Method and device for predicting remaining life of key structural positions of wind turbines

Abstract

The present invention provides a method and device for predicting the remaining life of a key structural position of a wind turbine. The method includes: establishing a digital mirror model of the wind turbine and an extrapolated digital mirror model of the external environment of the wind farm; performing combined simulation on the possible operating states of the wind turbine and the wind conditions in which it is located, extracting simulation results corresponding to the working conditions and establishing a simulation result database; establishing a wind turbine site load database based on the actual operating condition statistical data of the site and the wind condition data that may be experienced; performing equivalent fatigue load calculation and analysis on the standard design load data and the site load data based on the equivalent fatigue load algorithm; establishing a remaining life analysis model for the key structural position of the wind turbine, and performing analysis and calculation on the remaining life of the key structural position of a single unit and the entire machine. The method and device for predicting the remaining life of the key structural position of a wind turbine provided by the present invention can realize efficient and accurate online prediction and analysis of the remaining life of a wind turbine.

Description

Technical Field

[0001] The invention relates to the technical field of wind power generation, and in particular to a method and a device for predicting the remaining service life of a key structural position of a wind turbine generator set. Background Art

[0002] At present, my country's wind power industry is developing rapidly, especially the construction of offshore wind power and large bases. The units in its wind farms are restricted by environmental conditions and factors of the wind farm itself. There are situations that are inconsistent with the design conditions or even seriously deteriorate, which puts higher requirements on the safe and reliable operation of the unit equipment. As the capacity of wind turbines continues to increase, the unit's deadweight, impeller diameter, and tower height have also increased exponentially. Especially in low wind speed areas, in order to obtain better wind resources, ultra-high towers and large impeller units are usually used, resulting in a significant increase in unit loads. There is a great deal of uncertainty in the life, safety and reliability of the units, which brings serious challenges to the safe operation of wind farms. Due to the limitation of onshore resources, large-scale offshore wind power development is being implemented both at home and abroad, especially deep-sea large impellers and large-capacity units are gradually put into commercial operation. Offshore units are greatly affected by the environment and economy, especially in offshore wind power operation and maintenance. Due to the great difficulty in implementation, the operation and maintenance costs are extremely high. Therefore, it is necessary to carry out life and status monitoring of the units, accurately locate the safety hazards in the key structural positions of the units, and then reasonably arrange inspections and maintenance, appropriately adjust the operating status and related control parameters, etc., in order to greatly reduce the operation and maintenance costs of the units, prevent serious accidents, improve the reliability of the units, and improve the economic benefits of offshore wind power.

[0003] For the operation monitoring and life analysis of wind turbines, the SCADA system data during the historical operation period and the oil and vibration data extracted by the CMS system are traditionally used to extract and analyze characteristic parameters, and combined with the stress and strain sensor data at the on-site structural components or key positions, in order to achieve the purpose of remaining life analysis and accident warning. However, both types of systems are low-frequency historical operation data of the unit, and have a low correlation with the unit's operating status, especially the unit's life, making it difficult to ensure the accuracy requirements of the remaining life analysis. At the same time, the life calculation is usually offline feedback, which makes it difficult to achieve efficient online digital prediction. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for predicting the remaining life of a key structural position of a wind turbine generator set. Through this method, efficient and accurate online prediction and analysis of the remaining life of a wind turbine generator set can be achieved, thus overcoming the deficiencies in the prior art.

[0005] In order to solve the above technical problems, the present invention provides a method for predicting the remaining life of a key structural position of a wind turbine generator set, the method comprising: establishing a digital mirror model of the wind turbine generator set and an extrapolated digital mirror model of the external environment of a wind farm, wherein the digital mirror model of the wind turbine generator set is consistent with the actual situation of the unit; designing load conditions according to the IEC61400-1 standard, performing combined simulation on the possible operating states and wind conditions of the wind turbine generator set, extracting simulation results corresponding to the conditions and establishing a simulation result database; establishing a site load database of the wind turbine generator set based on the statistical data of the actual operating conditions of the site and the possible wind conditions data statistically obtained by a state monitoring system or a SCADA system, in combination with the digital mirror model of the wind turbine generator set; performing equivalent fatigue load calculation and analysis on the standard design load data and the site load data according to the equivalent fatigue load algorithm, and establishing an equivalent fatigue load database; establishing a remaining life analysis model for the key structural position of the wind turbine generator set, and performing analysis and calculation on the remaining life of the key structural position of a single unit and the entire machine.

