27058 results about "Turbine" patented technology

A gas turbine engine comprises a fan, a core turbine engine coupled to the fan, a fan case housing the fan and the core turbine engine, a plurality of outlet guide vanes extending between the core turbine engine and the fan case, and an acoustic spacing. The fan comprises a plurality of fan blades that define a fan diameter and a BEAL. The fan case comprises an inlet and an inlet length between the inlet and the fan. The acoustic spacing comprises a distance between the fan and the plurality of outlet guide vanes, and in combination with the BEAL determines an acoustic spacing ratio of the gas turbine engine.

An online testing and diagnosis method for vibration characteristics of blades of wind turbine is disclosed. Steps of testing and diagnosing blade vibration comprises: S1: installing vibration sensors at key positions of a blade, designing an adaptive data acquisition strategy, and automatically adjusting a sampling rate according to a vibration amplitude and environmental changes monitored in a real time; S2: extracting key features reflecting health status of the blade from massive data, and evaluating an impact of wind speed, temperature, and environmental factors on vibration characteristics; S3: designing a customized deep learning model for damages of the blade of a wind turbine, extracting a time sequence data and a vibration signal, identifying a damage among different types of damages and evaluating a damage degree; and S4: automatically adjusting a warning threshold based on a real-time data stream and a historical trend, and drafting a preventive maintenance plan.

The invention discloses a wind turbine generator hoisting construction tower drum operation system and a construction method thereof, and relates to the technical field of intelligent control. The problems that in the prior art, a static tension balance mechanism cannot restrain bending moment abrupt change, steel-concrete interface stress concentration causes microcrack propagation, the wave dynamic load compensation capacity is insufficient, and dynamic rigidity attenuation early warning is lacked are solved. Comprising a dynamic load prediction module, a multi-mode vibration suppression module, an offset compensation module and a digital twinborn decision module, a hoisting load is solved in real time through a multi-physics field coupling model and an improved time sequence deep learning algorithm, and interface crack propagation is suppressed in combination with traveling wave offset control and sweep frequency vibration. An improved Morison equation is adopted to drive a two-stage hydraulic servo to compensate a wave dynamic load, and a digital twin closed-loop correction mechanism is constructed based on a 5G URLLC protocol; the tower drum hoisting precision, the structural safety and the operation reliability under the complex working condition are remarkably improved.

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 wind power plant generating capacity improving method based on active wake flow yaw control optimization. The method comprises the following steps: 1) acquiring basic information data of a wind power plant; 2) establishing a CFD simulation model of the wind power plant according to the basic information data; 3) optimizing the arrangement scheme of the laser wind finding radar, and obtaining the operation data of the wind turbine generator in real time; 4) constructing a single-machine analysis wake flow model and a multi-machine wake flow superposition model, respectively comparing with the flow field simulation result data and the wind measurement result of the laser wind measurement radar, and correcting a wake flow speed distribution expression to obtain wind speed and wind direction prediction data at the hub center height of each machine site in the wind power plant; and 5) constructing a whole field optimization control model, taking maximum output as an optimization target to obtain an optimal yaw control strategy set of each wind turbine generator, and enabling the related wind turbine generators to execute yaw actions. The invention belongs to the technical field of wind power generation control, and solves the problem that the wake flow loss among wind power units in a wind power plant is difficult to reduce by the existing control strategy.

The invention relates to the technical field of wind power generation, and discloses a wind turbine generator gearbox fault diagnosis method and device and computer equipment, and the method comprises the steps: collecting multi-source data of a wind turbine generator gearbox; respectively extracting a time domain feature, a frequency domain feature, a temperature feature and a working condition feature of the multi-source data; performing weighted feature fusion on the time domain feature, the frequency domain feature, the temperature feature and the working condition feature to obtain a real-time fusion feature; obtaining historical health data of the wind turbine generator gearbox, establishing a health degree reference of the wind turbine generator gearbox based on the historical health data, and constructing a health degree model based on the real-time fusion features and the health degree reference; and performing fault diagnosis on the wind turbine generator gearbox by adopting the health degree model. According to the method, multi-source data of the gearbox of the wind turbine generator is combined, and gearbox health state evaluation and fault type identification are realized through dynamic feature fusion and deep learning modeling.

