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WO2021122347A1 - Procédé de détermination d'une durée de vie restante d'une turbine éolienne et turbine éolienne - Google Patents

Procédé de détermination d'une durée de vie restante d'une turbine éolienne et turbine éolienne Download PDF

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Publication number
WO2021122347A1
WO2021122347A1 PCT/EP2020/085671 EP2020085671W WO2021122347A1 WO 2021122347 A1 WO2021122347 A1 WO 2021122347A1 EP 2020085671 W EP2020085671 W EP 2020085671W WO 2021122347 A1 WO2021122347 A1 WO 2021122347A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
load
wind energy
energy installation
sensor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/EP2020/085671
Other languages
German (de)
English (en)
Inventor
Luis VERA-TUDELA
Annette FÖRSTER
Robert ERDMANN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Polytech Wind Power Technology Germany GmbH
Original Assignee
fos4X GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by fos4X GmbH filed Critical fos4X GmbH
Publication of WO2021122347A1 publication Critical patent/WO2021122347A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/82—Forecasts
    • F05B2260/821—Parameter estimation or prediction
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/331—Mechanical loads
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/332—Maximum loads or fatigue criteria
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/80—Devices generating input signals, e.g. transducers, sensors, cameras or strain gauges
    • F05B2270/804—Optical devices
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/80—Devices generating input signals, e.g. transducers, sensors, cameras or strain gauges
    • F05B2270/807—Accelerometers
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/80—Devices generating input signals, e.g. transducers, sensors, cameras or strain gauges
    • F05B2270/808—Strain gauges; Load cells
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the present disclosure relates to a method for determining a remaining useful life of a wind turbine and to a wind turbine.
  • the invention relates to a method and a wind energy installation in which the remaining useful life of the wind energy installation can be determined from load data and fatigue stresses on the wind energy installation estimated therefrom.
  • Embodiments of the present disclosure provide methods for determining a remaining useful life of a wind energy installation according to claim 1 and a wind energy installation according to claim 10.
  • a method for determining a remaining useful life of a wind energy installation comprises: acquisition of load data by means of at least one load sensor arranged on the wind turbine, creation of a time curve from the acquired load data, estimation of a fatigue load from the time curve of the acquired load data by means of a data processing device, assignment of the estimated fatigue load to operating conditions and / or a status of the wind turbine , and determining the remaining useful life from the estimated fatigue load, their assignment to operating conditions and / or the status of the wind turbine and an expected value for the remaining useful life.
  • a wind turbine comprising a sensor for capturing data and a data processing device.
  • the data processing device is set up to: create a time profile from the recorded load data, estimate a fatigue load from the time profile of the recorded load data by means of a data processing device, assign the estimated fatigue load on operating conditions and / or a status of the wind energy installation, and determining the remaining useful life from the estimated fatigue stress, its assignment to operating conditions and / or the status of the wind energy installation and an expected value for the remaining useful life.
  • FIG. 1 schematically, by way of example, a wind park with three wind turbines according to the embodiments described herein;
  • FIG. 2 shows an exemplary wind turbine according to embodiments
  • FIG. 3 shows a flow chart to illustrate an exemplary method for determining a remaining useful life of a wind energy installation according to embodiments
  • FIG. 4 shows an exemplary system with a wind turbine and an online-based storage and server service in accordance with embodiments;
  • FIG. 5 shows an exemplary wind turbine in operation according to embodiments.
  • FIG. 6 shows a diagram to illustrate a specific service life consumption over time.
  • the assessment of the fatigue loads can be carried out by means of blade load measurements, which are carried out with sensors that are placed in at least one part of the wind turbine, ie in at least one blade, drive train, tower upper part, tower lower part, transition piece or tower base, etc. with data-controlled, model-based and hybrid approaches, virtual sensors can be established in all other positions.
  • the measurements can be carried out onshore or offshore.
  • Fatigue loads can be estimated from measured time series.
  • the fatigue loads can be assigned to the operating conditions and the state of the wind energy plants.
