CN114251238A - Variable pitch motor temperature anomaly detection method and equipment - Google Patents
Variable pitch motor temperature anomaly detection method and equipment Download PDFInfo
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- CN114251238A CN114251238A CN202111443199.1A CN202111443199A CN114251238A CN 114251238 A CN114251238 A CN 114251238A CN 202111443199 A CN202111443199 A CN 202111443199A CN 114251238 A CN114251238 A CN 114251238A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
Abstract
The disclosure provides a variable pitch motor temperature anomaly detection method and equipment. The method comprises the following steps: acquiring historical temperature data of each variable pitch motor and historical fan operation data related to the temperature of the variable pitch motor from SCADA historical operation data of the wind turbine generator; establishing a temperature prediction model of each variable pitch motor based on historical temperature data of each variable pitch motor and historical operating data of a fan related to the temperature of each variable pitch motor; determining a temperature abnormity early warning rule corresponding to each variable pitch motor by using the temperature prediction model of each variable pitch motor; inputting real-time running data of the fan related to the temperature of each variable pitch motor into a temperature prediction model of the corresponding variable pitch motor, and acquiring predicted temperature data of each variable pitch motor; and determining whether the temperature of each variable pitch motor is abnormal or not according to the predicted temperature data, the real-time temperature data and the temperature abnormity early warning rule of each variable pitch motor.
Description
Technical Field
The disclosure relates to the field of wind power generation, in particular to a method and equipment for detecting temperature abnormity of a variable pitch motor.
Background
The variable pitch system is one of key systems of a wind generating set (hereinafter referred to as a wind generating set or a fan), the core task of the variable pitch system is to capture wind energy, wind resources are used to the maximum extent, and the effect of the environment on the system is obvious. The variable pitch motor is a key driving component of a variable pitch system, and if the temperature of the variable pitch motor is abnormal, the wind turbine generator is stopped in a short time due to faults; the service life of the variable pitch motor can be shortened for a long time, the damage of the variable pitch motor is accelerated, and meanwhile, the variable pitch motor is predicted to run in a sick state, so that the stable running of the wind turbine generator is influenced.
Therefore, it is very important to accurately identify the temperature abnormality of the pitch motor and determine the pitch motor with the temperature abnormality.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide a method for detecting temperature abnormality of a pitch motor, a computer-readable storage medium, a control device, and a wind turbine generator, which are used for detecting temperature abnormality of each pitch motor and accurately positioning the pitch motor with abnormal temperature.
According to an embodiment of the disclosure, a method for detecting temperature abnormality of a pitch motor is provided, the method including: acquiring historical temperature data of each variable pitch motor and historical fan operation data related to the temperature of the variable pitch motor from SCADA historical operation data of the wind turbine generator; establishing a temperature prediction model of each variable pitch motor based on historical temperature data of each variable pitch motor and historical operating data of a fan related to the temperature of each variable pitch motor; determining a temperature abnormity early warning rule corresponding to each variable pitch motor by using the temperature prediction model of each variable pitch motor; inputting real-time running data of the fan related to the temperature of each variable pitch motor into a temperature prediction model of the corresponding variable pitch motor, and acquiring predicted temperature data of each variable pitch motor; and determining whether the temperature of each variable pitch motor is abnormal or not according to the predicted temperature data, the real-time temperature data and the temperature abnormity early warning rule of each variable pitch motor.
According to an embodiment of the disclosure, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, implements a pitch motor temperature anomaly detection method according to the disclosure.
According to an embodiment of the present disclosure, there is provided a control apparatus including: a processor; a memory storing a computer program that, when executed by the processor, implements a pitch motor temperature anomaly detection method according to the present disclosure.
According to an embodiment of the present disclosure, a wind turbine is provided, the wind turbine comprising a control device according to the present disclosure.