[0006] In some embodiments, the wind turbine digital mirror model includes a physical model of the wind turbine and a control system model, and the wind farm external environment extrapolation digital mirror model includes external environment data and a point extrapolation model.

[0007] In some embodiments, the standard design load simulation database is based on the IEC61400-1 standard design operating condition combination, combines simulations of possible operating states and wind conditions of the wind turbine, and extracts simulation results corresponding to the operating conditions to establish a database.

[0008] In some embodiments, the site load simulation database is a combined simulation performed in advance based on the historical operating conditions of the wind turbine generator set and the wind conditions that the wind turbine generator set may experience.

[0009] In some embodiments, the equivalent fatigue load is the equivalent fatigue load after load data is converted.

[0010] In some embodiments, the load results all use an equivalent fatigue load of 1 Hz.

[0011] In some embodiments, the equivalent fatigue load calculation method is:

[0012]

[0013] Among them, DEL eq The 10-minute fatigue load obtained for the simulation can be used with the equivalent load, R i is the i-th level load range of the fatigue load spectrum, n i is the number of times within the i-th load range, N eq is 600 equivalent cycles (corresponding to 1 Hz), and m is the slope of the S / N curve of the relevant material.

[0014] In some embodiments, a remaining life analysis model for key structural positions of a wind turbine is established, and remaining life analysis and calculation of key structural positions of a single unit and the entire unit are performed, including: based on real-time wind condition statistical data at each specific point and historical operating conditions of each wind turbine, the calculated equivalent fatigue load is extracted in a search manner by combining them as query conditions, and the damage of a single unit under a certain combination of conditions is calculated by comparing the loads at the same key structural position, and then the total damage is calculated according to Miner's linear cumulative damage theory, thereby obtaining the remaining life coefficient of the key structural position.

[0015] In some embodiments, the damage of a certain position of a single unit under a certain operating condition-air density-inflow angle-turbulence intensity-wind shear coefficient-sector-wind speed combination is:

[0016]

[0017] Among them, D ij is the damage of the i-th sector at the j-th wind speed under the site load condition, DEL site_ij is the 1Hz equivalent fatigue load under the design load condition, DEL site_ij is the equivalent fatigue load at the jth wind speed in the i-th sector under the site load condition, p ij is the occurrence frequency at the jth wind speed in the ith sector under the site load condition, and m is the slope of the S / N curve.

[0018] In addition, the present invention also provides a device for predicting the remaining life of a key structural position of a wind turbine generator set, the device comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method for predicting the remaining life of a key structural position of a wind turbine generator set as described above.

[0019] After adopting such a design, the present invention has at least the following advantages:

[0020] (1) The present invention uses digital mirroring technology and unit status monitoring to simulate the operating status of the unit in the actual site environment, realize online calculation and analysis of the remaining life of the key structural positions of the wind turbine, and timely discover the weak links of the unit, reduce the risk of unit safety accidents, and further provide optimization and improvement solutions for wind turbine design and manufacturing, thereby improving the reliability of the unit;

[0021] (2) The present invention provides a decision-making basis for rationalizing the unit operation and maintenance plan, reducing the unit's unplanned downtime, accurately locating the source of the fault, and improving the maintenance efficiency by monitoring and calculating the remaining life and fatigue damage of the key structural positions of the wind turbine unit;

[0022] (3) The present invention completes the large-scale unit design load and site load data simulation before the remaining life calculation and analysis, and can improve the efficiency and accuracy of the remaining life calculation and analysis through online query and iterative update. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0024] Figure 1 is a flow chart of a method for predicting the remaining life of a key structural position of a wind turbine generator set provided by an embodiment of the present invention;

