CN108869174A - A kind of blade of wind-driven generator intrinsic frequency operating condition compensation method of Nonlinear Modeling - Google Patents

A kind of blade of wind-driven generator intrinsic frequency operating condition compensation method of Nonlinear Modeling Download PDF

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CN108869174A
CN108869174A CN201810622308.8A CN201810622308A CN108869174A CN 108869174 A CN108869174 A CN 108869174A CN 201810622308 A CN201810622308 A CN 201810622308A CN 108869174 A CN108869174 A CN 108869174A
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modeling
blade
wind
frequency
intrinsic frequency
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CN108869174B (en
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侯成刚
胡翔
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Xian Jiaotong University
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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
    • F03D7/00—Controlling wind motors 
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Wind Motors (AREA)

Abstract

The wind electricity blade intrinsic frequency operating condition compensation method based on Nonlinear Modeling that the invention discloses a kind of, including：1) history data collection is divided into several modeling data subsets further according to the power P size of wind-driven generator SCADA, each modeling data subset is then respectively divided into modeling training dataset and model measurement data set；2) element in each modeling data subset is normalized；3) corresponding observation dot-blur pattern D is constructed according to the modeling training dataset after normalization, then according to the observation dot-blur pattern D of building and its corresponding model measurement data set XtestConstruct corresponding intrinsic frequency prediction model；4) duty parameter of current unit is substituted into corresponding intrinsic frequency prediction model, then the current fixed frequency of the current fixed frequency of actual measurement and prediction is made into additive operation, finally carry out the compensation of Natural Frequency of Blade operating condition using fixed frequency offset value.This method can accurately realize the operating condition compensation of blade of wind-driven generator fixed frequency.

