CN113803220A - Method, device and system for detecting fatigue damage of wind generating set and controller - Google Patents
Method, device and system for detecting fatigue damage of wind generating set and controller Download PDFInfo
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- CN113803220A CN113803220A CN202010554838.0A CN202010554838A CN113803220A CN 113803220 A CN113803220 A CN 113803220A CN 202010554838 A CN202010554838 A CN 202010554838A CN 113803220 A CN113803220 A CN 113803220A
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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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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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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
Abstract
The application provides a method, a device, a system and a controller for detecting fatigue damage of a wind generating set. The method for detecting the fatigue damage of the wind generating set comprises the following steps: acquiring torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data; determining structural stress time sequence data of a generator hot spot region according to the torque load time sequence data, and determining a first fatigue damage value of the hot spot region according to the structural stress time sequence data; determining thermal stress time sequence data of the hot spot region according to the temperature time sequence data, and determining a second fatigue damage value of the hot spot region according to the thermal stress time sequence data; determining a third fatigue damage value of the hot spot region according to the eccentric magnetic tension load time sequence data; and determining a total fatigue damage value according to the first fatigue damage value, the second fatigue damage value and the third fatigue damage value. According to the method and the device, various loads causing fatigue damage are considered when the total fatigue damage value is determined, and good data support is provided for the residual life prediction of the wind generating set.
Description
Technical Field
The application relates to the technical field of fatigue detection, in particular to a method, a device, a system and a controller for detecting fatigue damage of a wind generating set.
Background
In the actual operation process of the wind generating set, the wind generating set is subjected to the action of random alternating load, and the stability and the safety of the wind generating set are tested at all times. With the technology becoming mature, research on-line monitoring and health diagnosis of the wind generating set is increasing. The generator support structure (including the stator support and the rotor support) contains a large number of welds, and fatigue damage is a major factor leading to its failure.
At present, the fatigue damage of the generator structure is generally predicted in the development stage of the wind generating set. For example, according to the general standard such as IEC61400, the fatigue load is obtained through load simulation, and then the fatigue damage is estimated. However, the existing fatigue damage prediction method is only suitable for the development stage of the wind generating set, and cannot accurately obtain the real fatigue damage of the wind generating set in the actual operation process in real time, so that the residual life of the wind generating set in the actual operation process cannot be effectively predicted.
Disclosure of Invention
The application provides a method, a device, a system and a controller for detecting fatigue damage of a wind generating set aiming at the defects of the prior art, and is used for solving the technical problem that the real fatigue damage of the wind generating set in the actual operation process cannot be obtained in the prior art.
In a first aspect, an embodiment of the present application provides a method for detecting fatigue damage of a wind turbine generator system, including:
acquiring torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data of the generator;
determining structural stress time sequence data of a hot spot region of the generator according to the torque load time sequence data, and determining a first fatigue damage value of the hot spot region according to the structural stress time sequence data;
determining thermal stress time sequence data of the hot spot region according to the temperature time sequence data, and determining a second fatigue damage value of the hot spot region according to the thermal stress time sequence data;
determining a third fatigue damage value of the hot spot region according to the eccentric magnetic tension load time sequence data;
and determining the total fatigue damage value of the hot spot region according to the first fatigue damage value, the second fatigue damage value and the third fatigue damage value.
In a second aspect, an embodiment of the present application provides a detection apparatus for a fatigue damage of a wind turbine generator system, including:
the data acquisition module is used for acquiring torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data of the generator;
the first fatigue determining module is used for determining structural stress time sequence data of a hot spot region of the generator according to the torque load time sequence data and determining a first fatigue damage value of the hot spot region according to the structural stress time sequence data;
the second fatigue determining module is used for determining thermal stress time sequence data of the hot spot region according to the temperature time sequence data and determining a second fatigue damage value of the hot spot region according to the thermal stress time sequence data;
the third fatigue determining module is used for determining a third fatigue damage value of the hot spot region according to the eccentric magnetic tension load time sequence data;
and the total fatigue determining module is used for determining a total fatigue damage value of the hot spot region according to the first fatigue damage value, the second fatigue damage value and the third fatigue damage value.
In a third aspect, an embodiment of the present application provides a controller for a wind farm, including:
a memory;
a processor electrically connected to the memory;
the memory stores a computer program, and the computer program is executed by the processor to implement the method for detecting fatigue damage of the wind turbine generator system provided by the first aspect of the embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides a detection system for a wind turbine generator system, including: a torque load detector, a temperature detector, an eccentric magnetic pull load detector and a controller of a wind farm provided in the third aspect of the embodiments of the present application;
the torque load detector, the temperature detector and the eccentric magnetic tension load detector are all in communication connection with the controller;
the torque load detector, the temperature detector and the eccentric magnetic tension load detector are respectively used for detecting the torque load, the temperature and the eccentric magnetic tension load of the generator to obtain torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data.
In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for detecting fatigue damage of a wind turbine generator system provided in the first aspect of the embodiment of the present application is implemented.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
the method and the device can acquire various load time sequence data (specifically including torque load time sequence data, thermal load time sequence data and eccentric magnetic tension load time sequence data) of the wind driven generator acquired in real time, and according to the various load time sequence data, a first fatigue damage value under the action of a torque load, a second fatigue damage value under the action of a thermal load and a third fatigue damage value under the action of an eccentric magnetic tension load can be acquired, so that a total fatigue damage value based on the first fatigue damage value, the second fatigue damage value and the third fatigue damage value can be acquired; based on the process, the method and the device can obtain the real load data of various loads causing fatigue damage, and comprehensively process and calculate the real load data of the various loads, so that the total fatigue damage value of the wind generating set is determined, the fatigue damage data support with higher accuracy is provided for the residual life prediction of the wind generating set, the fatigue state of the wind generating set can be mastered accurately in real time, and the occurrence of fatigue fracture accidents is avoided.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic diagram of the relationship between the eccentric magnetic pull force of a generator and the length of an air gap between a stator and a rotor of the generator;
FIG. 2 is a schematic illustration of relative eccentricity of a stator and rotor of a generator;
FIG. 3 is a schematic view of a generator assembly of a typical direct drive wind turbine generator system;
fig. 4 is a schematic flow chart of a method for detecting fatigue damage of a wind turbine generator system according to an embodiment of the present application;
FIG. 5 is a graphical representation of an S-N curve representing the relationship between stress range and fatigue strength of a material (as characterized by the number of stress cycles at failure of the material) in an example of the present application;
FIG. 6 is an out-of-plane bending moment M of the hub center plane in the embodiment of the present applicationyzAnd fatigue damage of the generator rotating for one circle;
fig. 7 is a schematic flow chart of another method for detecting fatigue damage of a wind turbine generator system according to an embodiment of the present application;
fig. 8 is a schematic structural framework diagram of a detection device for fatigue damage of a wind generating set according to an embodiment of the present application;
FIG. 9 is a schematic structural framework diagram of a wind farm in an embodiment of the present application;
FIG. 10 is a schematic structural framework diagram of a controller of a wind farm according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the present application, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar parts or parts having the same or similar functions throughout. In addition, if a detailed description of the known art is not necessary for illustrating the features of the present application, it is omitted. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
The inventor of the application researches and discovers that the generator fatigue load of the wind generating set mainly has three parts: torque load of the generator, thermal load caused by heating of the winding and eccentric magnetic tension load.
