CN104747368A - Method and system for optimizing power of wind turbine generator - Google Patents
Method and system for optimizing power of wind turbine generator Download PDFInfo
- Publication number
- CN104747368A CN104747368A CN201510040771.8A CN201510040771A CN104747368A CN 104747368 A CN104747368 A CN 104747368A CN 201510040771 A CN201510040771 A CN 201510040771A CN 104747368 A CN104747368 A CN 104747368A
- Authority
- CN
- China
- Prior art keywords
- data
- model
- vibration amplitude
- power
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B10/00—Integration of renewable energy sources in buildings
- Y02B10/30—Wind power
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Landscapes
- Wind Motors (AREA)
Abstract
The invention relates to a method and system for optimizing power of a wind turbine generator, wherein the method for optimizing power of the wind turbine generator comprises the following steps: 1, collecting operation data of the wind turbine generator; 2, processing the collected data, namely selecting the data of second level time resolution, and removing failure data; 3, establishing a generated power model P(t) and a vibration amplitude model A(t) according to the processed data; 4, selecting the generated power model and the vibration amplitude model which are the minimum in mean square error values to be an optimal generated power model and an optimal vibration amplitude model; 5, according to the optimal generated power model and the optimal vibration amplitude model, establishing a power optimization function and a vibration optimization function. According to the method and system, the power optimization of the wind turbine generator is researched from the perspective of control models, and the method and system for optimizing power of the wind turbine generator are established.
Description
Technical field
The present invention relates to field, particularly relate to a kind of method and system of Wind turbines power optimization.
Background technique
In Wind Power Generation Industry, the operation and maintenance cost in blower fan later stage is one of capital expenditure of wind energy turbine set.In traditional analysis thinking, production capacity and the vibration of blower fan are optimized from aerodynamics and amechanical angle.But the blower fan in conventional thought is modeled in engineering practice considerable restraint.
Summary of the invention
Technical problem to be solved by this invention optimizes the power of Wind turbines, provides a kind of method and system of Wind turbines power optimization.
The technological scheme that the present invention solves the problems of the technologies described above is as follows: a kind of method of Wind turbines power optimization, comprises the steps,
Step 1, gathers running of wind generating set data;
The described data collected are processed, namely choose the data of level time resolution second by step 2, reject fault data;
Step 3, according to data construct generated output model P (t) after process and vibration amplitude model A (t);
Step 4, the generated output model that Select Error mean square value is minimum and vibration amplitude model are optimal power generation power module and optimum vibration amplitude model;
Step 5, according to optimal power generation power module and optimum vibration amplitude model buildings power optimization function and vibration majorized function.
On the basis of technique scheme, the present invention can also do following improvement.
Further, the data gathered in step 1 comprise the following operational parameter data collection of Wind turbines, wind speed v (t), driftage y (t), and wind direction d (t) becomes vane angle p (t), temperature T (t).
Further, the fault data in step 2 comprises data because Wind turbines fault collection arrives and obviously abnormal data.
Further, P (t) in step 3=f (v (t), y (t), d (t), p (t), T (t))+error, A (t)=g (v (t), y (t), d (t), p (t), T (t))+error, wherein f (), g () the regression class algorithm that is data mining, t is the working time of Wind turbines, and error is the error amount of described generated output model and vibration amplitude model.
Further, also comprise in step 4 and determine described optimal power generation power module and wind speed v (t) corresponding to described optimum vibration amplitude model, driftage y (t), wind direction d (t), become the parameter value of vane angle p (t) and temperature T (t).
Further, in step 4, treated data are divided into two-part at random, wherein 70% for the training data of generated output model and vibration amplitude model training, and other 30% for the predictive data set of MSE error mean square.
Further, before carry out step 5, resampling can be adopted to the non-equilibrium data of training data part, make the quantity in parameter wind speed v (t) in described generated output model P (t) and vibration amplitude model A (t), driftage y (t), wind direction d (t), change vane angle p (t) and temperature T (t) each interval in five dimension state spaces be no less than predefined value n1, n1 represents interval censored data quantity predefined value.