By adopting the variable pitch motor temperature anomaly detection method, the computer readable storage medium, the control device and the wind turbine generator according to the embodiment of the disclosure, at least one of the following technical effects can be realized: influence factors of the temperature of each variable pitch motor are fully considered, the early warning period of the abnormal temperature of the variable pitch motor is prolonged, the accurate positioning of the variable pitch motor with the abnormal temperature is facilitated, the operation and maintenance cost of the fan is effectively reduced, the abnormal temperature detection of the variable pitch motor is realized, the variable pitch motor is prevented from being broken down or further damaged, and the unstable operation condition of the fan is avoided.
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The above and other objects and features of the present disclosure will become more apparent from the following description when taken in conjunction with the accompanying drawings.
FIG. 1 is a flow chart of a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure;
FIG. 2 is another flow diagram of a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure;
FIG. 3 is another flow diagram of a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure;
FIG. 4 is another flow diagram of a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure;
FIG. 5 is a block diagram of a control device according to an embodiment of the present disclosure;
FIG. 6 is a block diagram of a control device according to another embodiment of the present disclosure;
fig. 7 is a field list according to an embodiment of the present disclosure.
Detailed Description
The variable pitch system is an important component of the wind turbine generator, and the variable pitch motor is a main driving component of the variable pitch system. The temperature of the variable-pitch motor is monitored, so that the problem of a variable-pitch system can be found in time, the fault shutdown loss of the wind turbine generator can be reduced, the safety accidents of the wind turbine generator caused by the abnormal variable-pitch system can be reduced, and the normal and stable operation of the wind turbine generator can be guaranteed.
The invention provides a method And equipment for detecting the temperature abnormality of a variable-pitch motor of a wind turbine generator, aiming at the problem of the temperature abnormality of the variable-pitch motor of the wind turbine generator, which can detect the temperature abnormality of the variable-pitch motor of the wind turbine generator based on SCADA (Supervisory Control And Data Acquisition) transient Data acquired by an SCADA (Supervisory Control And Data Acquisition) system. The method can combine the working principle and structure of a variable pitch motor of the wind turbine generator set to select proper SCADA transient data; acquiring required wind generating set operation data (for example, measuring point data can comprise generator rotating speed, active power, environment temperature, fan state, variable pitch angle, variable pitch speed, variable pitch motor temperature, time and the like); cleaning and preprocessing the acquired data, and screening SCADA data meeting certain working condition conditions; constructing derivative variables related to the temperature of the variable pitch motor, and establishing a variable pitch motor temperature prediction model based on an XGboost algorithm; and designing a model evaluation index and a threshold value thereof based on the standard such as mean square error or root mean square error, thereby effectively monitoring the temperature abnormality of the variable pitch motor.
According to the technical scheme provided by the invention, the temperature influence factor of the variable-pitch motor of the wind turbine generator is considered, and the abnormal variable-pitch motor temperature detection method and the abnormal variable-pitch motor temperature detection equipment are designed, so that the operation and maintenance cost of the fan can be effectively reduced, the abnormal variable-pitch motor temperature detection is realized, the fault or further damage of the variable-pitch motor is prevented, the abnormal variable-pitch motor temperature is accurately positioned, the service life of the variable-pitch motor is prolonged, and the stable operation of the wind turbine generator is ensured.
The following description of specific embodiments is provided in connection with the accompanying drawings to assist the reader in obtaining a thorough understanding of the methods, apparatus, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and/or systems described herein will be apparent to those skilled in the art after reviewing the disclosure of the present application. For example, the order of operations described herein is merely an example, and is not limited to those set forth herein, but may be changed as will become apparent after understanding the disclosure of the present application, except to the extent that operations must occur in a particular order. Moreover, descriptions of features known in the art may be omitted for clarity and conciseness.
The features described herein may be embodied in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein have been provided to illustrate only some of the many possible ways to implement the methods, apparatus and/or systems described herein, which will be apparent after understanding the disclosure of the present application.
As used herein, the term "and/or" includes any one of the associated listed items and any combination of any two or more.
Although terms such as "first", "second", and "third" may be used herein to describe various elements, components, regions, layers or sections, these elements, components, regions, layers or sections should not be limited by these terms. Rather, these terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section referred to in the examples described herein could also be referred to as a second element, component, region, layer or section without departing from the teachings of the examples.