In order to obtain the optimal wind energy capture coefficient, the generator needs to adjust the torque of the generator to control the rotating speed; the change of the torque causes the stress fluctuation of the structure of the generator, and further causes the fatigue damage of the structure.
The loss is inevitably generated in the running process of the generator, the efficiency of the current generator is about 95 percent, and about 5 percent of loss can occur in a heating mode, so that the temperature of the structure of the generator is increased; due to wind uncertainty and environment temperature uncertainty, the generator temperature changes, which causes the structure thermal stress fluctuation, and further causes thermal fatigue damage.
The stator and the rotor of the generator have the magnetic tension effects with equal magnitude and opposite directions, the magnetic tension density is related to the air gap of the generator, and the expression of the magnetic tension density of the generator is as follows:
pmag＝aδ2+ b δ + c expression (1)
In the expression (1), pmagA, b and c are constants for the magnetic pull density of the generator, and delta is the length of an air gap between a stator and a rotor of the generator.
Under an ideal state, an air gap between a stator and a rotor of the generator is uniform, magnetic tension is uniformly distributed, and the generator rotates under the action of the magnetic tension, so that stress fluctuation cannot be caused, and fatigue damage cannot occur. However, due to the influences of processing and assembling errors, wind load, gravity load and the like, inevitable eccentricity exists between the stator and the rotor, and the length of an air gap is different at each position of the circumference of the generator; under the action of the central load of the hub and gravity, relative displacement can be generated between the stator and the rotor; the air gap also changes due to the temperature rise in the generator operating state.
The above factors can cause uneven distribution of magnetic tension and periodic change of stress in the operation of the generator, so that the generator is always under the action of eccentric magnetic tension in the working process, and the rotation of the generator under the action of the eccentric magnetic tension is a main source of fatigue of the generator. The following equation gives the circumferential distribution of air gap lengths taking into account the above-mentioned effects:
an example of a circumferential distribution of the air gap length δ taking into account the above mentioned effects is given by:
δ＝δ0-ΔδT-(e0+e1) cos theta expression (2)
In the expression (2), δ0Is the initial air gap length; delta deltaTAir gap length changes due to temperature; e.g. of the type0To make the eccentricity; e.g. of the type1The center load of the wheel hub is eccentric under the action of gravity; θ is the circumferential position.
Fig. 1 shows the relationship between the magnetic pull density and the air gap length, and the abscissa of fig. 1 is the air gap length, in mm (millimeters),the ordinate is the magnetic tensile density in kN/m2(kilonewtons per square meter). Fig. 2 shows a schematic diagram of relative eccentricity between a stator and a rotor of a generator, where in fig. 2, the outer circle represents the stator, the inner circle represents the rotor, θ is the circumferential position as described above, and δ -e and δ + e show changes in the length of the air gap between the stator and the rotor, where e ═ e0+e1。
The inventor of the application considers the factors, provides a method, a device, a system and a controller for detecting fatigue damage of a wind generating set, and aims to solve the technical problems in the prior art.
The method, the device, the system and the controller for detecting the fatigue damage of the wind generating set provided by the application are applicable to various wind generating sets, including but not limited to direct-drive wind generating sets, semi-direct-drive wind generating sets and double-fed wind generating sets, and fig. 3 shows a generator assembly structure of a typical direct-drive wind generating set.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments.
The embodiment of the application provides a method for detecting fatigue damage of a wind generating set, and as shown in fig. 4, the method comprises the following steps:
s401, torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data of the generator are obtained.
Optionally, when acquiring data, the embodiment of the application may acquire torque load time sequence data, temperature time sequence data, and eccentric magnetic tension load time sequence data in one detection period; the time length and range of the detection period can be set according to actual requirements, and for example, any one time period such as one week and one hour can be set as one detection period.
Optionally, when data is acquired, a hot spot area of the generator is used as a monitoring point, and torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data of the hot spot area are monitored and acquired in real time.
The time sequence data in the embodiment of the application represent a plurality of data arranged in time sequence, the torque load time sequence data comprises a plurality of torque load data arranged in time sequence, the temperature time sequence data comprises a plurality of temperature data arranged in time sequence, and the eccentric magnetic tension load time sequence data comprises a plurality of eccentric magnetic tension load data arranged in time sequence.
The hot spot area in the embodiment of the application is a fatigue local maximum damage position of a support structure (including a stator support and a rotor support) of a generator in a development stage of a wind generating set, generally is a position of a bearing weld joint, and the hot spot areas of different wind generating sets are not completely consistent and need to be selected according to development calculation results.
Optionally, the temperature timing data comprises: internal temperature timing data and external ambient temperature timing data. The internal temperature time series data includes internal temperature data of a plurality of generators arranged in time series, and the external environment temperature time series data includes external environment temperature data of a plurality of generators arranged in time series.
Alternatively, the internal temperature of the generator may be characterized by the temperature of the major components in the generator, including the core, stator support, rotor support, magnet steel, windings, etc., and in one example, the internal temperature of the generator may be characterized by any one or more of the core temperature, stator support temperature, rotor support temperature, magnet steel temperature, and winding temperature of the generator.
S402, determining structural stress time sequence data of a hot spot region of the generator according to the torque load time sequence data, determining a first fatigue damage value of the hot spot region according to the structural stress time sequence data, and then executing the step S405.
