CN113847216A - Method, device and equipment for predicting state of fan blade and storage medium - Google Patents
Method, device and equipment for predicting state of fan blade and storage medium Download PDFInfo
- Publication number
- CN113847216A CN113847216A CN202111196259.4A CN202111196259A CN113847216A CN 113847216 A CN113847216 A CN 113847216A CN 202111196259 A CN202111196259 A CN 202111196259A CN 113847216 A CN113847216 A CN 113847216A
- Authority
- CN
- China
- Prior art keywords
- fan blade
- icing
- power
- actual
- historical
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Wind Motors (AREA)
Abstract
The application discloses a state prediction method, device, equipment and storage medium of a fan blade, and relates to the technical field of wind power generation. The method comprises the following steps: acquiring actual environmental parameters and actual power values of 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 environment temperature in the actual environment parameters; and predicting whether the target fan blade is in the icing state or not based on the power drop index and the icing risk index. By the method, the prediction of whether the fan blade is in the icing state or not 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 or not is improved, the cost of predicting the icing state is reduced, the icing state prediction method is not limited in use, and the method 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 a cold area, in order to improve the 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 blade icing fault needs to be eliminated in time.
In the related art, it is generally determined whether the fan blade is frozen according to a preset freezing temperature threshold by combining temperature data of the fan blade collected by various sensors or ultrasonic detection technologies, where the sensors may be infrared sensors, optical fiber sensors, and the like.
However, in the above technology, the prediction of whether the fan blade is in the icing state is performed only depending on the temperature data collected by the sensors arranged on the fan blades, so that the prediction effect of 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 detection method can ensure the detection accuracy of the icing state of the fan blade and expand the application range of the detection technology at the same time, and the technical scheme is as follows:
in one aspect, a method for predicting the state of a fan blade is provided, and the method includes:
acquiring actual environmental parameters and actual power values of target fan blades;
acquiring a desired power value of the target fan blade based on the actual environment parameter;
obtaining a power droop index based on the actual power value and the expected power value, the power droop index indicating a 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 environment temperature in the actual environment parameter;
and predicting whether the target fan blade is in an icing state or not based on the power drop index and the icing risk index.
In another aspect, a device for predicting the state of a fan blade is provided, the device comprising:
the actual data acquisition module is used for acquiring actual environmental parameters and actual power values of the target fan blade;
a desired power value obtaining module, configured to obtain a desired power value of the target fan blade based on the actual environment parameter;
a power droop index obtaining module, configured to obtain a power droop index based on the actual power value and the expected power value, where the power droop 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 parameter;
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 drop index and the icing risk index.
In one possible implementation, the icing status prediction module is configured to predict that the target fan blade is in an icing status in response to the power droop 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 acquisition module is used for responding to the prediction that the target fan blade is in the icing state and acquiring the duration of the target fan blade in the icing state;
and the icing alarm module is used for responding to the condition that the duration is greater than the duration threshold value and sending icing alarm.
In a possible implementation manner, the expected power value obtaining module is configured to input the actual environment 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 environmental parameter samples and power value labels corresponding to the environmental parameter samples.
In a possible implementation manner, the environmental parameter sample and the power value tag corresponding to the environmental parameter sample are obtained by preprocessing a historical environmental parameter and a historical power value corresponding to the historical environmental parameter;
the pre-processing comprises at least one of the following operations:
cleaning the historical environment parameters and the historical power value hollow values corresponding to the historical environment parameters;
cleaning the historical environment parameters, dead numbers in historical power values corresponding to the historical environment parameters and interpolation data;
and cleaning low-temperature data in historical environment parameters and historical power values corresponding to the historical environment parameters, wherein the low-temperature data refer to the historical environment parameters and the historical power values corresponding to the historical environment parameters, and the historical environment parameters and the historical power values corresponding to the historical environment parameters have corresponding historical environment temperatures lower than a temperature threshold.
In one possible implementation, the power droop index is equal to a 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 an icing risk index of the target fan blade based on an actual temperature in the actual environment parameter in combination with a temperature-icing risk index model;
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, the computing 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 that are loaded and executed by the processor to implement the method of predicting a condition of a fan blade of the above aspect.
In another aspect, a computer-readable storage medium is provided that stores at least one instruction for execution by a processor to implement a method of condition prediction 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 the processor executes the computer instructions to cause the computer device to execute the state prediction method of the fan blade provided in the above-mentioned various optional implementations.
