5255 results about "Wind power generator" patented technology

The invention provides a wind generating set fault monitoring method and system based on voiceprint recognition, and the method comprises the steps: collecting gear box voiceprint signal data in real time, recording signal fluctuation caused by gear surface wear, and obtaining an original signal data set containing a frequency spectrum high-frequency component enhancement feature; a time-frequency analysis method is adopted for the original signal data set, non-linear interference of vibration signals is recognized, multi-scale decomposition is carried out, initial wear frequency spectrum narrow-band characteristics and medium-term harmonic components are separated out, and a frequency spectrum component set is obtained; extracting characteristic parameters related to modulation depth abnormal fluctuation from the frequency spectrum component set, identifying a gear pair meshing frequency change rule, and determining a distribution mode of a tooth surface contact noise proportion; and if the feature matching result shows that the deviation between the spectrum high-frequency component diffusion distortion feature and the reference feature library exceeds a threshold value, adding the tooth surface fatigue crack noise feature into the feature library to obtain a target feature library.

The invention relates to the technical field of state monitoring of wind generating sets, and discloses a fault early warning and service life prediction method and system for a wind generating set, and the method comprises the steps: obtaining first state data, second state data and image data of a target wind generating set, and forming multi-dimensional data; fusing the multi-dimensional data by using a multi-modal fusion model to obtain multi-modal data fusion features of the target wind generating set; and performing fault early warning and / or life prediction on the target wind generating set based on the multi-modal data fusion features. By integrating the multi-modal data, the problem that fault features are difficult to comprehensively capture by a single data source is solved, fault early warning and service life prediction are performed by utilizing the multi-modal data fusion features, the false report and missing report rate of faults is reduced, accurate quantitative prediction of the remaining service life of the wind generating set is realized in combination with the data driving model, and the prediction efficiency is improved. By improving the accuracy of fault early warning and life prediction, the wind generating set is effectively operated and maintained in advance.

The invention discloses a power equipment fault prediction method based on multi-modal data and related equipment, and belongs to the technical field of intelligent monitoring and fault prediction of power equipment, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of the power equipment; extracting features of the preprocessed multi-modal operation data and performing global feature fusion to obtain multi-modal features; inputting the multi-modal features into a pre-trained fault prediction model to obtain a fault category and an occurrence probability of the power equipment; the fault prediction model is obtained by inputting the multi-modal features into a hybrid neural network for training. Multi-modal operation data of power equipment is collected, key features are extracted by using a deep learning model, multi-modal data fusion is performed, and a fault prediction model is constructed based on a hybrid neural network. Real-time fault early warning and maintenance suggestions are provided, the operation reliability of power equipment is improved, the unplanned shutdown risk is reduced, and the method is suitable for health management of transformer substations, high-voltage power transmission equipment and wind generating sets.

The invention relates to the technical field of fault diagnosis, in particular to a wind driven generator fault diagnosis method and system based on Mamba-ResNet. The method comprises the following steps: carrying out feature extraction and feature fusion by utilizing preprocessed data, namely constructing adaptive window short-time Fourier transform (AW-STFT) to carry out dynamic time-frequency resolution analysis, carrying out parallel feature extraction and constructing a multi-dimensional heterogeneous feature vector, and carrying out a cross-modal adaptive gating fusion mechanism based on a bidirectional cross gating unit; the method comprises the following steps: constructing a Mamba-ResNet hybrid deep network model architecture; performing model training on the constructed network model architecture; and performing fault diagnosis on the wind driven generator by using the trained model architecture. A tedious manual feature design process in a traditional method is avoided, and the automation level and adaptability of a diagnosis system are remarkably improved.

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

The invention 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 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 intelligent power distribution, in particular to an optical storage and charging integrated system based on a distributed soft bus, which comprises hardware equipment: a photovoltaic module, a wind driven generator, an energy storage module, a charging pile, acquisition equipment, a photovoltaic controller, a DCDC module and a mains supply / thermoelectric generator; communication and control: a distributed soft bus, a time sensitive network (TSN) and an intelligent control unit (EMS); according to the scheme, photovoltaic, wind power, energy storage, charging piles and other hardware equipment are connected through a distributed soft bus, real-time transmission of control instructions is achieved through a TSN, data are analyzed through intelligent algorithms such as a charging and discharging model and a multi-task learning model, an optimization strategy is generated, multi-energy cooperative control (including photovoltaic direct supply and energy recovery in the case of faults) is executed through an EMS, and the energy efficiency is improved. A user can set priorities through a terminal to participate in scheduling, and finally model parameters are corrected based on feedback data to form a light storage and charging integrated system of data acquisition, prediction optimization, intelligent control, user interaction and closed-loop evolution.