Optionally, determining structural stress timing data of a hot spot region of the generator from the torque load timing data comprises: and determining structural stress time sequence data of the hot spot region according to the torque load time sequence data and the mapping relation between the torque load and the structural stress.
Optionally, the mapping between torque loads and structural stresses is predetermined by: and determining the mapping relation between the plurality of sample torque load data and the plurality of sample structure stress data by an analytic method or a finite element method as the mapping relation between the temperature and the thermal stress. The sample torque load data may be historical torque load data and the sample structural stress data may be historical structural stress data at the historical torque load.
In one example, the mapping between the determined torque loads and structural stresses is represented as follows:
σtorq＝f(Mtorq) Expression (3)
In the expression (3), MtorqRepresenting the torque load, σ, of the hot spot region of the generatortorqRepresenting structural stresses in the hot spot region of the generator.
Torque load timing data Mtorq(t) substituting expression (3) to obtain structural stress time sequence data sigmatorq(t)，σtorq(t)＝f(Mtorq(t))。
Optionally, determining a first fatigue damage value of the hot spot region according to the structural stress time series data includes:
determining stress cycle times of different stress ranges and average stress in structural stress time sequence data according to a rain flow counting method, wherein the stress cycle times are used as structural stress cycle times; and for each stress range in the structural stress time sequence data, determining a first fatigue damage value according to the stress range and a relation curve of the stress range and the structural stress cycle number.
According to Miner's fatigue cumulative damage theory, fatigue damage can be linearly superimposed. Fatigue damage caused by each stress cycle is related to the stress range and the mean stress value, and is not related to the loading sequence. According to a rain flow counting method, structural stress time sequence data can be divided into independent stress cycles to obtain stress cycle times of different stress ranges and average stress, namely a Markov matrix.
FIG. 5 illustrates a representation of stress range and fatigue strength of a material (in material)Characterization of the number of stress cycles at failure) of the material, the abscissa of the curve being the fatigue strength of the material (i.e. the duration number of cycles N in fig. 5), and the ordinate of the curve being the stress range (i.e. the stress range Δ σ of the stress range in fig. 5)RIn the unit of N/mm2I.e., newtons per square millimeter), the points on the curve represent: the number of stress cycles for which the material fails in a certain stress range; the portion of fig. 5 indicated by the arrow labeled 1 indicates a position having a fatigue strength value of 2.0E +06 (i.e., a power of six of 2 times 10), and the portion indicated by the arrow labeled 2 indicates a position having a fatigue strength value of 5E6 (i.e., a power of six of 5 times 10), at which the S-N curve has an inflection point; the part labeled 3 represents the position (i.e., endurance limit) where the fatigue strength value is 1.0E +8 (i.e., 1 times 10 to the power of eight); m represents the slope of the S-N curve in a logarithmic coordinate system; the numbers in each curve represent the fatigue level; the respective abscissa in fig. 5 means similar to 2.0E +06 and 1.0E + 8.
Under the condition of known stress range and stress cycle number, a corresponding fatigue damage value can be obtained through an S-N curve, and the specific principle is as follows: for a known stress range, a fatigue strength value (i.e., a stress cycle number) corresponding to the stress range can be obtained on the S-N curve, and a ratio of the stress cycle number to the fatigue strength value is determined, so that a corresponding fatigue damage value can be obtained.
In one example, for each stress range of the structural stress, a fatigue strength value (i.e., structural stress cycle number) corresponding to the stress range can be obtained on the S-N curve, a ratio of the structural stress cycle number to the fatigue strength value is determined, and a first fatigue damage value under the torque load of the generator can be obtained.
And S403, determining thermal stress time sequence data of the hot spot region according to the temperature time sequence data, determining a second fatigue damage value of the hot spot region according to the thermal stress time sequence data, and then executing the step S405.
Optionally, determining thermal stress time series data of the hot spot region according to the temperature time series data includes: and determining thermal stress time sequence data of the hot spot region according to the temperature time sequence data and the mapping relation between the temperature and the thermal stress.
Optionally, the mapping between temperature and thermal stress is predetermined by:
acquiring a plurality of sample temperature data and a plurality of sample thermal stress data of a hot spot area; the sample temperature data comprises sample internal temperature data and sample external environment temperature data; and determining the mapping relation between the plurality of sample temperature data and the plurality of sample thermal stress data by an analytic method or a finite element method as the mapping relation between the temperature and the thermal stress. The sample temperature data may be historical temperature data and the sample thermal stress data may be historical thermal stress data at a historical temperature.
The inventor of the application discovers that in the running process of the generator, the winding can generate heat to enable the temperature of the generator to rise, the temperature difference of the generator can reach more than 100 ℃ (centigrade) from a shutdown state to a continuous full-power state, and the thermal stress fluctuation caused by the temperature difference can not be ignored. The thermal stress is caused by the structure uneven thermal strain, the thermal expansion coefficient of the metal structure is constant, and the thermal strain is proportional to the structure temperature, so in one example, it can be assumed that the thermal stress is proportional to the difference between the internal temperature and the external temperature of the generator (difference between the internal temperature and the external ambient temperature), and the mapping relationship between the sample temperature data and the sample thermal stress data can be expressed as follows:
σT＝A(Twire-T0) Expression (4)
In expression (4), TwireIndicates the core temperature (as internal temperature), T, of the generator0Representing the external ambient temperature of the generator, A being the structural thermal stress coefficient, σTIndicating thermal stress.
In an optional embodiment, the sample temperature data and the sample thermal stress data are both data under the condition of extreme temperature rise, and the mapping relation between the temperature and the thermal stress is determined to be the mapping relation between the temperature and the thermal stress under the condition of extreme temperature rise.
The extreme temperature rise working condition in the embodiment of the application can be obtained through fluid simulation, and the simulation working condition can be set as: the highest environment temperature, the running of the generator at rated power and the normal work of a heat dissipation system are realized; the maximum environment temperature is the maximum temperature of the actual operation environment of the wind generating set and can be obtained according to historical environment temperature data of the expected installation area of the wind generating set.
Optionally, the mapping relationship between the temperature and the thermal stress under the extreme temperature rise condition may be specifically determined by the following method: and substituting the obtained sample internal temperature data, the sample external environment temperature data and the sample thermal stress data under the extreme temperature rise working condition into an expression (4) to determine a structural thermal stress coefficient A, namely determining the mapping relation between the temperature and the thermal stress under the extreme temperature rise working condition.
