CN114251238B - Method and equipment for detecting abnormal temperature of variable pitch motor - Google Patents
Method and equipment for detecting abnormal temperature of variable pitch motor Download PDFInfo
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- CN114251238B CN114251238B CN202111443199.1A CN202111443199A CN114251238B CN 114251238 B CN114251238 B CN 114251238B CN 202111443199 A CN202111443199 A CN 202111443199A CN 114251238 B CN114251238 B CN 114251238B
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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
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Abstract
The disclosure provides a method and equipment for detecting temperature abnormality of a variable pitch motor. The method comprises the following steps: acquiring historical temperature data of each variable pitch motor and fan historical operation data related to the temperature of the variable pitch motor from SCADA historical operation data of the wind turbine; based on historical temperature data of each variable-pitch motor and historical operation data of a fan related to the temperature of the variable-pitch motor, a temperature prediction model of each variable-pitch motor is established; determining a temperature abnormality early warning rule of the variable-pitch motors by using a temperature prediction model of each variable-pitch motor; inputting real-time running data of fans related to the temperature of each variable-pitch motor into a temperature prediction model of the variable-pitch motor, and obtaining predicted temperature data of each variable-pitch motor; and 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.
Description
Technical Field
The disclosure relates to the field of wind power generation, in particular to a method and equipment for detecting temperature abnormality of a variable pitch motor.
Background
The pitch system is used as one of key systems of a wind generating set (hereinafter referred to as a wind generating set or a fan for short), has the core task of capturing wind energy, maximally uses wind resources, and has more remarkable effect on the system by the environment. The pitch motor is a key driving component of a pitch system, and if the temperature of the pitch motor is abnormal, the wind turbine can be stopped in a short period; the service life of the variable-pitch motor is shortened in a long period, so that the variable-pitch motor is damaged during acceleration, and 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 important to accurately identify the temperature abnormality of the pitch motor and determine the temperature abnormality of the pitch motor.
Disclosure of Invention
An embodiment of the disclosure aims to provide a temperature anomaly detection method for a pitch motor, a computer-readable storage medium, control equipment and a wind turbine generator, which are used for detecting temperature anomalies for each pitch motor and accurately positioning the pitch motor with the temperature anomalies.
According to an embodiment of the present disclosure, there is provided a method for detecting a temperature abnormality of a pitch motor, the method including: acquiring historical temperature data of each variable pitch motor and fan historical operation data related to the temperature of the variable pitch motor from SCADA historical operation data of the wind turbine; based on historical temperature data of each variable-pitch motor and historical operation data of a fan related to the temperature of the variable-pitch motor, a temperature prediction model of each variable-pitch motor is established; determining a temperature abnormality early warning rule of the variable-pitch motors by using a temperature prediction model of each variable-pitch motor; inputting real-time running data of fans related to the temperature of each variable-pitch motor into a temperature prediction model of the variable-pitch motor, and obtaining predicted temperature data of each variable-pitch motor; and 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.
According to an embodiment of the present 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 present disclosure.
According to an embodiment of the present disclosure, there is provided a control apparatus including: a processor; and a memory storing a computer program which, when executed by the processor, implements the pitch motor temperature anomaly detection method according to the present disclosure.
According to an embodiment of the present disclosure, a wind turbine is provided, 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 equipment and the wind turbine generator according to the embodiment of the disclosure, at least one of the following technical effects can be achieved: the influence factors of the temperature of each variable pitch motor are fully considered, the early warning period of the temperature abnormality of the variable pitch motor is prolonged, the variable pitch motor with abnormal temperature is accurately positioned, the operation and maintenance cost of a fan is effectively reduced, the abnormal temperature detection of the variable pitch motor is realized, the variable pitch motor is prevented from being failed or further damaged, and the unstable running condition of the fan is avoided.
Drawings
The foregoing and other objects and features of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings.
FIG. 1 is a flow chart of a method of detecting a temperature anomaly of a pitch motor according to an embodiment of the present disclosure;
FIG. 2 is another flow chart of a method of detecting a temperature anomaly of a pitch motor according to an embodiment of the present disclosure;
FIG. 3 is another flow chart of a method of detecting a temperature anomaly of a pitch motor according to an embodiment of the present disclosure;
FIG. 4 is another flow chart of a method of detecting a temperature anomaly of a pitch motor 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 pitch system is an important component of the wind turbine, and the pitch motor is a main driving component of the 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 failure shutdown loss of the wind turbine can be reduced, the safety accidents caused by the abnormality of the variable-pitch system of the wind turbine can be reduced, and the normal and stable operation of the wind turbine can be guaranteed.