The invention relates to the technical field of wind power prediction, and particularly provides a generation power prediction method and system for a wind generating set, and the method comprises the steps: firstly obtaining a continuous operation data sequence of equipment state parameters and environment parameters containing timestamp marks, and then carrying out the time sequence feature analysis, generating an equipment state feature sequence and an environmental condition feature sequence, then performing correlation modeling on the two features through feature collaborative analysis operation to obtain a coupling feature sequence reflecting multi-factor collaborative influence, and inputting the coupling feature sequence into a pre-trained power prediction model to obtain a power prediction result; an initial power prediction sequence of a target time period is generated through time context learning and power value mapping operation, finally, an error calibration model is constructed based on historical data, the initial power prediction sequence is dynamically adjusted, and a calibration power prediction sequence is generated and output to a power dispatching system for power generation plan arrangement. And the accuracy of generating power prediction of the wind generating set is effectively 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 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 parameter simulation, in particular to a parameterized simulation model design method based on a standard part structure. The method comprises the following steps: collecting bolt connection data of standard components in the wind generating set; blade surface vibration feature information is extracted according to the standard component bolt connection data; acquiring stress concentration distribution data at the joint based on the blade surface vibration characteristic information; calculating a local deformation accumulated value of the standard part according to the stress concentration distribution data at the joint; measuring the friction coefficient variation trend of the contact surface based on the local deformation accumulated value of the standard component; deducing pre-tightening force attenuation characteristics according to the change trend of the friction coefficient of the contact surface; and constructing a standard part fatigue damage prediction model based on the pre-tightening force attenuation characteristics. According to the method, the vibration characteristics of the blade surface are collected, the stress concentration distribution at the bolt connection position is indirectly extracted, and the bottleneck that the internal connection state is difficult to directly sense is broken through.

The invention provides an active-disturbance-rejection full-wind-speed power smoothing method based on kinetic energy of a fan rotor, and belongs to the technical field of wind power control. The technical problem that the output power of a traditional wind turbine generator is unstable due to random fluctuation of the wind speed is solved. According to the technical scheme, the method comprises the following steps that S1, rotor kinetic energy is calculated based on a wind turbine kinetic model, and the effect of the rotor kinetic energy in power fluctuation stabilization is analyzed; s2, designing a first-order linear active disturbance rejection controller for rotating speed loop control; s3, designing a second-order linear active disturbance rejection controller for variable pitch control; and S4, dynamically generating an active power reference value by combining the kinetic energy of the rotor and an active disturbance rejection control technology, and controlling by coordinating a rotating speed ring and a variable pitch. The method has the beneficial effects that power fluctuation can be remarkably reduced under wind speed fluctuation and complex working conditions, the smoothness of power output is improved, meanwhile, the anti-interference capability and the dynamic stability of the system are enhanced, and efficient and stable operation of the wind driven generator system is ensured.

The invention discloses a wind generating set fault prediction method and system based on edge computing, and the method comprises the steps: collecting the vibration, temperature, rotating speed and other parameters of a wind generating set in real time through a data collection module deployed by edge computing equipment, carrying out the preliminary screening and packaging through a built-in data processing unit, and synchronizing to a constructed digital twinborn model; establishing an edge calculation fault analysis model based on model data, identifying potential fault features, comparing a physical unit operation state with a digital twin model simulation state parameter by parameter, performing preliminary diagnosis in combination with the fault features, evaluating a unit health state according to a fault mechanism model, and outputting a fault prediction result and part information. The system is correspondingly provided with six units including a data acquisition interaction unit, a data processing unit and a model construction updating unit, and all the units work cooperatively, so that accurate prediction and health management of faults of the wind generating set are realized.

The invention provides a variable-speed variable-pitch wind generating set torque control method and system and a computer readable medium, and the method comprises the steps: designing a rolling optimization torque and variable pitch instruction based on model predictive control (MPC) and sliding mode surface constraint on the basis of constructing a discretization model comprising a pneumatic mechanism, a transmission chain and a variable pitch mechanism; the robustness is enhanced in combination with the self-adaptive sliding mode surface design, the control gain is dynamically adjusted through fuzzy logic, and the power tracking precision and the mechanical loss suppression are further considered; meanwhile, the delay is reduced with the assistance of wind speed prediction feed-forward compensation, the method has high robustness and adaptive capacity, wind speed fluctuation and parameter time variation can be effectively handled, equipment is protected while the power generation efficiency is guaranteed, and an efficient and reliable solution is provided for wind power generation control.

