4221 results about "Wind power generation" patented technology

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.

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, 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 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.

The invention relates to the technical field of power system operation and control, and discloses a primary and secondary frequency modulation cooperative control system of a hybrid energy storage coupling wind generating set, which comprises a wind power generation unit, a hybrid energy storage unit and a central cooperative controller. A total power demand including inertia response and primary and secondary frequency modulation is synthesized, a primary and secondary frequency modulation cooperative distribution module generates a dynamic adjustment weight by using an S-type nonlinear function based on a frequency change rate and frequency deviation coupling relationship, and an energy relay compensation mechanism is introduced to fill a power gap in a switching process. And the SOC adaptive constraint and execution module applies direction selective boundary constraint to the state of charge, and controls hybrid energy storage to perform internal energy self-balancing in a frequency modulation dead zone. According to the invention, seamless connection of multi-time scale frequency modulation is realized, frequency secondary drop is effectively prevented, and the frequency support capability and the self-recovery capability of the system are improved.

The invention discloses a wind-solar complementary intelligent charging system and heat management optimization. The system comprises a wind power generation unit, a photovoltaic power generation unit, an energy storage unit, an intelligent control unit and a heat management unit, the wind power generation unit adopts an efficient wind generating set, is provided with an intelligent control system, and can automatically adjust a pitch angle and a yaw angle according to real-time wind speed and wind direction; the photovoltaic power generation unit selects a high-efficiency photovoltaic cell assembly and tracks the position of the sun in real time in combination with an intelligent light following system; and the energy storage unit adopts a high-performance energy storage battery pack and a battery management system. Through accurate prediction and analysis of local wind and light resources, dynamic configuration and cooperative power generation of the wind power generation unit and the photovoltaic power generation unit are realized. Compared with a traditional fixed configuration mode, local wind and light resources can be fully utilized, and the power generation efficiency is improved. Meanwhile, the multi-energy complementary cooperative power generation technology is adopted, organic combination of wind and light power generation and energy storage is achieved, and stable power supply of the system is ensured.

The invention relates to the technical field of wind power generation, and discloses a wind power plant wind speed correction method and system based on dynamic space-time modeling, and the method comprises the steps: obtaining a whole power curve, obtaining the whole wind speed of a historical period, and constructing a multi-modal training data set; inputting a convolutional neural network to extract local features, inputting a long-short-term memory network, calculating the correlation of each time step feature, obtaining an attention weight, and finally obtaining global feature representation; setting two multi-layer perceptron branches to carry out wind speed prediction correction to obtain a common weather branch prediction value and an extreme weather branch prediction value; constructing a correction curve of each sector and obtaining a correction curve prediction value; and according to the common weather branch prediction value, the extreme weather branch prediction value and the correction curve prediction value, carrying out weighted fusion to obtain a final wind speed correction value. According to the method, the correction precision and robustness are improved, and the interpretability and applicability of the model are enhanced.

The invention discloses a twinborn cooperative intelligent operation and maintenance method for a wind power tower group, and relates to the technical field of wind power generation towers, and the method comprises the following steps: S1, sensor deployment and real-time data collection; the method comprises the following steps: acquiring operation state data and environmental parameters through a sensor network deployed in each wind power generation tower in a wind power plant; s2, digital twinborn body construction and early warning are carried out; constructing a corresponding digital twinborn body for each wind power generation tower, wherein the digital twinborn body integrates a physical mechanism model and a prediction module driven by real-time data; generating a fault early warning signal and a health degree evaluation result based on the deviation between the simulation output of the digital twin and the sensor data; s3, multi-agent collaborative decision making is carried out; step S4, performing closed-loop optimization; according to the twinborn cooperative intelligent operation and maintenance method for the wind power tower group, the fault early warning precision is improved, the service life prediction error is compressed, the maintenance efficiency is improved, and the wake flow loss is remarkably reduced.