Further, in step 5 before building majorized function, need parameter wind speed v (t), driftage y (t), wind direction d (t), become vane angle p (t) and the controlled optimization of temperature T (t).
Further, in step 5, power optimization function is Tp=max{P (t) }, Tp refers to the performance number after power optimization, vibration majorized function is TA=max{A (t) }, wherein TA refers to the vibration after optimization, C=max (Tp|TA<=C), C represents vibration limit value, namely vibration can power under acceptable conditions best.
The another kind of technological scheme that the present invention solves the problems of the technologies described above is as follows: a kind of system of Wind turbines power optimization, comprises data acquisition module, data processing module, model construction module, optimal models builds module and majorized function builds module;
Described data acquisition module is for gathering running of wind generating set data;
Described data processing module is used for the described data collected to process, and namely chooses the data of level time resolution second, rejects fault data;
Described model construction module is used for data construct generated output model P (t) after according to process and vibration amplitude model A (t);
Described optimal models structure module is used for the minimum generated output model of Select Error mean square value and vibration amplitude model is optimal power generation power module and optimum vibration amplitude model;
Described majorized function builds module for according to optimal power generation power module and optimum vibration amplitude model buildings power optimization function and vibration majorized function.
The invention has the beneficial effects as follows: from the power optimization of the angle research Wind turbines of Controlling model, consider the optimization of Transmitted chains and the vibration of tower cylinder simultaneously.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of Wind turbines power optimization of the present invention;
Fig. 2 is the system construction drawing of Wind turbines power optimization of the present invention;
Fig. 3 be for GW1500 data scrubbing after power curve;
Fig. 4 be for GW1500 data scrubbing after change oar curve;
Fig. 5 be for GW1500 data scrubbing after speed curves;
Fig. 6 is the error time sequence chart for GW1500;
Fig. 7 is the standardized change oar curve confidence interval figure for GW1500;
Fig. 8 be for the optimization of GW1500 before and after change propeller angle figure;
Fig. 9 is that after the optimization for GW1500, power deducts the power diagram do not optimized.
Embodiment
Be described principle of the present invention and feature below in conjunction with accompanying drawing, example, only for explaining the present invention, is not intended to limit scope of the present invention.
The present invention, from the power optimization of the angle research Wind turbines of Controlling model, considers the optimization of Transmitted chains and the vibration of tower cylinder simultaneously in optimization aim.The present invention has inquired into data digging method to optimize the basic ideas framework of blower fan production capacity and vibration, and achieves by change oar control power optimization with an Engineering Projects.
As shown in Figure 1, a kind of method of Wind turbines power optimization, comprises the steps:
Step 1, gathers running of wind generating set data, with the running of wind generating set time for variable, gather the following operational parameter data collection of Wind turbines, wind speed v (t), driftage y (t), wind direction d (t), becomes vane angle p (t), temperature T (t);
Step 2, data processing, selects the data of right times resolution, according to engineering experience, generally chooses the data of second rank, rejects fault data, comprises the data because Wind turbines fault collection arrives and obviously abnormal data.The process of this data processing is conducive to the degree of accuracy improving data, ensures the validity of subsequent calculations.
Step 3, according to data construct generated output model P (t) after process and vibration amplitude model A (t), wherein P (t)=f (v (t), y (t), d (t), p (t), T (t))+error, A (t)=g (v (t), y (t), d (t), p (t), T (t))+error, wherein f (.), the regression class algorithm that g (.) is data mining, what can realize above-mentioned effect in data mining algorithm has neural network, support vector machine, k nearest neighbor and random forest method etc., error is the error amount of model.
Step 4, the generated output model that Select Error mean square value is minimum and vibration amplitude model are optimal power generation power module and optimum vibration amplitude model, and determine this optimal power generation power module and wind speed v (t) corresponding to optimum vibration amplitude model, driftage y (t), wind direction d (t), become the parameter value of vane angle p (t) and temperature T (t).In this process, treated data are divided into two-part at random, wherein 70% for the training data of model training, and other 30% for the predictive data set of MSE (i.e. error mean square), and wherein MSE adopts predictive data set to be to prevent overfitting.