Alternatively, after the mapping relationship between the temperature and the thermal stress shown in expression (4) is determined, the internal temperature and the external environment temperature in the temperature time series data are substituted into expression (4), and the thermal stress time series data can be obtained.
Alternatively, the sample internal temperature data may be the internal temperature of a major component in the generator, such as any one or more of the core temperature, stator support temperature, rotor support temperature, magnet steel temperature and winding temperature of the generator.
In one example, when any one of the above temperature data is selected as the sample internal temperature data, the any one temperature data may be substituted for T in expression (4)wireTo obtain the corresponding mapping relation sigmaTObtaining a corresponding fatigue damage value as a second fatigue damage value based on the mapping relation; in another example, when any of the plurality of temperature data described above is selected as the sample internal temperature data, each temperature data may be substituted into T in expression (4), respectivelywireTo obtain a plurality of corresponding mapping relations sigmaTAnd obtaining a plurality of corresponding fatigue damage values based on the plurality of mapping relations, and selecting the maximum value in the plurality of fatigue damage values as a second fatigue damage value.
Optionally, determining a second fatigue damage value of the hot spot region according to the thermal stress time sequence data includes:
determining stress cycle times of different stress ranges and average stress in thermal stress time sequence data according to a rain flow counting method, wherein the stress cycle times are used as the thermal stress cycle times; and for each stress range in the thermal stress time sequence data, determining a second fatigue damage value according to the stress range and a relation curve of the stress range and the thermal stress cycle number.
The principle of determining the number of thermal stress cycles according to the rain flow counting method is the same as the principle of determining the number of structural stress cycles described above, and details are not repeated here.
The principle of determining the second fatigue damage value from the stress range of the thermal stress and the relationship curve of the stress range and the number of thermal stress cycles is as described above. In one example, for each stress range of the thermal stress, a fatigue strength value (i.e., a thermal stress cycle number) corresponding to the stress range can be obtained on the S-N curve, a ratio of the thermal stress cycle number to the fatigue strength value is determined, and a second fatigue damage value under the action of the thermal load of the generator can be obtained.
S404, determining a third fatigue damage value of the hot spot region according to the eccentric magnetic tension load time sequence data, and then executing the step S405.
Optionally, the third fatigue damage value is determined according to the eccentric magnetic tension load time sequence data and the relation curve between the eccentric magnetic tension load and the fatigue strength.
Optionally, the relation curve between the eccentric magnetic tension load and the fatigue damage caused by the eccentric magnetic tension load is predetermined by:
acquiring a plurality of sample eccentric magnetic tension load data and a plurality of sample fatigue damage data of fatigue damage caused by eccentric magnetic tension; determining a relation curve between the eccentric magnetic tension load data of the multiple samples and the fatigue damage data of the multiple samples as a sample relation curve; and fitting the sample relation curve to obtain a fitted curve which is used as a relation curve between the eccentric magnetic tension load and fatigue damage caused by the eccentric magnetic tension.
The sample eccentric magnetic tension load data can be historical eccentric magnetic tension load data, and the sample fatigue damage data can be historical fatigue damage data under the historical eccentric magnetic tension load.
The inventors of the present application have studied to find that in the expression (2)Air gap length change delta caused by temperatureTLess influence on fatigue damage, eccentric magnetic tension (e)0+e1) Is the main cause of fatigue. Thus, in an alternative embodiment, the calculation can be simplified assuming that the generator is always operating at a limit temperature when calculating the fatigue damage caused by the magnetic pull, as shown by the following formula, the eccentricity e1Is a function of the hub center load:
e1＝f(Fx,Fy,Fz,Mx,My,Mz) Expression (5)
In the expression (5), Fx,Fy,Fz,Mx,My,MzThe hub center load.
Since the out-of-plane bending moment of the rotating plane of the generator is the main factor causing the eccentricity of the generator, the expression (5) can be simplified as follows:
e1＝f(Myz) Expression (6)
In expression (6), MyzThe bending moment outside the central plane of the hub,
through the simplification, the fatigue damage caused by the eccentric magnetic pull force can be considered to be only equal to the out-of-center-plane bending moment M of the hubyzIn this regard, in an alternative embodiment, the hub center-plane bending moment M may be adjusted accordinglyyzAs an eccentric magnetic pull load.
Optionally, the acquiring the eccentric magnetic tension load time series data comprises: acquiring time sequence data of bending moment outside a hub central plane; the method specifically comprises the following steps: the blade root stress at the blade root of the impeller is obtained, blade root bending moment is determined according to the blade root stress, and the hub center bending moment can be obtained through translation of moment.
Optionally, the acquiring the sample eccentric magnetic tension load data comprises: obtaining a sample MyzAnd (4) data.
Optionally based on a plurality of samples MyzData and multiple sample fatigue damage data for fatigue damage caused by eccentric magnetic pull,can determine the external bending moment M of the central plane of the hubyzAnd fatigue damage caused by eccentric magnetic tension, and measuring the bending moment M outside the central plane of the hub according to the eccentric magnetic tension load time sequence datayzAnd the bending moment M outside the central plane of the hubyzAnd fatigue damage caused by the eccentric magnetic tension, and determining a third fatigue damage value under the action of the eccentric magnetic tension load.
Alternatively, the sample fatigue damage data may be determined by: determining the sample M according to the rain flow counting methodyzStress cycle times for different stress ranges and average stresses in the data; according to the sample MyzThe stress range of the data, and the stress range versus the number of stress cycles (i.e., S-N curve), a sample fatigue damage value can be determined. The principle of the determination method of the sample fatigue damage data is similar to the determination method of the first fatigue damage value and the second fatigue damage value, and the detailed description is omitted here.
Optionally, the sample M is obtainedyzBoth the data and the sample fatigue damage data may be data in a preset unit cycle, and the time length and range of the unit cycle may be set according to actual requirements, for example, the time range of one rotation (herein, one rotation in angle) of the generator may be set as one unit cycle.
In one example, based on a sample M within one revolution of the generatoryzObtaining a sample M by data and sample fatigue damage datayzActual relationship curves between the data and the sample fatigue damage data, as shown by the solid line in fig. 6; according to the data of the solid line in FIG. 6, the bending moment M outside the center plane of the hub can be fityzAnd fatigue damage of one revolution of the generator (i.e. M)yzThe d curve) as shown by the dashed line in fig. 6.