The technical scheme provided by the application can comprise the following beneficial effects:
the method has the advantages that whether the fan blade is in the icing state or not is comprehensively predicted through the obtained power drop index and the obtained icing risk index, wherein the power drop 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 ambient temperature, so that the prediction of whether the fan blade is in the icing state or not based on the operation data and the ambient parameters of the fan blade is realized, the accuracy of predicting whether the fan blade is in the icing state or not is improved, meanwhile, the installation of an additional sensor required in the icing state prediction is reduced, the icing state prediction method is not limited in use, 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 present 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 present application;
FIG. 2 illustrates a schematic structural diagram of a system used in a method for predicting a condition of a wind turbine blade according to an exemplary embodiment of the present disclosure;
FIG. 3 illustrates a flow chart of a method of predicting a condition of a wind turbine blade provided by an exemplary embodiment of the present application;
FIG. 4 illustrates a flow chart of a method of predicting a condition of a wind turbine blade provided by 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 shown in an exemplary embodiment of the present application;
FIG. 7 illustrates a schematic diagram of an icing warning flow provided by an exemplary embodiment of the present application;
FIG. 8 illustrates a block diagram of a state prediction device for a fan blade provided in an exemplary embodiment of the present application;
FIG. 9 is a block diagram illustrating the structure of a computer device according to an example embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
It is to be understood that reference herein to "a number" means one or more and "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Wind turbine generator system work in cold areas, and the fan in this environment receives meteorological conditions influences such as frost ice, rime and wet snow, very easily takes place the blade phenomenon of icing, and then triggers a series of consequences, probably causes following harm:
1) the blade airfoil of the frozen fan is changed, so that the wind energy capture capacity is reduced, and in addition, the blade is attached with an ice layer, so that the energy required by the rotation of the blade is increased, and finally, the power loss of wind power generation is caused;
fig. 1 illustrates a wind speed power scatter diagram according to an exemplary embodiment of the present application, as shown in fig. 1, when a fan blade is in a normal operating state, a relationship between a wind speed and a fan power conforms to a wind speed power curve 110, and when the fan blade is in an icing state, a wind energy capturing capability of the fan blade is reduced due to a change of a wing profile of the fan blade, so that the fan power is reduced, as shown in fig. 1, a point in an area 120 is the fan power corresponding to the 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, so that the inherent modal parameters of the blade part are influenced, and the blade is induced to break;
3) after the fan blades are frozen and accumulated to a certain degree, the ice layer is broken and flies out under the influence of self weight, and is easy to hit wind field inspection personnel, so that personal accidents are caused.
Therefore, the blade icing fault can be immediately detected and eliminated, and the method has important significance for prolonging the service life of the wind power equipment and preventing major safety accidents.
In the operation and maintenance process of the fan power station in the cold area, whether the fan blade of the wind turbine generator is in the icing state needs to be checked. The application provides a state prediction method for 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 convenience of understanding, terms referred to 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, monitoring control, process control and the like in the fields of electric power, metallurgy, petroleum, chemical industry, gas, railways 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 wind turbine power plant 201 and a monitoring platform 202.
The wind turbine power station 201 comprises a plurality of wind turbines, wherein each wind turbine comprises a wind turbine blade and a nacelle. In this embodiment, the wind turbine power station 201 may be provided with a plurality of sensors for acquiring data in the staged power station, and illustratively, the wind turbine power station may include a temperature sensor, a wind speed sensor, and the like for acquiring parameters such as ambient temperature and wind speed in the wind turbine power station 201, and sending the acquired data to the monitoring platform 202.
The wind turbine power station 201 is connected with the monitoring platform 202 through a wired or wireless network.
The monitoring platform 202 is a computer device having functions of storing data sent by the wind turbine power station 201, processing the data, generating an alarm record, and the like, and the computer device may be a server cluster or a cloud server formed by one or a plurality of servers, or the computer device may also be implemented as a terminal.
For ease of description, in the method embodiments described below, the monitoring platform 102 is described as a computer device illustrating the method of predicting the condition of a wind turbine blade provided herein.
FIG. 3 illustrates a flow diagram of a method for predicting a condition of a wind turbine blade, which may be performed by a computer device, which may be implemented as the monitoring platform shown in FIG. 2, according to an exemplary embodiment of the present disclosure, and may include the following steps:
and step 310, acquiring the actual environment parameters and the actual power values of the target fan blades.
In one possible implementation, the target fan blade is any one of a plurality of fan blades in a fan plant, the actual environmental parameter and the actual power value of the target fan blade are obtained based on data collected by a SCADA system in the fan, and the SDCADA system is used for realizing functions of data collection, equipment control, measurement, parameter adjustment, and the like.
And step 320, acquiring the expected power value of the target fan blade based on the actual environment parameter.
Wherein the expected power value is indicative of a power value that would be generated by the target fan blade operating normally under the environmental parameter in the non-icing condition.
Generally speaking, 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 droop index is set to measure the magnitude of the difference between the actual power value and the expected power value.
And 340, acquiring an icing risk index of the target fan blade based on the real-time environment temperature in the actual environment parameters.
The icing risk index is used for indicating the probability that the target fan blade is iced at the ambient temperature, and generally speaking, the lower the ambient temperature is, the higher the icing risk index of the target fan blade is, and the higher the ambient temperature is, the lower the icing risk index of the target fan blade is.