The terminology used herein is for the purpose of describing various examples only and is not intended to be limiting of the disclosure. The singular is also intended to include the plural unless the context clearly indicates otherwise. The terms "comprises," "comprising," and "having" specify the presence of stated features, quantities, operations, elements, components, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, quantities, operations, components, elements, and/or combinations thereof.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in general dictionaries) should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal sense.
Further, in the description of the examples, when it is considered that detailed description of well-known related structures or functions will cause a vague explanation of the present disclosure, such detailed description will be omitted.
FIG. 1 is a flow chart of a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure.
As shown in fig. 1, in step S11, historical temperature data of each pitch motor and historical operating data of a fan related to the temperature of the pitch motor are obtained from SCADA historical operating data of the wind turbine.
In the embodiment of the present disclosure, the wind turbine may be one or more wind turbines of the same model located in the same wind farm, or wind turbines of different models in multiple wind farms, and for the latter two, when performing data processing, the operation data of the wind turbines of the same model located in the same wind farm needs to be merged. Specifically, SCADA historical operating data of the wind turbine generator can be obtained through a wind power plant or an SCADA system of the wind turbine generator. Determining temperature influence factors of the variable pitch motor based on the working principle of the variable pitch motor and the structure of the wind turbine generator; reading SCADA historical operating data of the wind turbine generator, and performing cleaning and preprocessing operations on the read SCADA historical operating data to obtain historical temperature data of each variable pitch motor and historical operating data of a fan related to the temperature of the variable pitch motor. In this way, derived variables, such as the time of continuous pitching of individual blades of the wind turbine, the month of the present year, the hour of the present day, etc., may be determined based on historical operating data of the wind turbine related to the temperature of the pitch motor. A variable pitch motor temperature prediction model based on XGboost can be constructed for each variable pitch motor by utilizing derivative variables (for example, for three variable pitch motors of one or one wind turbine generator, at least three variable pitch motor temperature prediction models exist); in the model testing process after the model training, an evaluation index threshold (such as a root mean square error threshold or a mean square error threshold) between a predicted value and an actual value in the wind power plant can be counted; in the model application process, an evaluation index (for example, root mean square error or mean square error) of the predicted value and the actual value can be counted and compared with an evaluation index threshold value, so that whether the temperature of each variable pitch motor is abnormal or not can be judged.
FIG. 7 shows a field list corresponding to SCADA historical operating data according to an embodiment of the disclosure. In the example shown in FIG. 7, the historical SCADA operation data includes historical operation data of three pitch motors. As shown in FIG. 7, the "common" field indicates that the variable is required for the temperature prediction model for all three pitch motors, and the "independent" field indicates that each pitch motor can only use the corresponding field. In addition, if a pitch motor current field and other temperature influence factor fields which are not shown in fig. 7 exist in the SCADA historical operation data, the pitch motor current field and the other temperature influence factor fields can also be used as input variables of the temperature prediction model. The variable fields shown in fig. 7 are merely examples, but the present disclosure is not limited thereto. The specific parameters can be adjusted according to the actual wind turbine project.
In embodiments of the present disclosure, historical operating data of the wind turbine related to the temperature of the pitch motor may include: time factor data and/or non-time factor data affecting the temperature of the pitch motor.
The non-time factor data may include at least one of: the method comprises the following steps of generator rotating speed, active power, environment temperature, unit operation state data, variable pitch angle and variable pitch speed of the wind turbine generator. Further, the time factor data may include at least one of: the continuous pitch control time of each blade, the month of the pitch control motor, and the time of the pitch control motor in the day of operation.
How to obtain the SCADA historical operation data of the wind turbine is described below with reference to FIG. 2. FIG. 2 is another flow chart of a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure.
In step S21, SCADA historical operating data for each wind turbine in one or more wind farms may be obtained. For example, data reading is performed: and reading the SCADA historical operation data of each wind turbine generator according to a field list shown in FIG. 7. Because a large amount of sample data is needed in the training process of the temperature prediction model, the data reading period can be determined according to the number of fans of the same type in the wind field, and the number of the fans and the data reading period generally need to meet the conditions: the number of all fans × the data reading period of each fan > -a predetermined time (for example, 1800 days) for which the data amount is guaranteed to be sufficient. In the application process of the temperature prediction model, the data reading period of a single fan also meets a preset period, (for example, 3 days).