[0025] Figure 2 It is a structural diagram of a device for predicting the remaining life of a key structural position of a wind turbine generator set provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0027] The present invention provides a method and device embodiment for predicting the remaining life of a key structural position of a wind turbine, which realizes online prediction of the remaining life of a wind turbine based on a digital mirrored wind turbine model and actual operating status monitoring data. By adopting this method, the remaining life of the key structural position of the unit can be predicted quickly, providing basic data support for unit overhaul and maintenance, unit intelligent control optimization, etc., reducing the accident rate and improving the economic benefits of the wind farm.

[0028] Reference Figure 1 As shown, the remaining life prediction method of the key structural position of the wind turbine generator set in this embodiment includes the following steps:

[0029] Step 1: Establish a wind turbine simulation and wind farm external environment extrapolation digital mirror model, the wind turbine digital mirror model includes a unit physical model and a control system model. The unit physical model includes basic mass, inertia, size, damping, material properties, stiffness, strength and other parameters, the control system model is expressed as controller software or code, the control logic and control response mode are consistent with the actual unit, the dynamic response of the established simulation digital mirror model should be consistent with the actual condition of the unit, and the accuracy of the digital mirror model is continuously corrected through simulation and actual measurement comparison, such as tower and blade modes, operating status data, key position load measurement data, wind energy utilization coefficient curve, power curve, thrust coefficient curve, etc.

[0030] The wind farm external environment extrapolation digital mirror model includes external environment data and point extrapolation model. Among them, the external environment data mainly includes basic wind condition data and wind farm geographical environment data. The basic wind condition data includes wind measurement data of the wind farm wind tower, the wind measurement system of each unit cabin, the wind measurement data of the nearby meteorological station or wind tower, and the atmospheric mesoscale data of the area. The data covers basic parameters such as average wind speed distribution, wind direction, standard turbulence intensity, effective turbulence intensity, wind shear coefficient, inflow angle, air density, air pressure, humidity, etc.; the wind farm geographical environment data includes unit arrangement, elevation, roughness, geographical data of nearby wind farms, etc., and is continuously accumulated and updated. The point data extrapolation model is a model that obtains the wind condition data of each specific point of each unit in the wind farm through online real-time extrapolation calculation of the above-mentioned basic wind conditions and geographical environment data. The model calculates the wind condition data of each specific point of each unit in the wind farm after being affected by terrain, unit wake, obstacles, tower shadows, nearby wind farms, etc.

[0031] The extrapolation model can jointly correct the wind condition data at the unit point obtained by the extrapolation model calculation through the wind condition data measured by the terrain-corrected ground laser radar, the anemometer and wind vane placed on the cabin, the laser wind measuring device, and the ultrasonic wind measuring device, and at the same time integrate the mesoscale meteorological data at an altitude of 200m within 20KM of the wind farm center, so as to increase the accuracy of the extrapolation model.

[0032] Step 2: Design load conditions according to the IEC61400-1 standard, conduct combined simulation of the possible operating states and wind conditions of the wind turbine, extract the simulation results corresponding to the conditions and establish a database. The simulation time period is 10 minutes. The main output positions of the load are the span-wise section position of the blade and the connection between the blade and the hub, the section position of the tower and the connection between the tower and the nacelle, the hub position, the connection between the hub and the main shaft, the connection position between the main frame casting and the weldment, etc. The load coordinate system should adopt the universal coordinate system. In order to improve the simulation accuracy and further enhance the reliability of the remaining life prediction results, the wind speed interval in the preferred simulation is 0.1m / s and the air density interval is 0.05. According to the above standards, the conditions involving fatigue load calculation are mainly DLC1.2 normal power generation, DLC2.4 normal power generation and failure, and DLC6.4 shutdown or idling.

