blowing barrels can blow air outwards through a plurality of air blowing openings respectively, each air blowing barrel comprises a brushless motor, and the brushless motors can provide strong wind power so that the body can be quickly blow-dried. The brushless motor is high in power, and the lithium battery assembly is arranged to supply power to the brushless motor, so that use safety can be guaranteed, and family circuit short circuit caused by overhigh power is avoided. Moreover, under the action of the lifting mechanism, the lifting frame can ascend and descend relative to the fixed frame, so that the lifting frame drives the blowing cylinder to ascend and descend; under the action of the rotating mechanism, the blowing cylinder can swing relative to the lifting frame so as to enlarge the blowing range. In conclusion, the air outlet position of the body dryer can be adjusted up and down, the air outlet range is large, the wind power is strong, the body can be quickly dried, and the air drying effect is good.

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 relates to the technical field of fan blade detection, and provides a wind power plant fan blade detection method and system based on three-dimensional point clouds, and the method comprises the following steps: carrying out the multi-angle scanning of a fan blade through a laser scanner, and obtaining the three-dimensional point cloud data of the fan blade; performing registration processing on the three-dimensional point cloud data, and unifying the three-dimensional point cloud data into the same coordinate system to obtain registered point cloud data; performing block acceleration processing on the registered point cloud data by adopting an octree partitioning algorithm to obtain block acceleration point cloud data; performing feature extraction and feature segmentation on the block acceleration point cloud data to obtain a key feature region of the fan blade; performing defect identification analysis on the key feature region based on a point cloud network enhancement model to obtain a defect detection result, the defect detection result comprising a defect position and a defect type; and generating a detection report containing the defect position and the defect type, and visually displaying the detection report. According to the invention, the integrity of data acquisition and the precision of defect identification are improved, and the efficiency of fan blade detection is improved.

The invention is suitable for the technical field of flying dust monitoring, and provides a road construction site all-weather flying dust online monitoring method and system, and the method comprises the following steps: collecting flying dust data and wind power information through a sensor; the particulate matter concentration is judged, and the particulate matter concentration exceeding the standard and the corresponding coordinate position, wind speed data and wind direction data are screened out; determining a flying dust source area according to the coordinate position, the wind speed data and the wind direction data; a road transportation monitoring video is called, and the area where the transportation vehicle is located is determined according to the time information and the road transportation monitoring video; a road construction map is called, and the area where the transport vehicle is located is marked in the road construction map; and comparing and analyzing the flying dust source area with a road construction map, determining a potential flying dust source position, and sending the potential flying dust source position to an inspection terminal. According to the invention, the flying dust pollution source can be accurately tracked, and the inspection personnel can quickly position the potential flying dust pollution source according to the information.

The invention relates to the field of image recognition, in particular to a wind power construction intelligent safety management method and system based on intelligent AI monitoring. The method comprises the following steps: obtaining an omnibearing real-time image flow of a wind power construction area, carrying out super-resolution deep convolution optimization and operator three-dimensional image segmentation, and extracting an operator three-dimensional image frame; three-dimensional point cloud modeling of the construction area is carried out based on the image flow, real-time image frame position positioning rendering is carried out according to a three-dimensional image frame, and a real-time twinborn model of the construction area is constructed; performing operation dynamic behavior analysis and behavior deviation degree quantitative analysis based on a twin model to obtain the behavior deviation degree of the operator; and according to the behavior deviation degree, carrying out early prediction analysis on illegal behaviors, making an adaptive risk early warning decision, and constructing an operation behavior risk early warning strategy. According to the invention, through real-time operation behavior identification and environmental risk analysis, the intelligence and safety level of wind power construction are improved.

The invention discloses an online prediction method for a transient frequency track of a power grid under coexistence of wind power low voltage ride through and off-grid, and belongs to the technical field of operation and control of a power system. A multi-source heterogeneous data fusion monitoring system is constructed, power grid and wind turbine generator data are collected, faults are recognized through an improved algorithm, and feature vectors are output; building an energy flow model based on a fault result, calculating a trajectory divergence index by using technologies such as phase-space reconstruction, estimating power vacancy, and obtaining a power unbalance sequence; designing a prediction algorithm by using the sequence, predicting a frequency trajectory in combination with an improved K-nearest neighbor algorithm and a trajectory feature library, and introducing a confidence coefficient to evaluate a correction error; and finally, establishing a three-level control response system, and implementing multi-time scale cooperative control according to a prediction result. The method can accurately predict the frequency trajectory, effectively deal with the wind power fault, improve the stability of the power grid and the wind power consumption capability, and provide powerful guarantee for the safe and stable operation of the power grid.

A joint simulation and operation operability evaluation method based on wind power operation and maintenance ship motion dynamic response and active compensation gangway ladder motion control comprises the steps that an operation and maintenance ship frequency domain seakeeping model is built, and a hydrodynamic coefficient library is calculated; wave exciting force is reconstructed based on a Cummins time domain motion equation, and ship motion response prediction is realized by adopting implicit generalized alpha time domain composite integral; the dynamic positioning system extracts low-frequency motion through a Kalman filter and optimizes thruster thrust distribution to stabilize the position of the ship. A gangway ladder dynamic model is established based on a Lagrange equation, real-time control over displacement, speed and acceleration of the top end of the gangway ladder is achieved through a feedforward-feedback composite control strategy, and nonlinear output of a hydraulic actuator is optimized. And realizing joint simulation of the coupling system through time domain synchronization and self-adaptive PID (Proportion Integration Differentiation) control. And checking gangway ladder movement based on a preset threshold value and a space boundary condition, and evaluating the operability of the climbing operation. According to the method, the limitation of traditional single-disciplinary analysis is broken through, and the forecasting precision of wind power operation and maintenance operation operability is effectively improved.