Description

A kind of blade of wind-driven generator intrinsic frequency operating condition compensation method of Nonlinear Modeling
Technical field
The invention belongs to blade of wind-driven generator intrinsic frequency operating conditions to compensate field, be related to a kind of based on Nonlinear Modeling Wind electricity blade intrinsic frequency operating condition compensation method.
Background technique
China's wind power generation industry is in high-speed development period at present, and wind-driven generator quantity increases year by year.Wherein, wind-force is sent out Motor blade is critical component in unit, and blade construction health is most important for unit safety operation.Therefore it realizes to wind-force The on-line monitoring of generator blade state has important engineering practical value.Natural Frequency of Blade characterizes the spy of blade construction Property, when such as crackle occurs damaging in blade, the rigidity of blade will reduce, and cause Natural Frequency of Blade to reduce, therefore currently based on Extensive concern of the Natural Frequency of Blade offset monitoring blade health status technology by domestic and foreign scholars, but Natural Frequency of Blade By unit operating condition included that wind speed, revolving speed, power, propeller pitch angle, temperature etc. are influenced, therefore even if in normal healthy state Under, Natural Frequency of Blade is also among variation, so can not directly using actual measurement intrinsic frequency to blade health status into Row assessment.Therefore need to solve the operating condition compensation problem of Natural Frequency of Blade.
Summary of the invention
It is an object of the invention to overcome the above-mentioned prior art, provide a kind of based on based on Nonlinear Modeling Wind electricity blade intrinsic frequency operating condition compensation method, this method can be realized the operating condition compensation of blade of wind-driven generator intrinsic frequency.
In order to achieve the above objectives, the wind electricity blade intrinsic frequency operating condition compensation side of the present invention based on Nonlinear Modeling Method includes the following steps：
1) history data collection when Wind turbines operate normally is chosen, the power P according to wind-driven generator SCADA is big It is small that history data collection is divided into several modeling data subsets, each modeling data subset is then respectively divided into modeling instruction Practice data set and model measurement data set；
2) element in each modeling data subset is normalized；
3) corresponding observation dot-blur pattern D is constructed according to the modeling training dataset after normalization, to construct intrinsic frequency Then prediction model utilizes the observation dot-blur pattern D and its corresponding model measurement data set X of buildingtestIt tests and verifies solid There is frequency predication model, wherein the corresponding intrinsic frequency prediction model of a modeling data subset；
4) power and unit duty parameter for obtaining current wind generator SCADA, according to current wind generator The power of SCADA finds corresponding intrinsic frequency prediction model, then substitutes into the duty parameter of current unit corresponding intrinsic In frequency predication model, the current fixed frequency of prediction is obtained, then the current fixed frequency of actual measurement and the current of prediction are fixed into frequency Rate makees additive operation, and using the result of operation as fixed frequency offset value, finally completes wind-force using fixed frequency offset value The operating condition of generator blade intrinsic frequency compensates.
The concrete operations of step 1) are：
History data collection when Wind turbines operate normally is chosen, the history data collection includes that blade is intrinsic Frequency, wind speed, power, revolving speed, propeller pitch angle and temperature, when P≤0, unit is in shutdown status, influences the machine of blade fixed frequency Group duty parameter is wind speed and temperature, then constructs first modeling data subset by Natural Frequency of Blade, wind speed and temperature data； When
P>0 or P<PIt is specifiedWhen, then unit operates under constant pulpous state state, and the duty parameter for influencing unit fixed frequency is Natural Frequency of Blade, wind speed, temperature and power then construct second by Natural Frequency of Blade, wind speed, temperature and power data and build Mould data subset；Work as P=PIt is specifiedWhen, unit operates under variable pitch state, and it is solid for blade to influence the duty parameter of blade fixed frequency There are frequency, wind speed, temperature and propeller pitch angle, then third modeling data is constructed by Natural Frequency of Blade, wind speed, temperature and propeller pitch angle Then each modeling data subset is respectively divided into modeling training dataset and model measurement data set by subset.
In modeling data subset 65% data are divided into modeling training dataset, by modeling data subset 35% Data are divided into model measurement data set.
Observation dot-blur pattern D expression formula be：
The fixed frequency prediction result X of intrinsic frequency prediction model outputpredictFor：
The invention has the advantages that：
Wind electricity blade intrinsic frequency operating condition compensation method of the present invention based on Nonlinear Modeling when specific operation, History data collection when Wind turbines are operated normally is divided into several modeling data subsets, then according to each modeling data Subset constructs corresponding intrinsic frequency prediction model, in compensation, need to only substitute into the duty parameter of current unit corresponding pre- It surveys in fixed frequency model, the current fixed frequency of prediction and the fixed frequency of actual measurement is then made into result that is poor, and will making difference As fixed frequency offset value, which is not influenced by duty parameter, finally according to fixed frequency offset value Carry out the blade of wind-driven generator intrinsic frequency operating condition compensation of Nonlinear Modeling, it is convenient and simple for operation, the accuracy of compensation compared with It is high.
Detailed description of the invention