After obtaining the SCADA historical operating data, the data may be purged. For example, the rows containing the null values may be deleted, all columns with null values may be deleted, and the temperature data for which the pitch motor temperature is 180 ℃ and-50 ℃ may be screened.
After the data cleansing is completed, the data may be pre-processed. For example, data with variable name "time" may be converted from string format to datetime format, and other data may be converted from string format to float format.
In addition, time factor data influencing the temperature of the variable pitch motor needs to be added. For example, the month and hour of time may be obtained from the obtained SCADA historical operating data or the preprocessed data. For example, continuous pitch time over a period of statistical data. The pitch speed can be all equal to a preset speed threshold value in the continuous pitch moment, the time is accumulated, and if the pitch speed is not continuous, the statistical calculation is started from 0 again. For other variables, the month and hour of the day of each data item can also be used as input variables for the temperature prediction model, considering that the temperature has a large relationship with the month and hour of the day. After all statistics are calculated, the "time" can be set as an index, the "time" data is deleted, and the deleted data is sorted.
After the above data processing is completed, the data may be merged or spliced.
In step S22, the SCADA historical operation data of the wind turbines of the same model in the same wind farm are merged. In the sample data preparation process of the temperature prediction model training, all fan data under the same model (the same logic model identification ID or the same model) of the same wind field need to be spliced or combined. Then, the data of the middle range (e.g., 75%) may be screened as a training set, for example, the maximum temperature values of the pitch motors may be obtained, and sorted according to an index corresponding to the maximum value of the maximum temperature values of the three pitch motors. In addition, data consolidation and screening may not be necessary during application of the temperature prediction model.
Referring again to fig. 1, in step S12, a temperature prediction model for each pitch motor is established based on historical temperature data for each pitch motor and historical operating data of the fan related to the temperature of the pitch motor.
According to the embodiment of the disclosure, for each variable pitch motor, historical temperature data of the variable pitch motor and historical operating data of a fan related to the temperature of the variable pitch motor can be used as output and input of an original temperature prediction model of the variable pitch motor respectively, and the original temperature prediction model of the variable pitch motor is trained to obtain a trained temperature prediction model corresponding to the variable pitch motor.
For example, the temperature prediction model may be trained offline and model tested. In the off-line training model process, a variable pitch motor temperature prediction model based on the XGboost can be constructed, and the importance distribution of each input variable or output variable in the XGboost model can be realized. And training and testing after eliminating variables with lower importance, and then preferentially selecting a suitable variable pitch motor temperature prediction model based on XGboost after comparison effects.
Referring to fig. 1 again, in step S13, a temperature abnormality warning rule corresponding to each pitch motor is determined by using the temperature prediction model of each pitch motor.
According to the embodiment of the disclosure, after the training and testing of the temperature prediction model are completed, the temperature abnormity early warning rule can be set for each variable pitch motor according to a large number of model test results.
FIG. 3 is another flow chart of a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure.
In step S31, historical operating data of the fan of each pitch motor, which is related to the temperature of the pitch motor, is input to the trained temperature prediction model corresponding to the pitch motor, so as to output test temperature data corresponding to the pitch motor. In the model testing process, the trained variable pitch motor temperature prediction model can be tested by utilizing the historical operating data of the fan related to the temperature of each variable pitch motor, and the testing temperature data output by the trained variable pitch motor temperature prediction model is obtained.
In step S32, a temperature anomaly early warning rule corresponding to each pitch motor is determined according to the test temperature data and the historical temperature data of each pitch motor. For example, the temperature anomaly warning rules may include: a mean square error between the predicted temperature data and the actual temperature data is greater than or equal to a first threshold; and/or the root mean square error between the predicted temperature data and the actual temperature data is greater than or equal to a second threshold. According to an embodiment of the present disclosure, the first threshold may be determined according to a mean square error between the historical temperature data and the test temperature data. Further, the second threshold may be determined based on a root mean square error between the historical temperature data and the test temperature data.