The invention provides a wind power plant unit state monitoring and fault early warning system and method based on deep learning, and belongs to the field of wind power generation and artificial intelligence. According to the system, a cloud edge collaborative architecture is adopted, an edge computing terminal operates a data-driven space-time prediction model and a physical digital twinborn model in parallel, and abnormity is preliminarily screened by calculating a double-track residual error and comparing the double-track residual error with a dynamic early warning threshold value. And when an exception occurs, the cloud platform receives multi-modal data including a sensor, a model state and an operation and maintenance text, performs deep root cause analysis by using a diagnosis model fused with a wind power fault knowledge graph, and generates an interpretable diagnosis report. According to the method, deep fusion of data and a physical model is realized, and the accuracy of fault monitoring, the interpretability of diagnosis and the intelligent level of operation and maintenance decision are remarkably improved through a data-physical double-track driving mode.

The invention discloses a wind power generation abnormal data analysis method and system, and relates to the technical field of data analysis, and the method comprises the following steps: constructing a wind direction change rate enhanced perception model, analyzing the potential omen of wind direction abrupt change based on an ultra-short time scale wind direction change trend curve and a wind speed fluctuation coupling index collected at multiple measurement points, and determining the wind direction abrupt change. Generating a risk early warning label of wind wheel pointing deviation; and based on the risk early warning label, executing a high-frequency yaw disturbance prediction mechanism, and predicting an inflow angle continuous offset window caused by yaw response lag by using a nonlinear time sequence evolution trend and a short-term wind direction reversal probability curve. The wind direction sudden change early warning is realized through multi-measuring-point wind direction enhanced perception and wind speed coupling analysis, the response precision is improved and the energy consumption is reduced in combination with predictive yaw compensation and torque balance, the yaw parameters are dynamically optimized by using adaptive closed loop and reinforcement learning, the inflow angle is kept stable for a long time, and the power generation efficiency and the structural safety are improved.

The invention discloses a semi-submersible wave energy-wind energy integrated power generation platform and a damping regulation and control method of a damping regulation and control system of the semi-submersible wave energy-wind energy integrated power generation platform. The platform comprises a semi-submersible wind driven generator platform, a wind driven generator set, a wave energy power generation device, an energy storage system, a sea area detection system and the damping regulation and control system. The wave energy power generation devices are divided into two groups on the inner side and the outer side of the platform, each wave energy power generation device comprises a platform inner side wave energy power generation device and a platform outer side wave energy power generation device, and each wave energy power generation device comprises a swing arm floater, a universal hinge, a connecting arm and a damping adjusting module. The semi-submersible wind power generation device and the wave power generation device are combined, and offshore renewable resources are comprehensively utilized; the six-buoy design is adopted, buoyancy is dispersed, stability and safety are improved, and meanwhile space is provided for a wave power generation device; the floaters are symmetrically arranged in a hexagonal shape and carry a damping regulation and control system, and the whole structure is more easily stabilized by adjusting the damping of the floaters in all directions.

The invention discloses an electric heating deicing system and method based on machine learning prediction and adaptive adjustment, and belongs to the technical field of wind power generation. The system comprises a multi-physical sensor array and a dynamic heating execution module which are arranged on a blade. The multi-physical sensor array and the dynamic heating execution module are both connected with a CAN bus; the CAN bus is connected with an intelligent control module, a cloud collaboration platform and an edge computing node. The method comprises the following steps: acquiring historical operation data of an original unit, and preprocessing the historical operation data to obtain preprocessed data; the original unit historical operation data comprises environmental parameters and blade state monitoring data; analyzing the preprocessed data based on a machine learning algorithm, and establishing a blade icing prediction model; real-time environment parameters and blade state monitoring data are obtained, and the blade surface icing condition is predicted based on the blade icing prediction model; and determining the heating requirement of the blade based on the surface icing condition of the blade, and carrying out deicing.

The invention discloses a wind power generation power prediction method and system based on deep learning, and the method comprises the steps: obtaining multi-source data related to wind power, carrying out the standardization processing and missing value filling of the multi-source data, and obtaining a wind power multi-source data set; extracting spatial features of meteorological satellite images in the wind power multi-source data set through a CNN (convolutional neural network), extracting features of time sequence meteorological data and wind power data based on an LSTM (long short-term memory) network, and extracting spatial layout features of a wind power plant by using a GCN graph convolutional network to obtain wind power feature data; the importance of different features in the wind power feature data is dynamically weighted through a space-time attention mechanism to obtain fused feature data; and inputting the fused feature data into a model for prediction, and outputting wind power generation power. A comprehensive feature system is constructed, and the wind power prediction precision is remarkably improved.

