CN113864137A - Fatigue life monitoring method and system for whole wind turbine generator - Google Patents
Fatigue life monitoring method and system for whole wind turbine generator Download PDFInfo
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- 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
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- 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
- F03D7/00—Controlling wind motors
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- 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
- F03D9/00—Adaptations of wind motors for special use; Combinations of wind motors with apparatus driven thereby; Wind motors specially adapted for installation in particular locations
- F03D9/20—Wind motors characterised by the driven apparatus
- F03D9/25—Wind motors characterised by the driven apparatus the apparatus being an electrical generator
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- 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
- F05B2270/10—Purpose of the control system
- F05B2270/109—Purpose of the control system to prolong engine life
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- 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
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Abstract
The embodiment of the application provides a fatigue life monitoring method and system for a full-scale wind turbine generator, and relates to the technical field of power generation equipment. The fatigue life monitoring method of the whole wind turbine generator comprises the following steps: acquiring unit data, wind parameter data and corresponding load data of a preset wind turbine; generating a fatigue load data set according to the set data, the wind parameter data and the corresponding load data of the preset wind turbine; loading the fatigue load data set into a preset finite element model to generate a fatigue damage data set; acquiring a fatigue damage effective value in the fatigue damage data set according to the set data and the wind parameter data of the wind turbine to be detected; and generating a fatigue life result of the wind turbine generator to be tested according to the fatigue damage effective value. The fatigue life monitoring method for the whole wind turbine generator set can monitor the fatigue life of key bearing parts of the wind turbine generator set in real time, and achieves the technical effect of online monitoring of the fatigue life of the whole wind turbine generator set.
Description
Technical Field
The application relates to the technical field of power generation equipment, in particular to a fatigue life monitoring method and system for a full-scale wind turbine generator.
Background
At present, whether a wind turbine generator can safely operate in a design life period is always a hot point of concern in the industry. The wind turbine generator system runs in a severe environment for a long time, parts bear loads caused by various complex factors, and fatigue damage is the most main damage form of the parts. Fatigue damage is not as obvious as damage caused by extreme loads, usually a tiny crack is generated and then gradually expands, and when the damage is discovered, the damage is often already at the later stage of the damage process, and the secrecy of the damage brings a potential and huge risk to the safe operation of a unit and even the safety of operation and maintenance personnel. If the fatigue life state of the large parts of the unit can be mastered, the fatigue safety countermeasure of the unit can be established pertinently, and the safe operation of the unit is ensured.
In the prior art, a current wind power plant is common in that a single-machine load test is performed when a prototype of the wind power plant is tested or authenticated, and the test time is usually not too long. At present, almost no wind power plant units are used for monitoring fatigue life load and life, and some wind power plant units are only tested and tested on individual wind power plant units. Fatigue life monitoring is carried out on each unit of the wind power plant, fatigue load needs to be measured, the measurement cost of the fatigue load is high, and if the measurement is carried out on each unit of the wind power plant, the required cost is very high. In order to guarantee the fatigue safety of each unit in the wind power plant, a method which can monitor the fatigue life of the key force bearing part of the unit in real time and is economical is needed to be found.
Disclosure of Invention
An object of the embodiment of the application is to provide a method, a system, an electronic device and a computer readable storage medium for monitoring the fatigue life of a full wind turbine, which can monitor the fatigue life of a key force-bearing component of the wind turbine in real time, and realize the technical effect of online monitoring of the fatigue life of the full wind turbine.
In a first aspect, an embodiment of the present application provides an online monitoring method for fatigue life of a full wind turbine, including:
acquiring unit data, wind parameter data and corresponding load data of a preset wind turbine;
generating a fatigue load data set according to the set data, the wind parameter data and the corresponding load data of the preset wind turbine;
loading the fatigue load data set into a preset finite element model to generate a fatigue damage data set;
acquiring a fatigue damage effective value in the fatigue damage data set according to the set data and the wind parameter data of the wind turbine to be detected;
and generating a fatigue life result of the wind turbine generator to be tested according to the fatigue damage effective value.
In the implementation process, the fatigue life monitoring method of the whole wind turbine generator set comprises the steps of selecting representative preset wind turbine generators to measure the generator set data, wind parameter data and corresponding load data of the generator sets in real time, generating a fatigue load data set, loading the fatigue load data set onto a finite element model of key components of the generator set, and establishing a fatigue damage database of different components of the generator set based on actually measured loads, so that the conversion from the fatigue load data set to the fatigue damage data set is realized; therefore, in the same wind power plant, according to the unit data and the wind parameter data of the wind power unit to be tested at any time interval, the corresponding fatigue damage effective value can be found in the fatigue damage data set, the fatigue damage values of all parts can be obtained after the fatigue damage of all time intervals is accumulated, and the fatigue life result is obtained. Therefore, the fatigue life of the key force bearing part of the wind turbine generator can be monitored in real time, and the technical effect of online monitoring of the fatigue life of the whole wind turbine generator is achieved.
Further, before the step of obtaining the set data, the wind parameter data and the corresponding load data of the preset wind turbine, the method further comprises:
and selecting preset wind generation sets in the wind power plant according to preset conditions, wherein the preset conditions comprise one or more of the set with the highest generation hours in the wind power plant, the set with the highest annual average wind speed in the wind power plant and the set with the highest turbulence degree in the wind power plant.