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

A method of forming a seal plate for an ACM, having steps of: forming a base having a first section exposed to flow from an ACM first stage turbine inlet when installed; a second section secured to an ACM compressor shroud; a third section secured to a first stage nozzle and ACM shroud when installed; a fourth section that is adjacent to a rotor blade of the ACM compressor rotor when installed, forming the base includes: printing thermoplastic polymer surfaces from thermoplastic polymers, that are disposed against each other, the thermoplastic polymer surfaces having differing CTEs; forming a lower support section on the base by printing along the discrete sections a mixture of a third thermoplastic polymer and a catalyst formed with metal; and forming an upper support section on the seal plate by depositing on the lower support section, along the discrete sections, via electrolysis deposition, a metallic coating.

The invention discloses a multi-modal large model interpretable diagnosis method and system for a wind turbine generator, and relates to the technical field of wind turbine generator fault diagnosis, comprising the step of combining multi-modal data (vibration, time sequence, image and text) and topological information to realize fault diagnosis through cross-modal contrast learning and topological modeling. The method comprises the steps of multi-modal feature extraction, standardization and alignment, and feature fusion through topology embedding optimization and a cross-modal attention mechanism. In the fault diagnosis process, dynamic correction and path reliability evaluation are introduced by using a regular Agent and a topology consistent Agent, weighted fusion is performed on each modal feature and a topology structure, and finally an accurate fault type and a component positioning result are output. Through combination of knowledge retrieval and a multi-Agent decision model, the adaptability and precision of fault diagnosis are improved, especially in a complex environment, the fault mode of the wind turbine generator can be effectively identified, and the system reliability is improved.

Disclosed in the present invention are a yaw wake control method and device for wind turbines in a wind farm. The method comprises the following steps: S1, inputting basic data of a wind farm; S2, by means of the basic data of the wind farm, and on the basis of an axial induction factor and a constraint condition, building a wind farm output optimization model; S3, solving the wind farm output optimization model to obtain an optimization result, the optimization result comprising an optimal yaw angle and an optimal axial induction factor; S4, converting the optimal axial induction factor into an optimal adjustment parameter; and S5, according to the optimal yaw angle and the optimal adjustment parameter, setting the operation of each wind turbine, so as to reduce the wake effect of the wind farm, thus maximizing the output of the wind farm. The present invention can reduce the wake effect of wind farms, thus maximizing the output of the wind farms.

The invention provides a wind turbine generator array wake flow management method and system for Doppler laser wind measurement, and relates to the technical field of laser, and the method comprises the steps: collecting the real-time wind speed data, wind direction data and turbulence intensity data of each generator in the front and back direction through deploying a Doppler laser radar in a wind turbine generator array; establishing a wake flow influence model containing a wake flow influence coefficient, an attenuation coefficient and a diffusion coefficient based on the data, and calculating to obtain a wake flow influence range, loss power and a recovery distance; according to the method, the wind turbine generator array is cooperatively controlled, and the power generation efficiency of the wind turbine generator array is optimized by adjusting the yaw angle or the pitch angle of the upstream unit and the rotating speed of each unit. The wind speed data acquisition precision of the wind field is improved, the wake flow influence evaluation accuracy is enhanced, and the overall power generation efficiency of the wind field is remarkably improved.

The invention belongs to the field of wind power plant grid-connected control, particularly relates to a wind power plant active power efficient distribution method considering the fatigue damage degree of fans, and aims to solve the problem that scheduling instruction power is uniformly distributed to all fans in the prior art. And the fan fault probability is increased due to the fact that the influence of power adjustment on fan fatigue damage is not considered. The method comprises the steps of fan load estimation based on stress analysis and energy conservation, low-complexity fatigue damage quantification based on an improved energy method, and construction of a multi-target optimization model with the minimum total fatigue load of a wind power plant and the most uniform fatigue load distribution between fans as targets based on a Gulin method, an entropy weight method and an improved matter element extension model. According to the method, rapid and accurate power distribution is realized, so that the fan runs in a proper state, the accumulation of fatigue damage and the risk of failure occurrence are reduced, and the overall running efficiency and economic benefit of a wind power plant are improved.