The invention discloses a full life cycle test method, system and equipment for a variable pitch system of a wind generating set, and the method comprises the steps: constructing a digital twin model of the variable pitch system, and simulating the performance of the variable pitch system under an extreme working condition through the digital twin model; deploying a self-adaptive nano sensor network, and testing and optimizing the variable pitch system; carrying out remote installation and calibration guidance on the tested and optimized variable pitch system by utilizing an augmented reality technology; through an autonomous learning monitoring mechanism, all-weather monitoring is carried out on the pitch system which is operated after installation and calibration, and potential problems and damage in operation of the pitch system are predicted; based on a biological inspiration self-repairing technology, repairing potential damage of the variable pitch system; the method comprises the following steps: acquiring operation data of a variable pitch system, proposing an upgrading scheme for the variable pitch system by virtue of an expert diagnosis mechanism, and verifying the upgrading scheme through a digital twinborn model; and an intelligent decommissioning evaluation platform is established, the residual life of the variable pitch system is evaluated, and a recovery scheme is formulated.

The invention relates to the technical field of intelligent power grids, in particular to a micro-grid energy storage optimization method and system based on big data analysis, and the method comprises the steps: obtaining the comprehensive prediction power generation through the prediction segmentation time, a wind power generator set and a solar power generator set, and obtaining the analysis power based on the prediction segmentation time and the comprehensive prediction power generation, an energy storage battery parameter set is obtained, if the analysis electric quantity is larger than or equal to a storage electric quantity threshold value, a target storage battery pack is retrieved from the energy storage battery packs through the energy storage battery parameter set, active equalizing charging is conducted on the target storage battery pack, and an updated storage battery pack is obtained; and after it is confirmed that a preset target energy supply battery pack is retrieved in the energy storage battery packs by using the analysis electric quantity and the energy storage battery parameter set, power is supplied by using the target energy supply battery pack, and an updated power supply battery pack is obtained. According to the method, the energy storage of the micro-grid can be accurately and intelligently optimized.

The invention discloses a wind driven generator structure high-cycle fatigue cross-platform simulation method based on multi-scale coupling modeling, and aims to accurately evaluate a fatigue damage state of a wind driven generator structure under the action of a high-cycle low-amplitude load. Firstly, a multi-scale model based on coupling of a fan overall structure finite element model unit and a local cracking area near-field dynamic model is established, and fan power time-history analysis is carried out based on a finite element analysis platform; then response data are transmitted to a processing platform, a rain flow counting method and a linear damage accumulation criterion are combined, the fatigue damage state of the connection key is determined, a fatigue life attenuation value is calculated, the fatigue cycle number needed by local crack propagation is determined, and the residual fatigue life of the unbroken key is updated; and finally, correcting the fan model, removing a newly added fracture key at the tip of the crack, and repeating dynamic time-history analysis and damage updating until the structure fails. According to the method, whole-process simulation and fatigue behavior analysis of fan fatigue crack propagation are realized, and important reference is provided for fan design optimization and maintenance.

Disclosed is an explainable method for monitoring a state of a generator of a wind turbine generator system on the basis of a spatio-temporal graph. The method includes: S1: acquiring data collected by a supervisory control and data acquisition (SCADA) system; S2: carrying out data understanding on the SCADA data, selecting features associated with the generator, and carrying out data preparation on the selected feature data, and obtaining valid data; S3: embedding the SCADA data, and forming a directed spatio-temporal graph data sequence; and S4: carrying out modeling of a normal behavior model of the generator on the constructed directed spatio-temporal graph data sequence, computing a full-graph-level residual and a node-level residual, computing a residual through an exponentially weighted moving average (EWMA) control chart method, carrying out full-graph-level state monitoring on the generator, forming a fault information transmission chain relation, and enhancing explainability and robustness of a monitoring result.