  • An equivalent (measured) design load envelope can be created on the basis of a selection of representative measurements that correspond to the construction site class for which the wind turbines are designed. The site conditions for this site class can be summarized based on this set of representative measurements. In this way, the expected service life loads can be defined not only for individual systems, but also for wind parks and fleets.
  • the instantaneous and seasonal fatigue load accumulation can be estimated to define the normal behavior of single plants, wind farms and fleets. Abnormal behavior can be recognized and communicated as a potential indication of crack development (current anomaly) or turbine overload (seasonal overload). Based on the equivalent (measured) and expected service life loads, an indicator for the remaining service life can be calculated and shown.
  • FIG. 1 shows a wind farm 100 with three wind energy installations 10 as an example.
  • the wind energy installations 10 are networked with one another, as shown in FIG. 1 by dashed lines.
  • the networking enables communication, for example real-time communication, between the individual wind turbines.
  • the networking also enables joint monitoring, control and / or regulation of the wind energy installations.
  • the wind energy plants can also be monitored, controlled and / or regulated individually.
  • a wind park can contain two or more wind energy systems, in particular five or more wind energy systems, such as ten or more wind energy systems.
  • the wind farm comprises at least two wind energy installations which are spatially arranged at a distance from one another.
  • FIG. 2 shows an exemplary wind power plant 10.
  • a plurality of sensors 11, 12, 13, 14, 15 are arranged on the wind power plant 10 according to FIG. 1 by way of example.
  • Sensor 11 can be, for example, a wind speedometer.
  • the sensors 11, 12, 13, 14 and 15 can acquire data.
  • the data can be relevant with regard to the operation of the wind energy installation.
  • a data processing device 16 can be provided.
  • the data processing device 16 can process the recorded data.
  • the processed data can be transmitted via a network interface 18.
  • the network interface 18 can be set up to connect the data processing device to a data network.
  • the network interface can be set up to send data processed by the data processing device 16 to an online-based storage and server service (reference number 20 in FIG. 4).
  • the recorded load data can be sent.
  • Sensors 12, 13, 14 and 15 can be sensors that record measurement data of a wide variety of parameters, for example a rotor, a gearbox or a generator on the wind turbine.
  • One or more sensors 11, 12, 13, 14 and 15 can be attached to a wind energy installation. Consequently, unless explicitly stated otherwise, at least one sensor or a combination of several sensors should always be assumed for the present disclosure, even if only “sensor” is used in the singular for simpler addressing.
  • a sensor 11, 12, 13, 14, 15 can therefore be arranged on the wind energy installation.
  • the sensor 11, 12, 13, 14, 15 can be arranged on a rotor blade of the wind energy installation, on a turbine of the wind energy installation, on a transmission of the wind energy installation, on a tower of the wind energy installation, etc., or it can be an external sensor.
  • the sensor 11, 12, 13, 14, 15 can be a load sensor 11, 12, 13, 14, 15.
  • the senor 11, 12, 13, 14, 15 can be an optical sensor.
  • the sensor 12, 13, 14, 15 can be a fiber optic sensor.
  • the sensor 12, 13, 14, 15 can be a fiber-optic strain sensor or an acceleration sensor.
  • At least one virtual sensor can be provided at a location of the wind turbine 10 where no sensor is located, using a data-based, model-based and / or hybrid approach, from the recorded load data or a physical model of the wind turbine.
  • the sensor 11, 12, 13, 14, 15 can be connected to the data processing device 16.
  • the sensor 11, 12, 13, 14, 15 can be connected to the data processing device 16 via a wired or wireless connection. If the sensor 11, 12, 13, 14, 15 and the data processing device 16 are arranged on parts of the wind turbine 10 that can move relative to one another, such as the rotor and the nacelle, a wireless connection can be advantageous.
  • a wireless connection can be implemented, for example, via radio, in particular via a Bluetooth standard or WLAN standard.
  • the data processing device 16 can, for example, use and / or be a digital processor unit (“DPU”).
  • the data recorded by the sensor can be primary data.
  • the data processing device 16 can be set up to process the primary data take place automatically and autonomously.
  • the data processing device 16 can be set up to process the primary data into secondary data.
  • the network interface 18 can be set up to send the secondary data.
  • the amount of data to be sent can thus be reduced