Aiming at the problem of abnormal temperature of a variable-pitch motor of a wind turbine, the invention provides a method and equipment for detecting abnormal temperature of the variable-pitch motor, which can detect the abnormal temperature of the variable-pitch motor of the wind turbine 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 the variable pitch motor of the wind turbine, and select proper SCADA transient data; collecting required wind generating set operation data (for example, measuring point data can comprise generator rotating speed, active power, environment temperature, fan state, pitch angle, pitch speed, pitch motor temperature, time and the like); cleaning and preprocessing the collected data, and screening SCADA data meeting certain working conditions; constructing derivative variables related to the temperature of the variable pitch motor, and establishing a variable pitch motor temperature prediction model based on XGBoost algorithm; model evaluation indexes based on the standards of mean square error or root mean square error and the like and threshold values thereof are designed, so that effective monitoring of abnormal temperature of the variable-pitch motor is realized.
According to the technical scheme provided by the invention, temperature influence factors of the variable-pitch motor of the wind turbine are considered, and the variable-pitch motor temperature abnormality detection method and device are designed, so that the operation and maintenance cost of a fan can be effectively reduced, the temperature abnormality detection of the variable-pitch motor is realized, the variable-pitch motor is prevented from being failed or further damaged, the variable-pitch motor with abnormal temperature is accurately positioned, the service life of the variable-pitch motor is prolonged, and the stable operation of the wind turbine is ensured.
The following description of specific embodiments is provided in connection with the accompanying drawings to assist the reader in a comprehensive understanding of the methods, apparatus, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, devices, and/or systems described herein will be apparent after an understanding of the present disclosure. For example, the order of operations described herein is merely an example and is not limited to those set forth herein, but may be altered as will be apparent after an understanding of the disclosure of the application, except for operations that must occur in a specific order. Furthermore, 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, devices, and/or systems described herein, which many possible ways will be clear after an understanding of the present disclosure.
As used herein, the term "and/or" includes any one of the listed items associated as well as 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 member, first component, first region, first layer, or first portion referred to in the examples described herein may also be referred to as a second member, second component, second region, second layer, or second portion 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. Singular forms also are intended to include plural forms unless the context clearly indicates otherwise. The terms "comprises," "comprising," and "having" specify the presence of stated features, amounts, operations, components, elements, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, amounts, operations, components, elements, and/or combinations thereof.
Unless defined otherwise, 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 this disclosure. Unless explicitly so defined herein, terms (such as those defined in a general dictionary) should be construed to have meanings consistent with their meanings in the context of the relevant art and the present disclosure, and should not be interpreted idealized or overly formal.
In addition, in the description of the examples, when it is considered that detailed descriptions of well-known related structures or functions will cause a ambiguous explanation of the present disclosure, such detailed descriptions will be omitted.
Fig. 1 is a flowchart of a method of detecting a temperature anomaly of a pitch motor according to an embodiment of the present disclosure.
As shown in fig. 1, in step S11, from the SCADA historical operation data of the wind turbine generator, historical temperature data of each pitch motor and fan historical operation data related to the pitch motor temperature are obtained.
In the embodiment of the disclosure, the wind turbine may be one or more wind turbines in the same wind farm and the same model, or wind turbines in different models in the same wind farm, or wind turbines in different models in multiple wind farms, and for the latter two, when data processing is performed, operation data of wind turbines in the same model in the same wind farm need to be combined. Specifically, SCADA historical operation data of the wind turbine can be obtained through a wind power plant or an SCADA system of the wind turbine. The temperature influence factor of the variable pitch motor can be determined based on the working principle of the variable pitch motor and the structure of the wind turbine generator; and (3) reading SCADA historical operation data of the wind turbine, and cleaning and preprocessing the read SCADA historical operation data to acquire historical temperature data of each variable-pitch motor and fan historical operation data related to the temperature of the variable-pitch motor. In this manner, derivative variables such as, for example, the continuous pitching time of each blade of the wind turbine, the month of the year, the hour of the day, etc., may be determined based on historical operational data of the wind turbine associated with the temperature of the pitch motor. A XGBoost-based pitch motor temperature prediction model (e.g., three pitch motors for one or one wind turbine, there are at least three pitch motor temperature prediction models) may be built for each pitch motor using derivative variables; during the model test after model training, an evaluation index threshold (e.g., a root mean square error threshold or a mean square error threshold) between the predicted value and the actual value in the wind farm may 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 illustrates a field list corresponding to SCADA history operation data according to an embodiment of the present disclosure. In the example shown in fig. 7, the SCADA historical operating data includes historical operating data for three pitch motors. As shown in fig. 7, the "common" field indicates that the variable is required for the temperature prediction model of all three pitch motors, and the "independent" field indicates that each pitch motor can only use the corresponding field. In addition, if other temperature influencing factor fields, such as a current field of the variable pitch motor, which are not shown in fig. 7, exist in the SCADA historical operation data, the current field can also be used as an input variable of the temperature prediction model. The variable fields shown in fig. 7 are merely examples, and the present disclosure is not limited thereto. The specific parameters can be adjusted according to actual wind turbine generator projects.
