CN116415403A - Method and related device for calculating residual life of large component in wind generating set - Google Patents
Method and related device for calculating residual life of large component in wind generating set Download PDFInfo
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Abstract
The application discloses a method for calculating the residual life of a large part in a wind generating set and a related device, wherein the method comprises the following steps: acquiring real-time operation data of the wind generating set; inverting the time sequence load of each major component key position in the wind generating set through the real-time operation data; according to the time sequence load, calculating fatigue damage of each large part hot spot position under the load action of each large part key position, wherein each large part hot spot position is in stress correlation with each large part key position; and calculating the residual life of each large part through the fatigue damage of each large part hot spot position. The method and the device solve the problem that in the prior art, when the actual load and the simulation load of the unit appear larger, the evaluation accuracy of the residual service life is poor.
Description
Technical Field
The application belongs to the technical field of wind power generation, and particularly relates to a method, a device, equipment, a computer readable storage medium and a computer program product for calculating the residual life of a large part in a wind generating set.
Background
Wind power plants operate in complex environments for a long time as heavy equipment that operates over a long period of time. The actual life of its major components is often in and out of design life, subject to actual control strategies, operating loads, and maintenance activities, etc. Therefore, it is necessary to build a high-precision monitoring model of the remaining life of the wind turbine generator system.
Currently, a mechanism model is generally used as a residual life monitoring model of a large component in a wind generating set. Residual life monitoring based on a mechanism model can be realized by reconstructing a working condition table through historical SCADA data, or establishing a simulation database, and further realizing monitoring through a table look-up mode. When the actual load and the simulation load of the unit are larger in output, the residual life evaluation accuracy is poor.
Disclosure of Invention
The embodiment of the application provides a method and a related device for calculating the residual life of a large part in a wind generating set, and aims to solve the problem that in the prior art, when the actual load and the simulation load of the set appear in a large way, the evaluation accuracy of the residual life is poor.
In one aspect, an embodiment of the present application provides a method for calculating a remaining life of a large component in a wind turbine generator system, where the method includes:
acquiring real-time operation data of the wind generating set;
inverting the time sequence load of each major component key position in the wind generating set through the real-time operation data;
according to the time sequence load, calculating fatigue damage of each large part hot spot position under the load action of each large part key position, wherein each large part hot spot position is in stress correlation with each large part key position;
and calculating the residual life of each large part through the fatigue damage of each large part hot spot position.
Optionally, while performing the step of calculating the remaining life of each large component by the fatigue damage of each large component hot spot location, the method further comprises:
and carrying out degradation trend monitoring according to the real-time operation data so as to update the residual life of any abnormal large part when the abnormal large part occurs.
Optionally, said calculating said remaining life of each large component from said fatigue damage at each said large component hot spot location comprises:
according to the real-time operation data, determining wind condition data and working condition data of the wind generating set;
constructing a damage matrix of each large part in each operation mode according to the fatigue damage, the wind condition data and the working condition data of each large part hot spot position;
performing damage accumulation on the damage matrix of each large part in each operation mode to obtain an accumulated damage result of each large part;
the remaining life of each large component is calculated from the accumulated damage results of each large component.
Optionally, the performing damage accumulation on the damage matrix of each large component in each operation mode to obtain an accumulated damage result of each large component includes:
obtaining probability matrixes corresponding to the damage matrixes in all operation modes, wherein the probability matrixes comprise fatigue damage probabilities in the wind condition data and the working condition data;
multiplying the damage matrix of each large part in each operation mode by the probability matrix corresponding to each damage matrix to obtain the accumulated damage value of each large part in each operation mode;
and adding the accumulated damage values of the large parts in each operation mode to obtain an accumulated damage result of each large part.