Referring to fig. 1 again, in step S14, the real-time operating data of the fan related to the temperature of each pitch motor is input to the temperature prediction model of the corresponding pitch motor, and the predicted temperature data of each pitch motor is obtained. By collecting the real-time operation data of the fan related to the temperature of each variable pitch motor, the corresponding predicted temperature data can be obtained by using the temperature prediction model aiming at each variable pitch motor so as to predict the temperature abnormity in real time.
In step S15, it is determined whether each pitch motor is abnormal in temperature according to the predicted temperature data, the real-time temperature data, and the temperature abnormality warning rule of each pitch motor. For example, in response to the predicted temperature data and the real-time temperature data of any one or more pitch motors meeting the temperature anomaly early warning rule, the corresponding pitch motor is determined to have temperature anomaly.
FIG. 4 is another flow chart of a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure.
In step S41, it is determined whether the predicted temperature data and the real-time temperature data of any one or more pitch motors satisfy the temperature anomaly early warning rule.
And in response to the fact that the predicted temperature data and the real-time temperature data of any one or more pitch motors meet the temperature abnormity early warning rule, determining that the corresponding pitch motor has temperature abnormity (step S42). And responding to the condition that the predicted temperature data and the real-time temperature data of no variable pitch motor meet the temperature abnormity early warning rule, and determining that the temperature of all variable pitch motors is normal (step S43). In addition, the temperature anomaly detection result can be displayed to field workers through a display device, or the detection result notification or temperature anomaly early warning is carried out through other audio or video equipment.
Therefore, real-time and accurate temperature abnormity detection can be carried out on each variable pitch motor, and the variable pitch motor with abnormal temperature can be accurately positioned. The method for detecting the temperature abnormality of the variable pitch motor according to the embodiment of the disclosure can be applied to variable pitch motors of various types of wind turbine generators (for example, megawatt wind turbine generators).
According to an embodiment of the present disclosure, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed, implements a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure.
In an embodiment of the disclosure, the computer readable storage medium may carry one or more programs which, when executed, may implement the following steps described with reference to fig. 1-4: acquiring historical temperature data of each variable pitch motor and historical fan operation data related to the temperature of the variable pitch motor from SCADA historical operation data of the wind turbine generator; establishing a temperature prediction model of each variable pitch motor based on historical temperature data of each variable pitch motor and historical operating data of a fan related to the temperature of each variable pitch motor; determining a temperature abnormity early warning rule corresponding to each variable pitch motor by using the temperature prediction model of each variable pitch motor; inputting real-time running data of the fan related to the temperature of each variable pitch motor into a temperature prediction model of the corresponding variable pitch motor, and acquiring predicted temperature data of each variable pitch motor; and determining whether the temperature of each variable pitch motor is abnormal or not according to the predicted temperature data, the real-time temperature data and the temperature abnormity early warning rule of each variable pitch motor.
A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing. The computer readable storage medium may be embodied in any device; it may also be present separately and not assembled into the device.
Fig. 5 is a block diagram of the control apparatus 5 according to an embodiment of the present disclosure.
Referring to fig. 5, the control apparatus 5 according to an embodiment of the present disclosure may include a memory 51 and a processor 52, the memory 51 having stored thereon a computer program 53, the computer program 53, when executed by the processor 52, implementing a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure.
In an embodiment of the present disclosure, when the computer program 53 is executed by the processor 52, the operations of the pitch motor temperature anomaly detection method described with reference to fig. 1 to 4 may be implemented: acquiring historical temperature data of each variable pitch motor and historical fan operation data related to the temperature of the variable pitch motor from SCADA historical operation data of the wind turbine generator; establishing a temperature prediction model of each variable pitch motor based on historical temperature data of each variable pitch motor and historical operating data of a fan related to the temperature of each variable pitch motor; determining a temperature abnormity early warning rule corresponding to each variable pitch motor by using the temperature prediction model of each variable pitch motor; inputting real-time running data of the fan related to the temperature of each variable pitch motor into a temperature prediction model of the corresponding variable pitch motor, and acquiring predicted temperature data of each variable pitch motor; and determining whether the temperature of each variable pitch motor is abnormal or not according to the predicted temperature data, the real-time temperature data and the temperature abnormity early warning rule of each variable pitch motor.
