CN108869174B - Nonlinear modeling wind driven generator blade natural frequency working condition compensation method - Google Patents
Nonlinear modeling wind driven generator blade natural frequency working condition compensation method Download PDFInfo
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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
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- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
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Abstract
The invention discloses a wind power blade natural frequency working condition compensation method based on nonlinear modeling, which comprises the following steps of: 1) dividing the historical operation data set into a plurality of modeling data subsets according to the power P of the SCADA of the wind driven generator, and then dividing each modeling data subset intoModeling a training data set and a model testing data set; 2) normalizing the elements in each modeling data subset; 3) constructing a corresponding observation memory matrix D according to the normalized modeling training data set, and then testing a data set X according to the constructed observation memory matrix D and a corresponding modeltestConstructing a corresponding natural frequency prediction model; 4) and substituting the working condition parameters of the current unit into the corresponding natural frequency prediction model, performing subtraction operation on the actually measured current fixed frequency and the predicted current fixed frequency, and finally performing working condition compensation on the natural frequency of the blade by using the fixed frequency deviation value. The method can accurately realize the working condition compensation of the fixed frequency of the wind driven generator blade.
Description
Technical Field
The invention belongs to the field of natural frequency working condition compensation of a wind driven generator blade, and relates to a nonlinear modeling wind power blade natural frequency working condition compensation method.
Background
At present, the wind power generation industry in China is in a high-speed development period, and the number of wind power generators is increased year by year. The blades of the wind driven generator are key components in the unit, and the structural health of the blades is vital to the safe operation of the unit. Therefore, the online monitoring of the state of the wind driven generator blade has important engineering practical value. The natural frequency of the blade represents the characteristic of a blade structure, when the blade is damaged, such as cracks, the rigidity of the blade is reduced, and the natural frequency of the blade is reduced, so that the existing technology for monitoring the health state of the blade based on the natural frequency deviation of the blade is widely concerned by researchers at home and abroad, but the natural frequency of the blade is influenced by the operation conditions of a unit, such as wind speed, rotating speed, power, pitch angle, temperature and the like, and even in a normal health state, the natural frequency of the blade is in change, so that the health state of the blade cannot be evaluated by directly utilizing the actually measured natural frequency. Therefore, the problem of working condition compensation of the natural frequency of the blade needs to be solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a nonlinear modeling wind power blade natural frequency working condition compensation method which can realize the working condition compensation of the natural frequency of a wind driven generator blade.
In order to achieve the aim, the wind power blade natural frequency working condition compensation method based on the nonlinear modeling comprises the following steps:
1) selecting a historical operation data set when the wind turbine generator normally operates, dividing the historical operation data set into a plurality of modeling data subsets according to the power P of a SCADA (supervisory control and data acquisition) system of the wind turbine generator, and then dividing each modeling data subset into a modeling training data set and a model testing data set respectively;
2) normalizing the elements in each modeling data subset;
3) constructing a corresponding observation memory matrix D according to the normalized modeling training data set to construct an inherent frequency prediction model, and then utilizing the constructed observation memory matrix D and a corresponding model test data set X thereoftestTesting and verifying the natural frequency prediction model, wherein one modeling data subset corresponds to one natural frequency prediction model;
4) the method comprises the steps of obtaining the power of a current wind driven generator data acquisition and monitoring system SCADA and unit working condition parameters, searching a corresponding natural frequency prediction model according to the power of the current wind driven generator data acquisition and monitoring system SCADA, substituting the working condition parameters of the current unit into the corresponding natural frequency prediction model to obtain a predicted current fixed frequency, carrying out subtraction operation on the actually measured current fixed frequency and the predicted current fixed frequency, taking an operation result as a fixed frequency deviation value, and finally completing working condition compensation of the natural frequency of the wind driven generator blade by using the fixed frequency deviation value.
The specific operation of the step 1) is as follows:
selecting a historical operation data set when the wind turbine generator normally operates, wherein the historical operation data set comprises natural frequency, wind speed, power, rotating speed, pitch angle and temperature of a blade, when P is less than or equal to 0, the generator is in a shutdown state, and the working condition parameters of the generator influencing the fixed frequency of the blade are the wind speed and the temperature, and constructing a first modeling data subset by the natural frequency, the wind speed and the temperature data of the blade; when P is present>0 or P<PRated valueWhen the unit operates in the constant paddle state, working condition parameters influencing the fixed frequency of the unit are the natural frequency, the wind speed, the temperature and the power of the blade, and a second modeling data subset is constructed by the natural frequency, the wind speed, the temperature and the power data of the blade; when P is equal to PRated valueHour and machineAnd when the group runs in a variable pitch state, working condition parameters influencing the fixed frequency of the blade are the natural frequency, the wind speed, the temperature and the pitch angle of the blade, a third modeling data subset is constructed by the natural frequency, the wind speed, the temperature and the pitch angle of the blade, and then each modeling data subset is respectively divided into a modeling training data set and a model testing data set.
65% of the data in the subset of modeling data is divided into a modeling training data set and 35% of the data in the subset of modeling data is divided into a model test data set.