In embodiments of the present disclosure, the fan historical operating data related to the pitch motor temperature 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: generator rotating speed, active power, environment temperature, unit running 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 time of each blade, the month of the operation of the pitch motor and the moment of the current day of the operation of the pitch motor.
How the SCADA history operation data of the wind turbine is obtained is described below with reference to FIG. 2. Fig. 2 is another flowchart of a method of detecting a temperature anomaly of a pitch motor according to an embodiment of the present disclosure.
In step S21, SCADA historical operational data for each wind turbine in one or more wind farms may be obtained. For example, data reading is performed: and reading SCADA historical operation data of each wind turbine according to a field list shown in FIG. 7. Because a large amount of sample data is required 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 general fan number and the data reading period need to satisfy the conditions: all fans number x average data read period per fan > = guaranteed data amount for a predetermined time (e.g., 1800 days). In the application process of the temperature prediction model, the data reading period of a single fan also needs to meet a predetermined period (for example, 3 days).
After the SCADA history operation data is obtained, the data can be cleaned. For example, rows containing null values may be deleted, columns with all null values deleted, and temperature data of the pitch motor temperature < = 180 ℃ and > = -50 ℃ are screened out.
After the data cleansing is completed, the data may be preprocessed. For example, data with variable name "rectime" may be converted from string format to datetime format, and other data from string format to float format.
In addition, time factor data affecting the temperature of the pitch motor is also added. For example, the month and hour of time may be obtained from the obtained SCADA history operation data or the preprocessed data. For example, a continuous pitch time over a range of statistical data periods. The time may be accumulated for a duration of continuous pitch time where pitch speed > = predetermined speed threshold, and if not continuous, the statistical calculation is restarted from 0. For other variables, the month and hour of the day of each piece of data can be used as input variables of the temperature prediction model, considering that the temperature is greatly related to the month and hour of the day. After all statistics are calculated, "rectime" may be set as an index, the "rectime" data may be deleted, and the deleted data may be sorted.
After the data processing is completed, the data can be combined or spliced.
In step S22, the SCADA historical operation data of the wind turbines of the same model in the same wind farm are combined. In the sample data preparation process of temperature prediction model training, all fan data under the same wind field and the same model (logic model identification ID or the same model) need to be spliced or combined. The data for the intermediate range (e.g., 75%) may then be screened as a training set, for example, the temperature maxima for each pitch motor may be obtained, sorted by index corresponding to the maximum of the temperature maxima for the three pitch motors. In addition, data merging and screening may not be necessary in the 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 for the fan associated with the pitch motor temperature.
According to the embodiment of the disclosure, for each pitch motor, the historical temperature data of the pitch motor and the historical operation data of the fan related to the temperature of the pitch motor can be used as the output and input of the original temperature prediction model of the pitch motor, and the original temperature prediction model of the pitch motor can be trained to obtain the trained temperature prediction model of the pitch motor.
For example, a temperature prediction model may be trained offline and model testing performed. In the offline training model process, a XGBoost-based pitch motor temperature prediction model may be constructed, and the importance distribution of each input variable or output variable in the XGBoost model may be based. Training and testing are carried out after variables with lower importance are removed, and then after the comparison effect is achieved, a proper variable pitch motor temperature prediction model based on XGBoost is selected preferentially.
Referring again to fig. 1, in step S13, a temperature abnormality warning rule for the variable-pitch motor is determined using a temperature prediction model for each variable-pitch motor.