Optionally, said calculating said remaining life of each large component from said accumulated damage results of each large component comprises:
acquiring the accumulated operation years of the wind generating set;
dividing the accumulated damage result of each large part with the accumulated operation years to obtain the annual loss value of each large part;
the remaining life of each large part is calculated by the following formula,
RULm＝(1-Damage)/L
where RULm is the remaining life of the large component m, damage is the cumulative Damage result of the large component m, and L is the annual loss value of the large component m.
Optionally, the real-time operation data includes unit state data and environmental parameters of the wind generating unit; inverting the time sequence load of each major component key position in the wind generating set according to the real-time operation data, wherein the time sequence load comprises the following steps:
inputting the unit state data and the environmental parameters into a digital twin model of the fan to perform data inversion so as to obtain a first inversion result;
when the time sequence load of the key position of the large part in the target wind generating set is absent in the first inversion result, obtaining the actual measurement load of the part of the position in the target wind generating set, wherein the wind generating set comprises the target wind generating set;
inputting the unit state data, the environmental parameters and the actual measured load into the digital twin model of the fan to obtain a second inversion result; and the union of the first inversion result and the second inversion result is the time sequence load of the key position of each large part.
In another aspect, an embodiment of the present application provides a remaining life calculating device for a large component in a wind generating set, the device including:
the acquisition module is used for acquiring real-time operation data of the wind generating set;
the inversion module is used for inverting the time sequence load of each key position of the large component in the wind generating set according to the real-time operation data;
the damage calculation module is used for calculating fatigue damage of each big part hot spot position under the load action of each big part key position according to the time sequence load, and each big part hot spot position is in stress association with each big part key position;
and the service life calculation module is used for calculating the residual service life of each large part through the fatigue damage of each large part hot spot position.
In yet another aspect, embodiments of the present application provide a remaining life computing device for a large component in a wind turbine, the device comprising:
the apparatus comprises: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the remaining life calculation method for large components in a wind turbine generator system as described above.
In yet another aspect, embodiments of the present application provide a computer storage medium having stored thereon computer program instructions that, when executed by a processor, implement a method of remaining life calculation for a large component in a wind turbine generator set as in the above aspect.
In yet another aspect, embodiments of the present application provide a computer program product having stored thereon computer program instructions which, when executed by a processor, implement a method of calculating a remaining life of a large component in a wind turbine generator set as in the above aspect.
According to the method and the related device for calculating the residual life of the large parts in the wind generating set, after the time sequence load of the key positions of the large parts in the wind generating set is obtained through inversion of real-time operation data of the wind generating set, fatigue damage to the hot spot positions of the large parts under the action of the load is calculated; and calculating the residual service life of each large part through fatigue damage of the hot spot position of each large part. The fatigue damage is obtained by time sequence loads of key positions of all large parts in the wind generating set, and the time sequence loads of the key positions of all the large parts are obtained by inversion according to real-time operation data. Thus, the real-time influence of the load on the component in the whole life cycle can be embodied. On the basis, the residual life of each large part obtained through fatigue damage calculation of the hot spot position of each large part is real-time, and the accuracy is improved, so that the problem that the evaluation accuracy of the residual life is poor when the actual load and the simulation load of the unit appear in the prior art is solved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.
FIG. 1 is a flow chart of a method for calculating remaining life of a large component in a wind turbine generator system according to one embodiment of the present application;
FIG. 2 is a schematic diagram of a refinement flow of S102 in a method for calculating remaining life of a large component in a wind turbine generator system according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a refinement flow of S104 in a method for calculating remaining life of a large component in a wind turbine generator system according to an embodiment of the present application;
FIG. 4 is a schematic view of a device for calculating remaining life of a large component in a wind turbine generator system according to another embodiment of the present application;
FIG. 5 is a schematic diagram of a remaining life calculating device for large components in a wind turbine generator system according to yet another embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application are described in detail below to make the objects, technical solutions and advantages of the present application more apparent, and to further describe the present application in conjunction with the accompanying drawings and the detailed embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative of the application and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by showing examples of the present application.