The invention relates to the technical field of wind power generation. The invention provides a fan main shaft backward movement monitoring and evaluating method and system. The method comprises the following steps: acquiring an axial displacement signal, a vibration signal, a bearing temperature signal and a lubrication state parameter of a fan main shaft in real time; based on a multi-parameter fusion algorithm, performing coupling analysis on the axial displacement, the vibration amplitude, the bearing temperature and the lubrication state to generate a comprehensive risk index; performing dynamic diagnosis on the comprehensive risk index in combination with a main shaft backward movement mechanism model, and identifying at least one fault root cause of axial force imbalance, thermal stress abnormality or lubrication failure; an alarm threshold value is dynamically adjusted according to wind speed change, temperature fluctuation and load working conditions, and graded early warning is triggered based on the fault root cause; and based on historical operation data and a real-time monitoring result, evaluating a main shaft backward movement risk level, and generating a decision report containing a maintenance priority and a technical improvement suggestion. The problem that existing fan main shaft backward movement monitoring depends on manual inspection and offline detection technologies, and limitation exists is solved.

A lubrication system for a gearbox assembly for a turbine engine. The gearbox assembly includes a gear assembly including one or more gears and one or more bearings. The lubrication system includes a sump. The sump is a primary reservoir that has a first lubricant level. The lubrication system also includes a secondary reservoir in the gearbox assembly. The secondary reservoir has a second lubricant level that is greater than the first lubricant level. A plurality of drain ports includes a first drain port and a second drain port. The lubrication system fills the secondary reservoir with lubricant between the first lubricant level and the second lubricant level and a portion of the lubricant drains though the second drain port. The one or more gears collects the lubricant to supply the lubricant from the secondary reservoir to the one or more gears or to the one or more bearings.

A method of operating a gas turbine engine comprising an engine core comprising a turbine, a compressor, a combustor arranged to combust a fuel, and a core shaft connecting the turbine to the compressor; a fan located upstream of the engine core; a gearbox that receives an input from the core shaft and outputs drive to the fan; an oil loop system arranged to supply oil to the gearbox; and a heat exchange system comprising an air-oil heat exchanger through which the oil flows, a fuel-oil heat exchanger through which the oil and the fuel flow; and a valve arranged to allow a proportion of the oil sent via at least one of the heat exchangers to be varied, is disclosed, the method comprising controlling the valve such that, under cruise conditions, an oil flow ratio of:rate⁢ of⁢ oil⁢ flow⁢ into⁢ air-oil⁢ heat⁢ exchangerrate⁢ of⁢ oil⁢ flow⁢ into⁢ fuel-oil⁢ heat⁢ exchangeris in the range from 0 to 0.59.

The invention discloses a distributed wind set virtual inertia active support control method suitable for a weak power grid, and the method comprises the steps: dynamically adjusting the inertia support strength according to the frequency disturbance of a power grid through adaptive virtual inertia adjustment; multi-mode cooperative control is adopted, and an operation mode is switched according to the frequency deviation and the change rate so as to provide an optimal response; by means of dynamic damping compensation, frequency oscillation is suppressed in combination with multiple factors and an optimization algorithm, and system damping is improved; multi-time-scale cooperative control is implemented, and inertia support, power adjustment and rotating speed recovery are coordinated at a millisecond level, a second level and a minute level; and rotor kinetic energy graded release is adopted, kinetic energy release is accurately controlled according to the frequency drop degree, and sudden rotation speed drop is avoided. In addition, additional frequency damping control is introduced to enhance suppression of frequency fluctuation. The method can significantly improve the response speed and adaptability of the wind turbine generator to the frequency fluctuation of the weak power grid, ensures the frequency stability of the power grid, and provides key technical support for stable grid connection of wind power in the weak power grid.

A turbine shroud assembly adapted for use with a gas turbine engine includes a shroud segment. The shroud segment includes a heat shield, an attachment flange, and a multi-layer coating. The heat shield extends circumferentially partway around the axis to define a portion of gas path for the gas turbine engine. The attachment feature extends radially outward from the heat shield. The multi-layer coating is applied to different surfaces of the heat shield and the attachment feature of the shroud segment.