In order to reduce calculated load, speed-up computation, resampling is adopted to the non-equilibrium data of training data part, make parameter wind speed v (t) in above-mentioned generated output model P (t) and vibration amplitude model A (t), driftage y (t), wind direction d (t), become vane angle p (t) with temperature T (t) in n (corresponding number of parameters, namely n is 5) quantity in each interval is no less than predefined value n1 (n1 depends on total training sample amount in dimension state space, under interval number determination condition, training sample is larger, n1 is larger, data volume after resampling can not exceed former training data), even if the quantity that parameter is tieed up in state space at certain n is no less than n1.So both remain original training data and can cover all possible point, additionally reduce the volume of data.
Step 5, according to optimal power generation power module and optimum vibration amplitude model buildings power optimization function and vibration majorized function; Before building majorized function, parameter should adopt controlled optimization, to guarantee that the result optimized has certain actual physical meaning; Be that to become the power optimization of vane angle be example with parameter, optimization aim can be decomposed into, Tp=max{P (t) }, Tp refers to the performance number after power optimization, wherein becoming vane angle change should controlled optimization within the specific limits, p_min<=p (t) <=p_max, its scope is currency+-someValue (giving tacit consent to 10 °).
Equally, the majorized function that vibration is optimized can be built, TA=max{A (t) }, wherein becoming vane angle change should in the controlled optimization of certain limit, p_min<=p (t) <=p_max, its scope is currency+-someValue (giving tacit consent to 10 °).
The simultaneously majorized function of optimizing power and vibration, variable element is for becoming propeller angle, and C=max (Tp|TA<=C), C represent vibration limit value, namely vibration can power under acceptable conditions best.
As shown in Figure 2, a kind of system of Wind turbines power optimization, comprise data acquisition module, data processing module, model construction module, optimal models build module, training data resampling module and majorized function build module;
It is variable that data acquisition module was used for time, gathers the following operational parameter data collection of Wind turbines, wind speed v (t), driftage y (t), and wind direction d (t) becomes vane angle p (t), temperature T (t);
Data processing module, for selecting the data of right times resolution, according to engineering experience, generally chooses the data of second rank, rejects fault data, comprises the data because Wind turbines fault collection arrives and obviously abnormal data.The process of this data processing is conducive to the degree of accuracy improving data, ensures the validity of subsequent calculations.
Model construction module is used for data construct generated output model P (t) after according to process and vibration amplitude model A (t), wherein P (t)=f (v (t), y (t), d (t), p (t), T (t))+error, A (t)=g (v (t), y (t), d (t), p (t), T (t))+error, wherein f (), the regression class algorithm that g () is data mining, what can realize above-mentioned effect in data mining algorithm has neural network, support vector machine, k nearest neighbor and random forest method etc., error is the error amount of model.
Optimal models builds module and is used for the minimum generated output model of Select Error mean square value and vibration amplitude model is optimal power generation power module and optimum vibration amplitude model, and determines this optimal power generation power module and wind speed v (t) corresponding to optimum vibration amplitude model, driftage y (t), wind direction d (t), becomes the parameter value of vane angle p (t) and temperature T (t).In this process, treated data are divided into two-part at random, wherein 70% for the training data of model training, and other 30% for the predictive data set of MSE (i.e. error mean square), and wherein MSE adopts predictive data set to be to prevent overfitting.
Training data resampling module is used for adopting resampling to the non-equilibrium data of training data part, in order to reduce calculated load, speed-up computation, resampling is adopted to the non-equilibrium data of training data part, make parameter wind speed v (t) in above-mentioned generated output model P (t) and vibration amplitude model A (t), driftage y (t), wind direction d (t), become vane angle p (t) with temperature T (t) in n (corresponding number of parameters, namely n is 5) quantity in each interval is no less than predefined value n1 (n1 depends on total training sample amount in dimension state space, under interval number determination condition, training sample is larger, n1 is larger, data volume after resampling can not exceed former training data), even if the quantity that parameter is tieed up in state space at certain n is no less than n1.So both remain original training data and can cover all possible point, additionally reduce the volume of data.