In the implementation process, the preset wind turbine generator is selected according to the preset conditions, so that the operation conditions of most wind turbine generators in the wind power plant can be effectively represented, and the accuracy of the fatigue life result is improved.
Further, the unit data includes unit state data and unit operation parameter data, and the step of obtaining the unit data, the wind parameter data and the corresponding load data of the preset wind turbine includes:
acquiring a load time sequence of the preset wind turbine generator in real time, wherein the load time sequence comprises a fan starting load sequence, a fan stopping load sequence, a fan idling load sequence and a fan power generation load sequence;
and synchronously obtaining the state data, the operation parameter data and the wind parameter data of the preset wind turbine generator corresponding to the load time sequence according to the load time sequence.
Further, the step of generating a fatigue load data set according to the set data, the wind parameter data and the corresponding load data of the preset wind turbine generator set includes:
generating a fatigue load data set according to the load time sequence, the unit operation parameter data and the wind parameter data of the preset wind turbine generator, wherein the fatigue load data set is a set based on a plurality of time sequence loads obtained by time length subdivision on the actual measurement load time sequence of the preset unit, and any time sequence load in the fatigue load data set corresponds to the unit operation parameter data and the wind parameter data of the preset wind turbine generator one to one.
Further, the step of loading the fatigue load data set into a preset finite element model to generate a fatigue damage data set includes:
substituting the load time sequence in the fatigue load data set into the finite element model to obtain a stress time sequence;
obtaining fatigue damage values corresponding to the load time sequence according to the stress time sequence, wherein the fatigue damage values corresponding to the load time sequence are in one-to-one correspondence with unit operation parameter data and wind parameter data of a preset wind turbine;
and generating the fatigue damage data set according to the fatigue damage values corresponding to the load time sequence.
In the implementation process, the fatigue damage is a linear accumulation process, the load time sequence in the fatigue load data set is substituted into the finite element model, S-N curves and rain flow statistics are assisted, and the actually measured fatigue load data set can be converted into the fatigue damage data set of the key component of the wind turbine generator through analysis and calculation.
Further, the step of obtaining the fatigue damage effective value in the fatigue damage data set according to the unit data and the wind parameter data of the wind turbine to be tested at any time interval comprises the following steps:
the method comprises the steps of calling unit data and wind parameter data of the wind turbine generator to be tested;
selecting a plurality of fatigue damage values of the set data and the wind parameter data of the wind turbine generator to be tested within a preset threshold value in the fatigue damage data set;
and obtaining the fatigue damage effective value according to the plurality of fatigue damage values.
In the implementation process, according to the unit data and the wind parameter data of the wind turbine to be tested at any time interval, a plurality of fatigue damage values which are closest to each other can be searched in the fatigue damage data set, then interpolation calculation can be carried out on the plurality of fatigue damage values or other calculation methods can be used, a fatigue damage effective value is obtained, and the accuracy of fatigue damage estimation of the wind turbine to be tested is ensured.
Further, the step of obtaining the effective fatigue damage value according to the plurality of fatigue damage values includes:
carrying out interpolation calculation on the plurality of fatigue damage values to obtain the fatigue damage effective value;
the step of generating the fatigue life result of the wind turbine generator to be tested according to the fatigue damage effective value comprises the following steps:
and obtaining the fatigue life result according to the fatigue damage effective values of the wind turbine generator to be tested at all time intervals.
Further, the step of generating the fatigue life result of the wind turbine generator to be tested according to the fatigue damage effective value includes:
and calling all the operation data and wind parameter data of the wind turbine generator to be tested.
And acquiring the fatigue damage effective values of different key bearing parts at each time interval, accumulating the effective values to obtain the fatigue damage result and the fatigue life result of each part, further obtaining the fatigue life result of the wind turbine generator to be tested, and further obtaining the fatigue life result of the whole wind turbine generator by the same method.
In a second aspect, an embodiment of the present application provides a system for monitoring fatigue life of a full wind turbine, including:
the load measurement module is used for acquiring unit data, wind parameter data and corresponding load data of a preset wind turbine;
the fatigue load generation module is used for generating a fatigue load data set according to the set data, the wind parameter data and the corresponding load data of the preset wind turbine;
the fatigue damage module is used for loading the fatigue load data set into a preset finite element model to generate a fatigue damage data set;
the fatigue damage estimation module is used for acquiring a fatigue damage effective value in the fatigue damage data set according to the set data and the wind parameter data of the wind turbine generator to be detected;
and the fatigue life module is used for generating a fatigue life result of the wind turbine generator to be tested according to the fatigue damage effective value.
Further, the fatigue life monitoring system for the full wind turbine generator further comprises:
the wind power generation system comprises a wind power generation plant selection module and a wind power generation plant selection module, wherein the wind power generation plant selection module is used for selecting preset wind power generation plants in the wind power generation plant according to preset conditions, and the preset conditions comprise one or more of the plants with the highest generation hours in the wind power generation plant, the plants with the highest annual average wind speed in the wind power generation plant and the plants with the highest turbulence degree in the wind power generation plant.