[0033] Step 3: Based on the actual operating condition statistics of the site and the possible wind conditions (including the wind conditions at each specific point of the unit obtained by the actual measurement and extrapolation model) data collected by the state monitoring system or SCADA system, a wind turbine site load database is established in combination with the wind turbine simulation digital mirror model. The historical operating conditions are obtained through the data statistics of the state monitoring system or wind turbine SCADA system installed at each key part of the unit. The preferred parameters characterizing the operating conditions of the unit are: the electric power, wind rotor speed, pitch angle, yaw position, wind rotor azimuth, grid connection, braking condition, unit status, etc. of the wind turbine generator set. It is mainly used to count the time accumulation and distribution of normal power generation, normal power generation and failure, shutdown or idling in different time scales in each operating unit at the site, and provide frequency distribution data for specific load site simulation. The site load simulation time cycle is 10 minutes. The main output positions of the load are the spanwise section position of the blade and the connection between the blade and the hub, the section position of the tower and the connection between the tower and the nacelle, the hub position, the connection between the hub and the main shaft, the connection position between the main frame casting and the weldment, etc. The load coordinate system should be the same as step 2. Because the site wind condition data is affected by the atmospheric environment, seasonality, etc., in order to cover the wind condition changes caused by these influencing factors as much as possible and improve the efficiency of the remaining life prediction, it is necessary to combine the possible wind conditions before performing the equivalent fatigue load calculation. The wind condition conditions are composed of wind speed, turbulence, wind direction, inflow angle, and air density. In the preferred simulation, the wind speed interval is 0.1m / s, the turbulence interval is 0.1%, the wind direction interval is 5°, the inflow angle interval is 0.1°, and the air density interval is 0.05. The working condition combination is the same as step 2.

[0034] Step 4: Perform equivalent fatigue load calculation and analysis on the standard design load data and site load data according to the equivalent fatigue load algorithm, and establish an equivalent fatigue load database. The equivalent fatigue load is the equivalent fatigue load after the load data in step 2 and step 3 are converted. In order to facilitate the comparison of the loads in the historical operation period, preferably, the load results all use the equivalent fatigue load of 1 Hz. The equivalent load amplitude DEL of the 10-minute fatigue load obtained by simulation is eq and the number of cycles N eq This set of results is considered to have the same destructive effect as the original load data. The equivalent fatigue load calculation method is:

[0035]

[0036] Among them, DEL eq The 10-minute load obtained by simulation can be expressed as the equivalent load amplitude, R i is the i-th level load range of the fatigue load spectrum, n i is the number of times within the i-th load range, N eqis 600 equivalent cycles (corresponding to 1 Hz), and m is the slope of the S / N curve of the relevant material.

[0037] Step 5: Establish a residual life analysis model for the key structural position of the wind turbine, and perform analysis and calculation of the remaining life of the key structural position of a single unit and the entire unit. The method for predicting the remaining life of the unit is: based on the real-time wind condition statistical data at each specific point and the historical operating conditions of each wind turbine, the equivalent fatigue load calculated in step 4 is extracted by retrieval in a combined manner, and the damage of a single unit under a certain combination of conditions is calculated by comparing the loads at the same key structural position, and then the total damage is calculated according to the Miner linear cumulative damage theory, and then the remaining life coefficient of the key structural position is obtained.

[0038] The damage of a single unit at a certain position under a certain operating condition-air density-inflow angle-turbulence intensity-wind shear coefficient-sector-wind speed combination is:

[0039]

[0040] Among them, D ij is the damage of the i-th sector at the j-th wind speed under the site load condition, DEL design is the 1Hz equivalent fatigue load under the design load condition, DEL site_ij is the equivalent fatigue load at the jth wind speed in the i-th sector under the site load condition, p ij is the occurrence frequency at the jth wind speed in the ith sector under the site load condition, and m is the slope of the S / N curve.

[0041] The design life of the unit and its components is 20 years, and the corresponding total damage is 1. The calculation method of the remaining life factor (RUL) of the key structural position is:

[0042]

[0043] Among them, D cert is the total damage obtained under design conditions, D site is the total damage obtained under site conditions.

