16481results about "Wind energy generation" patented technology

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

The invention discloses a stability analysis method of a network tracking / constructing hybrid station based on an impedance model, and relates to a modeling and stability evaluation technology of a wind power plant grid-connected system. The method comprises the following steps: firstly, respectively establishing sequence impedance models of a following network type direct-driven fan and a construction network type direct-driven fan based on a harmonic linearization method, and verifying the accuracy of the established models in different frequency bands by combining an impedance sweep frequency method; secondly, on the basis of considering the influence of line impedance, deducing to form an impedance network structure of a hybrid wind power plant grid-connected system, and constructing a stability judgment condition suitable for system analysis; and finally, based on the established impedance stability criterion, identifying possible oscillation risks of the system in subsynchronous and supersynchronous frequency bands. The method is suitable for wind power system stability analysis in various hybrid access scenes, and provides theoretical basis and technical support for subsequent system parameter setting, control strategy optimization and oscillation suppression measures.

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 provides a wind turbine generator voiceprint fault recognition method, and relates to the technical field of wind turbine generator state monitoring and fault diagnosis, and the method comprises the steps: carrying out the noise reduction of an original audio signal through variational mode decomposition, screening a target mode of which the frequency, energy and kurtosis accord with features, and reconstructing the signal; extracting a Mel frequency cepstrum coefficient and a sensing noise robust coefficient, and generating multi-dimensional voiceprint data in combination with statistical characteristics such as a frequency spectrum gravity center, a spectrum entropy, energy, kurtosis and a zero-crossing rate; constructing a support set based on the prototype network, realizing small sample fault classification by calculating the Euclidean distance between the feature vector and the prototype vector, and outputting a preliminary result; judging whether the voiceprint is abnormal according to a preset threshold value, if so, storing the voiceprint into a dynamic abnormal voiceprint knowledge base; frequently occurring abnormal samples are manually labeled and added into a support set, the prototype network is retrained to update the model, and continuous optimization of the fault recognition capability is achieved.

The invention discloses a method, a device and equipment for generating a stability control strategy of a wind power grid-connected system based on digital twinning and a medium. The method comprises the following steps: acquiring historical operation data and wind power operation data of the wind power grid-connected system; constructing a digital twinborn model and acquiring real-time measurement data according to the historical operation data in combination with a topological structure of the wind power grid-connected system; calling a preset filtering algorithm to perform state estimation optimization according to the real-time measurement data, and determining an optimal system state; extracting feature state vectors from the optimal system state and the wind power operation data; and predicting the feature state vector according to a built-in strategy through a preset target reinforcement learning model, generating a stability control strategy instruction, and issuing the stability control strategy instruction to a corresponding module in the wind power grid-connected system, thereby effectively improving the adaptability of wind power grid-connected control.

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

The invention discloses a matrix yaw system of a wind power plant. The matrix yaw system comprises a data acquisition module, a wind plant modeling module, a parameter extraction module and a yaw control module. The data acquisition module acquires a wind field original data set containing three-dimensional space coordinates and timestamps by using a multi-source sensor; the wind field modeling module calculates correlation between points by combining a covariance function through a Gaussian process regression algorithm, and constructs a three-dimensional dynamic wind field model; the parameter extraction module is used for extracting wind regime parameter vectors in the model by adopting a nearest neighbor interpolation algorithm on the basis of actual space coordinates of a fan; and the yaw control module utilizes a depth deterministic strategy gradient reinforcement learning model to generate a yaw angle adjustment instruction in combination with the wind regime parameter vector, the current yaw state of the fan and a preset maximum cumulative reward function. Through multi-module cooperation and intelligent algorithm optimization, accurate matching of wind field dynamic modeling and yaw control is achieved, and the energy efficiency and operation stability of the wind generating set are improved.

The invention discloses an on-site monitoring system for the performance of an elastic loose type anchorage system suitable for deep-sea buoys, and particularly relates to the field of deep-sea monitoring. The on-site monitoring system auxiliary comprises a central processing module, wherein the output end of the central processing module is separately connected with an information display module,an information storage module, an auxiliary recovery module and a driving module, and the input end of the central processing module is separately connected with a power supply module, a monitoring module, a correction module and a positioning module. According to the on-site monitoring system for the performance of the elastic loose type anchorage system suitable for the deep-sea buoys, the auxiliary recovery module is arranged, air bags are installed at a plurality of node positions on the anchorage system, a booster pump in a pressurization unit is used, outside air is conveyed into a floating ball unit through a pipeline unit, so that the retractable air bags are enabled to bulge upwards to float, the whole anchor system is directly floated in sections, people conveniently recycle theanchorage system greatly, the workload during the recycling process is reduced, and the practicability is high.