The expression of the observation memory matrix D is:
fixed frequency prediction result X output by natural frequency prediction modelpredictComprises the following steps:
the invention has the following beneficial effects:
according to the wind power generator blade natural frequency working condition compensation method based on the nonlinear modeling, during specific operation, a historical operation data set during normal operation of a wind power generator set is divided into a plurality of modeling data subsets, then a corresponding natural frequency prediction model is constructed according to each modeling data subset, during compensation, working condition parameters of the current set are only required to be substituted into the corresponding prediction fixed frequency model, then the difference is made between the predicted current fixed frequency and the actually measured fixed frequency, the difference result is used as a fixed frequency deviation value, the fixed frequency deviation value is not influenced by the working condition parameters, and finally the wind power generator blade natural frequency working condition compensation based on the nonlinear modeling is carried out according to the fixed frequency deviation value.
Drawings
FIG. 1 is a flow chart of the present invention for building a prediction model of a natural frequency prediction model;
FIG. 2 is a flow chart of the present invention for predicting the current natural frequency;
FIG. 3 is a diagram of the natural frequency prediction result in a unit shutdown state;
FIG. 4 is a diagram showing the results of the predicted relative errors of natural frequency, wind speed and temperature in the shutdown state of the unit;
FIG. 5 is a diagram of the natural frequency prediction result in the unit under the state of unchanged paddle operation;
FIG. 6 is a diagram showing the results of the predicted relative errors of natural frequency, wind speed, power and temperature under the operating state of the set of constant pitch;
FIG. 7 is a diagram of a natural frequency prediction result in a variable pitch operation state of a unit;
FIG. 8 is a diagram showing the results of the predicted relative errors of the natural frequency, the wind speed, the pitch angle and the temperature of the unit in the variable-pitch operating state.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1 and 2, the wind turbine blade natural frequency condition compensation method based on nonlinear modeling includes the following steps:
1) selecting a historical operation data set of the wind turbine generator set in normal operation for 1-3 months, dividing the historical operation data set into a plurality of modeling data subsets according to the power P of an SCADA (supervisory control and data acquisition) system of the wind turbine generator, and then dividing each modeling data subset into a modeling training data set and a model test data set respectively, wherein 65% of data in the modeling data subsets are divided into the modeling training data sets, and 35% of data in the modeling data subsets are divided into the model test data sets;
the specific operation of the step 1) is as follows: selecting a historical operation data set when the wind turbine generator normally operates, wherein the historical operation data set comprises natural frequency, wind speed, power, rotating speed, pitch angle and temperature of a blade, when P is less than or equal to 0, the generator is in a shutdown state, and the working condition parameters of the generator influencing the fixed frequency of the blade are the wind speed and the temperature, and constructing a first modeling data subset by the natural frequency, the wind speed and the temperature data of the blade; when P is present>0 or P<PRated valueIn the meantime, the unit operates in the constant paddle state, and the working condition parameters influencing the fixed frequency of the unit areConstructing a second modeling data subset by the natural frequency, the wind speed, the temperature and the power data of the blades; when P is equal to PRated valueWhen the unit operates in a variable pitch state, working condition parameters influencing the fixed frequency of the blades are the natural frequency, the wind speed, the temperature and the pitch angle of the blades, a third modeling data subset is constructed by the natural frequency, the wind speed, the temperature and the pitch angle of the blades, and then each modeling data subset is divided into a modeling training data set and a model testing data set.
2) Normalizing the elements in each modeling data subset;
3) constructing a corresponding observation memory matrix D according to the normalized modeling training data set to construct an inherent frequency prediction model, and then testing a data set X according to the constructed observation memory matrix D and the corresponding modeltestTesting and verifying the natural frequency prediction model, wherein one modeling data subset corresponds to one natural frequency prediction model, and the expression of the observation memory matrix D is as follows:
each column in D represents one normal state sample of the modeling data subset, which consists of n variables, D has a total of m sample sets,
representing the calculation of the euclidean distance between the two vectors.
The fixed frequency prediction result X output by the natural frequency prediction modelpredictComprises the following steps:
and then checking whether the prediction error of the model test data set meets the requirement, setting the prediction relative error of the natural frequency to be less than or equal to 2%, setting the prediction relative error of the working condition parameter to be less than or equal to 5%, and if the prediction error meets the requirement, indicating that the constructed model meets the requirement, if the prediction error does not meet the requirement, continuing to correct and model, then repeating the steps until the prediction error meets the requirement and the modeling is finished.
4) The method comprises the steps of obtaining the power of a current wind driven generator data acquisition and monitoring system SCADA and unit working condition parameters, searching a corresponding natural frequency prediction model according to the power of the current wind driven generator data acquisition and monitoring system SCADA, substituting the working condition parameters of the current unit into the corresponding natural frequency prediction model to obtain a predicted current fixed frequency, carrying out subtraction operation on the actually measured current fixed frequency and the predicted current fixed frequency, taking an operation result as a fixed frequency deviation value, and finally completing working condition compensation of the natural frequency of the wind driven generator blade by using the fixed frequency deviation value.