The invention discloses a dynamic monitoring method and system for settlement and inclination of a tower drum of a wind generating set, and relates to the technical field of safety monitoring of wind power generation infrastructure, the method comprises the following steps: synchronously collecting inclination data and positioning data of each monitoring point, and carrying out space-time alignment to obtain a fusion data set; decoupling the dynamic elastic response of the tower drum and the steady-state deformation of the foundation from the fused data through frequency domain analysis, and further calculating the overall inclination rate, the settlement amount and the non-uniform settlement rate of the tower drum; performing safety assessment based on the calculated parameters and a preset safety threshold, and determining a basic safety state and a risk mode; and finally, generating graded early warning information and decision information including risk positioning and trend prediction according to an evaluation result. Through the above mode, the method achieves the precise separation and monitoring of the dynamic deformation characteristics of the tower drum, improves the accuracy of safety evaluation and the timeliness of early warning, and provides an effective data support for the operation and maintenance decision of a wind generating set.

The invention relates to the technical field of wind power generation, in particular to a wind turbine generator health state diagnosis system and method. The system comprises a monitoring module used for acquiring multi-source data by using a preset sensor group; the calculation module is used for receiving the multi-source data and performing feature extraction operation on the multi-source data to obtain multi-source feature information; and the analysis module is used for fusing the multi-source feature information and evaluating the health state of the generator set based on a fusion result. Therefore, through the generator set health state diagnosis system, multi-sensor fusion, edge calculation and cloud intelligent analysis are integrated, the problem that the prior art is lack of system-level health state evaluation capability and intelligent diagnosis capability is solved, and real-time monitoring and early fault early warning of the health state of the whole wind turbine generator system are realized.

The invention discloses a wind power generation operation monitoring and control system and method, and belongs to the technical field of new energy power generation. The system comprises an analysis module, an optimization module, a control module and a feedback module. Wherein the analysis module is used for receiving operation data of wind power equipment, and realizing cross-domain data mapping, time sequence prediction and state evaluation based on a generative adversarial network and a Transform structure; the optimization module combines the prediction result and the equipment damage change, introduces uncertainty constraint and a multi-target weighting mechanism, and generates operation control parameters; the control module performs equipment operation control according to the parameters, and executes local emergency response under abnormal conditions; and the feedback module dynamically updates model parameters by using a control result, and optimizes a maintenance strategy through cloud reinforcement learning. The method can improve the operation stability, the operation and maintenance efficiency and the fault pre-judgment capability, and is suitable for the intelligent operation management of the wind power equipment.

The invention discloses a wind power generation energy storage load intelligent prediction and power distribution management method, and relates to the technical field of new energy power generation, and the method comprises the steps: generating a clock synchronization signal when a phase gradient quantity exceeds a stable threshold value, correcting the node voltage phase deviation of a power distribution network according to the clock synchronization signal, and outputting a whole network synchronization voltage waveform; inputting an energy storage charging and discharging control instruction into the power flow optimization model, and generating a voltage suppression control vector through a dynamic power deviation compensation algorithm; and extracting stability parameters in the wind power operation data, carrying out weight distribution and state matching on the energy storage charging and discharging control instruction and the voltage suppression control vector, and outputting a cooperative control instruction. According to the method, heterogeneous data such as wind speed, power and voltage are fused into multi-dimensional sequence parameters through a space-time dynamic coupling method, the phase gradient quantity is generated through phase field gradient extraction, a quantitative correlation model of wind speed fluctuation and power grid response is established, and the load prediction accuracy is improved.

