CN113847216B - Fan blade state prediction method, device, equipment and storage medium - Google Patents
Fan blade state prediction method, device, equipment and storage medium Download PDFInfo
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- CN113847216B CN113847216B CN202111196259.4A CN202111196259A CN113847216B CN 113847216 B CN113847216 B CN 113847216B CN 202111196259 A CN202111196259 A CN 202111196259A CN 113847216 B CN113847216 B CN 113847216B
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Classifications
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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
- F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
- F03D80/40—Ice detection; De-icing means
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2260/00—Function
- F05B2260/82—Forecasts
- F05B2260/821—Parameter estimation or prediction
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/325—Air temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/335—Output power or torque
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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 discloses a method, a device, equipment and a storage medium for predicting the state of a fan blade, and relates to the technical field of wind power generation. The method comprises the following steps: acquiring actual environment parameters and actual power values of the target fan blades; acquiring an expected power value of a target fan blade based on actual environment parameters; acquiring a power drop index based on the actual power value and the expected power value; acquiring an icing risk index of a target fan blade based on the real-time environmental temperature in the actual environmental parameters; and predicting whether the target fan blade is in an icing state based on the power sag index and the icing risk index. By the method, the prediction of whether the fan blade is in the icing state based on the operation data of the fan blade and the environmental parameters is realized, the accuracy of predicting whether the fan blade is in the icing state is improved, the cost of icing state prediction is reduced, and the icing state prediction method is not limited in use and can be widely applied.
Description
Technical Field
The embodiment of the application relates to the technical field of wind power generation, in particular to a method, a device, equipment and a storage medium for predicting the state of a fan blade.
Background
In a wind power generation scene, in cold areas, in order to improve wind power generation efficiency and reduce the influence on the service life of wind power equipment, whether a fan blade is in an icing state or not needs to be detected, and the icing fault of the blade needs to be eliminated in time.
In the related art, temperature data of a fan blade collected by combining various sensors or ultrasonic detection technologies is generally used for judging whether the fan blade is frozen or not according to a preset freezing temperature threshold value, wherein the sensors can be infrared sensors, optical fiber sensors and the like.
However, in the above technology, only depending on the temperature data collected by the sensors arranged on each fan blade, it is predicted whether the fan blade is in an icing state, so that the effect of predicting whether the fan blade is in the icing state is poor, and the prediction accuracy is low.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for predicting the state of a fan blade. The method can expand the application range of the detection technology while ensuring the accuracy of detecting the icing state of the fan blade, and the technical scheme is as follows:
In one aspect, a method for predicting a state of a fan blade is provided, the method comprising:
acquiring actual environment parameters and actual power values of the target fan blades;
acquiring an expected power value of the target fan blade based on the actual environment parameter;
based on the actual power value and the expected power value, obtaining a power sag index, wherein the power sag index is used for indicating the difference between the actual power value and the expected power value;
acquiring an icing risk index of the target fan blade based on the real-time environmental temperature in the actual environmental parameter;
and predicting whether the target fan blade is in an icing state based on the power sag index and the icing risk index.
In another aspect, a fan blade condition prediction apparatus is provided, the apparatus comprising:
the actual data acquisition module is used for acquiring actual environment parameters and actual power values of the target fan blades;
the expected power value acquisition module is used for acquiring the expected power value of the target fan blade based on the actual environment parameters;
a power drop index obtaining module, configured to obtain a power drop index based on the actual power value and the expected power value, where the power drop index is used to indicate a difference between the actual power value and the expected power value;
The icing risk index acquisition module is used for acquiring the icing risk index of the target fan blade based on the real-time environment temperature in the actual environment parameters;
and the icing state prediction module is used for predicting whether the target fan blade is in an icing state or not based on the power falling index and the icing risk index.
In one possible implementation, the icing condition prediction module is configured to predict that the target fan blade is in an icing condition in response to the power sag index indicating that the power generation performance of the target fan blade is below a performance threshold and the icing risk index indicating that the icing risk of the target fan blade is above a risk threshold.
In one possible implementation, the apparatus further includes:
the duration time acquisition module is used for responding to prediction that the target fan blade is in an icing state and acquiring the duration time that the target fan blade is in the icing state;
and the icing alarm module is used for responding to the fact that the duration time is longer than the duration threshold value and sending out icing warning.
In a possible implementation manner, the expected power value obtaining module is configured to input the actual environmental parameter into a power prediction model, and obtain the expected power value output by the power prediction model;
The power prediction model is obtained based on an environment parameter sample and power value labels corresponding to the environment parameter sample.