According to the embodiment of the disclosure, after training and testing of the temperature prediction model are completed, a temperature anomaly early warning rule can be set for each pitch motor according to a large number of model test results.
Fig. 3 is another flowchart of a method of detecting a temperature anomaly of a pitch motor according to an embodiment of the present disclosure.
In step S31, fan history operation data of each variable pitch motor related to the variable pitch motor temperature is input to a trained temperature prediction model of the variable pitch motor to output test temperature data of the variable pitch motor. In the model test process, the trained variable-pitch motor temperature prediction model can be tested by utilizing historical operation data of fans related to the temperatures of the variable-pitch motors, and test temperature data output by the trained variable-pitch motor temperature prediction model can be obtained.
In step S32, a temperature anomaly warning rule for the variable-pitch motors is determined according to the test temperature data and the historical temperature data of each variable-pitch motor. For example, the temperature anomaly pre-warning rules may include: the 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 the second threshold. According to embodiments of the present disclosure, the first threshold may be determined from 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 again to fig. 1, in step S14, fan real-time operation data related to the temperature of each pitch motor is input to a temperature prediction model of the pitch motor, and predicted temperature data of each pitch motor is obtained. By collecting real-time operation data of fans related to the temperature of each variable-pitch motor, corresponding prediction temperature data can be obtained by using a temperature prediction model for each variable-pitch motor so as to perform real-time temperature anomaly prediction.
In step S15, it is determined whether a temperature abnormality occurs in each variable-pitch motor according to the predicted temperature data, the real-time temperature data, and the temperature abnormality warning rule of each variable-pitch motor. For example, in response to the predicted temperature data and the real-time temperature data of any one or more of the variable-pitch motors meeting the temperature abnormality pre-warning rule, it is determined that the corresponding variable-pitch motor is abnormal in temperature.
Fig. 4 is another flowchart of a method of detecting a temperature anomaly of a pitch motor 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 of the pitch motors satisfy the temperature abnormality warning rule.
And in response to the predicted temperature data and the real-time temperature data of any one or more of the variable-pitch motors meeting the temperature abnormality early warning rule, determining that the corresponding variable-pitch motor has temperature abnormality (step S42). And in response to the predicted temperature data and the real-time temperature data of the non-variable-pitch motors meeting the temperature abnormality pre-warning rule, determining that all the variable-pitch motors are normal in temperature (step S43). In addition, the temperature abnormality detection result can be displayed to site staff through a display device, or the detection result notification or the temperature abnormality early warning can be carried out through other audio or video equipment.
Therefore, real-time and accurate temperature abnormality detection can be performed on each variable-pitch motor, and the variable-pitch motor with abnormal temperature can be accurately positioned. The method for detecting 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 turbines (for example, megawatt wind turbines).
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 abnormality detection method according to an embodiment of the present disclosure.
In an embodiment of the present 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 to 4: acquiring historical temperature data of each variable pitch motor and fan historical operation data related to the temperature of the variable pitch motor from SCADA historical operation data of the wind turbine; based on historical temperature data of each variable-pitch motor and historical operation data of a fan related to the temperature of the variable-pitch motor, a temperature prediction model of each variable-pitch motor is established; determining a temperature abnormality early warning rule of the variable-pitch motors by using a temperature prediction model of each variable-pitch motor; inputting real-time running data of fans related to the temperature of each variable-pitch motor into a temperature prediction model of the variable-pitch motor, and obtaining predicted temperature data of each variable-pitch motor; and 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.
The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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. A computer program embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing. The computer readable storage medium may be embodied in any device; or may exist alone without being assembled into the device.
Fig. 5 is a block diagram of the control device 5 according to an embodiment of the present disclosure.
Referring to fig. 5, a control apparatus 5 according to an embodiment of the present disclosure may include a memory 51 and a processor 52, with a computer program 53 stored on the memory 51, which when executed by the processor 52, implements a pitch motor temperature anomaly detection method according to an embodiment of the present disclosure.