Further, the load measurement module includes:
the load time sequence unit is used for acquiring a load time sequence of the preset wind turbine generator, and the load time sequence comprises a fan starting load sequence, a fan stopping load sequence, a fan idling load sequence and a fan generating load sequence;
and the operation parameter and wind parameter unit is used for synchronously obtaining the unit state data, the unit operation parameter data and the wind parameter data of the preset wind turbine generator corresponding to the load time sequence according to the load time sequence.
Further, the fatigue load generation module is specifically configured to generate a fatigue load data set according to the load time sequence, the set operation parameter data of the preset wind turbine generator, and the wind parameter data, where the fatigue load data set is a set of a plurality of time sequence loads obtained by time length subdivision on a preset set actual measurement load time sequence, and any time sequence load in the fatigue load data set corresponds to the set operation parameter data of the preset wind turbine generator and the wind parameter data one to one.
Further, the fatigue damage module comprises:
the stress time sequence unit is used for substituting the load time sequence in the fatigue load data set into the finite element model to obtain a stress time sequence;
the fatigue damage value unit is used for obtaining a fatigue damage value corresponding to the load time sequence according to the stress time sequence, and the fatigue damage value corresponding to the load time sequence is in one-to-one correspondence with the unit operation parameter data and the wind parameter data of a preset wind turbine;
and the fatigue damage data set unit is used for generating the fatigue damage data set according to the fatigue damage value corresponding to the load time sequence.
Further, the fatigue damage estimation module comprises:
the calling unit is used for calling the unit data and the wind parameter data of the wind turbine generator to be tested;
the selection unit is used for selecting a plurality of fatigue damage values of the wind turbine generator data and the wind parameter data of the wind turbine generator to be detected within a preset threshold value in the fatigue damage data set;
and the estimating unit is used for obtaining the fatigue damage effective value according to the plurality of fatigue damage values.
Further, the estimation unit is specifically configured to: and carrying out interpolation calculation on the plurality of fatigue damage values to obtain the fatigue damage effective value.
In a third aspect, an electronic device provided in an embodiment of the present application includes: memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to any of the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, cause the computer to perform the method according to any one of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product, which when run on a computer, causes the computer to perform the method according to any one of the first aspect.
Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the above-described techniques.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a method for monitoring fatigue life of a full-scale wind turbine generator provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of another method for monitoring fatigue life of a full-scale wind turbine generator according to an embodiment of the present application;
fig. 3 is a block diagram of a fatigue life monitoring system of a full-scale wind turbine provided in the embodiment of the present application;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
The embodiment of the application provides a method, a system, electronic equipment and a computer readable storage medium for monitoring the fatigue life of a full wind turbine generator, which can be applied to the fatigue life monitoring of key components of the wind turbine generator; the fatigue life monitoring method of the whole wind turbine generator sets comprises the steps of selecting representative preset wind turbine generators to measure the generator set data, wind parameter data and corresponding load data of the wind turbine generators in real time, generating a fatigue load data set, loading the fatigue load data set onto a finite element model of key components (blades, hubs, spindles, underframe, tower drum and the like) of the wind turbine generators, and establishing a fatigue damage database of different components of the wind turbine generators based on actual measurement loads, so that conversion from the fatigue load data set to the fatigue damage data set is achieved; therefore, according to the unit data and the wind parameter data of the wind turbine generator to be tested, the corresponding fatigue damage effective value can be found in the fatigue damage data set, the fatigue damage value of each part can be obtained after damage is accumulated, and a fatigue life result is obtained. Therefore, the fatigue life of the key force bearing part of the wind turbine generator can be monitored in real time, and the technical effect of monitoring the fatigue life of the whole wind turbine generator is achieved.
For example, for a preset wind turbine, the fatigue life result of the component can be obtained directly from the measured load through calculation and analysis.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for monitoring fatigue life of a full-scale wind turbine generator provided in an embodiment of the present application, where the method for monitoring fatigue life of a full-scale wind turbine generator includes the following steps:
s100: the method comprises the steps of obtaining unit data, wind parameter data and corresponding load data of a preset wind turbine.
Illustratively, a wind power plant comprises a plurality of wind power sets, the preset wind power set is a representative wind power plant, and fatigue life results of all the wind power sets of the whole wind power plant can be evaluated by analyzing set data, wind parameter data and corresponding load data of the wind power sets.
For example, the load data is measured and obtained in real time through load monitoring equipment installed on a preset wind turbine, and the turbine data, the wind parameter data and the load data at the same time can form a one-to-one correspondence relationship.
S200: and generating a fatigue load data set according to the set data, the wind parameter data and the corresponding load data of the preset wind turbine.
By way of example, loads, also referred to as loads, refer to external forces and other factors that cause internal forces and deformations to structures or components, or, as is customary, to the various direct actions applied to an engineered structure that cause the engineered structure or component to act, as is common: the structure dead weight, the floor live load, the roof dust load, the vehicle load, the crane load, the equipment power load and the wind, snow, ice, wave and other natural loads. In the embodiment of the application, the fatigue load data set refers to a set of load data of parts of the wind turbine generator subjected to various complex factors.