  • the data collected, processed and / or to be sent are not SCADA data.In practice, the system can be independent of SCADA data, even if SCADA data can be included as an additional source of information.
  • the network interface 18 can be set up to send the primary data.
  • the data processing can then take place in the online-based storage and server service 20. In practice, this allows the raw data to be retained, for example in the event that a new evaluation option arises later.
  • the data processing device 16 can be set up to process the acquired data in real time. Furthermore, the network interface 18 can be set up to send the processed data in real time. In practice, real-time monitoring of the Wind turbine 10 can be achieved. Alternatively or additionally, the processed data can be downsampled for transmission.
  • the network interface 18 is shown in FIG. 1 as an antenna.
  • the network interface 18 can, however, be any suitable network interface and can itself have a logic circuit or a processor circuit. According to embodiments described herein, the network interface 18 can use a cellular radio standard. However, the network interface 18 can also use a wired standard, such as a telephone line or a DSL line.
  • FIG. 3 shows a flow chart for
  • Illustration of an exemplary method 300 for determining a remaining useful life of a wind energy installation according to embodiments.
  • load data can be recorded by means of at least one load sensor 11, 12, 13, 14, 15 arranged on the wind energy installation 10.
  • the load sensor 11, 12, 13, 14, 15 can be a fiber optic
  • a time curve can be created from the recorded load data.
  • an estimation of a fatigue load from the time course of the recorded load data can be carried out by means of a
  • Data processing device 16 take place.
  • the estimated fatigue load can be assigned to operating conditions and / or a status of the wind energy installation 10.
  • the remaining useful life can be determined from the estimated fatigue load, its assignment to operating conditions and / or the status of the wind energy installation 10 and an expected value for the remaining useful life.
  • the data processing device 16 can be set up to carry out this and also other processes or operations of the wind energy installation 10.
  • the processes can be carried out automatically and / or autonomously.
  • the processes can be carried out without an operator, calibration and / or corrections.
  • the system can thus be set up as a plug-and-play and / or plug-and-forget.
  • the acquired data can be stored at a low frequency. This can offer the advantage that load or fatigue measurements can be recorded and stored for the entire life cycle of the wind energy installation 10.
  • FIG. 4 shows a system with a wind energy installation 10 and an online-based storage and server service 20, such as a cloud, according to the embodiments described herein.
  • the wind energy installation can be the wind energy installation from FIG. 1, for example.
  • the wind energy installation 10 can be connected to the online-based storage and server service 20 via a data connection.
  • the data connection can have been established via the network interface 18 of the wind energy installation 10.
  • the online-based storage and server service 20 can have a corresponding interface for establishing the data connection.
  • the system can further comprise a terminal device 30.
  • the terminal device 30 can be set up to receive data from the online-based storage and server service 20.
  • the terminal device 30 can be set up to receive data that was previously sent from the wind energy installation 10 to the online-based storage and server service 20.
  • data can be made available from the wind energy installation 10 to other devices, in particular in real time.
  • the terminal device 30 can, for example, output the determined remaining period of use.
  • the terminal 10 can, for example, also be a different wind energy installation in the same or a different wind farm. In practice, this allows data between several wind turbines can be exchanged. Furthermore, the wind energy installation 10 can also receive data from the online-based storage and server service 20. The data can be data that the wind energy installation 10 has previously uploaded itself. This can be, for example, historical data and / or further processed data that have been further processed in particular in the online-based storage and server service 20. In addition, the online-based storage and server service 20 can also send other data to the wind energy installation 10. This can be the data from other wind turbines, but also software updates, for example for the sensors 11, 12, 13, 14, 15, the data processing device 16 and / or the network interface 18. Thus, the data processing device 16 can be system-specific and remotely over the course of time be adjusted. Furthermore, findings that arise in the online-based storage and server service 20 can thus be transferred to other wind energy installations.
  • system 10 can be set up to communicate with a SCADA system 40.