In one possible implementation manner, the environmental parameter sample and the power value label corresponding to the environmental parameter sample are obtained based on preprocessing historical environmental parameters and historical power values corresponding to the historical environmental parameters;
the preprocessing includes at least one of the following operations:
cleaning the historical environment parameters and the historical power value hollow value corresponding to the historical environment parameters;
cleaning the historical environment parameters and dead numbers and interpolation data in the historical power values corresponding to the historical environment parameters;
and cleaning the historical environment parameters and low-temperature data in the historical power values corresponding to the historical environment parameters, wherein the low-temperature data refer to the historical environment parameters with the corresponding historical environment temperature lower than a temperature threshold value and the historical power values corresponding to the historical environment parameters.
In one possible implementation, the power drop index is equal to the ratio of the actual power value to the desired power value.
In a possible implementation manner, the icing risk index obtaining module is configured to obtain, in combination with a temperature-icing risk index model, an icing risk index of the target fan blade based on an actual temperature in the actual environmental parameter;
wherein the temperature-icing risk index model is a mathematical model constructed based on a sigmoid function.
In another aspect, a computing device is provided that includes a processor and a memory; the memory stores at least one instruction, at least one section of program, a code set or an instruction set, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to implement the fan blade state prediction method in the above aspect.
In another aspect, a computer readable storage medium is provided, the storage medium storing at least one instruction for execution by a processor to implement a method of predicting a state of a fan blade as described in the above aspect.
In another aspect, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions to cause the computer device to perform the method of predicting the state of the fan blade provided in the various alternative implementations described above.
The technical scheme provided by the application can comprise the following beneficial effects:
the method comprises the steps of comprehensively predicting whether the fan blade is in an icing state or not through the obtained power falling index and the icing risk index, wherein the power falling index is determined based on the actual power value and the expected power value of the fan blade, and the icing risk index is determined by the environment temperature, so that the prediction of whether the fan blade is in the icing state or not based on the operation data and the environment parameters of the fan blade is realized, the accuracy of predicting whether the fan blade is in the icing state is improved, and meanwhile, the installation of an additional sensor required in the icing state prediction is reduced, so that the use of the icing state prediction method is not limited, and the method can be widely applied.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 illustrates a wind speed power scatter plot as shown in an exemplary embodiment of the application;
FIG. 2 illustrates a schematic diagram of a system used in a method for predicting a status of a fan blade according to an exemplary embodiment of the present application;
FIG. 3 illustrates a flowchart of a method for predicting a state of a fan blade according to an exemplary embodiment of the present application;
FIG. 4 illustrates a flowchart of a method for predicting a state of a fan blade according to an exemplary embodiment of the present application;
FIG. 5 illustrates a timing diagram provided by an exemplary embodiment of the present application;
FIG. 6 illustrates a schematic diagram of a temperature-icing risk index model according to an exemplary embodiment of the present application;
FIG. 7 is a schematic diagram of an icing alarm procedure provided by an exemplary embodiment of the present application;
FIG. 8 illustrates a block diagram of a fan blade condition prediction apparatus provided in accordance with an exemplary embodiment of the present application;
fig. 9 is a block diagram of a computer device, according to an example embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
It should be understood that references herein to "a number" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The wind turbine works in cold areas, and a fan in the environment is influenced by meteorological conditions such as frost ice, rime and wet snow, so that the phenomenon of blade icing is extremely easy to occur, a series of consequences are further caused, and the following damage is possibly caused:
1) The wing profile of the frozen fan blade changes, so that the wind energy capturing capacity is reduced, and an ice layer is attached to the blade, so that the energy required by rotation of the blade is increased, and finally the wind energy power generation power loss is caused;
FIG. 1 shows a plot of wind speed and power scatter, as shown in FIG. 1, for an exemplary embodiment of the present application, where the relationship between wind speed and fan power is in accordance with a wind speed and power curve 110 in a normal operating state of a fan blade, and when the fan blade is in an icing state, the wind energy capturing capability of the fan blade is reduced due to the change of the wing profile of the fan blade, so that the fan power is reduced, as shown in FIG. 1, the point in the area 120 is the fan power at the corresponding wind speed in the icing state; wherein, this fan power is active power.
2) After the fan blade is frozen, the structural parameters of the blade part are directly changed, and then the inherent modal parameters of the fan blade are influenced to induce the blade to break;
3) After the fan blade is frozen and accumulated to a certain extent, the ice layer breaks and flies out under the influence of the self weight, so that the wind field inspection personnel are easily hit, and personal accidents are caused.