Fig. 3 is a diagram of a prediction result of the natural frequency in the unit shutdown state, and the predicted value and the measured value in fig. 3 are all overlapped, so that the prediction is accurate. Fig. 4 is a diagram showing the result of the prediction relative errors of the natural frequency, the wind speed and the temperature in the shutdown state of the unit, wherein the maximum prediction error is only 0.012%, and the prediction error meets the requirement.
Fig. 5 is a diagram of a natural frequency prediction result in a unit constant-pitch running state, and the predicted value and the measured value in fig. 5 are all overlapped, so that prediction is accurate. FIG. 6 is a diagram showing the results of the predicted relative errors of the natural frequency, the wind speed, the temperature and the power of the unit under the operating state of the variable propeller, wherein the maximum predicted error is only-0.025%, and the predicted error meets the requirements.
Fig. 7 is a prediction result diagram of the natural frequency of the unit in the variable pitch operation state, and the predicted value and the measured value in fig. 7 are all overlapped, so that the prediction is accurate. FIG. 8 is a diagram showing the results of the predicted relative errors of the natural frequency, the wind speed, the temperature and the pitch angle of the unit in the variable-pitch operating state, wherein the maximum predicted error is only-0.1%, and the predicted error meets the requirements.
Claims (5)
1. A wind power blade natural frequency working condition compensation method based on nonlinear modeling is characterized by comprising the following steps:
1) selecting a historical operation data set when the wind turbine generator normally operates, dividing the historical operation data set into a plurality of modeling data subsets according to the power P of a SCADA (supervisory control and data acquisition) system of the wind turbine generator, and then dividing each modeling data subset into a modeling training data set and a model testing data set respectively;
2) normalizing the elements in each modeling data subset;
3) constructing a corresponding observation memory matrix D according to the normalized modeling training data set to construct an inherent frequency prediction model, and then utilizing the constructed observation memory matrix D and a corresponding model test data set X thereoftestTesting and verifying the natural frequency prediction model, wherein one modeling data subset corresponds to one natural frequency prediction model;
4) the method comprises the steps of obtaining the power of a current wind driven generator data acquisition and monitoring system SCADA and unit working condition parameters, searching a corresponding natural frequency prediction model according to the power of the current wind driven generator data acquisition and monitoring system SCADA, substituting the working condition parameters of the current unit into the corresponding natural frequency prediction model to obtain a predicted current fixed frequency, carrying out subtraction operation on the actually measured current fixed frequency and the predicted current fixed frequency, taking an operation result as a fixed frequency deviation value, and finally completing working condition compensation of the natural frequency of the wind driven generator blade by using the fixed frequency deviation value.
2. The nonlinear-modeled wind turbine blade natural frequency condition compensation method according to claim 1, characterized in that the specific operation of step 1) is as follows:
selecting a historical operation data set when the wind turbine generator normally operates, wherein the historical operation data set comprises natural frequency, wind speed, power, rotating speed, pitch angle and temperature of a blade, when P is less than or equal to 0, the generator is in a shutdown state, and the working condition parameters of the generator influencing the fixed frequency of the blade are the wind speed and the temperature, and constructing a first modeling data subset by the natural frequency, the wind speed and the temperature data of the blade; when P is present>0 and P<PRated valueIn the time, the unit operates in the constant paddle state, and the working condition parameters influencing the fixed frequency of the unit are the natural frequency, the wind speed, the temperature and the power of the blade, and the working condition parameters are the natural frequency, the wind speed, the temperature and the power of the bladeThe power data construct a second subset of modeling data, PRated valueThe rated power generation power of the wind generating set; when P is equal to PRated valueWhen the unit operates in a variable pitch state, working condition parameters influencing the fixed frequency of the blades are the natural frequency, the wind speed, the temperature and the pitch angle of the blades, a third modeling data subset is constructed by the natural frequency, the wind speed, the temperature and the pitch angle of the blades, and then each modeling data subset is divided into a modeling training data set and a model testing data set.
3. The nonlinear-modeled wind turbine blade natural frequency condition compensation method according to claim 2, characterized in that 65% of data in the modeling data subset is divided into a modeling training data set, and 35% of data in the modeling data subset is divided into a model test data set.
4. The nonlinear-modeled wind turbine blade natural frequency condition compensation method according to claim 1, wherein an expression of an observation memory matrix D is as follows:
5. the wind turbine blade natural frequency condition compensation method based on nonlinear modeling according to claim 4, characterized in that a fixed frequency prediction result X output by a natural frequency prediction modelpredictComprises the following steps:
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| CN111412115A (en) * | 2020-04-07 | 2020-07-14 | 国家电投集团广西电力有限公司 | Novel wind power tower cylinder state online monitoring method and system |
| CN112594125A (en) * | 2020-11-29 | 2021-04-02 | 上海电机学院 | Automatic-shrinkage wind power generation blade and control method thereof |
| CN113847212B (en) * | 2021-10-29 | 2023-05-02 | 中国华能集团清洁能源技术研究院有限公司 | Wind turbine generator blade natural frequency monitoring method |
| CN115374653B (en) * | 2022-10-21 | 2022-12-20 | 宇动源(北京)信息技术有限公司 | NSET model-based wind driven generator temperature early warning method and related device |
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