  • system 10 in particular the online-based storage and server service 20, can be connected to the SCADA system 40 via an interface such as an API (“Application Programming Interface”)
  • API Application Programming Interface
  • the data can also be integrated into an existing second level SCADA software via an API.
  • the method can further comprise connecting the data processing device 16 to the data network.
  • the data processing device 16 can be connected to the data network via a network interface 18 as described herein.
  • FIG. 5 shows an exemplary wind energy installation 10 in operation. As can be seen in FIG. 5, a wind acts on the wind energy installation 10 along a wind direction shown as an example. The system is therefore in operation.
  • FIG. 6 shows a service life consumption of a wind energy installation 10 plotted over the operating time of the wind energy installation 10.
  • the service life consumption can be understood in the context of the present disclosure as the life expectancy, which has already been used up at a certain point in time and / or runs between 0% and 100%.
  • the service life consumption is inversely related to the remaining service life. In particular, a high service life consumption can mean a low remaining service life, and vice versa.
  • the remaining useful life can be estimated during operation.
  • the estimate of the remaining useful life can change during operation. For example, in (seasonal) high-load phases, the estimated remaining useful life can be corrected downwards, whereas in phases with low loads, the estimated remaining useful life can also (again) increase.
  • FIG. 6 shows a comparison of the remaining useful life estimated in accordance with the embodiments described herein (solid line) with the remaining useful life specified in the factory (dashed line). As can be seen, the remaining service life in the factory follows a fixed, predetermined course, whereas the remaining service life estimated in accordance with the embodiments described herein reacts individually to occurring loads and symptoms of fatigue.
  • Fatigue load accumulation can be estimated. The seasonal
  • Fatigue load accumulation can be the accumulated fatigue loads that occur over a season, for example. The seasonal
  • So fatigue accumulation can be a short-frequency component.
  • the instantaneous fatigue stress accumulation can be the accumulated fatigue stresses that occur over a short period of time, for example.
  • the seasonal fatigue load accumulation can thus be a high-frequency component.
  • abnormal behavior of the wind turbine 10 can be determined from the estimated instantaneous and / or seasonal fatigue stress accumulation.
  • an instantaneous abnormality can be determined from the current fatigue load accumulation and / or a seasonal abnormality can be determined from the seasonal fatigue load accumulation.
  • An instantaneous abnormality can for example crack formation or crack development.
  • a seasonal abnormality can be, for example, an overload / underload situation.
  • an envelope can also be created for an equivalent or measured design load based on a selection from the recorded load data.
  • the envelope can also be a statistical load distribution or load envelope.
  • the selection of the recorded load data can be characteristic of the location of the wind energy installation 10.
  • the present disclosure can solve the underlying problem by one or more of the following factors.
  • the fatigue loads used for turbine design are measurable and can be reproduced with equivalent (measured) loads that have been reconstructed or aggregated from other locations.
  • the expected lifetime loads can be reconstructed on the basis of the statistical aggregation of site conditions, which represent the assumption of the site class for the design of wind turbines.
  • the statistical reconstructions of fatigue loads and design conditions can either be used to improve the turbine design (feedback) or to increase performance (feed-forward).
  • the current and seasonal normal loads can be used to identify abnormal loads, i.e. crack development (current) and excessive
  • the accumulated loads can be used to evaluate wind turbines, wind farms and fleets so that the remaining useful life can be provided.
  • Another important performance metric for asset management can also be used
  • Wind turbines can be designed on the basis of a continuous increase in measurements and not on the basis of simulations and data from individual locations (prototypes).
  • the normal behavior can be used to identify an immediate crack formation and the design framework (loading) with the maintenance framework
  • Measurements can be based on electrical strain gauges or
  • Proximity sensors are carried out. Load and / or fatigue measurements can be carried out for the entire life cycle of a wind turbine.
  • Performance monitoring continuously and does not require any destructive testing, such as is required for structural condition monitoring.
  • the exposure can be based on estimates from other data by modeling (SCADA, other sensors for structural properties or for wind field measurements, e.g.