It is noted that relational terms such as first and second, and the like are 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. Moreover, 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 … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
A mechanism model, also known as a white-box model, is an accurate mathematical model built from objects, internal mechanisms of the production process, or delivery mechanisms of the material flow.
In the prior art, when a mechanism model is used for calculating the residual life of a large part in a wind generating set, an equivalent fatigue accumulation scheme is often adopted.
Specifically, the common method is to reconstruct a working condition table through site history SCADA (Supervisory Control And Data Acquisition, data acquisition and monitoring control) data, and further obtain the fatigue load of the wind generating set through working condition simulation calculation. Or establishing a simulation database under different wind conditions-sectors, and looking up a table to calculate the fatigue load of the unit according to the current wind conditions of the wind power plant. And finally, combining the S/N curve of the large part, and designing and calculating to obtain the residual service life of the large part.
However, these calculation schemes based on the mechanism model generally do not consider the difference between the simulation model and the actual load, and when the actual load and the simulation load appear in a larger way, the difference of damage of the component between the actual load and the simulation cannot be estimated, and because the theoretical model in the modeling of the physical model is simplified, higher uncertainty is introduced. Furthermore, for most transmission components, equivalent fatigue is not generally reflective of the consumption of its life. Therefore, in the scheme based on the mechanism model in the prior art, when the calculation of the residual life of a large part of the wind generating set is performed, particularly when the actual load and the simulation load of the set appear to be larger, the evaluation accuracy of the residual life is poor.
To solve the above-mentioned problems in the prior art, embodiments of the present application provide a method, an apparatus, a device, a computer readable storage medium, and a computer program product for calculating a remaining life of a large component in a wind turbine generator system. The following first describes a method for calculating the remaining life of a large component in a wind turbine generator set according to an embodiment of the present application.
Fig. 1 is a flow chart of a method for calculating remaining life of a large component in a wind generating set according to an embodiment of the present application. As shown in fig. 1, the method for calculating the remaining life of a large component in a wind generating set provided by the embodiment of the application includes the following steps:
s101, acquiring real-time operation data of the wind generating set.
S102, inverting time sequence loads of key positions of all large components in the wind generating set through real-time operation data.
S103, according to the time sequence load, calculating fatigue damage of each large part hot spot position under the load action of each large part key position, wherein each large part hot spot position is in stress correlation with each large part key position.
S104, calculating the residual service life of each large part through fatigue damage of the hot spot position of each large part.
According to the embodiment of the application, after the time sequence load of the key positions of all the large parts in the wind generating set is obtained through inversion of the real-time operation data of the wind generating set, the fatigue damage to the hot spot positions of all the large parts under the action of the load is calculated; and calculating the residual service life of each large part through fatigue damage of the hot spot position of each large part. The fatigue damage is obtained by time sequence loads of key positions of all large parts in the wind generating set, and the time sequence loads of the key positions of all the large parts are obtained by inversion according to real-time operation data. Thus, the real-time influence of the load on the component in the whole life cycle can be embodied. On the basis, the residual life of each large part obtained through fatigue damage calculation of the hot spot position of each large part is real-time, and the accuracy is improved, so that the problem that the evaluation accuracy of the residual life is poor when the residual life of the large part of the wind generating set is calculated in the prior art, particularly when the actual load and the simulation load of the set appear in a large way is solved.
In some embodiments, the large component of the wind turbine includes at least one of a fan blade, a drive train, a casting, a tower, and a bolt. The above-described device performing the calculation of the remaining life of the large component may be a boundary device or a field end device of the wind power plant.
In step S101, the real-time operation data may include environmental parameters of the operation of the wind turbine and turbine status data. The environmental parameters may include, among other things, real-time operating wind conditions (including, by way of example, wind direction, wind speed), temperature, humidity, altitude, etc. of the location where the wind farm is located. The unit status data may be obtained from the wind power unit and/or from sensors arranged in the wind power unit. In addition, a desired portion of the real-time operational data may be obtained from the SCADA data.