The invention discloses a floating wind power platform self-energized carbon capture and deep sea sealing integrated system, which comprises a floating platform foundation, which comprises a plurality of buoy parts, and the buoy parts are integrated through connecting parts; the double-path carbon capture subsystem comprises an air carbon capture module and a seawater carbon capture module; the air carbon capture module introduces air into the adsorption module through negative pressure, and selectively adsorbs CO2 in the air; the seawater carbon capturing module is used for separating out CO2 from seawater in an electrochemical separation mode; the carbon sequestration subsystem is used for compressing, liquefying and sequestration the CO2 conveyed by the double-path carbon capture subsystem; and the wind power function subsystem comprises a wind power generation tower and power distribution equipment for supplying power to each subsystem. The platform is reasonable in configuration and high in function integration degree, has the advantages of wind energy driving, autonomous operation, continuous carbon capture, on-site storage and the like, is particularly suitable for deep and far sea areas without seabed pipe networks and far away from shore-based power grids, and can be used for application scenes such as wind power plant carbon compensation and ocean carbon removal.

The invention discloses a wind power plant energy management system and a dispatching optimization system, and belongs to the field of wind power generation. The system comprises a data acquisition module, a wind speed prediction module, a wake effect analysis module, a power prediction and distribution module, an optimization scheduling module, a dynamic adjustment module, an energy efficiency evaluation module and a communication control module which work cooperatively. Through multi-source data fusion and dynamic collaborative optimization, the comprehensive performance of the wind power plant is remarkably improved, on the operating efficiency level, the system combines the space-time convolutional neural network and the multi-target optimization algorithm, high-precision prediction of minute-level wind speed and optimal distribution of whole-field power are achieved, energy loss caused by the wake effect is effectively reduced, and the wind power generation efficiency is improved. The output strategy is dynamically adjusted according to the health state of the fan, and the fatigue loss of the equipment is delayed while the generating capacity is maximized; on the power grid adaptability level, a self-learning mechanism based on the frequency disturbance qualified rate is introduced, and frequency modulation response parameters are optimized in real time.

The invention relates to the technical field of wind power generation, and particularly discloses a duct double-turbine supercharged windmill which comprises a duct and a blade type rotating structure arranged in the duct. The duct sequentially comprises a first contraction section, a first straight section, a second contraction section and a second straight section in the air flow direction, and the diameter of the first straight section is larger than that of the second straight section. The blade type rotating structure comprises a rotating shaft, a first-stage turbine and a second-stage turbine, the first-stage turbine and the second-stage turbine are arranged on the rotating shaft, the first-stage turbine and the second-stage turbine are correspondingly arranged on the first straight section and the second straight section respectively, and the rotating shaft is connected with the generator through a transmission structure. Structural improvement is carried out through the blade type rotating structure and the duct, the turbofan located at the front end drives the turbofan located at the rear end to rotate, and therefore the wind energy utilization rate is increased.

The invention belongs to the technical field of wind power generation equipment state monitoring and intelligent fault diagnosis, and discloses a wind turbine generator blade acoustic fault detection method based on transfer learning, and the method comprises the steps: building a standardized sample through acoustic signal simulation and multi-dimensional data enhancement, and extracting weak fault features through an STFT-Mel frequency spectrum; a dual-scale time-frequency attention module is introduced into the lightweight MobileNetV3, so that the focusing and recognition capability on early crack features is improved; by combining transfer learning with MMD domain difference and entropy minimization regularization, efficient alignment of a simulation domain and actually-measured wind field data is achieved, and the generalization performance of the model under the small sample condition is enhanced; a transverse difference spectrogram and longitudinal historical baseline self-evolution double-flow feature fusion mechanism is adopted, common-mode noise is suppressed, and single-blade positioning, progressive degradation early warning and synchronous aging recognition are achieved. The method is small in parameter quantity, low in calculation overhead and suitable for high-precision and low-cost intelligent detection of early damage of the wind turbine generator blades.

