21120 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 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.

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.

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.

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 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 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.

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. 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 relates to the technical field of wind turbine generator operation and maintenance, in particular to a wind turbine generator probability fatigue life prediction method and device, equipment and a medium. The method comprises the following steps: constructing a digital twinborn model of a wind turbine generator; based on operation load data, collected in real time, of the wind turbine generator, a digital twinborn model is adopted to determine corresponding stress data; and inputting the stress data into a pre-trained probabilistic fatigue life prediction model to obtain a current probabilistic fatigue life result of the wind turbine generator. According to the method, the current stress data of the wind turbine generator are calculated by constructing the digital twinborn model of the wind turbine generator, and the current probability fatigue life result of the wind turbine generator is obtained based on the pre-trained probability fatigue life prediction model. Therefore, according to the method, through real-time interaction of the physical unit and the virtual model, accurate prediction of the fatigue damage of the wind turbine generator is achieved, that is, the method can reflect the operation state and the fatigue damage condition of the wind turbine generator in time, and the real-time performance is high.

A disk-pack turbine for use, for example, in systems and methods in at least one embodiment for separating fluids including liquids and gases into subcomponents by passing the fluid through a fluid 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. The rotors and / or disks having waveform patterns on at least one side.

The invention relates to the technical field of wind power operation and maintenance technologies, and discloses a combined heat dissipation system of a fan, the system comprises a multi-source sensing module, a decision control module, a heat dissipation execution module and a safety early warning module, and by setting multi-mode cooperative control, when fan heat dissipation regulation and control are carried out, the heat dissipation of the fan can be controlled in a multi-mode cooperative mode; a dynamic matching mechanism of temperature field characteristics and heat dissipation modes is established, and air cooling, liquid cooling and phase change strengthening modes are automatically switched in real time according to the temperature rise rate and the heat distribution gradient, so that the system can accurately adapt to heat dissipation requirements under different working conditions, timely heat dissipation of a high-temperature core area is guaranteed, the heat dissipation response accuracy is improved, and the service life of the system is prolonged. By arranging the three-dimensional thermal field balancing system, the hot area distribution difference of the bearing, the winding and the power module is recognized in real time, the cooling liquid flow and the airflow guide angle are dynamically adjusted, the three-dimensional thermal unbalance phenomenon in a traditional heat dissipation mode is eliminated, and it is guaranteed that the temperature gradient of all areas in the motor is always kept within a safety threshold value.

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 provides a DLH-IHBA-based adaptive cycle engine control law optimization method, and belongs to the technical field of aero-engine control, and the method comprises the steps: building an adaptive cycle engine model, and determining an optimization variable based on the adaptive cycle engine model; according to the control mode of each self-adaptive cycle engine, constraint conditions and an objective function based on optimization variables are determined, the control modes comprise a steady state control mode and a transition state control mode, and the steady state control mode comprises a minimum fuel consumption control mode, a maximum thrust control mode and a minimum turbine front temperature control mode; and based on the objective function and the constraint condition, optimizing the control rule of each control mode by adopting a multi-dimensional learning prey strategy fused with a badger optimization algorithm to obtain an optimal control variable, and adopting Tent mapping as initial particle swarm position mapping. According to the scheme, the convergence speed of engine control rule optimization is increased, and the optimization effect and the engine performance are improved.

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 provides a fan gearbox dynamic integration fault detection method and system based on feature fusion, and relates to the technical field of data processing, and the method comprises the steps: obtaining each preliminary feature set; performing feature-to-feature and feature-fault nonlinear relation quantization on each preliminary feature set to obtain each preliminary screening feature set; analyzing the contribution degree of each feature in each preliminary screening feature set, executing feature fine screening, and generating each fine screening feature set; and calling a multi-agent integrated fault detection system, executing multi-layer agent information sharing and collaborative decision from bottom to top based on each fine screening feature set, and generating fan gearbox fault detection information. According to the method and the device, the technical problem of low fault detection accuracy caused by lack of deep feature mining and dependence on a single-machine diagnosis system in the prior art is solved, and the technical effect of improving the fault detection accuracy is achieved by constructing the multi-agent integrated fault detection system, so that the false alarm rate and the missing report rate are remarkably reduced.