For example, the real-time operational data may include a blade speed, a unit operational power, which may be obtained from a unit operational signal sent by the wind turbine.
The sensor may include a load sensor, a vibration sensor, and an acceleration sensor, for example. The real-time operational data obtained from the sensors may include acceleration, tilt angle, and the like. In other examples, the measured load of the partial unit may also be detected by a load sensor, and so on.
In some embodiments, in S102, inversion of the load of the critical position of each large component of the wind turbine generator set may be performed through the above-obtained real-time operation data, so as to learn and infer to obtain the time sequence load of the critical position of each large component at this time.
Specifically, the load inversion can be performed by using a constructed fan digital twin model, wherein the fan digital twin model is obtained by carrying out parameterization modeling on a wind generating set, and can be constructed in a dynamic modeling mode, a machine learning method or a combination of the two modes.
When the digital twin model of the fan is used for carrying out load inversion, input signals of the digital twin model comprise unit state data obtained from sensors and units, wherein the unit state data comprise blade rotation speed, running power, acceleration, rotation inclination angle and the like. On the basis, the system can also comprise actual measurement loads of part of the units, inversion is finally carried out through a digital twin model of the fan, and the output loads can comprise time sequence loads and equivalent fatigue loads of key positions.
The data used in carrying out load inversion can be determined according to the influence factors of the actual key positions. The load inversion of the part of the key positions can be obtained from the unit state data and the environment parameters of the wind generating set, and the real-time operation data used for the load inversion of the part of the key positions can be a combination of the unit state data, the environment parameters and the actual measured load.
In an alternative example, referring to fig. 2, in S102, the process of inverting the time-series load of each major component key position in the wind generating set by using the real-time operation data may include:
s201, inputting the state data and the environmental parameters of the unit into a digital twin model of the fan to perform data inversion so as to obtain a first inversion result.
S202, when the time sequence load of the key position of the large part in the target wind generating set is lost in the first inversion result, obtaining the actual measurement load of the part of the position in the target wind generating set, wherein the wind generating set comprises the target wind generating set.
S203, inputting the unit state data, the environmental parameters and the actually measured load into a digital twin model of the fan to obtain a second inversion result; the union of the first inversion result and the second inversion result is the time sequence load of each large part key position.
In the scheme, by means of the digital twin model of the fan and combining with flexible data input under different conditions, the time sequence load of key positions of different large parts can be ensured to be smoothly obtained.
It will be appreciated that the load inversion at each critical location may be performed using existing real-time operational data. If the real-time operation data are input into the fan digital twin model, when the time sequence load of a key position of a certain wind generating set (namely, a target wind generating set) cannot be output, namely, the time sequence load of the key position of the target wind generating set is lost, the actual measurement load of a part of positions detected by a load sensor can also be used as the input of the fan digital twin model, so that the time sequence load of each position of the target wind generating set can be calculated, and the inversion result obtained by calculation and the previous data inversion result are combined, namely, the time sequence load of the key position of each large part of each wind generating set in a wind power plant is obtained.
In S103, the fatigue damage of each large part hot spot position under the load action can be correspondingly calculated by using the time sequence load or the equivalent fatigue load of the key position obtained by inversion.
In some embodiments, fatigue damage at the hot spot location under the load timing may be calculated using fatigue damage models corresponding to different large components, respectively. The fatigue damage model inputs time sequence loads of key positions of the large parts, the time sequence loads can be load components in all directions, stress correlation exists on hot spot positions of the large parts by the load components in different directions, and the load components of the key positions of the large parts can affect fatigue damage of the hot spot positions of the large parts.
The fatigue damage model, also called a load (including history) -damage model, can be established specifically aiming at the physical characteristics of different large parts of the unit. In the actual use process of the fatigue damage model, model parameter correction can be carried out based on maintenance/inspection data of the wind generating set, so that the load-damage model is close to the real-time physical characteristics of the wind generating set, and errors are reduced.