The present application relates to the technical field of wind power generation, and discloses a control method and apparatus for a grid-forming doubly-fed wind turbine generator, a generator, and a medium. In embodiments of the present application, a current reference value of a grid connection point is determined on the basis of measurement parameters of the grid connection point in combination with an internal potential parameter of a grid-forming doubly-fed wind turbine generator and a virtual impedance reference value, a voltage reference value of a grid-side converter is further determined on the basis of the current reference value of the grid connection point, and the grid-side converter is modulated on the basis of the voltage reference value, thereby implementing closed-loop control of the grid-side converter, such that a difference between an output voltage value and the voltage reference value of the grid-side converter is less than a preset threshold.

The invention discloses a wind power generation system. The system comprises a ground device; the floating device comprises an adjusting air floating piece and a first annular air floating piece, the adjusting air floating piece is movably connected with the ground device, the first annular air floating piece is connected with the adjusting air floating piece, and a through duct is defined by the inner wall of the first annular air floating piece; the wind power generation device is arranged in the duct; and the position of the gravity center of the adjusting air floating piece in the extending direction of the duct is adjustable. According to the wind power generation system, the influence of airflow disturbance, temperature change and buoyancy lift gas leakage on the floating device can be reduced, the phenomenon that the pitching posture change amplitude is large is reduced, particularly, the included angle between the wind receiving face of the blade of the wind power generation device and the airflow direction is closer to the optimal value, the effective wind energy area flowing through the blade is increased, and the wind power generation efficiency is improved. And the fluctuation of the wind energy yield along with the posture is reduced, so that the wind energy utilization efficiency and the power generation output power are improved.

A comprehensive power supply method for agricultural irrigation equipment relates to the technical field of agricultural irrigation equipment power supply, and comprises the following steps: constructing a comprehensive power supply unit which comprises wind power generation equipment, photovoltaic power generation equipment and energy storage equipment; defining a water pump motor in the irrigation equipment as a critical load, and defining other equipment in the irrigation equipment as non-critical loads; adopting an ARI MA model to obtain prediction data of wind speed and illumination intensity; calculating the predicted power generation power of the wind power generation equipment based on the wind speed prediction data, and calculating the predicted power generation power of the photovoltaic power generation equipment based on the illumination intensity prediction data; constructing a comprehensive power supply strategy model; according to the comprehensive power generation power of each device in the comprehensive power supply unit and the consumption power of the irrigation device, a power supply mode with high irrigation stability is dynamically selected after analysis of the comprehensive power supply strategy model; according to the dynamically selected power supply mode, power is supplied to the irrigation equipment through a bidirectional inverter; the problem that traditional irrigation equipment is unstable in operation is solved.

The invention relates to the technical field of wind power generation, artificial intelligence and fluid mechanics, and discloses a prediction method and device for a multi-working-condition flow field of a wind driven generator based on a neural network, and the prediction method comprises the steps: obtaining a sample data set which comprises multiple groups of multi-working-condition data; and inputting the sample data set into a physical information field adversarial neural network model for training to obtain the total loss of forward propagation of the sample data in the field adversarial neural network model. According to the total loss, determining whether training of the wake flow field prediction model is completed; and under the condition that a new round of training is carried out on the wake flow field prediction model, back propagation is carried out on the total loss, and neural network parameters are optimized. In this way, a dual-branch loss collaborative optimization mechanism is formed. A physical information neural network and a domain adversarial neural network are combined, and complementary advantages of the two are fully exerted. When the data is limited or the distribution difference is large, high-precision and physically consistent wake flow field prediction can be realized.

The invention discloses a wind power generation device and system, and the device comprises a main bag body which extends in the front-back direction, and the peripheral surface of the main bag body is a rotary surface; the annular wing is arranged on the radial outer side of the main bag body in a sleeving mode, the annular wing and the main bag body are spaced, and an airflow channel is formed between the inner wall of the annular wing and the outer wall of the main bag body; the airflow channel is divided into a plurality of air channels through components, the air channels are arranged in the circumferential direction of the main bag body, and fan blades are arranged in at least part of the air channels. By means of the coaxial layout of the main bag body and the annular wing and the combination of division of an airflow channel by a component, efficient capture and energy conversion of wind energy are achieved. Compared with a traditional floating power generation device, the wind sweeping area of the fan blades can be remarkably reduced under the same power generation power, the economical efficiency of the wind power generation device is improved, and the technical feasibility of stepping towards a higher altitude and a higher wind energy density area is achieved.