The invention discloses a wind turbine generator running state analysis method and system based on rotating speed monitoring, and relates to the technical field of wind power generation, and the method comprises the steps: collecting wind turbine generator data and historical data of the wind turbine generator data, and carrying out the preprocessing of the collected wind turbine generator data; performing rotation speed monitoring verification based on fault tree analysis, training a machine learning model in combination with historical data, and performing fault diagnosis by using a rotation speed monitoring verification result; and automatically generating a repair scheme based on the fault diagnosis result, executing the repair scheme, and recovering the normal working state of the wind turbine generator. According to the wind turbine generator running state analysis method based on rotating speed monitoring, data modeling is carried out by using the optimal characteristics of the wind turbine generator, the correlation among the characteristics is captured through the multi-dimensional Gaussian model, the fault probability of each component is calculated, the fault diagnosis precision is improved, and the fault diagnosis efficiency is improved. And the limitation of only depending on a single data source in the prior art is overcome.

The invention relates to a structural fatigue evaluation method and system for a tower drum of a wind turbine generator, equipment and a storage medium, and the evaluation method comprises the steps: S1, obtaining the shape functions of a plurality of nodes on a target tower drum; the plurality of nodes are uniformly divided along the height direction of the target tower drum; s2, acquiring operation data of a target wind turbine generator; s3, inputting the operation data and the shape function on each node into a preset wind turbine generator model to obtain a bending moment time sequence corresponding to each node on the target tower; s4, according to the bending moment time sequence of each node, the accumulated damage degree of each node is determined; and S5, according to the accumulated damage degree of the at least one node, outputting a structural fatigue evaluation result of the target tower drum. According to the evaluation method, the structural fatigue condition of the tower drum can be accurately estimated without depending on an external sensor.

The invention discloses a multi-axial fatigue and reliability analysis method for a semi-submersible wind turbine supporting structure, which comprises the following steps of: firstly, determining an input load power spectral density function of each part of an offshore semi-submersible wind turbine by combining a motion theory, a wind speed spectrum theory and a wave spectrum theory of the offshore semi-submersible wind turbine; carrying out finite element analysis on the offshore wind turbine in a frequency domain according to the load power spectral density function, determining a dangerous point through a frequency domain method, calculating damage by using a W-B critical plane method, predicting the fatigue life of the dangerous point in a time domain, verifying that wind-induced damage is far greater than wave-induced damage, and finally solving a failure probability curve of the wind turbine support structure. And multi-axial fatigue and reliability analysis of the semi-submersible wind turbine supporting structure is completed. According to the method, the randomness of the marine environment and the calculation interval of the wind turbine load can be well unified, the accuracy of predicting the fatigue life of the wind turbine supporting structure is improved, and the method has universality for any type of wind turbine supporting structures and is easy and convenient to operate and wide in application range.

Systems and methods for providing lubrication to a gas turbine engine are described. An example gas turbine engine includes a plurality of bearings disposed along an axial length of a rotatable shaft to provide rotational support between a static structure and at least one of a compressor and a turbine of the gas turbine engine. The gas turbine engine further includes a fuel system operable to deliver a mixture of fuel and lubricant sequentially to at least one of the plurality of bearings and the combustor so that during operation of the gas turbine engine, at least the lubricant within the mixture is in a liquid phase prior to being received by the at least one of the plurality of bearings, and in a vapor phase once the lubricant is delivered to the at least one of the plurality of bearings.

The invention discloses a wind turbine generator monitoring and early warning system based on the Internet of Things sensing technology, and relates to the technical field of the Internet of Things, and the system comprises a data collection module which collects the operation state information of key parts of a wind turbine generator in real time, and transmits the operation state data to an edge calculation unit; the prediction module is used for identifying an abnormal mode, analyzing time domain and frequency domain characteristics of the vibration signal in combination with fast Fourier transform, detecting a mechanical fault, and obtaining a residual life prediction value according to a residual life prediction model; the preliminary early warning module is used for determining an equipment health state score based on fuzzy reasoning, and performing preliminary early warning locally according to the equipment health state score; and the grading early warning module is used for carrying out grading alarm on the wind turbine generator equipment according to the mechanical fault condition in combination with the equipment health state score, and providing maintenance decision suggestions. By integrating the Internet of Things technology, edge computing and artificial intelligence, intelligent monitoring and early warning of the wind turbine generator are achieved, and the reliability and safety of equipment operation are improved.

Disclosed is an assembly for a turbomachine comprising: ⋅—a second connection element (8) having a third end (81) which is connected to a turbine shaft (2) and is mounted on a second bearing (5), and a fourth end (82) connected to a mechanical transmission device (6), the second connection element (8) having a second radial compliance (SR2); and ⋅—a third connection element (9) having a fifth end (91) fixedly mounted on a casing (1), and a sixth end (92) connected to the mechanical transmission device (6), the third connection element (9) having a third radial compliance (SR3), wherein a ratio of the first radial compliance (SR1) to the third radial compliance (SR3) is strictly lower than 10% and / or a ratio of the first radial compliance (SR1) to the second radial compliance (SR2) is strictly lower than 4%.