In some embodiments, referring to FIG. 3, in S104, the process of computing an estimate of the remaining life of each large component from fatigue damage at the location of the large component hot spot may include:
s301, determining wind condition data and working condition data of the wind generating set according to the real-time operation data.
S302, constructing a damage matrix of each large part in each operation mode according to fatigue damage, wind condition data and working condition data of each large part hot spot position.
S303, performing damage accumulation on the damage matrix of each large part in each operation mode to obtain an accumulated damage result of each large part.
S304, calculating the residual service life of each large part according to the accumulated damage result of each large part.
In the scheme, the damage capture matrix under the real-time wind condition and the working condition is constructed by utilizing the fatigue damage of the hot spot position, so that damage accumulation is realized, the real-time residual life of each large part is obtained through calculation, the evaluation accuracy is high, and the problem that the evaluation accuracy of the residual life is poor when the actual load and the simulation load of the unit appear in the prior art is solved.
The wind condition data and the working condition data comprise contents related to the dimension defined by the damage matrix. It should be noted that, the damage matrix may include wind condition dimension and working condition dimension, and the subdivision may include wind speed, sector, turbulence, and unit operation state. Under different wind conditions and working condition dimensions, different fatigue damage of each large part is corresponding.
The wind condition data and the working condition data of each wind generating set can be directly obtained and/or indirectly calculated from the real-time operation data. For example, the unit operation state subdivision dimension under the large dimension of the working condition may include a shutdown state, a power generation state or a power limit state.
Under the condition of fixed wind conditions and working conditions, the fatigue damage of the large parts of each wind generating set can be filled in the damage matrix, so that the damage matrix of each large part in each operation mode is constructed.
It should be noted that, under the same wind conditions and operation conditions, it can be considered that the fatigue damage of the large components of each unit should have the same characteristics, so that the cumulative damage can be calculated in consideration of constructing the damage capture matrix. The damage capture matrix comprises a damage matrix, and also relates to fatigue damage probability under wind conditions and working conditions, and can be represented by the probability matrix, so that the accumulated damage values under different operation modes can be obtained by the product of the damage matrix and the probability matrix.
The probability matrix may be a probability joint distribution matrix formed by referring to wind resource information and a design operation mode in the initial stage of wind farm construction. In the operation process of the later-stage wind farm, the operation times under different wind conditions and working conditions during the operation can be recorded through a program, so that the actual operation probability can be obtained through calculation based on the recorded times of the program after a period of time, and the probability matrix is updated.
In some embodiments, the process of performing damage accumulation based on the damage matrix to obtain accumulated damage results for each large component may include: acquiring probability matrixes corresponding to the damage matrixes in each operation mode, wherein the probability matrixes comprise fatigue damage probabilities in wind condition data and working condition data; multiplying the damage matrix of each large part in each operation mode by the probability matrix corresponding to each damage matrix to obtain the accumulated damage value of each large part in each operation mode; and adding the accumulated damage values of the large parts in each operation mode to obtain the accumulated damage result of each large part.
In the scheme, the accumulated damage of each large part in each operation mode is obtained through the damage matrix and the probability matrix, so that the accumulated damage result of each large part is obtained, and a theoretical basis is provided for calculating the real-time residual life of each large part.
The following is exemplified by dimensions including a wind speed dimension i, a sector dimension j, and a run mode dimension k. And filling the fatigue damage of the large component A into the damage dimension of the damage matrix at the determined wind speed i and the determined sector j to form a damage matrix Dijk of the large component A in the current operation mode.
On the basis, a damage matrix D can also be obtained ijk Corresponding probability matrix P ijk Through D ijk *P ijk The cumulative damage value of the large component A in the current operation mode is obtained. And adding the accumulated damage values in each operation mode to obtain an accumulated damage result. The mathematical expression using cumulative impairments is Damage = Σd ijk ＊P ijk Damage is the cumulative Damage result.