The abscissa in FIG. 6 represents the hub center-plane out-of-plane bending moment M during one rotation of the generatoryz(kNm, k.m.) and the ordinate indicates the fatigue Damage value Damage for one revolution of the generator, y 3E-08E0.002xIs MyzFitting expression of the d-curve (where 3E-08 denotes 3 times 10 to the negative octave, E being the natural logarithm)Base number), each ordinate value in FIG. 6 has a meaning similar to 3E-08, e.g., 1.80E-07 represents the negative seventh power of 1.80 times 10, and the other ordinate values are the same; r2As a correlation coefficient, for characterizing the fitting accuracy, R2The closer to 1, the higher the fitting accuracy of the dotted line in the representation; as can be seen from the expression and trend of the dotted line in FIG. 6, MyzThe fatigue damage value of the generator rotating for one circle is in an exponential relation, and is represented by R in figure 62The fitting accuracy of the existing dotted line is higher as can be seen from the numerical value of (2).
In one example, based on M shown in FIG. 6yzD curve, for measured hub center plane bending moment M in the acquired eccentric magnetic tension load time sequence datayzAccording to the Myz-d-curve determining the fatigue damage value for one revolution of the generator, multiplying the fatigue damage value for one revolution of the generator by the different M in one detection periodyzNumber of revolutions of generator at level (which number of revolutions may be based on a real-time measured rotation angle of generator
Calculated), the fatigue damage value of the generator in one detection period can be obtained, and the fatigue damage value of the generator in one week (here, one week in time) or one day can be used as the third fatigue damage value under the action of the eccentric magnetic tension load according to the actual requirement.
S405, determining a total fatigue damage value of the hot spot region according to the first fatigue damage value, the second fatigue damage value and the third fatigue damage value.
In an alternative embodiment, the sum of the first fatigue damage value, the second fatigue damage value and the third fatigue damage value is determined, and the determined sum is used as the total fatigue damage value of the hot spot region.
In an optional embodiment, the first fatigue damage value, the second fatigue damage value and the third fatigue damage value obtained based on the torque load time sequence data, the thermal load time sequence data and the eccentric magnetic tension load time sequence data in one detection period are fatigue damage values in one detection period, and at this time, for a plurality of detection periods, the fatigue damage values in one detection period are obtainedTotal fatigue damage value D ofnThis can be determined as follows:
in the expression (7), DnRepresenting the total fatigue damage value, d, over n test periods (e.g. n days)M,iRepresenting a first fatigue damage value of the hot spot region under the action of the torque load in the ith detection period (for example, the ith day)T,iA second fatigue damage value, d, representing the hot spot area under the action of the thermal load in the ith detection periodecc,iRepresenting a third fatigue damage value of a hot spot area under the action of the eccentric magnetic tension load in the ith detection period; wherein n is an integer greater than 1, and i is [1, n ]]An integer within the range.
Optionally, as shown in fig. 7, the method for detecting fatigue damage of a wind turbine generator system according to the embodiment of the present application further includes, on the basis of the steps S401 to S405, the following steps S406 to S407:
and S406, predicting the residual life of the wind generating set according to the total fatigue damage value.
In an alternative embodiment, the value D is for the total fatigue damagenThe remaining life of the wind turbine generator system can be predicted as followsres：
The parameter n has the same meaning as above and is the number of detection cycles.
And S407, sending out early warning information when the remaining life is less than or equal to the life threshold.
The lifetime threshold in the embodiment of the present application may be preset according to actual needs or empirical values, and may be set to 5 years or 8 years, for example, or other time limit.
The early warning information in the embodiment of the application can be sent in various forms, such as light, sound, characters and the like. The early warning information can help an engineer to know the condition of the residual life in time, so that the engineer can be helped to detect flaws in a hot spot area in time, hidden dangers can be eliminated as soon as possible, and the safety of the wind generating set is guaranteed.
Optionally, the method for detecting fatigue damage of a wind turbine generator system provided by the embodiment of the present application further includes: and when the total fatigue damage value is greater than or equal to the damage threshold value, controlling the wind generating set to stop under a preset stop condition.
The damage threshold value and the shutdown condition in the embodiment of the application can be set according to actual requirements, for example, the shutdown condition can be set to control part of the wind generating sets to be shutdown in the scenes of power limitation, technical transformation, spot check and the like, and the wind generating sets which are determined in the embodiment of the application and are larger than or equal to the damage threshold value can be used as the priority sets for controlling the shutdown, so that the fatigue loss of the wind generating sets is reduced while the requirements of power limitation, technical transformation, spot check and the like are met.
Optionally, after acquiring the time series data of the eccentric magnetic tension load of the generator, the embodiment of the application further includes:
when any eccentric magnetic tension load data in the eccentric magnetic tension load time sequence data is larger than or equal to the load threshold value, adjusting at least one parameter of a yaw parameter and a blade pitch angle of the wind generating set to enable an engine room of the wind generating set to yaw or the blade pitch angle to change until the eccentric magnetic tension load data is smaller than the load threshold value.
According to the research of the inventor of the application, the eccentric magnetic tension load and the thermal load are main factors causing fatigue damage of the wind generating set, and the load threshold value is set for the eccentric magnetic tension load, so that when whether corresponding parameters of the wind generating set are adjusted or not is determined on the basis of the load threshold value, the parameter adjusting conditions can be accurately determined, meanwhile, the calculated amount is reduced, and the data processing speed is improved. The load threshold value of the embodiment of the application can be preset according to actual requirements or empirical values.
Based on the research, the embodiment of the application can also adopt corresponding measures to reduce the fatigue loss of the wind generating set based on the heat load data, and the related measures are introduced in the subsequent embodiments.
Based on the same inventive concept, the detection device for the fatigue damage of the wind generating set provided by the embodiment of the present application, as shown in fig. 8, includes: a data acquisition module 801, a first fatigue determination module 802, a second fatigue determination module 803, a third fatigue determination module 804, and a total fatigue determination module 805.
And the data acquisition module 801 is used for acquiring torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data of the generator.
The first fatigue determination module 802 is configured to determine structural stress time sequence data of a hot spot region of the generator according to the torque load time sequence data, and determine a first fatigue damage value of the hot spot region according to the structural stress time sequence data.
The second fatigue determining module 803 is configured to determine thermal stress time series data of the hot spot region according to the temperature time series data, and determine a second fatigue damage value of the hot spot region according to the thermal stress time series data.