The invention provides a transmission chain and a wind generating set. The transmission chain comprises a bearing seat, a transmission shaft and a transmission shaft, the main shaft is rotatably supported in the bearing seat and is provided with a front end and a rear end which are opposite to each other; the gear box comprises a box body and a planet carrier arranged in the box body, the box body is fixedly connected with the rear end of the bearing seat, and the planet carrier is rigidly connected with the rear end of the main shaft; and the thrust bearing is arranged in the gear box. According to the transmission chain, the overall structure is compact, the shafting and the gearbox are highly integrated, and the transmission chain has the advantages of being light in weight, low in overall cost and the like.

The invention provides a vibration reduction buffering and posture self-adaptive adjusting method in wind turbine generator cabin transportation, and relates to the technical field of wind turbine generator transportation, which comprises the following steps: collecting transportation vibration time sequence data, after wavelet denoising and normalization processing, adopting synchronous compression decomposition and nonlinear cross-scale feature mapping to extract a dominant frequency component, and then extracting the dominant frequency component; synchronously calculating time domain parameters (a root mean square value, a peak factor and a waveform factor) and frequency domain parameters (a frequency centroid and bandwidth energy distribution); inputting the multi-dimensional features into a graph convolutional neural network to realize vibration mode classification, constructing an attitude optimization model based on a classification result, performing cooperative solution through a multi-agent depth deterministic strategy gradient algorithm, and outputting an optimal attitude adjustment parameter; after the adjustment is implemented, new vibration data are collected to update network parameters, and closed-loop control of vibration feature recognition and attitude optimization is formed. Transportation vibration impact is effectively reduced, and the transportation stability and safety of wind power equipment are improved.

The invention relates to the technical field of wind power operation and maintenance management, and particularly provides an abnormal state detection method and system for a wind driven generator system. Joint feature extraction is carried out on the multi-source monitoring data flow through a feature coding network, a state evolution feature sequence and a cross-source association feature set are generated, a pre-trained anomaly diagnosis model is called to carry out fusion analysis on the features, and an anomaly diagnosis result containing anomaly confidence distribution information is generated; and after the abnormal type, the time dimension development track and the space dimension influence component identifier are analyzed, an equipment early warning instruction containing time and space positioning information is generated and sent to the target management terminal, so that the abnormal state of the wind driven generator system can be comprehensively and accurately detected, detailed abnormal information is provided in time, and the safety of the wind driven generator system is improved. And the equipment fault risk and the operation and maintenance cost are effectively reduced.

The invention belongs to the technical field of multi-body dynamics analysis, and discloses a wind driven generator transmission chain rigid-flexible coupling multi-body dynamics analysis method based on dynamic mode decomposition, and the method comprises the steps: firstly, enabling multi-degree-of-freedom time series data to be non-linearly embedded into a high-dimensional feature space through an encoder neural network; extracting a dominant mode by utilizing intrinsic orthogonal decomposition (POD), and constructing a low-dimensional feature space; parameterized dynamic mode decomposition and radial basis function regression are adopted, a mapping relation between system parameters and Koopman operators is established, and accurate prediction of dynamic characteristics under variable working conditions is achieved; and finally, reconstructing a physical response through a decoder, and optimizing model parameters in combination with an error driving mechanism. The problems that a traditional method is low in calculation efficiency, poor in nonlinear adaptability and difficult in multi-parameter coupling prediction are effectively solved, the efficiency and precision of transmission chain dynamic analysis are remarkably improved, and reliable technical support is provided for state monitoring and service life prediction of the wind turbine generator.

The invention discloses a wind driven generator blade lightning conductor broken line point positioning method based on a radio frequency transformer, and relates to the field of wind driven generator blade lightning conductor broken line point positioning. A to-be-detected line is connected in series with the radio frequency transformer before being injected into traveling waves, and the change of traveling wave current is converted into the change of transformer output voltage; therefore, the output voltage of the radio frequency transformer is detected to judge the arrival time difference of the incident wave and the reflected wave, and then the breakpoint position is judged by multiplying the wave velocity by the time difference and dividing by 2. According to the wind driven generator blade lightning conductor breaking point positioning method based on the radio frequency transformer, the position of the lightning conductor breaking point can be efficiently, accurately, conveniently and flexibly detected at low cost, positioning measurement of disconnection of a multi-core wire part can be achieved, the specific positions of the breaking point and an abnormal point of the lightning conductor can be effectively determined, and the working efficiency is improved. After the positions of the broken line point and the abnormal point are determined, specific positions are repaired in a targeted manner, so that the cost for replacing the lightning conductor is saved.