Therefore, the method for detecting and eliminating the blade icing fault in real time has important significance for prolonging the service life of wind power equipment and preventing important safety accidents.
In the operation and maintenance process of a fan power station in a cold region, whether the fan blades of the wind turbine generator are in an icing state or not needs to be checked. The application provides a method for predicting the state of a fan blade, which can improve the accuracy of predicting whether the fan blade is in an icing state or not and expand the application range of the detection technology. For ease of understanding, the terms involved in the embodiments of the present application are explained below.
SCADA (Supervisory Control And Data Acquisition, data acquisition and monitoring control System) System
The SCADA system is a DCS (Distributed Control System ) and an electric power automatic monitoring system based on a computer; the method can be applied to the fields of data acquisition and monitoring control, process control and the like in the fields of electric power, metallurgy, petroleum, chemical industry, fuel gas, railway and the like.
Fig. 2 is a schematic structural diagram of a system corresponding to a method for predicting a state of a fan blade according to an exemplary embodiment of the present application, where, as shown in fig. 2, the system includes: a fan power station 201 and a monitoring platform 202.
The fan power station 201 includes a plurality of wind turbines, wherein the wind turbines include fan blades and a nacelle. In the embodiment of the present application, a plurality of sensors may be disposed in the fan power station 201, for collecting data in the hierarchical power station, and illustratively, a temperature sensor, a wind speed sensor, etc. may be included to collect parameters such as an ambient temperature, a wind speed, etc. in the fan power station 201, and send the collected data to the monitoring platform 202.
The fan power station 201 is connected with the monitoring platform 202 through a wired or wireless network.
The monitoring platform 202 is a computer device with functions of storing data sent by the fan power station 201, processing the data, generating an alarm record and the like, and the computer device can be a server cluster or cloud server formed by one server or a plurality of servers, or the computer device can be implemented as a terminal, and the implementation form of the computer device is not limited by the application.
For convenience of description, in the following method embodiments, the monitoring platform 102 is described as a computer device to illustrate the method for predicting the state of a fan blade provided by the present application.
Fig. 3 is a flowchart illustrating a method for predicting a state of a fan blade according to an exemplary embodiment of the present application, where the method for predicting a state of a fan blade may be performed by a computer device, and the computer device may be implemented as the monitoring platform shown in fig. 2, and as shown in fig. 3, the method for predicting a state of a fan blade may include the following steps:
in step 310, the actual environmental parameters and the actual power values of the target fan blade are obtained.
In one possible implementation, the target fan blade is any one of a plurality of fan blades in a fan station, the actual environmental parameters and the actual power values of the target fan blade are acquired based on data acquired by an SCADA system in the fan, and the SDCADA system is used for achieving functions of data acquisition, equipment control, measurement, parameter adjustment and the like.
Step 320, obtaining the desired power value of the target fan blade based on the actual environmental parameters.
The expected power value is used for indicating the power value which can be generated by the target fan blade when the target fan blade works normally under the environment parameter under the non-icing state.
Step 330, based on the actual power value and the desired power value, obtaining a power sag index, wherein the power sag index is used for indicating the difference between the actual power value and the desired power value.
Generally, due to the influence of environmental factors, there is often a certain difference between the actual power value and the expected power value, and in the embodiment of the present application, a power drop index is set to measure the difference between the actual power value and the expected power value.
And step 340, acquiring the icing risk index of the target fan blade based on the real-time environmental temperature in the actual environmental parameters.
The icing risk index is used for indicating the icing probability of the target fan blade at the environmental temperature, and generally, the lower the environmental temperature is, the higher the icing risk index of the target fan blade is, the higher the environmental temperature is, and the lower the icing risk index of the target fan blade is.
Step 350, predicting whether the target fan blade is in an icing state based on the power sag index and the icing risk index.
In summary, according to the method for predicting the state of the fan blade provided by the embodiment of the application, whether the fan blade is in the icing state is comprehensively predicted by the obtained power falling index and the icing risk index, wherein the power falling index is determined based on the actual power value and the expected power value of the fan blade, and the icing risk index is determined by the environment temperature, so that the prediction of whether the fan blade is in the icing state based on the operation data and the environment parameters of the fan blade is realized, the accuracy of predicting whether the fan blade is in the icing state is improved, and meanwhile, the installation of additional sensors required in the icing state prediction is reduced, so that the use of the icing state prediction method is not limited and can be widely applied.