Majorized function builds module for building majorized function, and before building majorized function, parameter should adopt controlled optimization, to guarantee that the result optimized has certain actual physical meaning; Be that to become the power optimization of vane angle be example with parameter, optimization aim can be decomposed into,
Tp=max{P (t) }, Tp refers to the performance number after power optimization, wherein becoming vane angle change should controlled optimization within the specific limits, p_min<=p (t) <=p_max, its scope is currency+-someValue (giving tacit consent to 10 °).
Equally, the majorized function that vibration is optimized can be built, TA=max{A (t) }, wherein becoming vane angle change should in the controlled optimization of certain limit, p_min<=p (t) <=p_max, its scope is currency+-someValue (giving tacit consent to 10 °).
The simultaneously majorized function of optimizing power and vibration, variable element is for becoming propeller angle, and max (Tp|TA<=C), C represent vibration limit value, namely vibration can power under acceptable conditions best.
The machine type data of an existing GW1500, we will set up simple power optimization function, and variable element is for becoming vane angle.
Always have 45 groups of data, in modal analysis, we are research wherein 14 groups of variablees only: WROT.PtAngVal.Bl1, WROT.PtAngVal.Bl2, WROT.PtAngVal.Bl3, WMAN.State, DateTime, WTUR.PwrAt.InstMag.f, WNAC.ExlTmp.instMag.f, WNAC.TopBoxTmp, WNAC.IntTmp.instMag.f, WROT.PtCptTmp.Bl1, WROT.PtCptTmp.Bl2, WROT.PtCptTmp.Bl3, WROT.PtCnvTmp.Bl1, WROT.PtCnvTmp.Bl2, WROT.PtCnvTmp.Bl3.
First data scrubbing is carried out.Power curve after cleaning, change oar curve and speed curves are respectively as shown in Fig. 3, Fig. 4 and Fig. 5.
Wind speed ' NacWS', driftage ' YawPos', wind direction ' Wdir', the data become in oar ' PtAngBl3', temperature ' ExlTmp' are normalized, and ' PwrAct' data are also normalized, use neuron network (setting of attention parameters), Modling model, training set data is the initial data of random 70%, if Fig. 6 is error time sequence chart.As can be seen, error time sequence at most can not more than 15%, and overall MSE is 0.0001, and model can describe the relation of each variable and power very well.
To become vane angle ' PtAngBl3' as variable element, be optimized process to power, be optimized result, becomes propeller angle optimization and adopt controlled optimization, can adjust by the rule of the fixed percentage change of currency or currency+-fixed value change.Here is one and becomes 95% confidecne curve of oar curve according to limit, carrys out the example of control and optimize.If Fig. 7 is standardized change oar curve confidence interval figure, the curve 1 marked in Fig. 7 and curve 3 are the upper and lower of confidence interval, curve 2 represents P50, Fig. 8 is the change propeller angle figure before and after optimizing, change propeller angle in Fig. 8 before curve 1 representing optimized, change propeller angle after curve 2 representing optimized, Fig. 9 is that after optimizing, power deducts the power diagram do not optimized.
Calculate the result after optimization, result before and after optimization is analyzed, and result shows, the productivity ratio after optimization improve 0.6% originally, the physical change rule that the result before and after the optimization of change propeller angle is more realistic.
The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. a method for Wind turbines power optimization, is characterized in that, comprises the steps,
Step 1, gathers running of wind generating set data;
The described data collected are processed, namely choose the data of level time resolution second by step 2, reject fault data;
Step 3, according to data construct generated output model P (t) after process and vibration amplitude model A (t);
Step 4, the generated output model that Select Error mean square value is minimum and vibration amplitude model are optimal power generation power module and optimum vibration amplitude model;
Step 5, according to optimal power generation power module and optimum vibration amplitude model buildings power optimization function and vibration majorized function.
2. the method for Wind turbines power optimization according to claim 1, it is characterized in that, the data gathered in step 1 comprise the following operational parameter data collection of Wind turbines, wind speed v (t), driftage y (t), wind direction d (t), becomes vane angle p (t), temperature T (t).
3. the method for Wind turbines power optimization according to claim 1, is characterized in that, the fault data in step 2 comprises data because Wind turbines fault collection arrives and obviously abnormal data.