A method of operating a gas turbine engine including: an engine core with a turbine, compressor, a fuel combustor, and a core shaft connecting the turbine to the compressor; a fan; a gearbox that receives an input from the core shaft and outputs drive to the fan; an oil loop system arranged to supply oil to the gearbox; and a heat exchange system including: an air-oil heat exchanger through which the oil flows; a fuel-oil heat exchanger through which the oil and the fuel flow; a temperature sensor downstream of the fuel-oil heat exchanger; and a valve arranged to allow oil or air flow rate through at least one heat exchanger to be varied, the method including: determining if the fuel temperature has increased above a set threshold at cruise conditions; and in response, controlling the valve so as to change the flow rate through the heat exchanger.

The invention discloses a pitch angle dynamic compensation method based on multi-radar space-time synchronization, and relates to the technical field of wind power generation. In order to solve the problem that the pitch angle control precision is insufficient due to the fact that three-dimensional wind field space-time characteristics cannot be captured in traditional single-point wind speed measurement, a wind wheel plane three-dimensional space-time speed field is synchronously constructed through a horizontal-vertical radar array, and blade segmented aerodynamic load distribution is calculated in real time in combination with a flow field-structure coupling model; establishing a vorticity transport equation under a rotating coordinate system based on a vorticity-shearing force transfer function, mapping dynamic load deviation through a neural network to generate a reference variable pitch instruction, and fusing the tower shadow interference feature vector to perform pre-compensation; a quadratic programming algorithm is adopted to resist saturation optimization, so that the variable pitch rate strictly follows a dynamic constraint curve of a hydraulic system. According to the method, the problem of aerodynamic load phase delay is effectively solved, periodic load fluctuation caused by the tower shadow effect is reduced, and technical guarantee is provided for stable operation of a large wind turbine generator under the complex wind condition.

A turbine engine with an engine core defining an engine centerline and comprising a rotor and a stator. The turbine engine including a set of composite airfoils circumferentially arranged about the engine centerline and defining at least a portion of the rotor. An airfoil in the set of composite airfoils including a composite portion extending chordwise between a composite leading edge and a trailing edge and a leading edge protector coupled to the composite portion.

The invention discloses a wind turbine generator operation and maintenance knowledge base construction method based on a large model and mechanism self-learning. The wind turbine generator operation and maintenance knowledge base construction method comprises the steps of wind turbine generator operation and maintenance domain knowledge Schema definition and large model cue word template design used for wind turbine generator operation and maintenance knowledge extraction; obtaining operation and maintenance multi-modal data of the wind turbine generator, performing preprocessing, and performing knowledge extraction through a large model based on a designed cue word template; a dynamic knowledge association and wind turbine generator operation and maintenance knowledge base fault mechanism self-learning updating mechanism is established, operation and maintenance data and a knowledge graph are associated in real time, and the knowledge base is automatically learned and updated through an exception triggering mechanism; and constructing and storing a wind turbine generator operation and maintenance knowledge graph based on a knowledge extraction result, generating a semantic association sub-graph through clustering, generating a sub-graph clustering report, and realizing efficient knowledge retrieval. Based on the above content, the wind turbine generator operation and maintenance knowledge base which is efficient, accurate and updated in real time is constructed.

A system and method in at least one embodiment provides magnetic levitation to a vehicle above and / or partially about a rail or track using a flux field generator having a plurality of members having mated waveform patterns. In a further embodiment, the magnetic levitation also uses a distribution system. In a further embodiment, the plurality of members forms a disk-pack turbine. In a further embodiment, the flux field generator is not vehicle based.

A pair of waveform rotors and / or disks for use in a disk-pack turbine used, for example, in systems and methods in at least one embodiment for separating fluids including liquids and gases into subcomponents by passing the fluid into an expansion chamber and then through at least a portion of a waveform pattern present between at least two rotors and / or disks. The rotors and / or disks having waveform patterns on at least one side.

A system and method in at least one embodiment for separating fluids including liquids and gases into subcomponents by passing the fluid through an intake chamber into an expansion chamber and then through at least a portion of a waveform pattern present between at least two rotors and / or disks. In at least one embodiment, the waveform patterns include a plurality of hyperbolic waveforms axially aligned around a horizontal center of the system.