The control apparatus shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
The present disclosure also provides a wind turbine, where the wind turbine includes a control device according to an embodiment of the present disclosure, and the method for detecting temperature abnormality of a pitch motor as described above may be executed.
The method for detecting temperature abnormality of the pitch motor, the computer-readable storage medium, the control device, and the wind turbine generator according to the embodiment of the present disclosure have been described above with reference to fig. 1 to 5. However, it should be understood that: the control apparatus shown in fig. 5 is not limited to including the above-shown components, but some components may be added or deleted as needed, and the above components may also be combined.
Fig. 6 is a block diagram of a control device according to another embodiment of the present disclosure.
As shown in fig. 6, the control device may include a processor 62 and a display screen 63. Processor 62 may include a data input module 621, a data cleaning and preprocessing module 622, a feature extraction and model building module 623, and a pitch motor temperature anomaly detection module 624. The display screen 63 may include a results display module 631.
The data input module 621 can obtain the SCADA historical operation data and the SCADA real-time operation data of the wind turbine generator from the SCADA data source. The data washing and preprocessing module 622 can wash and preprocess the data acquired by the data input module 621. The cleaned and preprocessed data may be provided to a feature extraction and model building module 623 for use in building a temperature prediction model for each pitch motor.
The pitch motor temperature anomaly detection module 624 can obtain predicted temperature data of each pitch motor based on real-time running data of a fan related to the temperature of each pitch motor by using a temperature prediction model of each pitch motor, and then determine whether each pitch motor has temperature anomaly according to the predicted temperature data, the real-time temperature data and a temperature anomaly early warning rule of each pitch motor.
The pitch motor temperature anomaly detection module 624 may provide the pitch motor temperature anomaly detection result to the result display module 631 so that the detection result is displayed by the result display module 631.
The steps or operations corresponding to the modules in the processor 62 and the display screen 63 are described above with reference to fig. 1 to 4, and are not described again here for brevity, and the operations of the modules may be understood with reference to the steps in the above-described pitch motor temperature abnormality detection method.
By adopting the variable pitch motor temperature anomaly detection method, the computer readable storage medium, the control device and the wind turbine generator according to the embodiment of the disclosure, at least one of the following technical effects can be realized: the influence factors of the temperature of each variable pitch motor are fully considered, the early warning period of the abnormal temperature of the variable pitch motor is prolonged, the variable pitch motor with the abnormal temperature is favorably and accurately positioned, the operation and maintenance cost of the fan is effectively reduced, the abnormal temperature detection of the variable pitch motor is realized, the variable pitch motor is prevented from being broken down or further damaged, the variable pitch motor with the abnormal temperature is accurately positioned, the service life of the variable pitch motor is prolonged, and the stable operation of the wind turbine generator is ensured.
The control logic or functions performed by the various components or controllers in the control system may be represented by flowcharts or the like in one or more of the figures. These figures provide representative control strategies and/or logic that may be implemented using one or more processing strategies (e.g., event-driven, interrupt-driven, multi-tasking, multi-threading, and so forth). As such, various steps or functions illustrated may be performed in the sequence illustrated, in parallel, or in some cases omitted. Although not always explicitly illustrated, one of ordinary skill in the art will recognize that one or more of the illustrated steps or functions may be repeatedly performed depending on the particular processing strategy being used.
While the disclosure has been shown and described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made to these embodiments without departing from the spirit and scope of the disclosure as defined by the claims.