The invention discloses a wind power plant unit load reduction optimization method and system based on cabin type laser radar real-time wind measurement, and relates to the technical field of wind power generation. Constructing a load estimation model; performing real-time detection on a blade root bending moment predicted value sequence and a tower bottom vibration acceleration predicted value sequence, constructing a multi-objective optimization function including blade root bending moment, tower bottom vibration acceleration and power generation power, and setting a dynamic weight coefficient of the multi-objective optimization function according to three-dimensional wind field prediction sequence data; obtaining an optimal control variable through a multi-objective genetic algorithm based on the multi-objective optimization function; and according to the optimal control variable and vertical wind shearing in the three-dimensional area, differential adjustment is conducted on the pitch angle of each blade, and a personalized variable pitch angle sequence of each blade is obtained. According to the method, by establishing the optimization objective function fusing the load and the power, multi-variable collaborative optimization is achieved, and the safety and the economical efficiency of wind power plant unit operation are remarkably improved.

The invention discloses a blade bolt state monitoring system and method based on an acoustic emission technology in the technical field of state monitoring of wind power generation equipment. The method comprises the following steps: acquiring acoustic emission signal data of a blade bolt connection area acquired by an acoustic emission sensor array; sequentially carrying out preprocessing and feature extraction processing on the basis of the acoustic emission signal data, and then calculating a wavelet packet energy entropy value; judging whether a triggering condition of multi-sensor data fusion weighting judgment is met or not based on the wavelet packet energy entropy value; when any triggering condition is met, it is judged that the state of the bolt is abnormal, and after comprehensive state recognition is conducted through multi-sensor data fusion weighted judgment and modal acoustic emission wave velocity correction positioning, early warning information including the damage type, the severity degree and position information is output; when the triggering condition is not met, it is judged that the bolt state is normal, and circulating monitoring continues. According to the invention, accurate evaluation and early warning of the blade bolt state are realized through signal acquisition, processing, analysis and early warning.

The invention discloses an intelligent temperature control wind turbine generator blade electric heating deicing system and method, and belongs to the technical field of wind power generation. The system comprises an electric heating film arranged on a blade, a thermal infrared imager and a plurality of sensors, the electric heating film, the thermal infrared imager and the plurality of sensors are all connected with an intelligent control unit; the intelligent control unit is connected with an unmanned aerial vehicle carrying a laser radar and is used for regularly scanning ice layer distribution on the surface of the blade; and the electric heating film adopts zone control. The method comprises the following steps: acquiring environmental parameters and blade state monitoring data, and starting a deicing system; the method comprises the following steps: collecting deicing data from a deicing system in real time and preprocessing the deicing data to obtain preprocessed deicing data; establishing an intelligent temperature control model based on the environmental parameters and the blade state monitoring data; and inputting the pre-processed deicing data into an intelligent temperature control model, calculating to obtain dynamic adjustment data of the heating power, and transmitting the dynamic adjustment data to a deicing system for deicing.

The invention belongs to the technical field of wind power generation fault routing inspection and early warning, and discloses a wind power generation fault intelligent routing inspection and early warning system and a method thereof.After efficient scheduling is achieved through a routing inspection driving unit and abnormal signals are received, the unit fuses fault diffusion prediction and real-time environment data to plan a path, and potential fault influence areas are avoided; the unmanned aerial vehicle and the ground unmanned vehicle are called to execute air-ground three-dimensional inspection for the first-level abnormity, tasks in the same area are combined for the second-level abnormity and the third-level abnormity, and the sequence is optimized through path clustering; the latest idle equipment is preferentially selected during scheduling, the spare complement is automatically scheduled if the equipment fails, and the electric quantity and the communication state of the equipment are monitored in real time to plan return, so that the abnormal response time is shortened, and invalid inspection is reduced; by constructing a multi-dimensional acquisition and reinspection mechanism, a data acquisition unit covers mechanical, electrical and environmental data, and the sampling frequency is adjusted according to the difference of working conditions.