And step 350, predicting whether the target fan blade is in the icing state or not based on the power drop index and the icing risk index.
To sum up, the method for predicting the state of the fan blade provided by the embodiment of the present application comprehensively predicts whether the fan blade is in the icing state or not through the obtained power drop index and the icing risk index, wherein the power drop 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 ambient temperature, so that the prediction of whether the fan blade is in the icing state or not based on the operating data and the environmental parameters of the fan blade is realized, the accuracy of predicting whether the fan blade is in the icing state or not is improved, and meanwhile, the installation of an additional sensor required in the icing state prediction is reduced, so that the method for predicting the icing state is not limited in use and can be widely applied.
FIG. 4 illustrates a flow diagram of a method for predicting a condition of a wind turbine blade, which may be performed by a computer device, which may be implemented as the monitoring platform shown in FIG. 2, according to an exemplary embodiment of the present disclosure, and may include the following steps:
and step 410, acquiring the actual environment parameters and the actual power values of the target fan blades.
And step 420, acquiring a desired power value of the target fan blade based on the actual environment parameter.
In a possible implementation manner, the actual environment parameters are input into the power prediction model, and an expected power value output by the power prediction model is obtained;
the power prediction model is obtained based on the environmental parameter samples and the power value labels corresponding to the environmental parameter samples.
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 by 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 obtained based on data collected by an SCADA system in the wind turbine. The time period corresponding to the historical data (the historical environmental parameters and the historical power values corresponding to the historical environmental parameters) may be set based on actual requirements, for example, the time period may be one month, one quarter, one year, and the like.
The power value label is the historical power value corresponding to the environmental parameter sample.
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 the historical environment parameters and the historical power value hollow values corresponding to the historical environment parameters;
cleaning the historical environment parameters, dead numbers in historical power values corresponding to the historical environment parameters and interpolation data;
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 and the historical power values corresponding to the historical environment parameters, and the historical environment parameters and the historical power values corresponding to the historical environment parameters have corresponding historical environment temperatures lower than a temperature threshold.
The low-temperature data is cleaned so that the power prediction model can 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 environment attributes by utilizing a tree model based on the influence degree of whether the fan blade is in an icing state or not to obtain a sequencing result;
acquiring a target environment attribute 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 environment attributes can be obtained according to actual requirements, that is, important environment attributes in the environment attributes can be obtained according to the actual requirements as the target environment attributes, and parameters corresponding to the target environment attributes are obtained as environment parameters; illustratively, the target environmental attributes may include a pitch angle, turbulence, temperature, and nacelle position; accordingly, the environmental parameters may include wind speed values, angle of pitch values, temperature values, and cabin 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 is a prediction power value;
calculating a loss function value based on the power value label corresponding to the environment parameter sample and the prediction result corresponding to the environment parameter sample;
based on the loss function value, a parameter update is performed on the power prediction model.
Because the accuracy of the power value predicted by the power prediction model during application can only be ensured by making the prediction result (i.e., the predicted power value) obtained by the power prediction model based on the environmental parameter sample be the same as or similar to the power value label corresponding to the environmental parameter sample, multiple times of training are 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.
Based on the actual power value and the expected power value, a power droop index is obtained, step 430, which indicates the difference between the actual power value and the expected power value.
Wherein the power droop index is equal to a ratio of the actual power value to the desired power value, i.e.:
and 440, acquiring an icing risk index of the target fan blade based on the real-time environment temperature in the actual environment parameters.
To a certain extent, the ambient temperature can reflect the influence of the icing state on the fan power of the target fan blade, fig. 5 shows a timing diagram provided by an exemplary embodiment of the present application, part a of fig. 5 shows a time-ambient temperature timing diagram, part B of fig. 5 shows a time-fan power timing diagram, and as can be seen from the timing diagram of part a of fig. 5 and the timing diagram of part B, when the ambient temperature is lower than the specified temperature threshold, the fan blade is in an icing state, and in the icing state, the fan blade is in an abnormal working state due to the influence of icing, so that the power of the fan corresponding to the fan blade is reduced, as shown in FIG. 5, during a first time period 510, the fan blade is in a normal operating condition, during a second time period 520, the fan blade is in an icing condition, and during a third time period 530, the fan blade resumes a normal operating condition. Thus, an icing risk index for a fan blade may be determined based on changes in ambient temperature.
In one possible implementation, the icing risk index of the target fan blade may be obtained based on the real-time ambient temperature in the actual environmental parameter according to historical experience.
The historical experience may be a work experience or an expert experience, for example, when the ambient temperature is 0 degrees or below 0 degrees, the icing risk is high, and the icing risk index gradually increases as the ambient temperature decreases.
Or, in order to improve accuracy of obtaining the icing risk index, in one possible implementation manner, the icing risk index of the target fan blade is obtained based on the actual temperature in the actual environment parameter in combination with a temperature-icing risk index model;
the temperature-icing risk index model is a mathematical model constructed based on a sigmoid function.