4. the method for Wind turbines power optimization according to claim 2, it is characterized in that, P (t) in step 3=f (v (t), y (t), d (t), p (t), T (t))+error, A (t)=g (v (t), y (t), d (t), p (t), T (t))+error, wherein f (), the regression class algorithm that g () is data mining, t is the working time of Wind turbines, and error is the error amount of described generated output model and vibration amplitude model.
5. the method for Wind turbines power optimization according to claim 2, it is characterized in that, also comprise in step 4 and determine described optimal power generation power module and wind speed v (t) corresponding to described optimum vibration amplitude model, driftage y (t), wind direction d (t), become the parameter value of vane angle p (t) and temperature T (t).
6. the method for Wind turbines power optimization according to claim 1, it is characterized in that, in step 4, treated data are divided into two-part at random, wherein 70% for the training data of generated output model and vibration amplitude model training, and other 30% for the predictive data set of MSE error mean square.
7. the method for Wind turbines power optimization according to claim 6, it is characterized in that, before carry out step 5, resampling can be adopted to the non-equilibrium data of training data part, make the quantity in parameter wind speed v (t) in described generated output model P (t) and vibration amplitude model A (t), driftage y (t), wind direction d (t), change vane angle p (t) and temperature T (t) each interval in five dimension state spaces be no less than predefined value n1, n1 represents interval censored data quantity predefined value.
8. the method for Wind turbines power optimization according to claim 1, it is characterized in that, in step 5 before building majorized function, need parameter wind speed v (t), driftage y (t), wind direction d (t), become vane angle p (t) and the controlled optimization of temperature T (t).
9. the method for Wind turbines power optimization according to claim 1, it is characterized in that, in step 5, power optimization function is Tp=max{P (t) }, Tp refers to the performance number after power optimization, vibration majorized function is TA=max{A (t) }, wherein TA refers to the vibration after optimization, C=max (Tp|TA<=C), C represents vibration limit value, namely vibration can power under acceptable conditions best.
10. a system for Wind turbines power optimization, is characterized in that, comprises data acquisition module, data processing module, model construction module, optimal models builds module and majorized function builds module;
Described data acquisition module is for gathering running of wind generating set data;
Described data processing module is used for the described data collected to process, and namely chooses the data of level time resolution second, rejects fault data;
Described model construction module is used for data construct generated output model P (t) after according to process and vibration amplitude model A (t);
Described optimal models structure module is used for the minimum generated output model of Select Error mean square value and vibration amplitude model is optimal power generation power module and optimum vibration amplitude model;
Described majorized function builds module for according to optimal power generation power module and optimum vibration amplitude model buildings power optimization function and vibration majorized function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510040771.8A CN104747368B (en) | 2015-01-27 | 2015-01-27 | A kind of method and system of Wind turbines power optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510040771.8A CN104747368B (en) | 2015-01-27 | 2015-01-27 | A kind of method and system of Wind turbines power optimization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104747368A true CN104747368A (en) | 2015-07-01 |
| CN104747368B CN104747368B (en) | 2017-11-07 |
Family
ID=53587524
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510040771.8A Active CN104747368B (en) | 2015-01-27 | 2015-01-27 | A kind of method and system of Wind turbines power optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104747368B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105134482A (en) * | 2015-07-22 | 2015-12-09 | 扬州大学 | Gray combined modeling and optimized vibration controlling method of large intelligent draught fan blade system |