The present disclosure discloses a method for controlling coordinated operation of multiple wind generators to avoid secondary frequency drop. The method includes the following steps: first, the Doubly Fed Induction Generator (DFIG) required for frequency support is dynamically selected based on a real-time disturbance power of a power grid and the rotating speed level of each DFIG; second, the virtual inertial control parameters are adaptively regulated according to the system frequency and the change in the rotating speed of the DFIG participating in frequency support, ensuring that the frequency support requirements are satisfied; and finally, the remaining DFIGs are controlled to release the rotor kinetic energy to support the rotating speed recovery of the selected DFIG, so as to reduce the extra energy absorbed from the power grid in the process.

The invention provides an electrical automation control system and method. The method comprises the steps that multi-source heterogeneous data in the operation process of a wind driven generator is collected; performing feature coupling processing on the multi-source heterogeneous data to obtain dynamic association features among different state parameters during power generation of the motor rotor; determining stability margins of different control loops in the wind driven generator based on the time-varying characteristic of each dynamic correlation characteristic, and performing cooperative compensation on each control signal in the wind driven generator based on all the stability margins to obtain a multi-stage compensation control signal of the wind driven generator; according to the interference gradient determined by the multi-source heterogeneous data, the cooperation coefficient of each compensation control signal in the multi-stage compensation control signals is adjusted; and performing cooperative control on each control loop based on the multi-stage compensation control signal and the adjusted cooperative coefficient of each compensation control signal. According to the scheme provided by the invention, different control loops in the wind driven generator can be cooperatively controlled under the influence of the dynamic coupling relationship of the different control loops.

The invention relates to the technical field of wind generating sets, in particular to an operation fault monitoring and early warning method and system suitable for a wind generating set, and the method specifically comprises the steps: constructing an abnormal significant coefficient of a part in each time period through the waveform characteristics of a vibration signal spectrogram of the wind generating set and the comparison with a normal vibration spectrogram; constructing an abnormal vibration coupling index of each time period based on the difference between the abnormal saliency coefficients of all the components in each time period and the overall distribution size; fitting a wind speed power curve based on data points composed of wind speed and power data at each moment; analyzing the synchronism of the distance between each data point and the wind speed power curve and the abnormal vibration coupling index, and constructing an abnormal synchronization coefficient; performing fault monitoring on the wind generating set based on the abnormal synchronization coefficient; the influence of the dynamic load on the operation fault monitoring of the generator set is avoided, the operation fault of the generator set can be found in time, and the fault monitoring accuracy of the wind generating set is improved.

The invention relates to the technical field of wind driven generator monitoring, in particular to a fan fault early warning and diagnosis method fusing multi-modal time sequence data, which comprises the following steps: respectively acquiring vibration time sequence data, current time sequence data and temperature time sequence data of a fan, and respectively calculating a first feature sequence, a second feature sequence and a third feature sequence; generating a fault early warning signal based on a three-state polarity synchronous counting mechanism; calculating a dispersion statistical value, and executing chronic fault diagnosis; when the fan is in a low-load steady-state working condition, active lubrication state detection and feedback are executed; when the first feature sequence is calculated, synchronously executing adjoint oil product pollution diagnosis; and the environment temperature is judged through the temperature time sequence data, and low-temperature working condition adaptability compensation is executed under the low-temperature working condition. The main purpose of the invention is to solve the problems in the prior art that early fault signs cannot be effectively captured due to information loss during fusion processing of multi-modal data, and real faults and external interference are difficult to distinguish.

The utility model relates to the technical field of vibration damping devices, in particular to a wind driven generator tower tuning inerter vibration damping device which comprises a sliding support, the sliding support is mounted on a rest platform, and a mass block is slidably arranged on the sliding support in the horizontal direction; the counter-force frame assembly is fixedly connected to the rest platform, and an adjustable spring assembly and a liquid inerter damper assembly are connected between the counter-force frame assembly and the mass block; wherein the adjustable spring assembly and the liquid inerter damper assembly can rotate in the horizontal direction relative to the mass block and the reaction frame assembly, the adjustable spring assembly is used for buffering movement of the mass block in the horizontal direction, and the liquid inerter damper assembly is used for absorbing vibration of the mass block. According to the device, the damping effect of the device is improved through the amplification effect of inertial damping, the damping device can achieve balance of strong vibration energy with the small mass size, all the components of the device are supported to be disassembled and assembled, and installation and transportation are convenient.