[0044] The remaining life of critical structural locations is obtained by multiplying the corresponding RUL factor by the design life, using the formula:

[0045] RL=20×RUL

[0046] Among them, RL is the remaining life of the key position.

[0047] The digital mirror model of the present invention can predict the wind data of different time scales in the future based on the historical external environment data, predict and estimate the remaining life and possible damage of the key components of the unit in advance, and optimize the unit control strategy and operation and maintenance decision analysis. For example, when it is predicted that the unit may experience special wind conditions, the operating status of the unit can be adjusted in time. Through the remaining life analysis and operation status monitoring, the possible dangerous points and fault points of the unit can be discovered and announced in advance, providing a technical basis for the reasonable arrangement of operation and maintenance.

[0048] The digital mirror model of the present invention includes an iterative optimization system, which tracks, verifies and optimizes the model by comparing field load measurement with simulation and improving structure and control tuning. Field load measurement is performed in accordance with IEC TS 61400-13 standard, and the main monitoring locations are the root of the blade, the hub, the connection between the nacelle and the tower, the bottom of the tower, etc. The data obtained by the field load measurement is compared and verified with the data obtained by the unit simulation under the same working conditions, and then the relevant parameters of the digital mirror model are adjusted to make it as consistent as possible with the actual state of the unit.

[0049] Figure 2 The structure of the remaining life prediction device for the key structural position of a wind turbine is shown. Figure 2 For example, the wind turbine critical structure position remaining life prediction device 200 can be used to act as a remaining life prediction host in a wind turbine system. As described herein, the wind turbine critical structure position remaining life prediction device 200 can be used to implement the remaining life prediction function for critical structure positions in a wind turbine system. The wind turbine critical structure position remaining life prediction device 200 can be implemented in a single node, or the functions of the wind turbine critical structure position remaining life prediction device 200 can be implemented in multiple nodes in a network. Those skilled in the art should appreciate that the term wind turbine critical structure position remaining life prediction device includes devices in a broad sense, Figure 2 The wind turbine critical structure position remaining life prediction device 200 shown in the specification is only one example. The wind turbine critical structure position remaining life prediction device 200 is included for clarity of description, and is not intended to limit the application of the present invention to a specific wind turbine critical structure position remaining life prediction device embodiment or a certain type of wind turbine critical structure position remaining life prediction device embodiment. At least some of the features / methods described in the present invention may be implemented in a network device or component, such as a wind turbine critical structure position remaining life prediction device 200. For example, the features / methods in the present invention may be implemented in hardware, firmware and / or software installed and run on hardware. The wind turbine critical structure position remaining life prediction device 200 may be any device that processes, stores and / or forwards data frames through a network, such as a server, a client, a data source, etc. Figure 2As shown, the remaining life prediction device 200 for the key structural position of a wind turbine may include a transceiver (Tx / Rx) 210, which may be a transmitter, a receiver, or a combination thereof. Tx / Rx210 may be coupled to multiple ports 250 (e.g., an uplink interface and / or a downlink interface) for sending and / or receiving frames from other nodes. A processor 230 may be coupled to Tx / Rx 210 to process frames and / or determine which nodes to send frames to. The processor 230 may include one or more multi-core processors and / or a memory device 232, which may be used as a data storage device, a buffer, etc. The processor 230 may be implemented as a general-purpose processor, or may be part of one or more application-specific integrated circuits (ASICs) and / or digital signal processors (DSPs).

[0050] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Those skilled in the art may make some simple modifications, equivalent changes or modifications using the technical contents disclosed above, which all fall within the protection scope of the present invention.