Exemplarily, the unit data and the wind parameter data of a preset wind turbine generator and the corresponding fatigue load data can be obtained through the real-time measurement of the corresponding equipment; optionally, the time series of the measured fatigue load is subdivided through the operation parameters and the wind parameters according to the state of the wind turbine (the state of the wind turbine such as starting, stopping, idling, power generation, etc.), so as to form a fatigue load data set.
S300: and loading the fatigue load data set into a preset finite element model to generate a fatigue damage data set.
Illustratively, the finite element model is a model established when a finite element analysis method is applied, and is a group of unit assemblies which are only connected at nodes, only transmit force by virtue of the nodes and are only restrained at the nodes; finite element analysis is the use of finite element methods to analyze static or dynamic physical objects or physical systems. In this method an object or system is decomposed into a geometric model consisting of a number of interconnected, simple, independent points. The number of these individual points is limited in this method and is therefore called a finite element. Equilibrium equations derived from the actual physical model are used for each point, thereby creating a system of equations. This system of equations can be solved by linear algebra. The accuracy of finite element analysis cannot be improved infinitely, the accuracy of the solution is not improved after the number of elements reaches a certain height, and only the calculation time is continuously improved.
Exemplarily, the finite element model provided by the embodiment of the application refers to a finite element model of a key component of a wind turbine generator; the fatigue load data set is brought into a finite element model by preparing a corresponding S-N curve, and is converted into a fatigue damage data set of the key parts of the unit according to a fatigue damage theory and the corresponding S-N curve, wherein any numerical value in the data set corresponds to a unique unit state, an operation parameter and a wind parameter to form a corresponding relation.
Illustratively, the S-N curve is a curve representing the relationship between the fatigue strength and the fatigue life of a standard test piece under a certain cycle characteristic, also referred to as a stress-life curve, with the fatigue strength of the standard test piece of material as the ordinate and the logarithmic value lgN of the fatigue life as the abscissa. Fatigue Damage theory Fatigue Damage (FD) is the initiation of a crack and its continued propagation due to cyclic loading, and is generally a linear cumulative process.
S400: and acquiring a fatigue damage effective value in the fatigue damage data set according to the unit data and the wind parameter data of the wind turbine generator to be detected.
S500: and generating a fatigue life result of the wind turbine generator to be tested according to the fatigue damage effective value.
Exemplarily, a process of acquiring unit data and wind parameter data of a wind turbine to be tested, namely, acquiring unit data and wind parameter data of any fan in a wind power plant from self-operation; and finding a plurality of fatigue damage values which are closest to state parameters of the wind turbine generator to be tested in all operation periods from the established fatigue damage data set, and then carrying out interpolation calculation to obtain the fatigue damage value of the wind turbine generator to be tested in the period. According to the Maina fatigue damage linear accumulation theory, all the damages are superposed to obtain the fatigue damage values of different parts in the wind turbine generator to be tested, and further obtain the fatigue life value. Similar data extraction and calculation are carried out on all the units in the wind power plant, and a critical fatigue life result of the unit can be obtained, so that the online monitoring of the fatigue life of the whole unit is realized.
Illustratively, the fatigue life monitoring method provided by the embodiment of the application is combined with actual test and data analysis, the fatigue life of the whole unit in the wind power plant can be accurately monitored on line in real time, fatigue load measurement of the whole unit is not needed, cost is saved, and meanwhile, along with the longer the test time of the preset wind power plant is, the more data are accumulated, the higher the accuracy of the fatigue life result of the whole unit is, and an effective and practical method is provided for monitoring the fatigue life of the unit in the current wind power plant.
In some embodiments, the fatigue life monitoring method for the whole wind turbine generator sets comprises the steps of selecting representative preset wind turbine generators to measure the generator set data, wind parameter data and corresponding load data of the wind turbine generators in real time, generating a fatigue load data set, loading the fatigue load data set onto a finite element model of key components (blades, hubs, spindles, underframe, tower drum and the like) of the wind turbine generators, and establishing a fatigue damage database of different components of the wind turbine generators based on measured loads, so that the fatigue load data set is converted into the fatigue damage data set; therefore, according to the unit data and the wind parameter data of the wind turbine generator to be tested, the corresponding fatigue damage effective value can be found in the fatigue damage data set, the fatigue damage value of each part can be obtained after damage is accumulated, and a fatigue life result is obtained. Therefore, the fatigue life of the key force bearing part of the wind turbine generator can be monitored in real time, and the technical effect of monitoring the fatigue life of the whole wind turbine generator is achieved.
Referring to fig. 2, fig. 2 is a schematic flow chart of a method for monitoring fatigue life of a full-scale wind turbine generator according to an embodiment of the present application.
Exemplarily, at S100: before the step of obtaining the unit data, the wind parameter data and the corresponding load data of the preset wind turbine, the method further comprises the following steps:
s101: and selecting preset wind generation sets in the wind power plant according to preset conditions, wherein the preset conditions comprise one or more of the set with the highest generation hours in the wind power plant, the set with the highest annual average wind speed in the wind power plant and the set with the highest turbulence degree in the wind power plant.
Illustratively, the preset wind turbine generators are selected according to the preset conditions, so that the operation conditions of most wind turbine generators in the wind power plant can be effectively represented, and the accuracy of the fatigue life result is improved.