Fig. 6 shows a schematic diagram of a temperature-icing risk index model according to an exemplary embodiment of the present application, as shown in fig. 6, the temperature-icing risk index model (curve 610) is used for determining the icing risk index (ordinate) according to the ambient temperature (abscissa), and the mathematical formula corresponding to the temperature-icing risk index model can be expressed as:
y＝sigmoid(-x)
wherein x represents the ambient temperature and y represents the icing risk index.
And 450, responding to the power drop index indicating that the power generation performance of the target fan blade is lower than the performance threshold value and the icing risk index indicating that the icing risk of the target fan blade is higher than the risk threshold value, and predicting that the target fan blade is in an icing state.
Except that the power drop index indicates that the power generation performance of the target fan blade is lower than a performance threshold value, and the icing risk index indicates that the icing risk of the target fan blade is higher than a risk threshold value, the target fan blade is predicted to be in an icing state, and the target fan blade is predicted to be in a non-icing state under other conditions. That is to say:
responding to the power drop index to indicate that the power generation performance of the target fan blade is lower than a performance threshold value, and the icing risk index indicates that the icing risk of the target fan blade is lower than a risk threshold value, and predicting that the target fan blade is in a non-icing state;
or, in response to the power droop index indicating that the power generation performance of the target fan blade is above a performance threshold, the icing risk index indicating that the icing risk of the target fan blade is above a risk threshold, predicting that the target fan blade is in a non-icing state;
or in response to the power droop index indicating that the power generation performance of the target fan blade is lower than the performance threshold and the icing risk index indicating that the icing risk of the target fan blade is lower than the risk threshold, predicting that the target fan blade is in a non-icing state.
In order to prevent the target fan blade from being in the power reduction state due to the non-icing power reduction reason and to make a misjudgment on whether the target fan blade is in the icing state, in a possible implementation manner, after the target fan blade is predicted to be in the icing state, the reason causing the target fan blade to be in the power reduction state is obtained, and when the reason causing the target fan blade to be in the power reduction state is determined to be the non-icing power reduction reason, the target fan blade is predicted to be in the icing state.
The non-icing power reduction reasons comprise turbulence power reduction, generator over-temperature power reduction, gear box over-temperature power reduction and the like; different power reduction reasons correspond to different power reduction judgment mechanisms, for example, when determining whether the reason that the fan blade is in the power reduction state is turbulence power reduction, the determination can be performed by detecting whether the wind speed fluctuation exceeds a wind speed fluctuation threshold value; when determining whether the reason that the fan blade is in the power reduction state is over-temperature of the generator, determining by detecting whether the temperature of the generator exceeds a temperature threshold of the generator; when determining whether the cause of the fan blades being in the derated state is a gearbox over-temperature, the determination may be made by detecting whether a gearbox temperature exceeds a gearbox temperature threshold.
In one possible implementation, in response to predicting that the target fan blade is in an icing state, obtaining a duration of the target fan blade in the icing state;
in response to the duration being greater than the duration threshold, an icing warning is issued.
Because it is continuous that the fan blade is in the state of icing, in a possible implementation, the duration that the fan blade is in the state of icing is obtained through the sliding window method, and this process can be realized as: taking the marked freezing point as a starting point, obtaining the length of a time period containing the freezing point through a sliding window, 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 the starting point of the icing state determined based on the icing state prediction method; and when the duration of the icing state is longer than the duration threshold, sending an icing warning to indicate relevant personnel to inspect and maintain the fan blade.
To sum up, the method for predicting the state of the fan blade provided by the embodiment of the present application comprehensively predicts whether the fan blade is in the icing state or not through the obtained power drop index and the icing risk index, wherein the power drop 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 ambient temperature, so that the prediction of whether the fan blade is in the icing state or not based on the operating data and the environmental parameters of the fan blade is realized, the accuracy of predicting whether the fan blade is in the icing state or not is improved, and meanwhile, the installation of an additional sensor required in the icing state prediction is reduced, so that the method for predicting the icing state is not limited in use and can be widely applied.
FIG. 7 is a schematic diagram illustrating an icing warning process provided by an exemplary embodiment of the present application, as shown in FIG. 6, the icing warning process including the following steps:
And 720, evaluating the power generation performance of the fan blade.
The power generation performance evaluation corresponds to the process of calculating the power droop index in the embodiments of fig. 3 and 4, and is not described herein again.
And step 730, performing icing risk assessment on the fan blade.
The icing risk assessment corresponds to the process of obtaining the icing risk index in the embodiments of fig. 3 and 4, and is not described herein again.
And 740, judging whether the fan blade is in a state with low power generation performance and high icing risk, if so, executing the step 750, and if not, determining that the fan blade is in a non-icing state.
And 750, judging whether other power reduction reasons are eliminated, if so, executing 760, and if not, ending.
And 760, judging whether the icing duration is greater than the icing time threshold, if so, executing step 770, and otherwise, ending.