The invention discloses a fan blade diagnosis method and system based on voiceprint perception, and relates to the technical field of intelligent monitoring of wind power equipment, and the method comprises the steps: carrying out frequency domain compensation processing, stripping environment wind noise components, and generating a pure voiceprint signal based on a wind flow characteristic parameter set; based on the pure voiceprint signal, identifying a sensitive acoustic frequency band of the fan blade, exciting a sound wave phase synergistic effect in the sensitive acoustic frequency band, and generating an enhanced voiceprint signal; quantifying the internal damage depth of the fan blade material according to the enhanced voiceprint signal, and calculating a sound energy flow disorder degree index; according to the sound energy flow disorder degree index, the blade health state of the fan is judged through a multi-dimensional acoustic characteristic state space mapping mechanism, and a three-dimensional health assessment report is generated; through a physical level wind noise stripping technology, a transfer function matrix is generated based on vortex interference modeling and sound wave-airflow phase offset correction, frequency domain energy reweighted decoupling is realized, and environmental wind noise is accurately stripped in a strong turbulence environment.

The invention relates to the technical field of fan monitoring, and discloses a fan blade icing monitoring method and system, computer equipment, a medium and a product, the method comprises the following steps: collecting multi-source data, the multi-source data comprising data of a plurality of sensors arranged at preset positions of a fan blade, environmental meteorological data and fan operation data; fusing the multi-source data, and performing feature extraction on the fused data to obtain a comprehensive feature vector; monitoring the icing condition of the current fan blade based on the comprehensive feature vector and a preset deep learning model to obtain icing condition information; and performing early warning on the icing state of the fan blade based on the size relationship between the icing condition information and a preset threshold value. Through organic combination of multiple advanced technical means such as multi-source data fusion, deep learning, icing condition information prediction and early warning of the icing state of the fan blade, the problem of low accuracy of icing monitoring of the fan blade in a complex environment in the prior art is effectively solved.

The invention relates to the technical field of fan blade deicing, and discloses an intelligent temperature control type fan blade electric heating deicing control system and method, and the system comprises a meteorological sensing module which is used for collecting the environment temperature, humidity, wind speed and rainfall type; the blade state monitoring module is used for acquiring blade surface temperature distribution and ice layer thickness data; the dynamic decision-making module is used for generating a partition heating instruction based on the data of the weather sensing module and the blade state monitoring module; the temperature control execution module is used for converting the instruction into a gradient signal; the heating array module is used for executing electric heating deicing operation in the blade surface subareas; and the energy coordination module is used for dynamically constraining the total power of the system. The meteorological sensing module and the blade state monitoring module are integrated through the system, real-time collection and fusion of multi-source data of blade surrounding environment parameters and the thickness of an ice layer are achieved through the design, comprehensiveness and accuracy of icing monitoring are improved, a reliable basis is provided for dynamic decision making, and therefore the response capacity and early warning precision of the system to icing risks are improved.

The invention discloses a fan health state detection method and device based on multi-modal data, equipment and a medium, and relates to the technical field of intelligent fan monitoring, and the method comprises the steps: collecting fan images, sound and sensor data, extracting spatial features, synchronous vibration features and time sequence features through employing a multi-modal processing technology, and carrying out the detection of the health state of a fan; the features are integrated through the multi-mode self-adaptive space-time coupling decision network to generate health scores, the health state of the fan is evaluated according to the scores, early warning is performed, early risk detection and active early warning are achieved, and the accuracy and timeliness of fan fault detection are improved.

The invention provides a wind power plant AGC optimization control method and system based on model prediction control, and relates to the technical field of model control, and the method comprises the steps: building an adaptive prediction model, carrying out the online identification of parameters through a recursive least square method, constructing an active power optimization objective function, solving a control instruction sequence, designing a feedback compensator, and correcting a control instruction. And finally, issuing to each fan to execute and realize closed-loop control. According to the method, the AGC control precision of the wind power plant can be improved, the power fluctuation is reduced, and the power grid friendliness and the adaptability of an AGC control system are enhanced.