FIG. 4 is a flowchart illustrating a method for predicting a status of a fan blade according to an exemplary embodiment of the present application, where the method for predicting a status of a fan blade may be performed by a computer device, and the computer device may be implemented as the monitoring platform shown in FIG. 2, and as shown in FIG. 4, the method for predicting a status of a fan blade may include the following steps:
in step 410, the actual environmental parameters and the actual power values of the target fan blade are obtained.
Step 420, obtaining the desired power value of the target fan blade based on the actual environmental parameters.
In one possible implementation, inputting the actual environmental parameters into a power prediction model to obtain a desired power value output by the power prediction model;
the power prediction model is obtained based on an environment parameter sample and power value labels corresponding to the environment parameter sample.
In one possible implementation, the power prediction model is a model built based on a regression model, which may be illustratively a LightGBM model.
The environmental parameter sample and the power value label corresponding to the environmental parameter sample are obtained based on preprocessing the historical environmental parameter and the historical power value corresponding to the historical environmental parameter.
The historical environment parameters and the historical power values corresponding to the historical environment parameters are acquired based on data acquired by a SCADA system in the fan. The time period corresponding to the historical data (the historical environment parameter and the historical power value corresponding to the historical environment parameter) can be set based on actual requirements, for example, the time period can be one month, one quarter, one year and the like, and the time period for acquiring the historical data is not limited.
The power value label is the historical power value corresponding to the environmental parameter sample.
Wherein the preprocessing of the historical environment parameters and the historical power values corresponding to the historical environment parameters comprises at least one of the following operations:
cleaning a historical power value hollow value corresponding to the historical environment parameter;
cleaning historical environment parameters and dead numbers and interpolation data in the historical power values corresponding to the historical environment parameters;
and cleaning the historical environment parameters and low-temperature data in the historical power values corresponding to the historical environment parameters, wherein the low-temperature data refer to the historical environment parameters with the corresponding historical environment temperature lower than the temperature threshold value and the historical power values corresponding to the historical environment parameters.
The low-temperature data is cleaned to enable the power prediction model to learn the normal operation rule of the fan blade in a normal state (namely in a non-icing state).
In one possible implementation, the process of obtaining the environmental parameter may be implemented as:
sequencing at least two environmental attributes based on the influence degree of whether the fan blade is in an icing state by using a tree model to obtain a sequencing result;
acquiring target environment attributes from at least two environment attributes based on the sorting result;
and acquiring parameters corresponding to the target environment attributes as environment parameters.
The number of the obtained target environmental attributes can be obtained according to actual requirements, that is, important environmental attributes in the environmental attributes can be obtained according to the actual requirements to be the target environmental attributes, and parameters corresponding to the target environmental attributes can be environmental parameters; illustratively, the target environmental attribute may include pitch angle, turbulence, temperature, and nacelle position; accordingly, the environmental parameters may include wind speed values, pitch angle values, temperature values, and nacelle position coordinates.
In one possible implementation, the training process for the power prediction model may be implemented as:
Inputting the environmental parameter sample into a power prediction model to obtain a prediction result corresponding to the environmental parameter sample, wherein the prediction result refers to a prediction power value;
calculating a loss function value based on the power value label corresponding to the environmental parameter sample and the prediction result corresponding to the environmental parameter sample;
based on the loss function value, the power prediction model is updated with parameters.
Because the predicted result (i.e., predicted power value) obtained by the power prediction model based on the environmental parameter sample is the same as or similar to the power value label corresponding to the environmental parameter sample, the accuracy of the power value predicted by the power prediction model during application can be ensured, multiple times of training is required in the training process of the power prediction model, and each parameter in the power prediction model is iteratively updated until the power prediction model converges.
Step 430, based on the actual power value and the desired power value, obtaining a power sag index, wherein the power sag index is used for indicating the difference between the actual power value and the desired power value.
Wherein, the power drop index is equal to the ratio of the actual power value to the expected power value, namely:
step 440, acquiring the icing risk index of the target fan blade based on the real-time environmental temperature in the actual environmental parameters.
To a certain extent, the environmental temperature may represent the influence of the icing condition on the fan power of the target fan blade, fig. 5 shows a timing chart provided by an exemplary embodiment of the present application, part a in fig. 5 shows a time-environmental temperature timing chart, part B in fig. 5 shows a time-fan power timing chart, and when the environmental temperature is lower than the specified temperature threshold, the fan blade is in the icing condition, and in the icing condition, due to the influence of the icing, the fan blade is in an abnormal working condition, resulting in a reduction of the fan power corresponding to the fan blade, as shown in fig. 5, in a first period 510, the fan blade is in a normal working condition, in a second period 520, the fan blade is in the icing condition, and in a third period 530, the fan blade is restored to the normal working condition. Accordingly, the icing risk index of the fan blade may be determined based on changes in the ambient temperature.