In the embodiment of the present disclosure, the operation of the pitch motor temperature abnormality detection method described with reference to fig. 1 to 4 may be implemented when the computer program 53 is executed by the processor 52: acquiring historical temperature data of each variable pitch motor and fan historical operation data related to the temperature of the variable pitch motor from SCADA historical operation data of the wind turbine; based on historical temperature data of each variable-pitch motor and historical operation data of a fan related to the temperature of the variable-pitch motor, a temperature prediction model of each variable-pitch motor is established; determining a temperature abnormality early warning rule of the variable-pitch motors by using a temperature prediction model of each variable-pitch motor; inputting real-time running data of fans related to the temperature of each variable-pitch motor into a temperature prediction model of the variable-pitch motor, and obtaining predicted temperature data of each variable-pitch motor; and 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.
The control device shown in fig. 5 is only one example, and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.
The disclosure also provides a wind turbine generator, which includes the control device according to the embodiments of the disclosure, and may perform the method for detecting temperature anomalies of a variable pitch motor as described above.
A pitch motor temperature abnormality detection method, a computer-readable storage medium, a control device, and a wind turbine generator according to embodiments 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 include the above-shown components, but some components may be added or deleted as necessary, 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 63. Processor 62 may include a data input module 621, a data cleaning and preprocessing module 622, a feature extraction and model construction 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 may obtain SCADA historical operation data and SCADA real-time operation data of the wind turbine from a SCADA data source. The data cleansing and preprocessing module 622 may cleansing and preprocessing the data acquired by the data input module 621. The cleaned and preprocessed data may be provided to a feature extraction and model construction module 623 for building a temperature prediction model of each pitch motor.
The temperature anomaly detection module 624 of each variable pitch motor may utilize a temperature prediction model of each variable pitch motor to obtain predicted temperature data of each variable pitch motor based on real-time operation data of a fan associated with the temperature of each variable pitch motor, and then determine whether each variable pitch motor has a temperature anomaly according to the predicted temperature data of each variable pitch motor, the real-time temperature data, and the temperature anomaly pre-warning rules.
Pitch motor temperature anomaly detection module 624 may provide a result of pitch motor temperature anomaly detection to result display module 631 to display the detection result via result display module 631.
The steps or operations corresponding to the respective modules in the processor 62 and the display 63 are described above with reference to fig. 1 to 4, and for brevity, the operations of the respective modules may be understood with reference to the respective steps in the above-described method for detecting temperature abnormality of a pitch motor.
By adopting the variable pitch motor temperature anomaly detection method, the computer-readable storage medium, the control equipment and the wind turbine generator according to the embodiment of the disclosure, at least one of the following technical effects can be achieved: the influence factors of the temperature of each variable pitch motor are fully considered, the early warning period of the temperature abnormality of the variable pitch motor is prolonged, the variable pitch motor with the abnormal temperature is accurately positioned, the operation and maintenance cost of a fan is effectively reduced, the temperature abnormality detection of the variable pitch motor is realized, the variable pitch motor is prevented from being failed 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 a 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 flow diagrams or similar illustrations in one or more figures. These figures provide representative control strategies and/or logic that may be implemented using one or more processing strategies such as event-driven, interrupt-driven, multi-tasking, multi-threading, and the like. Accordingly, various steps or functions illustrated may be performed in the order illustrated, in parallel, or in some cases omitted. Although not always explicitly shown, one of ordinary skill in the art will recognize that one or more of the steps or functions illustrated may be repeatedly performed depending on the particular processing strategy being used.
Although the present disclosure has been shown and described with respect to the preferred embodiments, it will be understood by those skilled in the art that various modifications and changes may be made to these embodiments without departing from the spirit and scope of the disclosure as defined by the appended claims.
Claims (11)
1. The method for detecting the temperature abnormality of the variable pitch motor is characterized by comprising the following steps of:
Acquiring historical temperature data of each variable pitch motor and fan historical operation data related to the temperature of the variable pitch motor from SCADA historical operation data of the wind turbine;
Based on historical temperature data of each variable-pitch motor and historical operation data of a fan related to the temperature of the variable-pitch motor, a temperature prediction model of each variable-pitch motor is established;
determining a temperature abnormality early warning rule of the variable-pitch motors by using a temperature prediction model of each variable-pitch motor;
Inputting real-time running data of fans related to the temperature of each variable-pitch motor into a temperature prediction model of the variable-pitch motor, and obtaining predicted temperature data of each variable-pitch motor;
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 pre-warning rule of each variable-pitch motor,
Wherein, fan historical operating data associated with pitch motor temperature includes: time factor data and/or non-time factor data affecting a temperature of the pitch motor, the time factor data comprising at least one of: the continuous pitch time of each blade, the month of the operation of the pitch motor and the moment of the current day of the operation of the pitch motor;
The method for determining the temperature abnormality early warning rule of the variable-pitch motors by using the temperature prediction model of each variable-pitch motor comprises the following steps:
the method comprises the steps of inputting historical operation data of fans of all variable-pitch motors, which are related to the temperature of the variable-pitch motors, to a trained temperature prediction model of the variable-pitch motors so as to output test temperature data of the variable-pitch motors;
and determining a temperature abnormality early warning rule for the variable-pitch motors according to the test temperature data and the historical temperature data of each variable-pitch motor.