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Wind Motors (AREA)

Abstract

L'invention divulgue un procédé pour déterminer une durée de vie restante d'une turbine éolienne (10). Le procédé comprend les étapes consistant à : acquérir des données de chargement au moyen d'au moins un capteur de charge (11, 12, 13, 14, 15) disposé sur la turbine éolienne (10) ; créer un profil temporel à partir des données de chargement acquises ; estimer un chargement de fatigue à partir du profil temporel des données de chargement acquises à l'aide d'un dispositif de traitement de données (16) ; attribuer le chargement de fatigue estimé à des conditions de fonctionnement et/ou à un état de la turbine éolienne (10) ; et déterminer la durée de vie restante à partir du chargement de fatigue estimé, de son attribution à des conditions de fonctionnement et/ou l'état de la turbine éolienne (10) et d'une valeur attendue pour la durée de vie restante.
PCT/EP2020/085671 2019-12-20 2020-12-11 Procédé de détermination d'une durée de vie restante d'une turbine éolienne et turbine éolienne Ceased WO2021122347A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102019135628.8A DE102019135628A1 (de) 2019-12-20 2019-12-20 Verfahren zur Bestimmung einer Restnutzungsdauer einer Windenergieanlage und Windenergieanlage
DE102019135628.8 2019-12-20

Publications (1)

Publication Number Publication Date
WO2021122347A1 true WO2021122347A1 (fr) 2021-06-24

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PCT/EP2020/085671 Ceased WO2021122347A1 (fr) 2019-12-20 2020-12-11 Procédé de détermination d'une durée de vie restante d'une turbine éolienne et turbine éolienne

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Country Link
DE (1) DE102019135628A1 (fr)
WO (1) WO2021122347A1 (fr)

Cited By (2)

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Publication number Priority date Publication date Assignee Title
CN113719431A (zh) * 2021-11-03 2021-11-30 浙江中自庆安新能源技术有限公司 一种风机塔筒剩余寿命测量方法及系统
CN116484751A (zh) * 2023-06-21 2023-07-25 北京尚文汇通能源科技有限公司 一种风电机组部件的疲劳寿命评估方法及装置

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US20150211969A1 (en) * 2012-09-18 2015-07-30 Technische Universität München Method and device for monitoring the state of rotor blades
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US20060070435A1 (en) * 2003-02-03 2006-04-06 Lemieux David L Method and apparatus for condition-based monitoring of wind turbine components
US20150211969A1 (en) * 2012-09-18 2015-07-30 Technische Universität München Method and device for monitoring the state of rotor blades
US20150167637A1 (en) * 2013-12-12 2015-06-18 General Electric Company System and method for operating a wind turbine
EP3093486A1 (fr) * 2015-05-14 2016-11-16 Hitachi, Ltd. Système informatique, système de génération d'énergie éolienne et procédé de calcul de durée de vie restante ou d'endommagement par fatigue d'éolienne
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113719431A (zh) * 2021-11-03 2021-11-30 浙江中自庆安新能源技术有限公司 一种风机塔筒剩余寿命测量方法及系统
CN113719431B (zh) * 2021-11-03 2022-02-08 浙江中自庆安新能源技术有限公司 一种风机塔筒剩余寿命测量方法及系统
CN116484751A (zh) * 2023-06-21 2023-07-25 北京尚文汇通能源科技有限公司 一种风电机组部件的疲劳寿命评估方法及装置
CN116484751B (zh) * 2023-06-21 2023-09-05 北京尚文汇通能源科技有限公司 一种风电机组部件的疲劳寿命评估方法及装置

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