At step 770, an icing warning is issued.
To sum up, the method for predicting the state of the fan blade provided by the embodiment of the present application comprehensively predicts whether the fan blade is in the icing state or not through the obtained power drop index and the icing risk index, wherein the power drop 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 ambient temperature, so that the prediction of whether the fan blade is in the icing state or not based on the operating data and the environmental parameters of the fan blade is realized, the accuracy of predicting whether the fan blade is in the icing state or not is improved, and meanwhile, the installation of an additional sensor required in the icing state prediction is reduced, so that the method for predicting the icing state is not limited in use and can be widely applied.
Fig. 8 is a block diagram illustrating a state prediction apparatus for a fan blade according to an exemplary embodiment of the present application, where the state prediction apparatus for a fan blade, as shown in fig. 8, includes:
an actual data obtaining module 810, 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 droop index obtaining module 830, configured to obtain a power droop index based on the actual power value and the expected power value, where the power droop index is used to indicate a difference between the actual power value and the expected power value;
an icing risk index obtaining module 840, configured to obtain an icing risk index of the target fan blade based on the real-time environment temperature in the actual environment parameter;
an icing condition prediction module 850 configured to predict whether the target fan blade is in an icing condition based on the power droop 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 droop 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 acquisition module is used for responding to the prediction that the target fan blade is in the icing state and acquiring the duration of the target fan blade in the icing state;
and the icing alarm module is used for responding to the condition that the duration is greater than the duration threshold value and sending icing alarm.
In a possible implementation manner, the expected power value obtaining module 820 is configured to input the actual environment 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 environmental parameter samples and power value labels corresponding to the environmental parameter samples.
In a possible implementation manner, the environmental parameter sample and the power value tag corresponding to the environmental parameter sample are obtained by preprocessing a historical environmental parameter and a historical power value corresponding to the historical environmental parameter;
the pre-processing comprises at least one of the following operations:
cleaning the historical environment parameters and the historical power value hollow values corresponding to the historical environment parameters;
cleaning the historical environment parameters, dead numbers in historical power values corresponding to the historical environment parameters and interpolation data;
and cleaning low-temperature data in historical environment parameters and historical power values corresponding to the historical environment parameters, wherein the low-temperature data refer to the historical environment parameters and the historical power values corresponding to the historical environment parameters, and the historical environment parameters and the historical power values corresponding to the historical environment parameters have corresponding historical environment temperatures lower than a temperature threshold.
Wherein the interpolation data may be artificial difference data.
In one possible implementation, the power droop index is equal to a ratio of the actual power value to the desired power value.
In a possible implementation manner, the icing risk index obtaining module 840 is configured to obtain an icing risk index of the target fan blade based on an actual temperature in the actual environment parameter in combination with a temperature-icing risk index model;
to sum up, the method for predicting the state of the fan blade provided by the embodiment of the present application comprehensively predicts whether the fan blade is in the icing state or not through the obtained power drop index and the icing risk index, wherein the power drop 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 ambient temperature, so that the prediction of whether the fan blade is in the icing state or not based on the operating data and the environmental parameters of the fan blade is realized, the accuracy of predicting whether the fan blade is in the icing state or not is improved, and meanwhile, the installation of an additional sensor required in the icing state prediction is reduced, so that the method for predicting the icing state is not limited in use and can be widely applied.
Fig. 9 is a block diagram illustrating the structure of a computer device 900 according to an example embodiment. The computer device may be implemented as the monitoring platform in the above-mentioned aspect of the present application. The computer apparatus 900 includes a Central Processing Unit (CPU) 901, a system Memory 904 including a Random Access Memory (RAM) 902 and a Read-Only Memory (ROM) 903, and a system bus 905 connecting the system Memory 904 and the CPU 901. The computer device 900 also includes a basic Input/Output system (I/O system) 906, which facilitates the transfer of information between 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 through an input output controller 910 connected to the system bus 905. The basic input/output system 906 may 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, 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 Compact Disc-Only Memory (CD-ROM) drive.
Without loss of generality, the computer-readable media may comprise computer storage media and communication media. 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 Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, CD-ROM, Digital Versatile Disks (DVD), or other optical, magnetic, or other magnetic storage devices. Of course, those skilled in the art will appreciate that the computer storage media is not limited to the foregoing. The system memory 904 and mass storage device 907 described above may be collectively referred to as memory.
According to various embodiments of the present application, the computer device 900 may also operate as a remote computer connected to a network via a network, such as the Internet. That is, the computer device 900 may be connected to the network 912 through the network interface unit 911 coupled to the system bus 905, or the network interface unit 911 may be used to connect to other types of networks or remote computer systems (not shown).