In some embodiments, after obtaining the cumulative damage results for each of the large components, calculating the remaining life of each of the large components based on the cumulative damage results for each of the large components may include: acquiring the accumulated operation years of the wind generating set; dividing the accumulated damage result of each large part with the accumulated operation period to obtain the annual loss value of each large part; based on the Miner linear cumulative damage theory, the remaining life of each large component can be calculated by the following equation (1).
RULm＝(1-Damage)/L (1)
Where RULm is the remaining life of the large component m, damage is the cumulative Damage result of the large component m, and L is the annual loss value of the large component m.
In the scheme, inversion of the load of the key position of the wind generating set is carried out through the digital twin model, the real-time influence of the load on the component in the whole life cycle is considered, and the actual load is obtained. And the method is input into a high-precision fatigue damage model, so that the online real-time calculation of the residual service life of the wind generating set can be realized, the certainty is high, the problem that the actual load and the simulation load of the set are large in and out is avoided, and the accuracy of the calculation of the residual service life can be improved.
In some embodiments, while performing S104, the remaining life calculating method of the large component in the wind turbine generator set further includes:
and carrying out degradation trend monitoring according to the real-time operation data so as to update the residual life of the abnormal large part when any large part is abnormal.
The degradation trend monitoring is actually monitoring the abnormal state of the large component of the wind generating set, and can be realized by a constructed physical-based degradation model, wherein the degradation model can be constructed by a trend fitting method, a machine learning method and the like.
Based on the degradation trend monitoring, the characteristic state signal of the large component can be diagnosed, and when an abnormality warning occurs, the output result of the degradation model is updated. When the degradation model output result based on state monitoring is introduced into the residual life calculation, the abnormal signal is triggered to update the residual life of the abnormally large component. Therefore, the method for combining the physical model and the mechanism model can realize the calculation of the residual usable life of the real-time unit in all aspects.
Fig. 4 shows a schematic hardware structure of a device for calculating remaining life of a large component in a wind turbine generator system according to an embodiment of the present application. Referring to fig. 4, the apparatus includes:
an acquisition module 410, configured to acquire real-time operation data of the wind generating set;
the inversion module 420 is used for inverting the time sequence load of each major component key position in the wind generating set through real-time operation data;
the damage calculation module 430 is configured to calculate fatigue damage of each big component hot spot position under the time sequence load action of each big component key position, where each big component hot spot position has stress association with each big component key position;
the life calculation module 440 is configured to calculate a remaining life of each large component through fatigue damage of each large component hot spot location.
In an embodiment, the apparatus further comprises:
and the updating module is used for monitoring the degradation trend according to the real-time operation data so as to update the residual life of the abnormal large part when any large part is abnormal.
In another embodiment, a lifetime calculation module includes:
the determining unit is used for determining wind condition data and working condition data of the wind generating set according to the real-time operation data;
the construction unit is used for constructing a damage matrix of each large part in each operation mode according to fatigue damage, wind condition data and working condition data of each large part hot spot position;
the damage accumulation unit is used for carrying out damage accumulation on the damage matrix of each large part in each operation mode so as to obtain an accumulated damage result of each large part;
and a calculation unit for calculating the remaining life of each large component from the accumulated damage results of each large component.
In still another embodiment, the damage accumulation unit includes:
the first acquisition subunit is used for acquiring probability matrixes corresponding to the damage matrixes in all operation modes, wherein the probability matrixes comprise fatigue damage probabilities in wind condition data and working condition data;
the first operation subunit is used for multiplying the damage matrix of each large part in each operation mode by the probability matrix corresponding to each damage matrix to obtain the accumulated damage value of each large part in each operation mode;
and the accumulation subunit is used for adding the accumulated damage values of the large components in each operation mode to obtain the accumulated damage result of each large component.