And a third fatigue determining module 804, configured to determine a third fatigue damage value of the hot spot region according to the eccentric magnetic tension load time series data.
The total fatigue determining module 805 is configured to determine a total fatigue damage value of the hot spot region according to the first fatigue damage value, the second fatigue damage value, and the third fatigue damage value.
Optionally, the second fatigue determination module 803 is specifically configured to: and determining thermal stress time sequence data of the hot spot region according to the temperature time sequence data and the mapping relation between the temperature and the thermal stress.
Optionally, the second fatigue determination module 803 is specifically configured to: determining stress cycle times of different stress ranges and average stress in thermal stress time sequence data according to a rain flow counting method, wherein the stress cycle times are used as the thermal stress cycle times; and for each stress range in the thermal stress time sequence data, determining a second fatigue damage value according to the stress range and a relation curve of the stress range and the thermal stress cycle number.
Optionally, the third fatigue determination module 804 is specifically configured to: and determining a third fatigue damage value according to the eccentric magnetic tension load time sequence data and a relation curve between the eccentric magnetic tension load and the fatigue damage caused by the eccentric magnetic tension load.
Optionally, the detection apparatus 800 for wind generating set fatigue damage that this application implementation provided still includes: the service life prediction module and the early warning module.
The service life prediction module is used for predicting the residual service life of the wind generating set according to the total fatigue damage value; and the early warning module is used for sending out early warning information when the residual service life is less than or equal to the service life threshold value.
Optionally, the detection apparatus 800 for wind generating set fatigue damage that this application implementation provided still includes: and a parameter adjusting module.
The parameter adjusting module is used for adjusting at least one parameter of a yaw parameter and a blade pitch angle of the wind generating set when any eccentric magnetic tension load data in the eccentric magnetic tension load time sequence data is larger than or equal to a load threshold value after acquiring the eccentric magnetic tension load time sequence data of the generator, so that an engine room of the wind generating set is enabled to yaw or the blade pitch angle is changed until the eccentric magnetic tension load data is smaller than the load threshold value.
Optionally, the detection apparatus 800 for wind generating set fatigue damage that this application implementation provided still includes: and a control module.
And the control module is used for controlling the wind generating set to stop under the preset stop condition when the total fatigue damage value is greater than or equal to the damage threshold value.
The detection apparatus 800 for the fatigue damage of the wind generating set according to the embodiment of the present application may execute any one of the foregoing methods for detecting the fatigue damage of the wind generating set, and the implementation principles thereof are similar, and the content not shown in detail in the embodiment may refer to the foregoing aspect embodiments, and is not described herein again.
Based on the same inventive concept, the embodiment of the application provides a detection system of a wind generating set, and the detection system comprises: a torque load detector, a temperature detector, an eccentric magnetic pull load detector and a controller of a wind farm (also called a farm level controller).
The torque load detector, the temperature detector and the eccentric magnetic tension load detector are all in communication connection with a controller of the wind power plant.
The torque load detector, the temperature detector and the eccentric magnetic tension load detector are respectively used for detecting the torque load, the temperature and the eccentric magnetic tension load of the generator to obtain torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data.
Fig. 9 shows a schematic diagram of a wind farm, in the example shown in fig. 9, the torque load detector, the temperature detector, and the eccentric magnetic pull load detector are all in communication connection with a controller of the wind farm through a main controller (also referred to as a single-machine controller or a fan controller) of the wind turbine, the torque load detector, the temperature detector, and the eccentric magnetic pull load detector transmit detected torque load time sequence data, temperature time sequence data, and eccentric magnetic pull load time sequence data to the main controller of the wind turbine, and the main controller of the wind turbine can forward the torque load time sequence data, the temperature time sequence data, and the eccentric magnetic pull load time sequence data to the controller of the wind farm.
Optionally, the torque load detector of the embodiment of the present application may be integrated in a main controller of the wind turbine generator system.
Optionally, the temperature detector of the embodiment of the present application includes at least two temperature sensors, and at least one temperature sensor is respectively disposed inside and outside the generator and is respectively configured to detect an inside temperature and an outside ambient temperature of the generator.
In an alternative embodiment, when the temperature detector includes two temperature sensors, one temperature sensor may be disposed inside the generator, such as at any one of the back of the iron core, the stator support, the rotor support, the magnetic steel, the winding, and the like, for detecting the internal temperature of the generator in real time; the other is arranged outside the generator and is used for detecting the external environment temperature of the generator in real time.
In another alternative embodiment, when the temperature detector includes three or more temperature sensors, one temperature sensor may be disposed at any one of the inside and the outside of the generator, two or more temperature sensors may be disposed at another position, and two or more temperature sensors may be disposed at the inside and the outside of the generator, respectively. The part provided with more than two temperature sensors can detect the temperatures of a plurality of position points in real time, and further more accurately determine the temperature of the part according to the temperatures of the position points, for example, determine the temperature of the part according to the average temperature of the position points.
Optionally, the temperature detector in the embodiment of the present application includes at least one strain sensor for detecting stress of the blade in real time, where the stress is used to derive a blade root bending moment, and the blade root bending moment may be used to synthesize a hub center bending moment of the generator.
In an alternative embodiment, at least one strain sensor may be provided at the root (i.e. root) or at the middle of the blade to measure the stress at the root or middle of the blade; in another alternative embodiment, the eccentric magnetic tension load detector may obtain the blade root bending moment by measuring the displacement difference at different positions of the blade.
Alternatively, the sampling frequency of the torque load detector, the temperature detector and the eccentric magnetic tension load detector can be set according to actual requirements, for example, the sampling frequency of the torque load detector can be set to 1Hz (hertz), the sampling frequency of the temperature detector can be set to 0.1Hz, and the sampling frequency of the eccentric magnetic tension load detector can be set to 1 Hz.
The embodiment of the application provides a controller of a wind power plant, and the controller comprises: the storage and the processor are electrically connected.
The memory stores a computer program, and the computer program is executed by the processor to implement any detection method of the wind generating set provided by the embodiment of the application.
In an alternative embodiment, the present application provides a controller for a wind farm, as shown in fig. 10, the controller 1000 includes: the memory 1001 and the processor 1002 are electrically connected, such as by a bus 1003, and the memory 1001 and the processor 1002 are electrically connected.