2. The method of claim 1, wherein the non-time factor data comprises at least one of: generator rotating speed, active power, environment temperature, unit running state data, variable pitch angle and variable pitch speed of the wind turbine generator.
3. The method of claim 1, wherein the wind turbines are one or more wind turbines in the same wind farm and of the same model.
4. A method according to claim 3, wherein obtaining SCADA historical operational data of the wind turbine comprises the steps of:
Acquiring SCADA historical operation data of each wind turbine in one or more wind power plants;
and merging SCADA historical operation data of wind turbines of the same model in the same wind power plant.
5. The method of claim 1, wherein building a temperature prediction model for each pitch motor based on historical temperature data for each pitch motor and historical operating data for the fan associated with the pitch motor temperature comprises:
Aiming at each variable-pitch motor, the historical temperature data of the variable-pitch motor and the historical operation data of the fan related to the temperature of the variable-pitch motor are respectively used as the output and the input of an original temperature prediction model of the variable-pitch motor, and the original temperature prediction model of the variable-pitch motor is trained to obtain a trained temperature prediction model of the variable-pitch motor.
6. The method of claim 1, wherein the temperature anomaly pre-warning rule comprises:
the 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 the second threshold.
7. The method of claim 6, wherein the step of providing the first layer comprises,
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.
8. The method of claim 1, wherein determining whether a temperature anomaly has occurred for each pitch motor based on predicted temperature data, real-time temperature data, and temperature anomaly pre-warning rules for each pitch motor comprises:
And responding to the predicted temperature data and the real-time temperature data of any one or more variable-pitch motors to meet the temperature abnormality early warning rule, and determining that the corresponding variable-pitch motors are abnormal in temperature.
9. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the pitch motor temperature anomaly detection method according to any one of claims 1 to 8.
10. A control apparatus, characterized in that the control apparatus comprises:
A processor;
a memory storing a computer program which, when executed by a processor, implements the pitch motor temperature anomaly detection method according to any one of claims 1 to 8.
11. A wind power plant, characterized in that it comprises a control device according to claim 10.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105134510A (en) * | 2015-09-18 | 2015-12-09 | 北京中恒博瑞数字电力科技有限公司 | State monitoring and failure diagnosis method for wind generating set variable pitch system |
| CN112598148A (en) * | 2020-09-30 | 2021-04-02 | 新天绿色能源股份有限公司 | Fan variable pitch motor temperature fault early warning method based on collaborative expression and LightGBM algorithm |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8874276B2 (en) * | 2007-12-19 | 2014-10-28 | Vestas Wind Systems A/S | Event-based control system for wind turbine generators |
| WO2018228648A1 (en) * | 2017-06-14 | 2018-12-20 | Kk Wind Solutions A/S | Independent monitoring system for a wind turbine |
| CN109376426B (en) * | 2018-10-23 | 2023-04-07 | 株洲中车时代电气股份有限公司 | Wind power grid-connected power scheduling method and device |
| CN110414155B (en) * | 2019-07-31 | 2022-09-30 | 北京天泽智云科技有限公司 | Fan component temperature abnormity detection and alarm method with single measuring point |
| CN112613554B (en) * | 2020-12-21 | 2024-03-22 | 国家电投集团江苏新能源有限公司 | Wind driven generator variable pitch system fault prediction method and system |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105134510A (en) * | 2015-09-18 | 2015-12-09 | 北京中恒博瑞数字电力科技有限公司 | State monitoring and failure diagnosis method for wind generating set variable pitch system |
| CN112598148A (en) * | 2020-09-30 | 2021-04-02 | 新天绿色能源股份有限公司 | Fan variable pitch motor temperature fault early warning method based on collaborative expression and LightGBM algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 姚万业等.基于显著性差异分析的风机变桨电机故障预警.2016,第32卷(第2期),第50-54页. * |
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