In yet another embodiment, the computing unit includes:
the second acquisition subunit is used for acquiring the accumulated operation years of the wind generating set;
the second operation subunit is used for dividing the accumulated damage result of each large part with the accumulated operation years to obtain the annual loss value of each large part;
a calculating subunit for calculating the remaining life of each large component by the following formula,
RULm＝(1-Damage)/L
where RULm is the remaining life of the large component m, damage is the cumulative Damage result of the large component m, and L is the annual loss value of the large component m.
In yet another embodiment, the real-time operational data includes unit status data and environmental parameters of the wind turbine unit; the inversion module includes:
the primary inversion unit is used for carrying out data inversion by inputting the state data and the environmental parameters of the unit into the digital twin model of the fan so as to obtain a first inversion result;
the load acquisition unit is used for acquiring actual measurement load of part of positions in the target wind generating set when time sequence load of key positions of large components in the target wind generating set is lost in the first inversion result, wherein the wind generating set comprises the target wind generating set;
the secondary inversion unit is used for inputting the state data, the environmental parameters and the actually measured load of the unit into a digital twin model of the fan so as to obtain a second inversion result; the union of the first inversion result and the second inversion result is the time sequence load of each large part key position.
Fig. 5 shows a schematic hardware structure of a remaining life calculating device of a large component in a wind generating set according to an embodiment of the present application. In the remaining life computing device of the large component in the wind park, a processor 501 and a memory 502 storing computer program instructions may be included.
In particular, the processor 501 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
In particular embodiments, memory 502 may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, memory 502 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to the remaining life calculation method of a large component in a wind turbine generator set according to the above aspects of the disclosure.
The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement the remaining life calculation method of the large component in the wind turbine generator system according to any of the above embodiments.
In one example, the remaining life computing device of the large component in the wind turbine may also include a communication interface 503 and a bus 510. As shown in fig. 5, the processor 501, the memory 502, and the communication interface 503 are connected to each other by a bus 510 and perform communication with each other.
The communication interface 503 is mainly used to implement communication between each module, apparatus, unit and/or device in the embodiments of the present application.
The remaining life calculating device of the large component in the wind generating set can execute the remaining life calculating method of the large component in the wind generating set in the embodiment of the application, so that the remaining life calculating method and the device of the large component in the wind generating set described in connection with fig. 1 and 5 are realized.
In addition, in combination with the method for calculating the remaining life of the large component in the wind generating set in the embodiment, the embodiment of the application can be realized by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by the processor, implement a method for calculating the remaining life of a large component in a wind turbine generator system according to any of the above embodiments.
In addition, the embodiment of the application also provides a computer program product, which comprises a computer program, and the computer program can realize the steps of the embodiment of the method and the corresponding content when being executed by a processor.
It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be different from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood 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 which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, which are intended to be included in the scope of the present application.
Claims (10)
1. A method for calculating remaining life of a large component in a wind turbine generator system, comprising:
acquiring real-time operation data of the wind generating set;
inverting the time sequence load of each major component key position in the wind generating set through the real-time operation data;
according to the time sequence load, calculating fatigue damage of each large part hot spot position under the load action of each large part key position, wherein each large part hot spot position is in stress correlation with each large part key position;
and calculating the residual life of each large part through the fatigue damage of each large part hot spot position.
2. The method of claim 1, wherein while performing the step of calculating the remaining life of each large component through the fatigue damage of each large component hot spot location, the method further comprises:
and carrying out degradation trend monitoring according to the real-time operation data so as to update the residual life of any abnormal large part when the abnormal large part occurs.
3. The method of claim 1, wherein said calculating said remaining life of each large component from said fatigue damage at each said large component hot spot location comprises:
according to the real-time operation data, determining wind condition data and working condition data of the wind generating set;
constructing a damage matrix of each large part in each operation mode according to the fatigue damage, the wind condition data and the working condition data of each large part hot spot position;
performing damage accumulation on the damage matrix of each large part in each operation mode to obtain an accumulated damage result of each large part;
the remaining life of each large component is calculated from the accumulated damage results of each large component.