In some embodiments, according to the unit exploitable report, in combination with the actual operating data of the unit, a representative unit (a preset wind turbine) for fatigue load measurement can be selected in the wind power plant, the number of the representative units is not less than 2, and generally 3 or more are suggested, and online load measurement is performed on the representative unit. The representative unit should include: 1) the unit with the highest generation hours in the wind power plant; 2) the unit with the highest annual average wind speed in the wind power plant; 3) the unit with the highest turbulence degree in the wind power plant. The number of electricity generation hours refers to the number of hours that the unit is in an electricity generation state within a preset time; turbulence, also known as turbulence intensity, is a measure of the degree of pulsation in the velocity of the gas stream, and the magnitude of the pulsation is usually expressed as the ratio of the mean square sum of the velocity of the pulsation to the time-average velocity.
Illustratively, the unit data includes unit state data and unit operation parameter data, S100: the method comprises the steps of obtaining unit data, wind parameter data and corresponding load data of a preset wind turbine, and comprises the following steps:
s110: acquiring a load time sequence of a preset wind turbine generator in real time, wherein the load time sequence comprises a fan starting load sequence, a fan stopping load sequence, a fan idling load sequence and a fan generating load sequence;
s120: and synchronously obtaining the state data, the operation parameter data and the wind parameter data of the preset wind turbine generator corresponding to the load time sequence according to the load time sequence.
Illustratively, each load time sequence represents the load state of the unit in a period of time, and the time length of each load time sequence can be set by itself, which is not limited herein.
Illustratively, the unit state data refers to unit operation states including a starting state, a stopping state, an idling state and a power generation state; the unit operation parameter data comprises power, rotating speed, pitch angle and the like; wind parameter data includes wind speed, turbulence, wind direction, etc.
Exemplarily, S200: the method comprises the following steps of generating a fatigue load data set according to preset unit data, wind parameter data and corresponding load data of a wind turbine, wherein the steps comprise:
s210: generating a fatigue load data set according to the load time sequence, the unit operation parameter data of the preset wind turbine generator and the wind parameter data, wherein the fatigue load data set is a set based on a plurality of time sequence loads after time length subdivision is carried out on the preset unit actual measurement load time sequence, and any time sequence load in the fatigue load data set corresponds to the unit operation parameter data and the wind parameter data of the preset wind turbine generator one to one.
For example, the load to be measured in the embodiment of the present application may specifically include one or more of a blade root, a main shaft front end, and a tower; wherein, the load of the blade root comprises a flapping bending moment and a shimmy bending moment; the load at the front end of the main shaft comprises wind wheel pitching bending moment, lateral bending moment, main shaft torque and the like; the load of the tower barrel comprises tower top pitching bending moment, tower top lateral bending moment, tower top torque, tower bottom pitching bending moment, tower bottom lateral bending moment and the like.
In some implementation scenarios, the load time sequence of the preset wind turbine is extracted in real time, and meanwhile, the unit operation parameter data (such as power, rotation speed, pitch angle and the like) synchronized with the load time sequence is extracted, and the synchronized wind parameter data (such as wind speed, turbulence, wind direction and the like) is extracted. The load time series can be divided into the following four types according to the unit state:
1) the starting process of the fan is as follows: a fan starting load sequence FQ, wherein the duration of an FQ single body is defined as 90s (including a complete fan starting process), and each FQ corresponds to the synchronous unit operation parameter data and the synchronous wind parameter data;
2) the fan is stopped: a fan shutdown load sequence FT, wherein the time length of a single FT is defined as 90s (including a complete fan shutdown process), and each FT corresponds to the synchronous unit operation parameter data and wind parameter data thereof;
3) the fan idling state: the duration of each FK monomer is defined as 600s (corresponding to the duration of a turbulent load working condition in IEC 61400-1), and each FK corresponds to synchronous unit operation parameter data and wind parameter data;
4) the power generation state of the fan is as follows: the generator power generation load sequence FF is characterized in that the duration of an FF monomer is defined as 600s (corresponding to the duration of a turbulent load working condition in IEC 61400-1), and each FF corresponds to the synchronous unit operation parameter data and wind parameter data thereof. Therefore, all the measured fatigue load time sequences are divided into four types according to the state of the unit, and are further divided into fatigue load sequence monomers with the duration of 90s or 600s, and form a one-to-one correspondence relation with the operation parameters and the wind parameters of the unit (wherein the most important load is the correspondence relation with the wind speed and the power of the unit), so that a measured fatigue load data set in the wind power plant is formed.
It should be noted that the above-mentioned time length of the fatigue load sequence is only an example and not a limitation, and the time length of the fatigue load sequence can be set according to the actual requirement.
Exemplarily, S300: loading the fatigue load data set into a preset finite element model, and generating a fatigue damage data set, wherein the step comprises the following steps:
s310: substituting the load time sequence in the fatigue load data set into a finite element model to obtain a stress time sequence;
s320: obtaining fatigue damage values corresponding to the load time sequence according to the stress time sequence, wherein the fatigue damage values corresponding to the load time sequence are in one-to-one correspondence with preset unit operation parameter data and wind parameter data of the wind turbine;
s330: and generating a fatigue damage data set according to the fatigue damage values corresponding to the load time sequence.