In one possible implementation, the icing risk index for the target fan blade may be obtained based on real-time ambient temperature in the actual ambient parameters based on historical experience.
The historical experience may be work experience or expert experience, for example, when the ambient temperature is 0 degrees or less, the risk of icing is high, and the icing risk index increases gradually as the ambient temperature decreases.
Or, in order to improve accuracy of the icing risk index acquisition, in one possible implementation, acquiring an icing risk index of the target fan blade based on an actual temperature in the actual environmental parameters in combination with a temperature-icing risk index model;
the temperature-icing risk index model is a mathematical model constructed based on sigmoid functions.
Fig. 6 shows a schematic diagram of a temperature-icing risk index model according to an exemplary embodiment of the present application, and as shown in fig. 6, the temperature-icing risk index model (curve 610) is used to determine an icing risk index (ordinate) according to an ambient temperature (abscissa), and a mathematical formula corresponding to the temperature-icing risk index model may be expressed as:
y＝sigmoid(-x)
where x represents the ambient temperature and y represents the icing risk index.
In step 450, in response to the power drop index indicating that the power generation performance of the target fan blade is below the performance threshold and the icing risk index indicating that the icing risk of the target fan blade is above the risk threshold, the target fan blade is predicted to be in an icing condition.
When the power drop index indicates that the power generation performance of the target fan blade is lower than the performance threshold and the icing risk index indicates that the icing risk of the target fan blade is higher than the risk threshold, predicting that the target fan blade is in an icing state, and predicting that the target fan blade is in a non-icing state under other conditions. That is to say:
responsive to the power drop index indicating that the power generation performance of the target fan blade is below a performance threshold, the icing risk index indicating that the icing risk of the target fan blade is below a risk threshold, predicting that the target fan blade is in a non-icing state;
or, in response to the power drop index indicating that the power generation performance of the target fan blade is above the performance threshold, the icing risk index indicating that the icing risk of the target fan blade is above the risk threshold, predicting that the target fan blade is in a non-icing state;
or, in response to the power drop index indicating that the power generation performance of the target fan blade is below the performance threshold, the icing risk index indicating that the icing risk of the target fan blade is below the risk threshold, predicting that the target fan blade is in a non-icing condition.
In order to prevent the situation that the target fan blade is in the power-down state due to the non-icing power-down reason, the error judgment is made on whether the target fan blade is in the icing state or not, in one possible implementation manner, after the target fan blade is predicted to be in the icing state, the reason that the target fan blade is in the power-down state is acquired, and when the reason that the target fan blade is in the power-down state is determined to be the non-icing power-down reason, the target fan blade is predicted to be in the icing state.
The non-icing power reduction reasons comprise turbulence power reduction, power reduction caused by over-temperature of a generator, power reduction caused by over-temperature of a gear box and the like; different power reduction reasons correspond to different power reduction judging mechanisms, for example, when determining whether the reason that the fan blade is in a power reduction state is turbulence power reduction, the fan blade power reduction judging mechanism can determine whether the wind speed fluctuation exceeds a wind speed fluctuation threshold value or not; when determining whether the reason that the fan blade is in the power-down state is that the generator is over-temperature, determining whether the temperature of the generator exceeds a temperature threshold of the generator or not; in determining whether the reason the fan blade is in the reduced power state is an over temperature of the gearbox, a determination may be made by detecting whether the gearbox temperature exceeds a gearbox temperature threshold.
In one possible implementation, in response to predicting that the target fan blade is in an icing condition, obtaining a duration that the target fan blade is in an icing condition;
responsive to the duration being greater than the duration threshold, an icing alert is issued.
Since the fan blade is in icing state continuously, in one possible implementation manner, the duration of the fan blade in icing state is obtained through a sliding window method, and the process can be implemented as follows: acquiring the length of a time period containing the freezing point through a sliding window by taking a marked freezing point as a starting point, and determining the time period containing the freezing point as the duration of the icing state of the fan blade, wherein the marked freezing point is an icing state starting point determined based on the icing state prediction method; and when the icing state duration is longer than the duration threshold, an icing warning is sent out to instruct related personnel to inspect and maintain the fan blade.