Fig. 1 is the flow chart that intrinsic frequency prediction model prediction model is established in the present invention；
Fig. 2 is the flow chart that current intrinsic frequency is predicted in the present invention；
Fig. 3 is intrinsic frequency prediction result figure under compressor emergency shutdown state；
Fig. 4 is the Relative Error result figure of intrinsic frequency, wind speed, temperature under compressor emergency shutdown state；
Fig. 5 is intrinsic frequency prediction result figure under unit not variable pitch operating status；
Fig. 6 be unit not under variable pitch operating status intrinsic frequency, wind speed, power, temperature Relative Error result figure；
Fig. 7 is intrinsic frequency prediction result figure under set pitch control operating status；
Fig. 8 is the Relative Error result figure of intrinsic frequency, wind speed, propeller pitch angle, temperature under set pitch control operating status.
Specific embodiment
The invention will be described in further detail with reference to the accompanying drawing：
Referring to Figure 1 and Figure 2, the wind electricity blade intrinsic frequency operating condition compensation method of the present invention based on Nonlinear Modeling Include the following steps：
1) the history data collection for choosing 1-3 months when Wind turbines operate normally, according to wind-driven generator SCADA's History data collection is divided into several modeling data subsets by power P size, and then each modeling data subset is respectively divided For modeling training dataset and model measurement data set, wherein in modeling data subset 65% data are divided into modeling instruction Practice data set, in modeling data subset 35% data are divided into model measurement data set；
The concrete operations of step 1) are：Choose history data collection when Wind turbines operate normally, the history fortune Line data set includes Natural Frequency of Blade, wind speed, power, revolving speed, propeller pitch angle and temperature, and when P≤0, unit is in shutdown status, The unit duty parameter for influencing blade fixed frequency is wind speed and temperature, then by Natural Frequency of Blade, wind speed and temperature data structure Build first modeling data subset；Work as P>0 or P<PIt is specifiedWhen, then unit operates under constant pulpous state state, influences the fixed frequency of unit The duty parameter of rate is Natural Frequency of Blade, wind speed, temperature and power, then by Natural Frequency of Blade, wind speed, temperature and power number According to second modeling data subset of building；Work as P=PIt is specifiedWhen, unit operates under variable pitch state, influences the work of blade fixed frequency Condition parameter is Natural Frequency of Blade, wind speed, temperature and propeller pitch angle, then is constructed by Natural Frequency of Blade, wind speed, temperature and propeller pitch angle Then each modeling data subset is respectively divided into modeling training dataset and model measurement data by third modeling data subset Collection.
2) element in each modeling data subset is normalized；
3) corresponding observation dot-blur pattern D is constructed according to the modeling training dataset after normalization, to construct intrinsic frequency Prediction model, then according to the observation dot-blur pattern D of building and its corresponding model measurement data set XtestIt tests and verifies solid There is frequency predication model, wherein the corresponding intrinsic frequency prediction model of a modeling data subset, wherein observation memory square Battle array D expression formula be：
Each column in D indicate a normal condition sample of modeling data subset, it is made of n variable, and D mono- is shared M sample set,It indicates to calculate the Euclidean distance between two vectors.
The then fixed frequency prediction result X of intrinsic frequency prediction model outputpredictFor：
Whether the prediction error of following testing model test data set meets the requirements, and the prediction that intrinsic frequency is arranged is opposite Error is less than or equal to 2%, and duty parameter Relative Error is less than or equal to 5%, and prediction error is met the requirements, then shows building Model is met the requirements, if prediction error is unsatisfactory for requiring, is needed to continue amendment modeling, is then repeated the above steps, until prediction Error, which meets the requirements modeling, to be terminated.
4) power and unit duty parameter for obtaining current wind generator SCADA, according to current wind generator The power of SCADA finds corresponding intrinsic frequency prediction model, then substitutes into the duty parameter of current unit corresponding intrinsic In frequency predication model, the current fixed frequency of prediction is obtained, then the current fixed frequency of actual measurement and the current of prediction are fixed into frequency Rate makees additive operation, and using the result of operation as fixed frequency offset value, finally completes wind-force using fixed frequency offset value The operating condition of generator blade intrinsic frequency compensates.
Fig. 3 is the prediction result figure of intrinsic frequency under compressor emergency shutdown state, and predicted value and measured value are all overlapped in Fig. 3, Prediction is accurate.Fig. 4 is the Relative Error result figure of intrinsic frequency, wind speed, temperature under compressor emergency shutdown state, wherein maximum Prediction error be only 0.012%, prediction error meet the requirements.
Fig. 5 is intrinsic frequency prediction result figure under the constant slurry operating status of unit, and predicted value and measured value are all heavy in Fig. 5 It closes, prediction is accurate.Fig. 6 be unit not intrinsic frequency under variable pitch operating status, wind speed, temperature, power Relative Error knot Fruit figure, wherein maximum prediction error is only -0.025%, and prediction error is met the requirements.
Fig. 7 is the prediction result figure that unit becomes intrinsic frequency under slurry operating status, and predicted value and measured value are all heavy in Fig. 7 It closes, prediction is accurate.Fig. 8 be set pitch control operating status under intrinsic frequency, wind speed, temperature, propeller pitch angle Relative Error knot Fruit figure, wherein maximum prediction error is only -0.1%, and prediction error is met the requirements.