| CN107179503A (en) * | 2017-04-21 | 2017-09-19 | 美林数据技术股份有限公司 | The method of Wind turbines intelligent fault diagnosis early warning based on random forest |
| WO2018014825A1 (en) * | 2016-07-21 | 2018-01-25 | 浙江运达风电股份有限公司 | Method and device for automatically calibrating wind alignment error of wind power generation unit |
| CN108594895A (en) * | 2018-05-31 | 2018-09-28 | 江苏大学 | Energy recovery device of automobile exhaust electric-control system based on thermo-electric generation and method |
| CN108869174A (en) * | 2018-06-15 | 2018-11-23 | 西安交通大学 | A kind of blade of wind-driven generator intrinsic frequency operating condition compensation method of Nonlinear Modeling |
| CN111091236A (en) * | 2019-11-27 | 2020-05-01 | 长春吉电能源科技有限公司 | Multi-classification deep learning short-term wind power prediction method classified according to pitch angles |
| CN112696312A (en) * | 2020-12-31 | 2021-04-23 | 中国华能集团清洁能源技术研究院有限公司 | Wind turbine generator control method and system |
| CN113048016A (en) * | 2019-12-27 | 2021-06-29 | 新疆金风科技股份有限公司 | Method and device for correcting wind deviation of wind generating set on line |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101592128A (en) * | 2008-05-29 | 2009-12-02 | 通用电气公司 | Determine and/or provide the method and apparatus of power output information of wind turbine farms |
| US20100280756A1 (en) * | 2009-04-29 | 2010-11-04 | California Institute Of Technology | High resolution wind measurements for offshore wind energy development |
| CN102102626A (en) * | 2011-01-30 | 2011-06-22 | 华北电力大学 | Method for forecasting short-term power in wind power station |
| US20120029892A1 (en) * | 2011-05-19 | 2012-02-02 | Matthias Thulke | Condition monitoring of windturbines |
| CN103225588A (en) * | 2012-01-31 | 2013-07-31 | 北京能高自动化技术股份有限公司 | Wind power generation power curve optimization method based on pattern recognition technology |
| CN103441527A (en) * | 2013-08-15 | 2013-12-11 | 国家电网公司 | Wind electricity connection system model based on measured data |
| CN104021424A (en) * | 2013-02-28 | 2014-09-03 | 国际商业机器公司 | Method and device used for predicting output power of blower in wind field |
-
2015
- 2015-01-27 CN CN201510040771.8A patent/CN104747368B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101592128A (en) * | 2008-05-29 | 2009-12-02 | 通用电气公司 | Determine and/or provide the method and apparatus of power output information of wind turbine farms |
| US20100280756A1 (en) * | 2009-04-29 | 2010-11-04 | California Institute Of Technology | High resolution wind measurements for offshore wind energy development |
| CN102102626A (en) * | 2011-01-30 | 2011-06-22 | 华北电力大学 | Method for forecasting short-term power in wind power station |
| US20120029892A1 (en) * | 2011-05-19 | 2012-02-02 | Matthias Thulke | Condition monitoring of windturbines |
| CN103225588A (en) * | 2012-01-31 | 2013-07-31 | 北京能高自动化技术股份有限公司 | Wind power generation power curve optimization method based on pattern recognition technology |
| CN104021424A (en) * | 2013-02-28 | 2014-09-03 | 国际商业机器公司 | Method and device used for predicting output power of blower in wind field |
| CN103441527A (en) * | 2013-08-15 | 2013-12-11 | 国家电网公司 | Wind electricity connection system model based on measured data |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105134482A (en) * | 2015-07-22 | 2015-12-09 | 扬州大学 | Gray combined modeling and optimized vibration controlling method of large intelligent draught fan blade system |
| CN105134482B (en) * | 2015-07-22 | 2018-03-06 | 扬州大学 | Large-scale intelligent fan blade System Grey color compositional modeling and the method for optimization vibration control |
| WO2018014825A1 (en) * | 2016-07-21 | 2018-01-25 | 浙江运达风电股份有限公司 | Method and device for automatically calibrating wind alignment error of wind power generation unit |
| CN107179503A (en) * | 2017-04-21 | 2017-09-19 | 美林数据技术股份有限公司 | The method of Wind turbines intelligent fault diagnosis early warning based on random forest |
| CN107179503B (en) * | 2017-04-21 | 2020-07-07 | 美林数据技术股份有限公司 | Wind turbine generator fault intelligent diagnosis and early warning method based on random forest |
| CN108594895A (en) * | 2018-05-31 | 2018-09-28 | 江苏大学 | Energy recovery device of automobile exhaust electric-control system based on thermo-electric generation and method |
| CN108594895B (en) * | 2018-05-31 | 2023-09-26 | 江苏大学 | Electric control system and method for automobile exhaust energy recovery device based on thermoelectric power generation |