In summary, according to the method for predicting the state of the fan blade provided by the embodiment of the application, whether the fan blade is in the icing state is comprehensively predicted by the obtained power falling index and the icing risk index, wherein the power falling index is determined based on the actual power value and the expected power value of the fan blade, and the icing risk index is determined by the environment temperature, so that the prediction of whether the fan blade is in the icing state based on the operation data and the environment parameters of the fan blade is realized, the accuracy of predicting whether the fan blade is in the icing state is improved, and meanwhile, the installation of additional sensors required in the icing state prediction is reduced, so that the use of the icing state prediction method is not limited and can be widely applied.
Fig. 7 is a schematic diagram of an icing alarm procedure according to an exemplary embodiment of the present application, as shown in fig. 6, and the icing alarm procedure includes the following steps:
step 710, obtaining operation data of the fan blade; the operational data includes actual environmental parameters and actual power values.
And step 720, evaluating the power generation performance of the fan blade.
The power generation performance evaluation corresponds to the process of calculating the power drop index in the embodiments of fig. 3 and 4, and is not described herein.
And 730, performing icing risk assessment on the fan blade.
The icing risk assessment corresponds to the procedure of acquiring the icing risk index in the embodiments of fig. 3 and 4, and is not described here again.
Step 740, determining whether the fan blade is in a state with low power generation performance and high icing risk, if so, executing step 750, and if not, determining that the fan blade is in a non-icing state.
Step 750, determining whether other power-down reasons are eliminated, if so, executing step 760, otherwise, ending.
Step 760, determining whether the icing duration is greater than the icing time threshold, if so, executing step 770, otherwise, ending.
In step 770, an icing warning is issued.
In summary, according to the method for predicting the state of the fan blade provided by the embodiment of the application, whether the fan blade is in the icing state is comprehensively predicted by the obtained power falling index and the icing risk index, wherein the power falling index is determined based on the actual power value and the expected power value of the fan blade, and the icing risk index is determined by the environment temperature, so that the prediction of whether the fan blade is in the icing state based on the operation data and the environment parameters of the fan blade is realized, the accuracy of predicting whether the fan blade is in the icing state is improved, and meanwhile, the installation of additional sensors required in the icing state prediction is reduced, so that the use of the icing state prediction method is not limited and can be widely applied.
Fig. 8 is a block diagram of a fan blade state prediction apparatus according to an exemplary embodiment of the present application, and as shown in fig. 8, the fan blade state prediction apparatus includes:
the actual data obtaining module 810 is configured to obtain an actual environmental parameter and an actual power value of the target fan blade;
a desired power value obtaining module 820, configured to obtain a desired power value of the target fan blade based on the actual environmental parameter;
a power sag index obtaining module 830, configured to obtain a power sag index based on the actual power value and the desired power value, where the power sag index is used to indicate a difference between the actual power value and the desired power value;
the icing risk index obtaining module 840 is configured to obtain an icing risk index of the target fan blade based on the real-time environmental temperature in the actual environmental parameter;
an icing condition prediction module 850 configured to predict whether the target fan blade is in an icing condition based on the power sag index and the icing risk index.
In one possible implementation, the icing condition prediction module 850 is configured to predict that the target fan blade is in an icing condition in response to the power sag index indicating that the power generation performance of the target fan blade is below a performance threshold and the icing risk index indicating that the icing risk of the target fan blade is above a risk threshold.
In one possible implementation, the apparatus further includes:
the duration time acquisition module is used for responding to prediction that the target fan blade is in an icing state and acquiring the duration time that the target fan blade is in the icing state;
and the icing alarm module is used for responding to the fact that the duration time is longer than the duration threshold value and sending out icing warning.
In a possible implementation manner, the expected power value obtaining module 820 is configured to input the actual environmental parameter into a power prediction model, and obtain the expected power value output by the power prediction model;
the power prediction model is obtained based on an environment parameter sample and power value labels corresponding to the environment parameter sample.
In one possible implementation manner, the environmental parameter sample and the power value label corresponding to the environmental parameter sample are obtained based on preprocessing historical environmental parameters and historical power values corresponding to the historical environmental parameters;
the preprocessing includes at least one of the following operations:
cleaning the historical environment parameters and the historical power value hollow value corresponding to the historical environment parameters;
Cleaning the historical environment parameters and dead numbers and interpolation data in the historical power values corresponding to the historical environment parameters;
and cleaning the historical environment parameters and low-temperature data in the historical power values corresponding to the historical environment parameters, wherein the low-temperature data refer to the historical environment parameters with the corresponding historical environment temperature lower than a temperature threshold value and the historical power values corresponding to the historical environment parameters.
Wherein the interpolation data may be artificial difference data.