Claims

1. A method for predicting the remaining life of a key structural position of a wind turbine, characterized in that: include: Establishing a digital mirror model of a wind turbine and an extrapolated digital mirror model of the external environment of a wind farm, wherein the digital mirror model of the wind turbine is consistent with the actual situation of the turbine; The wind turbine digital mirror model includes a physical model of the wind turbine and a control system model, and the wind farm external environment extrapolation digital mirror model includes external environment data and a point extrapolation model; the external environment data includes basic wind condition data and wind farm geographical environment data; The basic wind condition data includes wind measurement data from wind farm wind towers, wind measurement systems in the cabins of each unit, nearby meteorological stations or wind towers, and mesoscale atmospheric data in the area where the wind farm is located. The data covers basic parameters such as average wind speed distribution, wind direction, standard turbulence intensity, effective turbulence intensity, wind shear coefficient, inflow angle, air density, air pressure, and humidity. The geographical environment data of the wind farm includes unit layout, elevation, roughness, and geographical data of nearby wind farms, and is continuously accumulated and updated. The point extrapolation model is a model that obtains wind condition data at specific points of each unit in the wind farm through online real-time extrapolation calculation of the above basic wind condition data and wind farm geographical environment data. Design load conditions according to IEC61400-1 standard, conduct combined simulation of possible operating states of wind turbines and wind conditions, extract simulation results corresponding to the conditions and establish a simulation result database; Based on the actual operating condition statistics of the site and the possible wind condition data collected by the condition monitoring system or the SCADA system, combined with the digital mirror model of the wind turbine, a wind turbine site load database is established; Perform equivalent fatigue load calculation and analysis on standard design load data and site load data based on equivalent fatigue load algorithm, and establish equivalent fatigue load database; Establish a residual life analysis model for key structural positions of wind turbines, and perform analysis and calculation of the key structural positions of a single unit and the remaining life of the entire unit, including: According to the real-time wind condition statistical data at each specific point and the historical operating conditions of each wind turbine, they are combined as query conditions to extract the calculated equivalent fatigue load in a search manner. The damage of a single unit under a certain combination of conditions is calculated by comparing the loads at the same key structural position. The total damage is then calculated based on Miner's linear cumulative damage theory, and the remaining life coefficient of the key structural position is obtained.

2. The method for predicting the remaining life of a key structural position of a wind turbine according to claim 1, characterized in that: The standard design load simulation database is based on the IEC61400-1 standard design operating condition combination, which combines the possible operating states and wind conditions of the wind turbines and extracts the simulation results corresponding to the operating conditions to establish a database.

3. The method for predicting the remaining life of a key structural position of a wind turbine according to claim 1, characterized in that: The site load simulation database is based on the historical operating conditions of the wind turbines and is simulated in advance in combination with the wind conditions that the wind turbines may experience.

4. The method for predicting the remaining life of a key structural position of a wind turbine according to claim 1, characterized in that: The equivalent fatigue load is the equivalent fatigue load after load data conversion.

5. The method for predicting the remaining life of a key structural position of a wind turbine according to claim 4, characterized in that: The load results all adopt the equivalent fatigue load of 1 Hz.

6. The method for predicting the remaining life of a key structural position of a wind turbine according to claim 4, characterized in that: The calculation method of equivalent fatigue load is: Among them, DEL eq The 10-minute fatigue load obtained for the simulation can be used with the equivalent load, R i is the i-th level load range of the fatigue load spectrum, n i is the number of times within the i-th load range, N eq is 600 equivalent cycles, corresponding to 1 Hz, and m is the slope of the S / N curve of the relevant material.

7. The method for predicting the remaining life of a key structural position of a wind turbine according to claim 1, characterized in that: The damage of a single unit at a certain position under a certain operating condition-air density-inflow angle-turbulence intensity-wind shear coefficient-sector-wind speed combination is: Among them, D ij is the damage of the i-th sector at the j-th wind speed under the site load condition, DEL design is the 1Hz equivalent fatigue load under the design load condition, DEL site_ij is the equivalent fatigue load at the jth wind speed in the i-th sector under the site load condition, p ij is the occurrence frequency at the jth wind speed in the ith sector under the site load condition, and m is the slope of the S / N curve.

8. A device for predicting the remaining life of a key structural position of a wind turbine, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the remaining life prediction method for key structural positions of a wind turbine generator set according to any one of claims 1 to 7.

Images