| CN108869174A (en) * | 2018-06-15 | 2018-11-23 | 西安交通大学 | A kind of blade of wind-driven generator intrinsic frequency operating condition compensation method of Nonlinear Modeling |
| CN111091236A (en) * | 2019-11-27 | 2020-05-01 | 长春吉电能源科技有限公司 | Multi-classification deep learning short-term wind power prediction method classified according to pitch angles |
| CN113048016A (en) * | 2019-12-27 | 2021-06-29 | 新疆金风科技股份有限公司 | Method and device for correcting wind deviation of wind generating set on line |
| CN112696312A (en) * | 2020-12-31 | 2021-04-23 | 中国华能集团清洁能源技术研究院有限公司 | Wind turbine generator control method and system |
| WO2022142412A1 (en) * | 2020-12-31 | 2022-07-07 | 中国华能集团清洁能源技术研究院有限公司 | Wind turbine generator control method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104747368B (en) | 2017-11-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104747368A (en) | Method and system for optimizing power of wind turbine generator | |
| CN104632521B (en) | A kind of wind power optimization system and method based on drift correction | |
| CN110023850B (en) | Method and control device for controlling a technical system | |
| La Cava et al. | Automatic identification of wind turbine models using evolutionary multiobjective optimization | |
| CN111045326B (en) | Tobacco shred drying process moisture prediction control method and system based on recurrent neural network | |
| EP3724490B1 (en) | Method and apparatus for cooperative controlling wind turbines of a wind farm | |
| CN112292642B (en) | Control device for controlling a technical system and method for configuring a control device | |
| CN105302973A (en) | MOEA/D algorithm based aluminum electrolysis production optimization method | |
| CN105888971A (en) | Active load reducing control system and method for large wind turbine blade | |
| CN108615097A (en) | A kind of wind speed forecasting method, system, equipment and computer readable storage medium | |
| Lalonde et al. | Comparison of neural network types and architectures for generating a surrogate aerodynamic wind turbine blade model | |
| CN103020743A (en) | Ultra-short-term wind speed forecasting method for wind power plant | |
| Wang et al. | Multi-objective deep reinforcement learning for optimal design of wind turbine blade | |
| CN105389442A (en) | Reverse design method for coupling genetic algorithm, neural network and numerical simulation | |
| EP3741991B1 (en) | Method for dynamic real-time optimization of the performance of a wind park and wind park | |
| CN105389614A (en) | Implementation method for neural network self-updating process | |
| CN116484675A (en) | Crack propagation life prediction method and system for ship engine blade | |
| Song et al. | Application of surrogate-assisted global optimization algorithm with dimension-reduction in power optimization of floating offshore wind farm | |
| CN115288929A (en) | Wind turbine generator yaw control method and system considering load calculation | |
| CN108364071A (en) | A kind of adaptive modeling wind power prediction method based on genetic programming algorithm | |
| CN110210657B (en) | Fan power prediction method and system based on single machine model and computer storage medium | |
| CN103994858B (en) | Dynamic balance detection control method based on biogeographical intelligent optimization support vector machine algorithm | |
| CN111178593A (en) | Photovoltaic system output power prediction method and device | |
| CN117117858B (en) | Wind turbine generator power prediction method, device and storage medium | |
| CN103970013A (en) | Implementation method for deaerator control on basis of genetic algorithm and fuzzy control |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: 430000 No. 999, hi tech Avenue, East Lake Development Zone, Hubei, Wuhan Patentee after: WINDMAGICS (WUHAN) Co.,Ltd. Address before: 430074, building 15, building A, overseas talent building, future science and Technology City, 999 hi tech Avenue, East Lake hi tech Zone, Hubei, Wuhan Patentee before: WINDMAGICS (WUHAN) RENEWABLE ENERGY TECHNOLOGY Co.,Ltd. |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: Method and system for optimizing power of wind turbine generator Effective date of registration: 20200708 Granted publication date: 20171107 Pledgee: Hubei Guo Chuang financing Company limited by guarantee Pledgor: WINDMAGICS (WUHAN) Co.,Ltd. Registration number: Y2020980003870 |