Illustratively, the fatigue damage is a linear accumulation process, and the actually measured fatigue load data set can be converted into the fatigue damage data set of the key component of the wind turbine generator set through analysis and calculation by substituting the load time sequence in the fatigue load data set into a finite element model and assisting with S-N curve and rain flow statistics.
In some embodiments, a finite element model of a key component of a wind turbine is prepared, and hubs of the wind turbine are prepared with respective corresponding S-N curves; and substituting the load time sequence in the fatigue load data set established in the S200 into a finite element model for analysis and calculation to obtain stress time sequences corresponding to different parts, carrying out rain flow statistics on the stress time sequences, and further obtaining fatigue damage values of the different parts corresponding to the load time sequences through analysis and calculation according to a fatigue damage linear accumulation theory and by referring to an S-N curve, wherein the fatigue damage values and the operation parameters and the wind parameters of the unit form a one-to-one correspondence (the most important correspondence is to the wind speed and the power of the unit), so that the actually measured fatigue load database is converted into an actually measured unit key part fatigue damage database.
Illustratively, the rain flow statistics is rain flow counting method, and the counting method has the main function of simplifying the actually measured load history into a plurality of load cycles for fatigue life estimation and fatigue test load spectrum compilation. The method is based on a double-parameter method, considers two variables of dynamic strength (amplitude) and static strength (mean value), and accords with the inherent characteristics of fatigue load. The rain flow counting method is mainly used in the engineering field, and is particularly widely applied to fatigue life calculation.
Exemplarily, S400: the method comprises the following steps of acquiring a fatigue damage effective value in a fatigue damage data set according to unit data and wind parameter data of a wind turbine generator to be detected, wherein the steps comprise:
s410: the method comprises the steps of calling unit data and wind parameter data of a wind turbine generator to be tested;
s420: selecting a plurality of fatigue damage values of the set data and the wind parameter data of the wind turbine generator to be tested within a preset threshold value in the fatigue damage data set;
s430: and obtaining a fatigue damage effective value according to the plurality of fatigue damage values.
Illustratively, according to the unit data and the wind parameter data of the wind turbine generator to be measured at any time interval, a plurality of fatigue damage values which are closest to each other can be searched in the fatigue damage data set, then interpolation calculation can be carried out on the plurality of fatigue damage values or other calculation methods can be used for obtaining the fatigue damage effective value, and the accuracy of fatigue damage estimation of the wind turbine generator to be measured is guaranteed.
In some embodiments, an example of the obtaining method for selecting the closest fatigue damage value in the fatigue damage data set according to the unit data and the wind parameter data of the wind turbine to be tested is as follows: assuming that the wind speed of the historical operating data of a certain 600s set is taken to be 10m/s and the power is 1.5MW, searching the wind speed from a damage database, wherein the power deviation does not exceed 5%, namely the wind speed [9.5m/s, 10.5m/s ] and the damage data points of the power [1.425MW, 1.575MW ] are simultaneously detected, and assuming that N damage data points meet the requirements, obtaining N fatigue damage values; furthermore, mathematical fitting is carried out on the N fatigue damage values to obtain a fitting curve, and damage values corresponding to the unit state parameters (10 m/s, 1.5 MW) in the 600s period can be obtained through the fitting curve.
Exemplarily, S430: the step of obtaining a fatigue damage effective value from the plurality of fatigue damage values includes:
and carrying out interpolation calculation on the plurality of fatigue damage values to obtain the fatigue damage effective value.
Exemplarily, after the plurality of fatigue damage values are subjected to interpolation calculation, a fatigue damage effective value corresponding to any unit data and wind parameter data of the wind turbine generator to be tested in a certain period (load time sequence) can be obtained; according to the fatigue damage linear accumulation theory, the fatigue damage effective values at all time intervals are accumulated, and the fatigue life result of the wind turbine generator to be tested from the self-running can be obtained.
In some embodiments, unit state data, unit operation parameter data and wind parameter data of any wind turbine (except for a preset wind turbine) in a wind power plant from the self-operation are obtained, a plurality of fatigue damage values of corresponding components close to the unit state data, the unit operation parameter data and the wind parameter data (most mainly wind speed and power) of the unit are found from the fatigue damage data set, interpolation calculation is carried out to obtain fatigue damage values of different components of the unit in the time period, then damages in all the time periods are overlapped according to a linear accumulated fatigue damage theory, accumulated fatigue damage of the corresponding components is obtained, and further a fatigue life result of the components is obtained.
In some embodiments, the calculation is performed on the whole set except the preset wind turbine set according to the method of S500 to obtain a set fatigue life result of the key bearing component of each set in the wind turbine set based on the measured load, and the preset wind turbine set directly converts the fatigue load of the preset wind turbine set into the fatigue life result of different components; by means of the method, the fatigue life monitoring method can obtain the fatigue life calculation results of all the units in the wind power plant, and achieves online monitoring of the fatigue life of key components of the whole unit of the wind power plant.