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 turbine generator intelligent control system and method based on rotation laser radar and artificial intelligence fusion. The wind turbine generator intelligent control system comprises a dynamic scanning monitoring module which can carry out three-dimensional point cloud data collection on the azimuth angle range of 0-300 m and + / -90 degrees in front of a generator; the space-time feature processing module comprises a voxel grid denoising unit, a coordinate conversion unit and a space-time convolutional neural network ST-CNN; and a hybrid optimization control module. A variable-speed rotating multi-line laser radar is adopted, a dynamic scanning strategy is combined, and accurate monitoring of a large-range three-dimensional wind field in front of a unit is achieved. A space-time convolutional neural network ST-CNN is constructed, spatial features and time sequence information are creatively fused, and future wind conditions are accurately predicted. A hybrid control algorithm combining model predictive control MPC and deep reinforcement learning DRL is proposed, and control parameters are dynamically generated by taking power generation efficiency and unit load as optimization targets. From hardware deployment to software configuration, a complete and highly integrated intelligent control system is formed.

The invention discloses a power coupling-considered capacity ratio estimation method for a network-constructing / network-following multi-machine hybrid system, and the method comprises the steps: building a GFL-GFM hybrid system model, combining VSG power coupling small signal modeling and a dynamic relative gain matrix (DRGA), and quantifying the coupling strength of active / reactive power to output voltage; the frequency domain impedance characteristic of the GFM converter is simplified, and an ideal voltage source series equivalent reactance equivalent model is constructed; deriving an analytical relationship between a generalized short-circuit ratio (gSCR) and a capacity ratio based on the model, and generating a ratio formula in combination with engineering boundary sub-scenes to form a ratio range suitable for multiple scenes; for coupling time-varying characteristics, a closed-loop mechanism of'mixed mode-coupling sensing-correction calculation-execution feedback 'is constructed, and the ratio is dynamically adjusted. According to the method, coupling quantization and multi-machine coordination control are deeply fused, the problem that the external characteristic model precision and the multi-working-condition adaptability are difficult to consider under complex environment coupling is effectively solved, and the engineering applicability of the hybrid fan system is enhanced.

The present disclosure provides a fault measurement impedance calculation method and device for a hybrid grid-following / grid-forming wind power system. The method comprises: acquiring characteristics of a grid-following converter and a grid-forming converter, and corresponding low-voltage ride-through control strategies; establishing an equivalent circuit corresponding to a fault state; performing simplified analysis on a hybrid wind power system to obtain an equivalent network diagram; obtaining a branch circuit diagram under individual action of each power excitation; obtaining short-circuit currents and grid-connected point voltages on two sides of the system corresponding to the individual action of each power excitation; performing superimposition to obtain a total short-circuit current and a total grid-connected point voltage at the two sides of the hybrid wind power system; and obtaining a measurement impedance at a protection installation point on a new energy side. In the method, a measurement impedance value at a protection installation point of a hybrid wind power system after a fault occurs can be accurately calculated, so that the setting calculation of distance protection is ensured, so as to accurately reflect fault characteristic information of a hybrid AC system, thereby providing a basis for the distance protection applicability research of the hybrid wind power system, and thus ensuring the safe and stable operation of new energy systems.

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 provides a self-adaptive variable-pitch control method and system based on turbulence intensity dynamic compensation, and relates to the field of wind power generation. Firstly, a real-time turbulence data set in the operation environment of a wind turbine generator is obtained, and the real-time turbulence data set comprises multi-dimensional turbulence characteristic units which are continuously collected and provided with time sequence marks; performing feature extraction on the real-time turbulence data set to obtain turbulence feature distribution containing spatial distribution features and dynamic change features, then generating a dynamic compensation strategy containing a related compensation parameter set according to the turbulence feature distribution, and adjusting control parameters of a preset variable pitch control model based on the dynamic compensation strategy to obtain the variable pitch control model. And finally, a pitch angle adjusting mechanism of the wind turbine generator is controlled through the corrected parameters, self-adaptive adjustment of the pitch angle is achieved, and therefore the operation stability and the output power stability of the wind turbine generator under the complex turbulence working condition can be improved, the damage risk of parts can be reduced, and the service life of the wind turbine generator can be prolonged.

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 provides an optimization scheduling method and system for power distribution of an electrolytic cell group under a wind power fluctuation condition, and relates to the technical field of wind power grid-connected power generation control, and the method comprises the steps: collecting wind power plant power data and electrolytic cell operation parameters, carrying out the adaptive segmentation processing of the power data, constructing a fluctuation feature space, predicting the fluctuation risk, and carrying out the optimization scheduling of the power distribution of the electrolytic cell group. And an electrolytic cell regulation capability evaluation system is established and dynamically grouped, and a power regulation matrix is constructed to calculate an optimal power distribution proportion, so that coordinated regulation of wind power fluctuation by the electrolytic cell group is realized, and the system operation stability and the energy utilization efficiency are improved.