In one possible implementation, the power drop index is equal to the ratio of the actual power value to the desired power value.
In one possible implementation, the icing risk index obtaining module 840 is configured to obtain, in conjunction with a temperature-icing risk index model, an icing risk index of the target fan blade based on an actual temperature of the actual environmental parameters;
in summary, according to the method for predicting the state of the fan blade provided by the embodiment of the application, whether the fan blade is in the icing state is comprehensively predicted by the obtained power falling index and the icing risk index, wherein the power falling index is determined based on the actual power value and the expected power value of the fan blade, and the icing risk index is determined by the environment temperature, so that the prediction of whether the fan blade is in the icing state based on the operation data and the environment parameters of the fan blade is realized, the accuracy of predicting whether the fan blade is in the icing state is improved, and meanwhile, the installation of additional sensors required in the icing state prediction is reduced, so that the use of the icing state prediction method is not limited and can be widely applied.
Fig. 9 is a block diagram of a computer device 900, shown in accordance with an exemplary embodiment. The computer device can be implemented as a monitoring platform in the above scheme of the present application. The computer apparatus 900 includes a central processing unit (Central Processing Unit, CPU) 901, a system Memory 904 including a random access Memory (Random Access Memory, RAM) 902 and a Read-Only Memory (ROM) 903, and a system bus 905 connecting the system Memory 904 and the central processing unit 901. The computer device 900 also includes a basic Input/Output system (I/O) 906, which helps to transfer information between various devices within the computer, and a mass storage device 907, for storing an operating system 913, application programs 914, and other program modules 915.
The basic input/output system 906 includes a display 908 for displaying information and an input device 909, such as a mouse, keyboard, etc., for user input of information. Wherein the display 908 and the input device 909 are connected to the central processing unit 901 via an input output controller 910 connected to the system bus 905. The basic input/output system 906 can also include an input/output controller 910 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, the input-output controller 910 also provides output to a display screen, a printer, or other type of output device.
The mass storage device 907 is connected to the central processing unit 901 through a mass storage controller (not shown) connected to the system bus 905. The mass storage device 907 and its associated computer-readable media provide non-volatile storage for the computer device 900. That is, the mass storage device 907 may include a computer readable medium (not shown) such as a hard disk or a compact disk-Only (CD-ROM) drive.
The computer readable medium may include computer storage media and communication media without loss of generality. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, erasable programmable read-Only register (Erasable Programmable Read Only Memory, EPROM), electrically erasable programmable read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, CD-ROM, digital versatile disks (Digital versatile disc, DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will recognize that the computer storage medium is not limited to the one described above. The system memory 904 and mass storage device 907 described above may be collectively referred to as memory.
According to various embodiments of the application, the computer device 900 may also operate by being connected to a remote computer on a network, such as the Internet. I.e., the computer device 900 may be connected to the network 912 through a network interface unit 911 coupled to the system bus 905, or other types of networks or remote computer systems (not shown) may be coupled using the network interface unit 911.
The memory further includes one or more programs stored in the memory, and the central processor 901 implements all or part of the steps of the methods shown in fig. 3, 4, or 7 by executing the one or more programs.
Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the embodiments of the present application may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, these functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The embodiment of the application also provides a computer readable storage medium which is used for storing at least one instruction, at least one section of program, a code set or an instruction set, wherein the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by a processor to realize the state prediction method of the fan blade. For example, the computer readable storage medium may be ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions to cause the computer device to perform the method of predicting the state of the fan blade provided in the various alternative implementations described above.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (8)
1. A method of predicting the condition of a fan blade, the method comprising:
acquiring actual environment parameters and actual power values of the target fan blades;
acquiring an expected power value of the target fan blade based on the actual environment parameter;
based on the actual power value and the expected power value, obtaining a power sag index, wherein the power sag index is used for indicating the difference between the actual power value and the expected power value;
acquiring an icing risk index of the target fan blade based on the real-time environmental temperature in the actual environmental parameters by combining a temperature-icing risk index model, wherein the temperature-icing risk index model is a mathematical model constructed based on a sigmoid function;
acquiring a cause of the target fan blade in a reduced power state in response to the power drop index indicating that the power generation performance of the target fan blade is below a performance threshold and the icing risk index indicating that the icing risk of the target fan blade is above a risk threshold; and in the case that the reason that the target fan blade is in the power-down state is not other power-down reason, predicting that the target fan blade is in an icing state, wherein the other power-down reason refers to other power-down reasons except the reason that icing leads to power-down, and the other power-down reasons comprise at least one of the following: turbulence power reduction, generator over-temperature power reduction, and gearbox over-temperature power reduction.