4. A method according to claim 3, wherein said performing damage accumulation on the damage matrix of each large part in each operation mode to obtain an accumulated damage result of each large part comprises:
obtaining probability matrixes corresponding to the damage matrixes in all operation modes, wherein the probability matrixes comprise fatigue damage probabilities in the wind condition data and the working condition data;
multiplying the damage matrix of each large part in each operation mode by the probability matrix corresponding to each damage matrix to obtain the accumulated damage value of each large part in each operation mode;
and adding the accumulated damage values of the large parts in each operation mode to obtain an accumulated damage result of each large part.
5. A method according to claim 3, wherein said calculating said remaining life of each large component from said accumulated damage results of each large component comprises:
acquiring the accumulated operation years of the wind generating set;
dividing the accumulated damage result of each large part with the accumulated operation years to obtain the annual loss value of each large part;
the remaining life of each large part is calculated by the following formula,
RULm＝(1-Damage)/L
where RULm is the remaining life of the large component m, damage is the cumulative Damage result of the large component m, and L is the annual loss value of the large component m.
6. The method according to any one of claims 1-5, wherein the real-time operational data comprises unit status data and environmental parameters of the wind power unit; inverting the time sequence load of each major component key position in the wind generating set according to the real-time operation data, wherein the time sequence load comprises the following steps:
inputting the unit state data and the environmental parameters into a digital twin model of the fan to perform data inversion so as to obtain a first inversion result;
when the time sequence load of the key position of the large part in the target wind generating set is absent in the first inversion result, obtaining the actual measurement load of the part of the position in the target wind generating set, wherein the wind generating set comprises the target wind generating set;
inputting the unit state data, the environmental parameters and the actual measured load into the digital twin model of the fan to obtain a second inversion result; and the union of the first inversion result and the second inversion result is the time sequence load of the key position of each large part.
7. A device for calculating the remaining life of a large component in a wind power plant, said device comprising:
the acquisition module is used for acquiring real-time operation data of the wind generating set;
the inversion module is used for inverting the time sequence load of each key position of the large component in the wind generating set according to the real-time operation data;
the damage calculation module is used for calculating fatigue damage of each big part hot spot position under the load action of each big part key position according to the time sequence load, and each big part hot spot position is in stress association with each big part key position;
and the service life calculation module is used for calculating the residual service life of each large part through the fatigue damage of each large part hot spot position.
8. A remaining life computing device for a large component in a wind turbine, the device comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method for calculating the remaining life of a large component in a wind turbine generator set according to any one of claims 1 to 6.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon computer program instructions, which when executed by a processor, implement a method for calculating the remaining life of a large component in a wind power plant according to any of claims 1-6.
10. A computer program product, characterized in that it has stored thereon computer program instructions which, when executed by a processor, implement a method for calculating the remaining life of a large component in a wind power plant according to any of claims 1-6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117272666A (en) * | 2023-10-08 | 2023-12-22 | 上海勘测设计研究院有限公司 | Blade fatigue life calculation method of floating offshore wind turbine |
| CN117782570A (en) * | 2024-02-28 | 2024-03-29 | 南京典格信息技术有限公司 | Mesh ad hoc network-based life prediction system and method for offshore wind turbine |
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2021
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117272666A (en) * | 2023-10-08 | 2023-12-22 | 上海勘测设计研究院有限公司 | Blade fatigue life calculation method of floating offshore wind turbine |
| CN117272666B (en) * | 2023-10-08 | 2024-04-05 | 上海勘测设计研究院有限公司 | Blade fatigue life calculation method of floating offshore wind turbine |
| CN117782570A (en) * | 2024-02-28 | 2024-03-29 | 南京典格信息技术有限公司 | Mesh ad hoc network-based life prediction system and method for offshore wind turbine |
| CN117782570B (en) * | 2024-02-28 | 2024-05-14 | 南京典格信息技术有限公司 | Mesh ad hoc network-based life prediction system and method for offshore wind turbine |
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