The memory further includes one or more programs, the one or more programs are stored in the memory, and the central processor 901 implements all or part of the steps of the method shown in fig. 3, fig. 4 or fig. 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, the 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 present application further provides a computer-readable storage medium for storing at least one instruction, at least one program, a code set, or an instruction set, which is loaded and executed by a processor to implement the method for predicting the state of a fan blade. For example, the computer readable storage medium may be a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
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 the processor executes the computer instructions to cause the computer device to execute the state prediction method of the fan blade provided in the above-mentioned various optional implementations.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention 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 invention 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 will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for predicting the condition of a fan blade, the method comprising:
acquiring actual environmental parameters and actual power values of target fan blades;
acquiring a desired power value of the target fan blade based on the actual environment parameter;
obtaining a power droop index based on the actual power value and the expected power value, the power droop index indicating a 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 environment temperature in the actual environment parameter;
and predicting whether the target fan blade is in an icing state or not based on the power drop index and the icing risk index.
2. The method of claim 1, wherein predicting whether the target fan blade is in an icing condition based on the power droop index and the icing risk index comprises:
predicting that the target fan blade is in an icing condition in response to the power droop 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.
3. The method of claim 2, further comprising:
in response to predicting that the target fan blade is in an icing state, obtaining a duration of the target fan blade in the icing state;
in response to the duration being greater than a duration threshold, an icing warning is issued.
4. 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 acquiring the expected power value output by the power prediction model;
the power prediction model is obtained based on environmental parameter samples and power value labels corresponding to the environmental parameter samples.
5. The method of claim 4, wherein the environmental parameter samples and the 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 pre-processing comprises at least one of the following operations:
cleaning the historical environment parameters and the historical power value hollow values corresponding to the historical environment parameters;
cleaning the historical environment parameters, dead numbers in historical power values corresponding to the historical environment parameters and interpolation data;
and cleaning low-temperature data in historical environment parameters and historical power values corresponding to the historical environment parameters, wherein the low-temperature data refer to the historical environment parameters and the historical power values corresponding to the historical environment parameters, and the historical environment parameters and the historical power values corresponding to the historical environment parameters have corresponding historical environment temperatures lower than a temperature threshold.
6. The method of claim 1, wherein the power droop index is equal to a ratio of the actual power value to the desired power value.
7. The method of claim 1, wherein obtaining the icing risk index for the target fan blade based on the real-time ambient temperature in the actual environmental parameter comprises:
acquiring an icing risk index of the target fan blade based on the actual temperature in the actual environment parameter 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.
8. A state prediction apparatus for a fan blade, the apparatus comprising:
the actual data acquisition module is used for acquiring actual environmental parameters and actual power values of the target fan blade;
a desired power value obtaining module, configured to obtain a desired power value of the target fan blade based on the actual environment parameter;
a power droop index obtaining module, configured to obtain a power droop index based on the actual power value and the expected power value, where the power droop 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 parameter;
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 drop index and the icing risk index.
9. A computer device, wherein the computer device comprises a processor and a memory; the memory has stored therein at least one instruction, at least one program, set of codes or set of instructions that is loaded and executed by the processor to implement a method of condition prediction of a wind turbine blade according to any of claims 1 to 7.