2. The method according to claim 1, wherein the method further comprises:
acquiring a duration of time that the target fan blade is in the icing state in response to predicting that the target fan blade is in the icing state;
responsive to the duration being greater than a duration threshold, an icing alert is issued.
3. The method of claim 1, wherein the obtaining the desired power value for the target fan blade based on the actual environmental parameter comprises:
inputting the actual environment parameters into a power prediction model, and obtaining the expected power value output by the power prediction model;
the power prediction model is obtained based on an environment parameter sample and power value labels corresponding to the environment parameter sample.
4. The method of claim 3, wherein the environmental parameter samples and power value labels corresponding to the environmental parameter samples are obtained based on preprocessing historical environmental parameters and historical power values corresponding to the historical environmental parameters;
the preprocessing includes at least one of the following operations:
cleaning the historical environment parameters and the historical power value hollow value corresponding to the historical environment parameters;
Cleaning the historical environment parameters and dead numbers and interpolation data in the historical power values corresponding to the historical environment parameters;
and cleaning the historical environment parameters and low-temperature data in the historical power values corresponding to the historical environment parameters, wherein the low-temperature data refer to the historical environment parameters with the corresponding historical environment temperature lower than a temperature threshold value and the historical power values corresponding to the historical environment parameters.
5. The method of claim 1, wherein the power drop index is equal to a ratio of the actual power value to the desired power value.
6. A fan blade condition prediction apparatus, the apparatus comprising:
the actual data acquisition module is used for acquiring actual environment parameters and actual power values of the target fan blades;
the expected power value acquisition module is used for acquiring the expected power value of the target fan blade based on the actual environment parameters;
a power drop index obtaining module, configured to obtain a power drop index based on the actual power value and the expected power value, where the power drop index is used to indicate a difference between the actual power value and the expected power value;
The icing risk index acquisition module is used for acquiring the icing risk index of the target fan blade based on the real-time environmental temperature in the actual environmental parameters by combining a temperature-icing risk index model, wherein the temperature-icing risk index model is a mathematical model constructed based on a sigmoid function;
the icing state prediction module is used for responding to the power drop index indicating that the power generation performance of the target fan blade is lower than a performance threshold value and the icing risk index indicating that the icing risk of the target fan blade is higher than a risk threshold value, and acquiring a reason for causing the target fan blade to be in a power-down state; and in the case that the reason that the target fan blade is in the power-down state is not other power-down reason, predicting that the target fan blade is in an icing state, wherein the other power-down reason refers to other power-down reasons except the reason that icing leads to power-down, and the other power-down reasons comprise at least one of the following: turbulence power reduction, generator over-temperature power reduction, and gearbox over-temperature power reduction.
7. A computer device, the computer device comprising a processor and a memory; the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, which are loaded and executed by the processor to implement the method for predicting the state of a fan blade according to any one of claims 1 to 5.
8. A computer readable storage medium having stored therein at least one instruction, at least one program, code set, or instruction set, the at least one instruction, the at least one program, the code set, or instruction set being loaded and executed by a processor to implement the method of predicting the state of a fan blade of any one of claims 1 to 5.
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| CN202111196259.4A CN113847216B (en) | 2021-10-14 | 2021-10-14 | Fan blade state prediction method, device, equipment and storage medium |
| PCT/SG2022/050725 WO2023063887A2 (en) | 2021-10-14 | 2022-10-11 | Method and apparatus for predicting state of wind turbine blade, and device and storage medium therefor |
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| CN202111196259.4A CN113847216B (en) | 2021-10-14 | 2021-10-14 | Fan blade state prediction method, device, equipment and storage medium |
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| CA3207396A1 (en) * | 2022-07-27 | 2024-01-27 | Borealis Wind Inc. | Wind turbine ice protection system |
| CN116310940A (en) * | 2022-12-29 | 2023-06-23 | 苏州斯曼克磨粒流设备有限公司 | Risk assessment method and system for running state of electromechanical equipment |
| CN116707035B (en) * | 2023-08-07 | 2023-09-29 | 江苏蔚风能源科技有限公司 | Active power control method depending on low wind speed dynamic programming |
| CN117638926B (en) * | 2024-01-25 | 2024-04-05 | 国能日新科技股份有限公司 | New energy power prediction method and device based on icing and power coupling modeling |
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| WO2023063887A3 (en) | 2023-07-20 |
| CN113847216A (en) | 2021-12-28 |
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