Optionally, the memory 1001 is used for storing application program codes for implementing the scheme of the present application, and the processor 1002 controls the execution. The processor 1002 is configured to execute the application program codes stored in the memory 1001 to implement any one of the detection methods of the wind turbine generator system provided by the embodiments of the present application.
The Memory 1001 may be a ROM (Read-Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read-Only Memory) or other optical disk storage, optical disk storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.
The Processor 1002 may be a CPU (Central Processing Unit), a general-purpose Processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or other Programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 1002 may also be a combination of computing functions, e.g., comprising one or more microprocessors, a combination of DSPs and microprocessors, and the like.
Optionally, the controller 1000 may also include a transceiver 1004. The transceiver 1004 may be used for reception and transmission of signals. The transceiver 1004 may allow the electronic device 1000 to communicate wirelessly or wiredly with other devices to exchange data. It should be noted that the transceiver 1004 is not limited to one in practical application.
Optionally, the controller 1000 may further include an input unit 1005. The input unit 1005 may be used to receive input numeric, character, image, and/or sound information, or to generate key signal inputs related to user settings and function control of the electronic apparatus 1000. The input unit 1005 may include, but is not limited to, one or more of a touch screen, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, a camera, a microphone, and the like.
Optionally, the controller 1000 may further include an output unit 1006. Output unit 1006 may be used to output or display information processed by processor 1002. The output unit 1006 may include, but is not limited to, one or more of a display device, a speaker, a vibration device, and the like.
While FIG. 10 illustrates a controller 1000 having various devices for a wind farm, it is to be understood that not all of the illustrated devices are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
Based on the same inventive concept, embodiments of the present application provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements any one of the detection methods of the wind turbine generator system provided by the embodiments of the present application.
The computer readable medium includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs, RAMs, EPROMs (Erasable Programmable Read-Only Memory), EEPROMs, flash Memory, magnetic cards, or fiber optic cards. That is, a readable medium includes any medium that stores or transmits information in a form readable by a device (e.g., a computer).
The embodiment of the application provides a computer-readable storage medium suitable for any one of the above detection methods for a wind turbine generator system, which is not described herein again.
By applying the embodiment of the application, at least the following beneficial effects can be realized:
1) the method and the device can acquire various load time sequence data (specifically including torque load time sequence data, thermal load time sequence data and eccentric magnetic tension load time sequence data) of the wind driven generator acquired in real time, and according to the various load time sequence data, a first fatigue damage value under the action of a torque load, a second fatigue damage value under the action of a thermal load and a third fatigue damage value under the action of an eccentric magnetic tension load can be acquired, so that a total fatigue damage value based on the first fatigue damage value, the second fatigue damage value and the third fatigue damage value can be acquired; based on the process, the method and the device can obtain the real load data of various loads causing fatigue damage, and comprehensively process and calculate the real load data of the various loads, so that the total fatigue damage value of the wind generating set is determined, the fatigue damage data support with higher accuracy is provided for the residual life prediction of the wind generating set, the fatigue state of the wind generating set can be mastered accurately in real time, and the occurrence of fatigue fracture accidents is avoided.
2) According to the method and the device, the residual life of the wind generating set can be accurately predicted in real time based on the obtained total fatigue damage value, early warning information is sent out based on the prediction result, and possible dangers are reminded, so that measures can be taken in time to prevent and treat the dangers, the safety of the wind generating set is improved, and the service life of the wind generating set is prolonged.
3) According to the method and the device, the wind generating set with large fatigue damage can be subjected to load management according to the total fatigue damage value of the wind generating set, so that more accurate field-level scheduling management is realized, and all wind generating sets of the wind power plant can be used in a balanced manner.
4) According to the embodiment of the application, the mapping relation between various types of load data and stress data can be established, the corresponding stress time sequence data can be accurately determined based on the established mapping relation and the actually measured load time sequence data, and then the fatigue damage components (namely the first fatigue damage value, the second fatigue damage value and the third fatigue damage value) are determined according to the stress time sequence data.
5) The embodiment of the application can establish the mapping relation between the load data and the stress data (such as between torque load and structural stress and between temperature and thermal stress) by an analytic method or a finite element method, can be suitable for various application scenes of simple data rules and complex data rules, and has a wide application range; after the stress data are determined, the fatigue damage components (such as the first fatigue damage value and the second fatigue damage value) of the generator under the corresponding load can be rapidly determined by combining a rain flow counting method.
6) According to the embodiment of the application, based on the actually measured load data (such as eccentric magnetic tension load time sequence data) and fatigue damage data of the generator in the actual operation process, the relation between the corresponding load data and the fatigue damage caused by the corresponding load data is determined, a corresponding relation curve is obtained, and then the fatigue damage value (such as a third fatigue damage value) caused by any actually measured load data is calculated on the basis of the relation curve, so that the fatigue damage value of the generator in the actual operation process can be obtained.
Those of skill in the art will appreciate that the various operations, methods, steps in the processes, acts, or solutions discussed in this application can be interchanged, modified, combined, or eliminated. Further, other steps, measures, or schemes in various operations, methods, or flows that have been discussed in this application can be alternated, altered, rearranged, broken down, combined, or deleted. Further, steps, measures, schemes in the prior art having various operations, methods, procedures disclosed in the present application may also be alternated, modified, rearranged, decomposed, combined, or deleted.
In the description of the present application, it is to be understood that the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless otherwise specified.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present application, and it should be noted that, for those skilled in the art, several modifications and decorations can be made without departing from the principle of the present application, and these modifications and decorations should also be regarded as the protection scope of the present application.
Claims (18)
1. A method for detecting fatigue damage of a wind generating set is characterized by comprising the following steps:
acquiring torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data of the generator;
determining structural stress time sequence data of a hot spot region of the generator according to the torque load time sequence data, and determining a first fatigue damage value of the hot spot region according to the structural stress time sequence data;
determining thermal stress time sequence data of the hot spot region according to the temperature time sequence data, and determining a second fatigue damage value of the hot spot region according to the thermal stress time sequence data;
determining a third fatigue damage value of the hot spot region according to the eccentric magnetic tension load time sequence data;
and determining a total fatigue damage value of the hot spot region according to the first fatigue damage value, the second fatigue damage value and the third fatigue damage value.
