22877results 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 relates to the technical field of devices for adjusting, controlling or stabilizing power or frequency in a power grid, in particular to a rapid frequency modulation method for a wind turbine generator based on disturbance adaptive compensation, which comprises the following steps of: constructing a dynamic disturbance sensing data set by collecting frequency deviation, a frequency deviation change rate and fan state parameters; a window length and a weight coefficient of a short-time window sliding mean algorithm are dynamically optimized by adopting a genetic algorithm, background noise interference of a power grid is suppressed, and high-precision disturbance characteristics are extracted. And reducing frequency interference of inertia identification by using a reverse test signal. And updating boundary layer parameters of the sliding mode control model through an online incremental learning algorithm, dynamically adjusting the output priority based on the disturbance energy entropy, and eliminating power conflicts. A multi-island genetic algorithm is introduced to optimize a mode switching threshold value, and frequency modulation safe exit is realized in combination with adaptive power ramp rate limitation. According to the method, the frequency response speed and the multi-source cooperation efficiency in the dynamic multi-disturbance and inertia time-varying scene are remarkably improved, and the frequency secondary drop risk is reduced.

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

Systems, methods, and articles for a portable power case are disclosed. The portable power case is comprised of at least one battery and at least one PCB. The portable power case is operable to supply power to a transceiver. The portable power case is operable to be charged using a DC power source (e.g., solar panel, wind turbine, water turbine). A plurality of portable power cases, DC power sources, and transceivers are operable to form a mesh network.

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

A method and apparatus for evaluating vulnerability of monopile foundations of offshore wind turbines are provided. The method includes collecting offshore wind farm location data and wind-wave characteristic data, and simulating a wind-wave time course according to the offshore wind farm location data and the wind-wave characteristic data; determining a wind-wave dynamic load based on the wind-wave course; obtaining lateral soil resistance data of monopile foundations with a plurality of rock-soil strength parameters, and inputting the wind-wave dynamic load into a 3D finite element model; using the lateral soil resistance data of monopile foundations with a plurality of rock-soil strength parameters as boundary conditions of the 3D finite element model; and giving a limit state of monopile foundations, and determining vulnerability of monopile foundations on basis of the dynamic response result of the monopile foundations and the limit state of monopile foundations.

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

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

The invention discloses a 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 discloses a transient voltage control method and a transient voltage control system for a network following-construction hybrid wind power plant. The method comprises the following steps: 1) acquiring wind turbine generator parameters and wind turbine generator demand parameters; 2) based on the wind turbine generator parameters and the wind turbine generator demand parameters, establishing a control parameter-output frequency linearization model and a control parameter-reactive power linearization model of the network construction type wind turbine generator; 3) constructing a hybrid wind power plant double-model transient voltage controller based on the linearized model; as for the grid-forming type wind turbine generator set, the output frequency fluctuation of the grid-forming type wind turbine generator set during the transient voltage period is restrained by optimizing control parameters; for a grid-forming type wind turbine generator and a grid-following type wind turbine generator, the active power, the reactive power and the flux weakening current are optimized to reduce the terminal voltage deviation of the wind turbine generators, and meanwhile the kinetic energy storage capacity is maximized. Node voltage fluctuation can be effectively restrained, the kinetic energy storage capacity of the wind turbine generator is maximized, and the transient voltage supporting capacity of the hybrid wind power plant is enhanced.

The invention discloses a fan operation real-time dynamic adjustment control method and system based on environmental data. The method comprises the steps that multi-dimensional dynamic parameters of the environment where a fan is located are collected in real time based on a distributed sensor network; constructing a dynamic coupling prediction model, inputting the dynamic coupling prediction model into a bidirectional verification module based on multi-dimensional dynamic parameters, and obtaining an environment state prediction matrix in a future time window; based on the environment state prediction matrix, a combined adjustment instruction set of the fan pitch angle, the yaw angle and the generator torque is obtained, and the instruction set comprises main strategy parameters and an emergency tolerance interval; based on an incremental learning mechanism, model parameters are dynamically updated according to the deviation between actual environment parameters and predicted values, and the updating period is adaptively adjusted along with the change of turbulence intensity. The method has the advantages that accurate prediction and optimal control of the fan in a complex environment are achieved, the limitation that a traditional data driving model lacks physical interpretability is broken through, and the running stability and efficiency of the fan are improved.

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

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

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

The invention provides a receiving-end power grid emergency frequency control method based on an optimal frequency track, and the method comprises the steps: firstly obtaining a speed regulator-prime mover model and parameters of a synchronous unit, calculating the maximum frequency deviation under the optimal frequency track through an optimal control algorithm, building an equivalent transfer function of a wind power plant participating in frequency response based on the deviation, and carrying out the emergency frequency control of the wind power plant. For an expected accident, an evaluation model is constructed by taking a frequency safety limit value as a constraint, extra demands of system inertia and frequency modulation intensity are solved, a frequency response model containing virtual inertia and droop control is established by combining multi-type frequency modulation resource characteristics and a wind power plant equivalent model, and a frequency response model containing virtual inertia and droop control is established by taking control cost minimization as a target. And the emergency power control quantity of each resource and the corresponding virtual control parameter are optimized and solved in combination with the constraint condition, and finally the virtual inertia coefficient of the network construction type new energy station is adjusted according to the inertia and frequency modulation requirements. Various flexible frequency modulation resources are comprehensively considered, and the solving process of the emergency frequency control optimization model is simplified.