Claims (13)
1. A method for detecting temperature abnormality of a variable pitch motor is characterized by comprising the following steps:
acquiring historical temperature data of each variable pitch motor and historical fan operation data related to the temperature of the variable pitch motor from SCADA historical operation data of the wind turbine generator;
establishing a temperature prediction model of each variable pitch motor based on historical temperature data of each variable pitch motor and historical operating data of a fan related to the temperature of each variable pitch motor;
determining a temperature abnormity early warning rule corresponding to each variable pitch motor by using the temperature prediction model of each variable pitch motor;
inputting real-time running data of the fan related to the temperature of each variable pitch motor into a temperature prediction model of the corresponding variable pitch motor, and acquiring predicted temperature data of each variable pitch motor;
and determining whether the temperature of each variable pitch motor is abnormal or not according to the predicted temperature data, the real-time temperature data and the temperature abnormity early warning rule of each variable pitch motor.
2. The method of claim 1, wherein historical wind turbine operating data related to pitch motor temperature comprises: time factor data and/or non-time factor data affecting the temperature of the pitch motor.
3. The method of claim 2,
the non-time factor data includes at least one of: the method comprises the following steps of (1) rotating speed, active power, ambient temperature, unit operation state data, variable pitch angle and variable pitch speed of a generator of a wind turbine generator; and/or
The time factor data includes at least one of: the continuous pitch control time of each blade, the month of the pitch control motor, and the time of the pitch control motor in the day of operation.
4. The method according to claim 1, characterized in that the wind turbines are one or more wind turbines of the same model in the same wind farm.
5. The method of claim 4, wherein obtaining SCADA historical operating data for the wind turbine comprises the steps of:
obtaining SCADA historical operation data of each wind turbine generator in one or more wind power plants;
and combining the SCADA historical operating data of the wind turbines of the same type in the same wind power plant.
6. The method of claim 1, wherein establishing a temperature prediction model for each pitch motor based on historical temperature data for each pitch motor and historical operating data for the wind turbine associated with the pitch motor temperature comprises:
and aiming at each variable pitch motor, respectively taking historical temperature data of the variable pitch motor and historical operating data of a fan related to the temperature of the variable pitch motor as the output and the input of an original temperature prediction model of the variable pitch motor, and training the original temperature prediction model of the variable pitch motor to obtain a trained temperature prediction model of the corresponding variable pitch motor.
7. The method according to any one of claims 1 to 6, wherein the determining the temperature anomaly early warning rule corresponding to the variable pitch motor by using the temperature prediction model of each variable pitch motor comprises:
inputting historical operating data of the fan of each variable pitch motor, which is related to the temperature of the variable pitch motor, into a trained temperature prediction model corresponding to the variable pitch motor so as to output test temperature data of the corresponding variable pitch motor;
and determining a temperature abnormity early warning rule corresponding to each variable pitch motor according to the test temperature data and the historical temperature data of each variable pitch motor.
8. The method of claim 7, wherein the temperature anomaly warning rules comprise:
a mean square error between the predicted temperature data and the actual temperature data is greater than or equal to a first threshold; and/or
The root mean square error between the predicted temperature data and the actual temperature data is greater than or equal to a second threshold.
9. The method of claim 8,
the first threshold is determined based on a mean square error between the historical temperature data and the test temperature data, and/or
The second threshold is determined based on a root mean square error between the historical temperature data and the test temperature data.
10. The method of claim 1, wherein determining whether the temperature of each variable pitch motor is abnormal according to the predicted temperature data, the real-time temperature data and the temperature abnormality early warning rule of each variable pitch motor comprises:
and determining that the temperature abnormality of the corresponding variable pitch motor occurs in response to the fact that the predicted temperature data and the real-time temperature data of any one or more variable pitch motors meet the temperature abnormality early warning rule.
11. A computer-readable storage medium having a computer program stored thereon, which, when being executed by a processor, carries out a pitch motor temperature anomaly detection method according to any one of claims 1 to 10.
12. A control apparatus, characterized in that the control apparatus comprises:
a processor;
a memory storing a computer program which, when executed by the processor, implements a pitch motor temperature anomaly detection method according to any one of claims 1 to 10.
13. Wind power plant, characterized in that it comprises a control device according to claim 12.
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