Referring to fig. 3, fig. 3 is a block diagram of a fatigue life monitoring system of a full wind turbine provided in an embodiment of the present application, where the fatigue life monitoring system of the full wind turbine includes:
the load measurement module 100 is used for acquiring unit data, wind parameter data and corresponding load data of a preset wind turbine;
the fatigue load generation module 200 is used for generating a fatigue load data set according to preset unit data, wind parameter data and corresponding load data of the wind turbine;
the fatigue damage module 300 is used for loading the fatigue load data set into a preset finite element model to generate a fatigue damage data set;
the fatigue damage estimation module 400 is used for acquiring a fatigue damage effective value in the fatigue damage data set according to the unit data and the wind parameter data of the wind turbine generator to be detected;
and the fatigue life module 500 is used for generating a fatigue life result of the wind turbine generator to be tested according to the fatigue damage effective value.
Illustratively, the fatigue life monitoring system for the full-scale wind turbine further comprises:
the wind power generation system comprises a wind power generation plant selection module, a wind power generation plant selection module and a wind power generation plant selection module, wherein the wind power generation plant selection module is used for selecting preset wind power generation plants in the wind power generation plant according to preset conditions, and the preset conditions comprise one or more of the plants with the highest generation hours in the wind power generation plant, the plants with the highest annual average wind speed in the wind power generation plant and the plants with the highest turbulence degree in the wind power generation plant.
Illustratively, the load measurement module 100 includes:
the load time sequence unit is used for acquiring a load time sequence of a preset wind turbine generator, and the load time sequence comprises a fan starting load sequence, a fan stopping load sequence, a fan idling load sequence and a fan generating load sequence;
and the operation parameter and wind parameter unit is used for synchronously obtaining the state data, the operation parameter data and the wind parameter data of the preset wind turbine generator corresponding to the load time sequence according to the load time sequence.
Illustratively, the fatigue load generating module 200 is specifically configured to generate a fatigue load data set according to the load time sequence, the unit operation parameter data of the preset wind turbine generator, and the wind parameter data, where the fatigue load data set is a set of a plurality of time sequence loads obtained by time length subdivision on the preset unit measured load time sequence, and any time sequence load in the fatigue load data set corresponds to the unit operation parameter data and the wind parameter data of the preset wind turbine generator one to one.
Illustratively, the fatigue damage module 300 includes:
the stress time sequence unit is used for substituting the load time sequence in the fatigue load data set into the finite element model to obtain a stress time sequence;
the fatigue damage value unit is used for obtaining a fatigue damage value corresponding to the load time sequence according to the stress time sequence, and the fatigue damage value corresponding to the load time sequence is in one-to-one correspondence with the unit operation parameter data and the wind parameter data of a preset wind turbine;
and the fatigue damage data set unit is used for generating a fatigue damage data set according to the fatigue damage values corresponding to the load time sequence.
Illustratively, the fatigue damage prediction module 400 includes:
the calling unit is used for calling unit data and wind parameter data of the wind turbine generator to be tested;
the selection unit is used for selecting a plurality of fatigue damage values of the unit data and the wind parameter data of the wind turbine generator to be detected within a preset threshold value in the fatigue damage data set;
and the estimating unit is used for obtaining the fatigue damage effective value according to the plurality of fatigue damage values.
Exemplarily, the estimation unit is specifically configured to: and carrying out interpolation calculation on the plurality of fatigue damage values to obtain a fatigue damage effective value.
It should be noted that the fatigue life monitoring system of the full-scale wind turbine generator shown in fig. 3 corresponds to the method embodiments shown in fig. 1 and fig. 2, and is not described herein again to avoid repetition.
Fig. 4 shows a block diagram of an electronic device according to an embodiment of the present disclosure, where fig. 4 is a block diagram of the electronic device. The electronic device may include a processor 510, a communication interface 520, a memory 530, and at least one communication bus 540. Wherein the communication bus 540 is used for realizing direct connection communication of these components. In this embodiment, the communication interface 520 of the electronic device is used for performing signaling or data communication with other node devices. Processor 510 may be an integrated circuit chip having signal processing capabilities.
The Processor 510 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor 510 may be any conventional processor or the like.
The Memory 530 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Read Only Memory (EPROM), an electrically Erasable Read Only Memory (EEPROM), and the like. The memory 530 stores computer readable instructions, which when executed by the processor 510, enable the electronic device to perform the steps involved in the method embodiments of fig. 1-2 described above.
Optionally, the electronic device may further include a memory controller, an input output unit.
The memory 530, the memory controller, the processor 510, the peripheral interface, and the input/output unit are electrically connected to each other directly or indirectly, so as to implement data transmission or interaction. For example, these elements may be electrically coupled to each other via one or more communication buses 540. The processor 510 is used to execute executable modules stored in the memory 530, such as software functional modules or computer programs included in the electronic device.
The input and output unit is used for providing a task for a user to create and start an optional time period or preset execution time for the task creation so as to realize the interaction between the user and the server. The input/output unit may be, but is not limited to, a mouse, a keyboard, and the like.
It will be appreciated that the configuration shown in fig. 4 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 4 or may have a different configuration than shown in fig. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof.
The embodiment of the present application further provides a storage medium, where the storage medium stores instructions, and when the instructions are run on a computer, when the computer program is executed by a processor, the method in the method embodiment is implemented, and in order to avoid repetition, details are not repeated here.