The invention relates to the technical field of new energy, in particular to a fan blade composite deicing system and method.The fan blade composite deicing system comprises a sensing layer, a decision-making layer, an execution layer and a feedback layer, and the decision-making layer comprises a graph neural network ice type recognition module, a CFD-icing coupling simulation module, a health management module and an energy consumption scheduling algorithm unit; the execution layer comprises a control unit and an execution unit, the control unit comprises a dynamic partition heating controller and a self-adaptive vibration controller, and the execution unit comprises an electric heating system and a vibration deicing system; compared with the prior art that a single deicing mode is usually adopted, and the adaptability to different ice type physical characteristics is poor, the scheme adopts a differential execution strategy, and partition electric heating control is carried out on different chord length areas of the blade based on the ice type recognition result; and high-frequency piezoelectric standing wave vibration is adopted for glaze ice, low-frequency eccentric wheel vibration is adopted for glaze ice, and the beneficial effects that the deicing efficiency is remarkably improved, invalid energy consumption is reduced, and damage to blades is reduced are achieved.

The invention relates to a wind power plant yaw control method based on a wind process. The method comprises the following steps: 1, establishing a wind power plant simulation model; 2, acquiring operation state data of the wind power plant as an initial state of the wind power plant simulation model; 3, acquiring a wind speed fluctuation sequence and a wind direction fluctuation sequence of the wind power plant as the state of the wind power plant yaw control model; 4, performing off-line learning to obtain optimal yaw angle set values under different wind regime sequences; enabling the wind power plant yaw control model to observe the environment and guide a wind turbine generator to act, and completing online learning; the wind power plant yaw control model is established, the optimal yaw angle of each wind turbine generator in the wind power plant is obtained through the wind speed sequence and the wind direction sequence, and the total generated power of the wind power plant is greatly improved; and the timeliness requirement of solving the wind power plant yaw control model in practical engineering is met.

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

The invention relates to the field of energy storage grid connection and operation control, and discloses a network construction / network following type energy storage optimization configuration method and system considering frequency support and wind power consumption, a medium and equipment, and the method comprises the steps: inputting wind power output data and microgrid related parameters into a constructed network construction / network following type energy storage configuration model; solving the model, optimizing the output of each device by taking the minimum daily average cost in a planning period as an optimization target, and obtaining a typical daily operation result of each device; taking random fluctuation of fan operation as disturbance power of frequency safety to check whether the micro-grid meets the frequency safety constraint or not, and according to a check result of the frequency safety constraint, updating a grid constructing type energy storage configuration scheme and a grid following type energy storage configuration scheme; whether the relative difference between the current optimal feasible solution and the optimal solution reaches a set value or not is judged, and if yes, the optimal configuration scheme and the corresponding costs are output; and if not, recording the optimal configuration scheme and the corresponding operation result of the network construction type energy storage and the network following type energy storage, and continuing to update the energy storage configuration scheme.

The present disclosure provides a method and device for calculating fault measurement impedance of a hybrid-connected grid-following and grid-forming wind power system. The method includes: obtaining characteristics of the grid-following and grid-forming converters and a corresponding low voltage ride-through control strategy; establishing an equivalent circuit under a corresponding fault state; conducting a simplified analysis on the wind power system to obtain an equivalent network diagram; obtaining a sub-circuit diagram of each power source excitation acting alone; obtaining short-circuit currents on both sides of the corresponding system and a grid connection point voltage; superimposing to obtain total short-circuit current on both sides of the wind power system and total grid connection point voltage; and obtaining a measurement impedance at a protection installation location on the new energy source side. The method can accurately calculate the measurement impedance value at the protection installation location after the wind power system has fault.

The invention discloses a fan inertia support evaluation and control method considering power grid inertia space-time distribution. Based on wide area measurement system data, a power grid inertia space-time distribution model is constructed, inertia contribution characteristics of different areas are analyzed, and the influence of extra-high voltage direct current transmission on inertia distribution is considered. And calculating the inertia response power of the fan, quantifying the support contribution of the virtual inertia of the fan based on the frequency change rate and the regional equivalent inertia, and establishing an inertia support efficiency evaluation index system. A dynamic feedback control strategy is put forward, a reinforcement learning or adaptive gain adjustment method is adopted in combination with the real-time distribution condition of the inertia of the power grid, the virtual inertia control parameters of the fan are optimized, and the response accuracy is improved. Based on a wide-area coordination optimization method, a fan inertia support scheduling strategy is optimized, and the system frequency stability is improved. The method can effectively improve the frequency supporting capability of the new energy power grid, improves the new energy consumption level, enhances the operation safety of the power grid, and is suitable for a high-proportion new energy grid-connected smart power grid.

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.

This invention introduces a kinetic energy recovery wind-wave integrated system for offshore wind power generation. The system consists of a semi-submersible platform equipped with a fan and an internal wave energy device. The device includes a shell housing a Power Take-Off (PTO) system, featuring a permanent magnet synchronous linear motor and an active controller. The motor's stator is fixed inside the shell, while its mover is connected to a counterweight block outside the stator, linked to the shell's top via a spring. Limiters are installed at both ends of the shell to restrict the counterweight block's movement. This system utilizes the wave energy device to absorb kinetic energy, which otherwise affects wind turbine stability, and converts it into usable electrical energy via the PTO system. This enhances the stability and safety of offshore wind turbine power generation.

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.