Claims (5)

1. a kind of wind electricity blade intrinsic frequency operating condition compensation method based on Nonlinear Modeling, which is characterized in that including following step Suddenly：
1) history data collection when Wind turbines operate normally is chosen, it will according to the power P size of wind-driven generator SCADA History data collection is divided into several modeling data subsets, and each modeling data subset is then respectively divided into modeling training number According to collection and model measurement data set；
2) element in each modeling data subset is normalized；
3) corresponding observation dot-blur pattern D is constructed according to the modeling training dataset after normalization, to construct intrinsic frequency prediction Then model utilizes the observation dot-blur pattern D and its corresponding model measurement data set X of buildingtestIt tests and verifies intrinsic frequency Rate prediction model, wherein the corresponding intrinsic frequency prediction model of a modeling data subset；
4) power and unit duty parameter for obtaining current wind generator SCADA, according to current wind generator SCADA's Power finds corresponding intrinsic frequency prediction model, and the duty parameter of current unit is then substituted into corresponding intrinsic frequency and is predicted In model, the current fixed frequency of prediction is obtained, then the current fixed frequency of actual measurement and the current fixed frequency of prediction work are subtracted each other Operation, and using the result of operation as fixed frequency offset value, finally wind-driven generator leaf is completed using fixed frequency offset value The operating condition of piece intrinsic frequency compensates.
2. the wind electricity blade intrinsic frequency operating condition compensation method according to claim 1 based on Nonlinear Modeling, feature It is, the concrete operations of step 1) are：
History data collection when Wind turbines operate normally is chosen, the history data collection includes the intrinsic frequency of blade Rate, wind speed, power, revolving speed, propeller pitch angle and temperature, when P≤0, unit is in shutdown status, influences the unit of blade fixed frequency Duty parameter is wind speed and temperature, then constructs first modeling data subset by Natural Frequency of Blade, wind speed and temperature data；When P>0 or P<PIt is specifiedWhen, then unit operates under constant pulpous state state, and the duty parameter for influencing unit fixed frequency is that blade is intrinsic Frequency, wind speed, temperature and power then construct second modeling data by Natural Frequency of Blade, wind speed, temperature and power data Collection；Work as P=PIt is specifiedWhen, unit operates under variable pitch state, influence blade fixed frequency duty parameter be Natural Frequency of Blade, Wind speed, temperature and propeller pitch angle then construct third modeling data subset by Natural Frequency of Blade, wind speed, temperature and propeller pitch angle, so Each modeling data subset is respectively divided into modeling training dataset and model measurement data set afterwards.
3. the wind electricity blade intrinsic frequency operating condition compensation method according to claim 2 based on Nonlinear Modeling, feature It is, in modeling data subset 65% data is divided into modeling training dataset, by modeling data subset 35% number According to being divided into model measurement data set.
4. the wind electricity blade intrinsic frequency operating condition compensation method according to claim 1 based on Nonlinear Modeling, feature It is, the expression formula of observation dot-blur pattern D is：
5. the wind electricity blade intrinsic frequency operating condition compensation method according to claim 4 based on Nonlinear Modeling, feature It is, the fixed frequency prediction result X of intrinsic frequency prediction model outputpredictFor：
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CN110298455A (en) * 2019-06-28 2019-10-01 西安因联信息科技有限公司 A kind of mechanical equipment fault intelligent early-warning method based on multivariable estimation prediction
CN111080981A (en) * 2019-12-30 2020-04-28 安徽容知日新科技股份有限公司 Alarm method and alarm system of equipment and computing equipment
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
CN113847212A (en) * 2021-10-29 2021-12-28 中国华能集团清洁能源技术研究院有限公司 Method for monitoring natural frequency of blades of wind turbine generator
CN115374653A (en) * 2022-10-21 2022-11-22 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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CN115374653A (en) * 2022-10-21 2022-11-22 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
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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