2. The detection method according to claim 1, wherein the temperature timing data includes: internal temperature timing data and external ambient temperature timing data;
determining thermal stress time sequence data of the hot spot region according to the temperature time sequence data comprises the following steps:
determining thermal stress time sequence data of the hot spot region according to the temperature time sequence data and a mapping relation between the temperature and the thermal stress;
the mapping relationship between the temperature and the thermal stress is predetermined by:
acquiring a plurality of sample temperature data and a plurality of sample thermal stress data of the hot spot area; the sample temperature data comprises sample internal temperature data and sample external environment temperature data;
determining a mapping relationship between the plurality of sample temperature data and a plurality of sample thermal stress data as a mapping relationship between the temperature and the thermal stress by an analytical method or a finite element method.
3. The method of claim 2, wherein determining a second fatigue damage value for the hot spot region from the thermal stress timing data comprises:
determining stress cycle times of different stress ranges and average stress in the thermal stress time sequence data according to a rain flow counting method, wherein the stress cycle times are used as the thermal stress cycle times;
and for each stress range in the thermal stress time sequence data, determining the second fatigue damage value according to the stress range and a relation curve of the stress range and the thermal stress cycle times.
4. The detection method according to claim 1, wherein the determining a third fatigue damage value of the hot spot region according to the eccentric magnetic tension load time series data comprises:
and determining the third fatigue damage value according to the eccentric magnetic tension load time sequence data and a relation curve between the eccentric magnetic tension load and the fatigue damage caused by the eccentric magnetic tension load.
5. The detection method according to claim 4, wherein a relationship curve between the eccentric magnetic tensile load and the fatigue damage caused by the eccentric magnetic tensile load is predetermined by:
acquiring a plurality of sample eccentric magnetic tension load data and a plurality of sample fatigue damage data of fatigue damage caused by the eccentric magnetic tension;
determining a relation curve between the eccentric magnetic tension load data of the plurality of samples and the fatigue damage data of the plurality of samples as a sample relation curve;
and fitting the sample relation curve to obtain a fitted curve which is used as a relation curve between the eccentric magnetic tension load and fatigue damage caused by the eccentric magnetic tension.
6. The detection method according to claim 1, further comprising:
predicting the residual life of the wind generating set according to the total fatigue damage value;
and when the residual service life is less than or equal to the service life threshold value, sending out early warning information.
7. The method of claim 1, wherein the step of obtaining the eccentric magnetic pull load time series data of the generator further comprises the following steps:
when any eccentric magnetic tension load data in the eccentric magnetic tension load time sequence data is larger than or equal to a load threshold value, adjusting at least one parameter of a yaw parameter and a blade pitch angle of the wind generating set to enable a cabin of the wind generating set to yaw or the blade pitch angle to change until the eccentric magnetic tension load data is smaller than the load threshold value.
8. The detection method according to claim 1, further comprising:
and when the total fatigue damage value is greater than or equal to the damage threshold value, controlling the wind generating set to stop under a preset stop condition.
9. The utility model provides a wind generating set fatigue damage's detection device which characterized in that includes:
the data acquisition module is used for acquiring torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data of the generator;
the first fatigue determining module is used for determining structural stress time sequence data of a hot spot region of the generator according to the torque load time sequence data and determining a first fatigue damage value of the hot spot region according to the structural stress time sequence data;
the second fatigue determining module is used for determining thermal stress time sequence data of the hot spot region according to the temperature time sequence data and determining a second fatigue damage value of the hot spot region according to the thermal stress time sequence data;
the third fatigue determining module is used for determining a third fatigue damage value of the hot spot region according to the eccentric magnetic tension load time sequence data;
and the total fatigue determining module is used for determining a total fatigue damage value of the hot spot region according to the first fatigue damage value, the second fatigue damage value and the third fatigue damage value.
10. The detection device of claim 9, further comprising:
the service life prediction module is used for predicting the residual service life of the wind generating set according to the total fatigue damage value;
and the early warning module is used for sending out early warning information when the residual service life is less than or equal to the service life threshold value.
11. The detection device of claim 9, further comprising:
the parameter adjusting module is used for adjusting at least one parameter of a yaw parameter and a blade pitch angle of the wind generating set when any eccentric magnetic tension load data in the eccentric magnetic tension load time sequence data is larger than or equal to a load threshold value after acquiring the eccentric magnetic tension load time sequence data of the generator, so that a cabin of the wind generating set is yawed or the blade pitch angle is changed until the eccentric magnetic tension load data is smaller than the load threshold value.
12. The detection device of claim 9, further comprising:
and the control module is used for controlling the wind generating set to stop under a preset stop condition when the total fatigue damage value is greater than or equal to a damage threshold value.
13. A controller for a wind farm, comprising:
a memory;
a processor electrically connected with the memory;
the memory stores a computer program for execution by the processor to implement the method of detecting fatigue damage of a wind turbine generator set according to any of claims 1-8.
14. A detection system of a wind generating set, comprising: a torque load detector, a temperature detector, an eccentric magnetic pull load detector and a controller of a wind farm according to claim 13;
the torque load detector, the temperature detector and the eccentric magnetic tension load detector are all in communication connection with the controller;
the torque load detector, the temperature detector and the eccentric magnetic tension load detector are respectively used for detecting the torque load, the temperature and the eccentric magnetic tension load of the generator to obtain torque load time sequence data, temperature time sequence data and eccentric magnetic tension load time sequence data.
15. The detection system according to claim 14, wherein the torque load detector is integrated in a main controller of the wind turbine generator set.
16. The detection system of claim 14, wherein the temperature probe comprises at least two temperature sensors;
at least one temperature sensor is respectively arranged inside and outside the generator and is respectively used for detecting the internal temperature and the external environment temperature of the generator.
17. The inspection system of claim 14, wherein the eccentric magnetic tension load detector comprises at least one strain sensor disposed at a root or a mid-section of the blade;
the at least one strain sensor is configured to detect a stress at a root or a mid-portion of the blade, the stress being used to determine a hub center bending moment.
18. A computer-readable storage medium, characterized in that a computer program is stored which, when being executed by a processor, carries out the method of detecting fatigue damage to a wind park according to any one of claims 1-8.
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| CN116052915A (en) * | 2022-12-30 | 2023-05-02 | 中国核动力研究设计院 | Nuclear reactor primary loop system fatigue state monitoring method and device |
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| CN116052915B (en) * | 2022-12-30 | 2024-01-23 | 中国核动力研究设计院 | Nuclear reactor primary loop system fatigue state monitoring method and device |
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| CN113803220B (en) | 2023-05-09 |
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