The invention belongs to the technical field of wind turbine yaw control, and relates to a wind turbine yaw cooperative control method and system based on a yaw wake flow model. The method comprises the following steps: selecting three wind turbines as target units; calculating a power loss rate to obtain a wake flow loss evaluation index; determining the wind turbine influenced by the wake flow according to the wake flow loss evaluation index; establishing a target function taking the total output power of the wind turbines as an optimization target, establishing a function relational expression between the yaw angle and the incoming flow wind speed of the downstream fan, and determining a relational expression between the incoming flow wind speed and the output power by utilizing a static power curve of the adjacent wind turbines; and the incoming flow wind speed is set, and the yaw angle of the wind turbine under the maximum output power is determined. On the basis of a three-dimensional yaw wake flow model of the wind turbine, a target function with the total output power of the three wind turbines as an optimization target is established, an optimization strategy of the three wind turbines is obtained through yaw power calculation, and active yaw cooperative control over the wind turbine units at different wind speeds is achieved.

The invention discloses a wind turbine generator state monitoring and fault tracing method and system, and relates to the technical field of wind power equipment monitoring. The method comprises the following steps: based on a plurality of vibration-acoustic emission sensors, collecting state sample data of the large fan component; feature index extraction is carried out; performing normalization processing on the feature indexes to obtain corresponding feature values; establishing a wind turbine generator large component fuzzy fault tree model; mapping the wind turbine generator large component fuzzy fault tree model into a Bayesian network model; optimizing the Bayesian network model based on the characteristic values; inputting the running state characteristic values of the large components of the fan transmission chain system under various working conditions into the optimized Bayesian network model for fault identification, and generating a running state evaluation result of the large components of the wind turbine generator; and if the large part of the wind turbine generator runs abnormally, the transfer entropy is introduced, and fault traceability diagnosis is carried out. According to the invention, the health state process monitoring of the large fan component and the fault tracing of the large fan component can be realized.

A turbine engine with an engine core defining an engine centerline and comprising a rotor and a stator. The turbine engine including a set of composite airfoils circumferentially arranged about the engine centerline and defining at least a portion of the rotor. An airfoil in the set of composite airfoils including a composite portion extending chordwise between a composite leading edge and a trailing edge and a leading edge protector coupled to the composite portion.

A turbine engine assembly generates an exhaust gas flow that is communicated through a core flow path. The exhaust gas flow is split into a first exhaust gas flow and a second exhaust gas flow. Water is extracted in a condenser from the second exhaust gas flow. The extracted water is transformed into a steam flow in an evaporator system utilizing thermal energy from at least the second exhaust gas flow. An exit flow from the condenser is communicated through an exhaust compressor and compressed to a higher pressure exit flow.

An airfoil is provided that includes a first end, a second end, a leading edge, a trailing edge, a pressure side and a suction side. The leading edge and the trailing edge are joined by the pressure side and the suction side to provide an exterior airfoil surface extending in a spanwise direction from the first end of the airfoil to the second end of the airfoil. The exterior airfoil surface are formed in conformance with a plurality of cross-section profiles of the airfoil described by a set of Cartesian coordinates set forth in Table 1. The Cartesian coordinates are provided by an axial coordinate scaled by a local axial chord, a circumferential coordinate scaled by the local axial chord, and a span location. The local axial chord corresponds to a width of the airfoil between the leading edge and the trailing edge at the span location.

The invention relates to the technical field of wind power generation, particularly provides a wind turbine blade aeroelasticity analysis method, system and equipment and a medium, and aims to solve the problem of aeroelasticity analysis of wind turbine blades. Therefore, the aeroelasticity analysis method for the wind turbine blade comprises the steps that the aerodynamic load of the blade is determined based on a pre-constructed aerodynamic model, and the aerodynamic model is constructed based on a vortex wake method of a lift line; based on a pre-constructed blade structure model and the aerodynamic load of the blade, determining structure response information of the blade; and analyzing the aeroelasticity of the blade based on the aerodynamic load and the structural response information of the blade. According to the method, the complex interaction between the unsteady aerodynamic load and the elastic deformation of the blade can be predicted, so that the influence of the aeroelastic effect on the power generation performance and fatigue life of the wind turbine is understood, and a reference is provided for reasonable design of the wind turbine blade.