10. A computer readable storage medium, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored, loaded and executed by a processor to implement a method of condition prediction of a fan blade according to any of claims 1 to 7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| 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 |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111196259.4A CN113847216B (en) | 2021-10-14 | 2021-10-14 | Fan blade state prediction method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113847216A true CN113847216A (en) | 2021-12-28 |
| CN113847216B CN113847216B (en) | 2023-09-26 |
Family
ID=78978248
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111196259.4A Active CN113847216B (en) | 2021-10-14 | 2021-10-14 | Fan blade state prediction method, device, equipment and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113847216B (en) |
| WO (1) | WO2023063887A2 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114593024A (en) * | 2022-04-06 | 2022-06-07 | 华润电力技术研究院有限公司 | Fan blade icing prediction method and related equipment |
| 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 |
| EP4311935A1 (en) * | 2022-07-27 | 2024-01-31 | Borealis Wind Inc. | Wind turbine ice protection system |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130031966A1 (en) * | 2010-04-12 | 2013-02-07 | Per Egedal | Method and system for determining a mass change at a rotating blade of a wind turbine |
| EP2626557A1 (en) * | 2012-02-08 | 2013-08-14 | Siemens Aktiengesellschaft | De-icing a rotor blade in dependence of a chill-factor |
| KR20140020405A (en) * | 2012-08-08 | 2014-02-19 | 삼성중공업 주식회사 | Apparatus for preventing or removing icing and wind power generator including the same |
| CN103608584A (en) * | 2011-06-07 | 2014-02-26 | 乌本产权有限公司 | Method for operating wind energy plant in icing condition |
| CN103899485A (en) * | 2014-04-24 | 2014-07-02 | 湘电风能有限公司 | Method for detecting freezing of blades when fan operates |
| DE202015003529U1 (en) * | 2015-05-18 | 2015-06-03 | Senvion Se | Computer program and system for rotor blade deicing and wind energy plant |
| US20150292486A1 (en) * | 2011-12-22 | 2015-10-15 | Vestas Wind Systems A/S | Wind turbine blade ice accretion detector |
| CN105089929A (en) * | 2014-05-21 | 2015-11-25 | 南车株洲电力机车研究所有限公司 | Wind generating set blade icing detection system and method |
| CN105298761A (en) * | 2015-11-06 | 2016-02-03 | 周志宏 | Freezing early warning and control method for wind generating unit and device of freezing early warning and control method |
| CN105464912A (en) * | 2016-01-27 | 2016-04-06 | 国电联合动力技术有限公司 | Method and device for detecting freezing of wind generating set blades |
| EP3015707A1 (en) * | 2014-10-31 | 2016-05-04 | Senvion GmbH | Wind turbine and method for de-icing a wind turbine |
| CN108119319A (en) * | 2016-11-29 | 2018-06-05 | 北京金风科创风电设备有限公司 | Method and device for identifying icing state of blade of wind generating set |
| CN108343566A (en) * | 2018-03-28 | 2018-07-31 | 长沙理工大学 | A kind of blade icing On-line Fault monitoring method and system based on running of wind generating set state |
| CN108915957A (en) * | 2018-06-12 | 2018-11-30 | 远景能源（江苏）有限公司 | A method of for monitoring the deformation of blade |
| CN109209790A (en) * | 2018-10-09 | 2019-01-15 | 浙江运达风电股份有限公司 | A kind of wind electricity blade icing estimation method based on data modeling |
| CN109522627A (en) * | 2018-11-01 | 2019-03-26 | 西安电子科技大学 | Fan blade icing prediction technique based on SCADA data |
| CN110285027A (en) * | 2019-04-30 | 2019-09-27 | 长沙理工大学 | Blade of wind-driven generator de-icing method, deicing system and terminal device |
| US20190323485A1 (en) * | 2016-12-22 | 2019-10-24 | Vestas Wind Systems A/S | Temperature control based on weather forecasting |
| WO2020057876A1 (en) * | 2018-09-19 | 2020-03-26 | Siemens Gamesa Renewable Energy Innovation & Technology S.L. | Ice detection method and system for a wind turbine |
| CN111102141A (en) * | 2019-12-13 | 2020-05-05 | 中国船舶重工集团海装风电股份有限公司 | Fan blade heating method, device, system and storage medium |
| CN113007041A (en) * | 2021-03-02 | 2021-06-22 | 山东中车风电有限公司 | Wind turbine generator blade icing detection system and detection method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109958588B (en) * | 2017-12-14 | 2020-08-07 | 北京金风科创风电设备有限公司 | Icing prediction method, icing prediction device, storage medium, model generation method and model generation device |
-
2021
- 2021-10-14 CN CN202111196259.4A patent/CN113847216B/en active Active
-
2022
- 2022-10-11 WO PCT/SG2022/050725 patent/WO2023063887A2/en active Search and Examination
Patent Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130031966A1 (en) * | 2010-04-12 | 2013-02-07 | Per Egedal | Method and system for determining a mass change at a rotating blade of a wind turbine |
| CN103608584A (en) * | 2011-06-07 | 2014-02-26 | 乌本产权有限公司 | Method for operating wind energy plant in icing condition |
| US20140091572A1 (en) * | 2011-06-07 | 2014-04-03 | Wobben Properties Gmbh | Method for operating a wind energy plant |
| US20150292486A1 (en) * | 2011-12-22 | 2015-10-15 | Vestas Wind Systems A/S | Wind turbine blade ice accretion detector |
| EP2626557A1 (en) * | 2012-02-08 | 2013-08-14 | Siemens Aktiengesellschaft | De-icing a rotor blade in dependence of a chill-factor |
| KR20140020405A (en) * | 2012-08-08 | 2014-02-19 | 삼성중공업 주식회사 | Apparatus for preventing or removing icing and wind power generator including the same |
| CN103899485A (en) * | 2014-04-24 | 2014-07-02 | 湘电风能有限公司 | Method for detecting freezing of blades when fan operates |
| CN105089929A (en) * | 2014-05-21 | 2015-11-25 | 南车株洲电力机车研究所有限公司 | Wind generating set blade icing detection system and method |