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

The invention belongs to the technical field of fan damage detection, and particularly discloses an unmanned aerial vehicle-based offshore fan blade damage detection system, which comprises a detection partition determination module, an unmanned aerial vehicle acquisition control module, a damage image acquisition module, a fan damage analysis module and a fan damage feedback terminal. According to the method, the damage point and the blade area are positioned through preliminary cruising of the unmanned aerial vehicle, and the fan array is divided, so that the dilemma that a traditional detection mode is easily interfered in an offshore high-wind-speed environment is changed, and the complicated offshore environment can be flexibly coped with; meanwhile, the shooting distance and the shooting time point of the unmanned aerial vehicle are adjusted by combining the space coordinates of the damage point, the real-time blade rotating speed and the real-time offshore wind condition to generate a shooting parameter instruction, so that the limitation and the monitoring dead angle limitation of traditional fixed-view-angle monitoring are broken through; and finally, crack analysis of different depth layers is carried out on the optical image and the ray image, so that the damage degree can be evaluated more comprehensively and accurately.

The invention provides a mountain wind power plant fan icing intelligent forecasting method and system, and relates to the field of fan icing forecasting. The method comprises the following steps: determining a meteorological element threshold value of a fan in an icing scene and a meteorological element threshold value of the fan in an ice melting scene; establishing a temperature-wind speed-power dynamic response model; constructing a three-layer linkage model for fan icing prediction; the first-layer model judges an icing condition in a preset range around the station based on a meteorological element threshold value in an icing scene; the second-layer model is started before the first-layer model judges the icing condition, and a convection radiation ice melting scheme is coupled when the meteorological condition reaches a meteorological element threshold value in an ice melting scene; and on the premise of the second-layer model, the third-layer model timely triggers graded early warning based on a temperature-wind speed-power dynamic response model, and outputs a forecasting and early warning result. According to the method, the regional adaptability and precision of fan icing forecasting are improved, risk level early warning is optimized, and about 10% of power generation loss of a wind power plant is reduced.

The invention provides a load frequency control method for a multi-region combined power system considering wind power integration, and the method comprises the following steps: building a combined state space model of the multi-region combined power system, comprising a state space sub-model of a wind power region, a state space sub-model of a thermal power region and tie line power deviation constraint relationships among the regions; a combined control topology of the multi-area combined power system is established, the combined control topology comprises a sub-control topology of a wind power area, a sub-control topology of a thermal power area and a tie line power deviation feedback topology, and the tie line power deviation feedback topology comprises feedback devices which are the total number of the wind power area and the thermal power area; and controlling equipment in the wind power area and the thermal power area to carry out power grid load frequency adjustment by using a control instruction output by the combined control topology. According to the technical scheme, dynamic response speed characteristics of wind power and thermal power and comprehensive frequency modulation requirements are considered, and optimal load frequency adjustment is carried out on the multi-region power system.

The present application provides a converter and a chopper transistor blocking control method therefor. According to the chopper transistor blocking control method, when switching transistors in a converter need to be blocked, switching transistors in a primary side circuit connected to a transformer of the converter are blocked, and switching transistors in a secondary side circuit are blocked when the current on a branch where a secondary side winding in the transformer is located is zero, or batch blocking of the switching transistors in the secondary side circuit is completed after the current on the branch where the secondary side winding is located recirculates to zero, thereby avoiding overvoltage damage to the switching transistors caused by the current on the branch where the secondary side winding is located recirculating by means of the junction capacitance of the switching transistors in the secondary side circuit.

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 relates to the technical field of wind turbine generator state monitoring, in particular to a wind turbine generator state monitoring method and system based on multi-source heterogeneous data fusion, and the system comprises a data collection unit, a dynamic noise processing unit, a damage feature analysis unit and a health state output unit. A data acquisition unit synchronously obtains blade strain, unit rotating speed and environment vibration signals through hardware timestamp alignment, and a dynamic noise processing unit removes rotating speed coupling noise by using a two-parameter coupling model, a temperature-pitch angle three-dimensional correction curved surface and closed-loop feedback in combination with variable step blanking and local band elimination protection. The damage feature analysis unit extracts microcrack features by adopting multi-scale wavelet packet decomposition and kurtosis detection, and performs cross validation by fusing a vibration mode confidence factor, and the health state output unit generates a topological graph containing a crack position, an expansion trend and an alarm confidence level, so that the problems of insufficient multi-source data fusion and false and missing judgment of damage are solved, and the safety of the system is improved. And the monitoring precision is improved.