The present application also provides a computer program product which, when run on a computer, causes the computer to perform the method of the method embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A fatigue life monitoring method for a full-scale wind turbine generator is characterized by comprising the following steps:
acquiring unit data, wind parameter data and corresponding load data of a preset wind turbine;
generating a fatigue load data set according to the set data, the wind parameter data and the corresponding load data of the preset wind turbine;
loading the fatigue load data set into a preset finite element model to generate a fatigue damage data set;
acquiring a fatigue damage effective value in the fatigue damage data set according to the set data and the wind parameter data of the wind turbine to be detected;
and generating a fatigue life result of the wind turbine generator to be tested according to the fatigue damage effective value.
2. The method for monitoring the fatigue life of the full range wind turbine generator set according to claim 1, wherein before the step of obtaining the set data and the wind parameter data of the preset wind turbine generator set, the method further comprises:
and selecting preset wind generation sets in the wind power plant according to preset conditions, wherein the preset conditions comprise one or more of the set with the highest generation hours in the wind power plant, the set with the highest annual average wind speed in the wind power plant and the set with the highest turbulence degree in the wind power plant.
3. The fatigue life monitoring method for the full-scale wind turbine generator according to claim 1, wherein the turbine generator data includes turbine generator state data and turbine generator operation parameter data, and the step of acquiring the turbine generator data, the wind parameter data and the corresponding load data of the preset wind turbine generator comprises:
acquiring a load time sequence of the preset wind turbine generator in real time, wherein the load time sequence comprises a fan starting load sequence, a fan stopping load sequence, a fan idling load sequence and a fan power generation load sequence;
and synchronously obtaining the state data, the operation parameter data and the wind parameter data of the preset wind turbine generator corresponding to the load time sequence according to the load time sequence.
4. The method for monitoring the fatigue life of the full range wind turbine generator set according to claim 3, wherein the step of generating a fatigue load data set according to the set data, the wind parameter data and the corresponding load data of the preset wind turbine generator set comprises:
generating a fatigue load data set according to the load time sequence, the unit operation parameter data and the wind parameter data of the preset wind turbine generator, wherein the fatigue load data set is a set based on a plurality of time sequence loads obtained by time length subdivision on the actual measurement load time sequence of the preset unit, and any time sequence load in the fatigue load data set corresponds to the unit operation parameter data and the wind parameter data of the preset wind turbine generator one to one.
5. The method for monitoring the fatigue life of the full-scale wind turbine generator set according to claim 4, wherein the step of loading the fatigue load data set into a preset finite element model to generate a fatigue damage data set comprises:
substituting the load time sequence in the fatigue load data set into the finite element model to obtain a stress time sequence;
obtaining fatigue damage values corresponding to the load time sequence according to the stress time sequence, wherein the fatigue damage values corresponding to the load time sequence are in one-to-one correspondence with unit operation parameter data and wind parameter data of a preset wind turbine;
and generating the fatigue damage data set according to the fatigue damage values corresponding to the load time sequence.
6. The method for monitoring the fatigue life of the whole wind turbine generator set according to claim 1, wherein the step of obtaining the effective fatigue damage value in the fatigue damage data set according to the set data and the wind parameter data of the wind turbine generator set to be tested comprises the following steps:
the method comprises the steps of calling unit data and wind parameter data of the wind turbine generator to be tested;
selecting a plurality of fatigue damage values of the set data and the wind parameter data of the wind turbine generator to be tested within a preset threshold value in the fatigue damage data set;
and obtaining the fatigue damage effective value according to the plurality of fatigue damage values.
7. The method for monitoring the fatigue life of the full-scale wind turbine generator according to claim 6, wherein the step of obtaining the effective fatigue damage value according to the plurality of fatigue damage values comprises:
carrying out interpolation calculation on the plurality of fatigue damage values to obtain the fatigue damage effective value;
the step of generating the fatigue life result of the wind turbine generator to be tested according to the fatigue damage effective value comprises the following steps:
and obtaining the fatigue life result according to the fatigue damage effective values of the wind turbine generator to be tested at all time intervals.
8. The utility model provides a full wind turbine generator system fatigue life monitoring system which characterized in that includes:
the load measurement module is used for acquiring unit data, wind parameter data and corresponding load data of a preset wind turbine;
the fatigue load generation module is used for generating a fatigue load data set according to the set data, the wind parameter data and the corresponding load data of the preset wind turbine;
the fatigue damage module is used for loading the fatigue load data set into a preset finite element model to generate a fatigue damage data set;
the fatigue damage estimation module is used for acquiring a fatigue damage effective value in the fatigue damage data set according to the set data and the wind parameter data of the wind turbine generator to be detected;
and the fatigue life module is used for generating a fatigue life result of the wind turbine generator to be tested according to the fatigue damage effective value.
9. An electronic device, comprising: memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method for fatigue life monitoring of a full range wind turbine as claimed in any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium having stored thereon instructions which, when run on a computer, cause the computer to perform the method of fatigue life monitoring of a full range wind turbine as claimed in any one of claims 1 to 7.
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Application publication date: 20211231 |