| EP3015707A1 (en) * | 2014-10-31 | 2016-05-04 | Senvion GmbH | Wind turbine and method for de-icing a wind turbine |
| DE202015003529U1 (en) * | 2015-05-18 | 2015-06-03 | Senvion Se | Computer program and system for rotor blade deicing and wind energy plant |
| CN105298761A (en) * | 2015-11-06 | 2016-02-03 | 周志宏 | Freezing early warning and control method for wind generating unit and device of freezing early warning and control method |
| CN105464912A (en) * | 2016-01-27 | 2016-04-06 | 国电联合动力技术有限公司 | Method and device for detecting freezing of wind generating set blades |
| CN108119319A (en) * | 2016-11-29 | 2018-06-05 | 北京金风科创风电设备有限公司 | Method and device for identifying icing state of blade of wind generating set |
| US20190323485A1 (en) * | 2016-12-22 | 2019-10-24 | Vestas Wind Systems A/S | Temperature control based on weather forecasting |
| CN108343566A (en) * | 2018-03-28 | 2018-07-31 | 长沙理工大学 | A kind of blade icing On-line Fault monitoring method and system based on running of wind generating set state |
| CN108915957A (en) * | 2018-06-12 | 2018-11-30 | 远景能源（江苏）有限公司 | A method of for monitoring the deformation of blade |
| WO2020057876A1 (en) * | 2018-09-19 | 2020-03-26 | Siemens Gamesa Renewable Energy Innovation & Technology S.L. | Ice detection method and system for a wind turbine |
| CN109209790A (en) * | 2018-10-09 | 2019-01-15 | 浙江运达风电股份有限公司 | A kind of wind electricity blade icing estimation method based on data modeling |
| CN109522627A (en) * | 2018-11-01 | 2019-03-26 | 西安电子科技大学 | Fan blade icing prediction technique based on SCADA data |
| CN110285027A (en) * | 2019-04-30 | 2019-09-27 | 长沙理工大学 | Blade of wind-driven generator de-icing method, deicing system and terminal device |
| CN111102141A (en) * | 2019-12-13 | 2020-05-05 | 中国船舶重工集团海装风电股份有限公司 | Fan blade heating method, device, system and storage medium |
| CN113007041A (en) * | 2021-03-02 | 2021-06-22 | 山东中车风电有限公司 | Wind turbine generator blade icing detection system and detection method |
Non-Patent Citations (2)
| Title |
|---|
| 叶春霖;邱颖宁;冯延晖;: "基于数据挖掘的风电机组叶片结冰故障诊断", 噪声与振动控制, no. 2 * |
| 黎楚阳;朱孟兆;焦健;张炜;张玉波;: "基于大数据分析的风机叶片结冰故障诊断", 自动化与仪器仪表, no. 03 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114593024A (en) * | 2022-04-06 | 2022-06-07 | 华润电力技术研究院有限公司 | Fan blade icing prediction method and related equipment |
| EP4311935A1 (en) * | 2022-07-27 | 2024-01-31 | 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 |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023063887A3 (en) | 2023-07-20 |
| CN113847216B (en) | 2023-09-26 |
| WO2023063887A2 (en) | 2023-04-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113847216B (en) | Fan blade state prediction method, device, equipment and storage medium | |
| KR102159692B1 (en) | solar photovoltatic power generation forecasting apparatus and method based on big data analysis | |
| US10600036B2 (en) | Wind power plant management system and method thereof | |
| EP3465359B1 (en) | System and method for controlling a dynamic system, in particular a wind turbine | |
| KR20160073945A (en) | System and method for managing wind plant | |
| Kreutz et al. | Machine learning-based icing prediction on wind turbines | |
| EP2514969B1 (en) | Monitoring wind turbine performance | |
| US20220412318A1 (en) | Method and System for Building Prescriptive Analytics to Prevent Wind Turbine Failures | |
| CN113049142A (en) | Temperature sensor alarm method, device, equipment and storage medium | |
| JP2013222423A (en) | Method and system for predicting power generation capacity, and method and system for managing health of wind power generation facilities | |
| EP3947961B1 (en) | A method for estimating remaining useful life of components of an operational wind turbine | |
| CN112682276B (en) | Fan blade icing state prediction method and device, medium and electronic equipment | |
| JP6315836B2 (en) | Windmill monitoring device, windmill monitoring method, and windmill monitoring program | |
| US9188021B2 (en) | Steam turbine blade vibration monitor backpressure limiting system and method | |
| Zhao et al. | Reliability analysis of wind turbine blades based on non-Gaussian wind load impact competition failure model | |
| TWI546762B (en) | Wind power generation equipment of the stress estimation device and wind power equipment, the stress estimation method, wind power generation system | |
| CN116306139A (en) | Intelligent monitoring method and system for service life of wind turbine blade | |
| CN110139983B (en) | Method and system for empirical evaluation of wind turbine generator operation | |
| CN112016739B (en) | Fault detection method and device, electronic equipment and storage medium | |
| CN116677570A (en) | Fault early warning method and system based on cabin temperature monitoring of offshore wind turbine | |
| De Oliveira-Filho et al. | Condition monitoring of wind turbine main bearing using SCADA data and informed by the principle of energy conservation | |
| Gray et al. | A practical approach to the use of scada data for optimized wind turbine condition based maintenance | |
| CN114611730A (en) | Differential operation and maintenance method, device, medium and terminal equipment for power transmission line | |
| Hellström | Development of a model for estimation of wind farm production losses due to icing | |
| CN114837903A (en) | Method and device for predicting fan blade state, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |