CN110094310B - Method for identifying wind power generator yaw wind disharmony - Google Patents
Method for identifying wind power generator yaw wind disharmony Download PDFInfo
- Publication number
- CN110094310B CN110094310B CN201910395581.6A CN201910395581A CN110094310B CN 110094310 B CN110094310 B CN 110094310B CN 201910395581 A CN201910395581 A CN 201910395581A CN 110094310 B CN110094310 B CN 110094310B
- Authority
- CN
- China
- Prior art keywords
- wind
- angle
- energy utilization
- data
- deviation
- 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.)
- Active
Links
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Abstract
The application relates to a method for identifying wind power generator yaw wind disharmony, which comprises the steps of collecting operation data of a wind power generator set; preprocessing the data, and removing the data in the data under the abnormal operation state; initializing an iteration counter and setting initial parameters; carrying out box separation processing on the wind speed, and calculating the maximum wind energy utilization index of different wind angle pairs under each wind speed box; calculating whether the dispersion of the maximum wind energy utilization index obtained by all wind angle boxes meets the requirement or not, fitting a curve enveloping a wind angle-wind energy utilization index dispersion point graph, and calculating a wind angle numerical value corresponding to the maximum wind energy utilization index point obtained by the curve; judging the morphological characteristics of the fitted curve, and reserving the curve meeting the index requirement; and calculating deviation angles and reliable indexes of all envelope curves meeting the index requirements, and judging the reliability of the result. The method can obtain more stable characteristic expression and more accurate result, can guide field operation and maintenance, and avoids the loss of generated energy.
Description
Technical Field
The application relates to a method for identifying wind power generator yaw wind disharmony, which is suitable for the technical field of wind power generation.
Background
Yaw vs. wind is a common phenomenon in wind generators. The actual wind angle of the blades and the wind angle corresponding to the wind are deviated due to the fact that the wind vane is not installed in a calibration mode, the wind vane is loosened or stuck, the wind vane is increased along with the use measurement error, the yaw system control is in fault, and the like, so that the generated energy loss is caused. Most methods for solving this problem using operational data are based on the assumption that the maximum wind energy that a wind turbine should capture should be around 0 degrees to the wind angle under normal conditions.
The method for identifying the problem of yaw misalignment and calculating the wind offset angle used in the prior art includes fitting the wind angle-power mean value of different wind speed intervals and calculating the wind angle position corresponding to the maximum power in the fitted curve. The method for screening the measured value of the yaw angle (namely the wind angle) and the maximum value of the corresponding generated power through the wind speed division section and obtaining the final offset through data fitting is proposed in the Chinese patent application CN 201410741958. The framework proposed in this patent application is similar to that of the prior art described above, except that the power mean is replaced by the generated power maximum, and the specific fitting and data processing method is not mentioned. In practical application, due to the complex wind conditions in the field, the wind speed and wind direction in the used SCADA (data acquisition and monitoring control system) data are measured by a wind speed and wind direction sensor from the cabin, and due to the influence of the blades, the measured wind speed and wind direction are different from those in front of the blades. Meanwhile, the data volume is obviously reduced when the wind angle is large due to less yaw motion at a large wind angle position, and meanwhile, due to the existence of measurement errors and singular values, the wind angle-power characteristic performances in different wind speed intervals are often inconsistent and are easily influenced by the singular value at the large wind angle position, so that the result reliability is poor.
The comparison between the theoretical and detected values of the yaw angle of the wind turbine set is proposed in chinese patent application CN 201110121958.2. Also, the referential of the theoretical value is decreased due to the problem of measurement, and the method, which only uses the currently detected yaw angle as a comparison object, is more susceptible to measurement errors and singular values, and results in inaccurate results.
There is a great need in the art for a method for detecting and identifying yaw versus wind error angles based solely on SCADA operational data without the use of additional equipment.
Disclosure of Invention
The application provides a method for identifying the yaw wind incorrectness of a wind driven generator, which describes the relationship of wind angle-wind energy utilization through preprocessing, filtering and characteristic enhancement of data and using more reasonable characteristics.
According to the application, the method for identifying the wind power generator yaw is not correct for the wind comprises the following steps:
(1) collecting operation data of a wind generating set;
(2) preprocessing the data, and removing the data in the data under the abnormal operation state;
(3) initializing an iteration counter, and setting initial parameters and outlier proportion;
(4) filtering the data according to the set initial parameters and the outlier proportion to enhance the feature expression;
(5) carrying out box separation processing on the wind speed, and calculating the maximum wind energy utilization index of different wind angle pairs under each wind speed box;
(6) calculating whether the dispersion of the maximum wind energy utilization index obtained by all wind angle boxes meets the requirement or not, fitting a curve enveloping a wind angle-wind energy utilization index dispersion point graph, and calculating a wind angle numerical value corresponding to the maximum wind energy utilization index point obtained by the curve;
(7) judging the morphological characteristics of the fitted curve, and reserving the curve meeting the index requirement;
(8) and calculating deviation angles and reliable indexes for all envelope curves meeting the index requirements, judging the reliability of the result, and taking the final wind angle deviation as the median of all results.
Preferably, the step (2) further comprises converting the data with the wind angle range of [0,360] into the range of [ -180,180], wherein if the original wind angle value is greater than 180, the converted wind angle value is-360; and (4) filtering the data outliers by using an isolated forest algorithm.
Preferably, the maximum wind energy utilization index in the step (5) is the median of the 99 th quantile of the wind energy utilization index and the maximum value of the wind energy utilization index in the wind corner box pair.
Preferably, in step (6), a standard deviation of the maximum wind energy utilization indexes obtained by all the wind angle boxes is calculated, if the standard deviation exceeds a set threshold, all the maximum wind energy utilization indexes are fitted to a curve capable of enveloping a dispersed point graph of the wind angle-wind energy utilization indexes, and a wind angle value corresponding to the maximum point of the wind energy utilization index obtained by the curve is calculated, wherein the wind angle value is a wind deviation angle under the wind speed box.
Preferably, in the step (7), data in all wind speed boxes are calculated in a circulating mode, wind angle-wind energy utilization index envelopes and corresponding wind deviation angles are reserved, and if the standard deviation of all the wind deviation angles is larger than a set value, dispersion control is performed on all the wind deviation angles, and envelopes needing to be reserved are output; and if the standard deviation of all the wind deviation angles is less than or equal to the set value, directly entering the next step. More preferably, the flow of the wind deviation angle dispersion control method is as follows: (a) calculating median of all deviation angles, and recording as M; (b) calculating the distance between all deviation angle values Xi and M, wherein the distance is calculated as abs (Xi-M); (c) deleting the points farthest from M; (d) recalculating the standard deviation and median of the retained deviation angles; (e) and (d) repeatedly executing the steps (b) to (d) until the standard deviation is smaller than the set value.
Preferably, in step (8), the reliability of the result is judged from the number of retained envelopes and the deviation angle dispersion by a judgment method in which the greater the number of retained envelopes, the smaller the deviation angle dispersion, the more the result conforms to the model assumption, and a high reliability level is given. If the obtained reliability level is low and the iteration count does not exceed the set iteration time threshold, continuing to run the next iteration and setting the outlier proportion to be accumulated on the basis of the previous iteration; and if the iteration is terminated, judging the final result and the reliability grade to be output according to the result and the reliability grade of each iteration in the result collector.
The method can obtain more stable characteristic expression and more accurate result; because a reliability judgment program is introduced, reasonable uncertainty management is carried out on data which do not meet the model assumption, and the output result is more credible; by more accurately judging the wind deviation angle, the field operation and maintenance can be guided, and the loss of the generated energy is avoided.
Drawings
FIG. 1 is a schematic flow chart of a method for identifying a yaw misalignment of a wind turbine according to the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
The method for identifying yaw misalignment of a wind turbine according to the present invention will be described with reference to fig. 1. The method of the present application comprises the steps of:
(1) the method comprises the steps of collecting operating data of the wind generating set from an SCADA system, wherein the collected operating data can comprise wind speed, rotating speed, blade angle, wind angle, active power, fan operating state and the like. The collection mode can be that an equipment sensor is arranged at a corresponding position of the wind generating set.
(2) Preprocessing the data and eliminating the data in the abnormal operation state in the data. The abnormal operation state may include the states of shutdown, fault, maintenance, power limit, etc., and the abnormal data may be removed by using the fan state identification or based on the fan state control logic.
Specifically, for example, a data labeling module may be provided, which performs state labeling on the operation data in the relevant time range according to the management operation data of the states of shutdown, fault, maintenance, limited power, and the like. For example, according to the operation maintenance record, the equipment operation data after startup can be marked as health state data, the equipment operation data a period of time before the shutdown time is fault data, and the equipment operation data during the shutdown time is invalid data; for another example, the device operation data of a period of time before the detection result can be marked according to the oil detection result of the fan gearbox, and similarly, the device operation data can be marked as the fan gearbox with fault risk or the fan gearbox is normal.
In addition, the range of values for the wind angle in different data is different, since the definition of the wind angle may be different in different systems. Therefore, the data with the range of the wind angle value [0,360] can be converted into the range of [ -180,180] by calculating the converted wind angle value to be equal to the original wind angle value-360 if the original wind angle value is greater than 180.
Then, a power characteristic variable wind energy utilization index (Cp) is calculated, which is calculated as follows:
(3) The iteration counter is initialized, setting initial parameters, including setting a proportion of outliers, which may be 1%, for example.
(4) And filtering the data according to the set initial parameters and the outlier proportion to enhance the feature expression, wherein the feature enhancement method can be used for isolating forests to remove irrelevant data and abnormal data.
The isolated forest is an anomaly detection algorithm, can filter anomaly points and outliers, and is favorable for keeping the filtered data in an approximate ellipse or circle due to the algorithm principle, so that the data more conforms to the relationship between the wind angle and the power described by a physical model, namely the quadratic function relationship with a downward opening. The outlier ratio is the ratio of the maximum outliers to be filtered.
(5) And performing bin (bin) processing on the wind speed, and circularly processing data in each wind speed bin, wherein the size of the wind speed bin can be 0.5. And calculating the maximum power characteristics of different wind angles under each wind speed box. Specifically, the wind angle is fixedly divided according to the interval size of 0.5, and the maximum wind energy utilization index in each wind angle box is calculated. Considering the influence of outliers, the maximum wind energy utilization index is defined as the 99 th quantile of the wind energy utilization index and the median of the maximum value of the wind energy utilization index in the wind corner box to control the influence of singular points.
(6) And calculating the standard deviation of the maximum wind energy utilization indexes obtained by all wind angle boxes. If the standard deviation exceeds a set threshold value, fitting all the maximum wind energy utilization indexes into a curve capable of enveloping a scattered point graph of the wind angle-wind energy utilization index by adopting a fitting method such as local polynomial regression, and calculating a wind angle value corresponding to the maximum point of the wind energy utilization index obtained by the curve, wherein the wind angle value is the wind deviation angle under the wind speed box.
(7) And circularly calculating data in all wind speed boxes and keeping the wind angle-wind energy utilization index envelope curve and the corresponding wind deviation angle. If the standard deviation of all the wind deviation angles is larger than 5, the dispersion control is carried out on all the wind deviation angles, and envelope curves needing to be reserved are output. And if the standard deviation of all the wind deviation angles is less than or equal to 5, directly entering the next step.
The wind deviation angle dispersion control method comprises the following steps: (a) calculating median of all deviation angles, and recording as M; (b) calculating the distance between all deviation angle values Xi and M, wherein the distance is calculated as abs (Xi-M); (c) deleting the points farthest from M; (d) recalculating the standard deviation and median of the retained deviation angles; (e) and (d) repeatedly executing the steps (b) to (d) until the standard deviation is less than 5.
(8) The reliability of the result is judged according to the number of the retained envelopes and the deviation angle dispersion, and the judgment method is that the more the number of the retained envelopes is, the smaller the deviation angle dispersion is, the more the result conforms to the model assumption, and a high reliability level is given. And (4) taking the median of results under all wind speed boxes by the deviation angle, and collecting the results through a result collector.
And according to the reliability grade obtained by calculation, if the reliability grade is lower and the iteration count does not exceed the set iteration number threshold, continuing to run the next iteration. The iteration counter is incremented. If iteration is continued, the outlier proportion is set to be accumulated on the basis of the last iteration, the default accumulation value is 0.25%, and then the steps (4) - (8) are repeated by using the new parameters. And if the iteration is terminated, judging the final result and the reliability grade to be output according to the result and the reliability grade of each iteration in the result collector. The judgment method is that the decision is made according to the times of different grades in the result collector and the result dispersion, the more the times of the results with high reliability grade are, the lower the result dispersion is, and the higher the output reliability grade is. The final yaw angle deviation is the median of all results.
Examples
The method for identifying the yaw wind incorrectness of the wind driven generator is described by one embodiment, and the specific implementation steps comprise the following steps:
(1) acquiring monitoring data of a device sensor;
(2) preprocessing data, and deleting the data of the equipment in abnormal operation states such as faults, maintenance, power limitation and the like according to the fan state identification in the data; for data without a fan state identification variable, data with a blade angle less than 5 degrees may be selected. The fan controls the rotating speed through the blade angle to achieve shutdown or power limitation, and the blade angle is smaller than 5 degrees when the fan is generally shut down or power limitation is achieved.
(3) And calculating the value range of the wind angle data, and judging whether the maximum value exceeds 180 degrees. For the variable whose maximum exceeds 180 degrees, 360 degrees are subtracted from the data greater than or equal to 180 degrees in the variable to transform the value range to [ -180,180 ].
(4) And (3) retaining data of wind speed between [3 and 9], calculating a wind energy utilization index, and retaining data of the wind energy utilization index between [0 and 10 ].
(5) Dividing the wind angle into a series of intervals of [ -180, -179), [ -179, -178), …, [179,180] with a fixed interval width, for example, dividing the wind angle into intervals of 1 interval width, filtering the wind energy utilization index data in each wind angle interval by using an isolated forest with a set initial anomaly point proportion of 1%, outputting the filtered data, and merging all the filtered data of the wind angle intervals.
(6) The wind speed is divided into intervals with fixed intervals, for example, the wind speed is divided into a series of intervals of [3,3.5 ], [3.5,4 ], …, [8.5,9] with the interval width of 0.5.
(7) And circularly or parallelly extracting data in each wind speed interval for individual calculation. And calculating the 99 th quantile of the wind energy utilization index and the median of the maximum value of the wind energy utilization index in each wind speed interval and each wind angle interval as the maximum wind energy utilization index.
(8) In each wind speed interval (i wind speed interval is represented by WSbin [ i ], n wind speed intervals in total), calculating the standard deviation of the maximum wind energy utilization indexes obtained in all wind angle intervals, and if the standard deviation exceeds a set threshold (such as 0.2), fitting the standard deviation of all the maximum wind energy utilization indexes into a curve by using a local polynomial regression method, and calculating the corresponding wind angle of the maximum wind energy utilization index under the curve, namely the wind angle deviation angle of the wind speed (WSbin [ i ] is represented by yaw _ error [ i ]).
(9) Obtaining wind angle deviation results of all wind speed intervals (yaw _ error [1], yaw _ error [2], …, yaw _ error [ i ], …, yaw _ error [ n ] (mean vector standard deviation), if the standard deviation is larger than 5, carrying out data processing on vectors to control dispersion to eliminate abnormal values until the standard deviation reaches a set parameter value or the data length is not smaller than 2).
(10) The reliability of the result is judged by using the deviation angle dispersion, and the judgment method is that the more the number of the reserved envelope lines is, the smaller the deviation angle dispersion is, the more the result accords with the model hypothesis, and a high reliability level is given. And taking the median of the results in all wind speed intervals by the deviation angle. The results of the above calculations are collected by a result collector. And according to the reliability grade obtained by calculation, if the reliability grade is lower and the iteration count does not exceed the set iteration number threshold, continuing to run the next iteration. The iteration counter is incremented.
And if the next iteration is carried out, adjusting the proportion of outliers and accumulating on the basis of the previous iteration, wherein the default accumulation value is 0.25%. And collecting results of all iterations until a set iteration number is reached.
And if the iteration is terminated, judging the final result and the reliability grade to be output according to the result and the reliability grade of each iteration in the result collector. The judgment method is that the decision is made according to the times of different grades in the result collector and the result dispersion, the more the times of the results with high reliability grade are, the lower the result dispersion is, and the higher the output reliability grade is. The final yaw angle deviation is the median of all results.
Although the embodiments disclosed in the present application are described above, the descriptions are only for the convenience of understanding the present application, and are not intended to limit the present application. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims.
Claims (6)
1. A method for identifying wind power generator yaw is not correct to wind is characterized by comprising the following steps:
(1) collecting operation data of a wind generating set;
(2) preprocessing the data, and removing the data in the data under the abnormal operation state;
(3) initializing an iteration counter, and setting initial parameters and outlier proportion;
(4) filtering the data according to the set initial parameters and the outlier proportion to enhance the feature expression;
(5) carrying out box separation processing on the wind speed, and calculating the maximum wind energy utilization index of different wind angle pairs under each wind speed box;
(6) calculating whether the dispersion of the maximum wind energy utilization index obtained by all wind angle boxes meets the requirement or not, fitting a curve enveloping the dispersed point graph of the wind angle-maximum wind energy utilization index, and calculating the wind angle value corresponding to the maximum wind energy utilization index point obtained by the curve;
(7) judging the morphological characteristics of the fitted curve, and reserving the curve meeting the index requirement;
(8) and calculating deviation angles and reliable indexes for all envelope curves meeting the index requirements, judging the reliability of the result, and taking the final wind angle deviation as the median of all results.
2. The method for identifying the yaw misalignment of the wind turbine as claimed in claim 1, wherein the step (2) further comprises converting the data of the range of the yaw misalignment of the wind turbine into the range of [ -180,180] in such a way that if the original value of the wind misalignment is greater than 180, the converted value of the wind misalignment is equal to-360.
3. The method for identifying the yawing improper wind turbine generator in the wind pair of claim 1 or 2, wherein the maximum wind energy utilization index in the step (5) is a median of a 99 th quantile of the wind energy utilization index and a maximum value of the wind energy utilization index in the wind pair angle box.
4. The method for identifying the yawing improper wind of the wind driven generator according to claim 1 or 2, wherein in the step (6), the standard deviation of the maximum wind energy utilization indexes obtained by all wind angle boxes is calculated, if the standard deviation exceeds a set threshold value, all the maximum wind energy utilization indexes are fitted to a curve capable of enveloping a scattered point graph of the wind angle-maximum wind energy utilization indexes, and the value of the wind angle corresponding to the maximum wind energy utilization index point obtained by the curve is calculated, wherein the value is the wind deviation angle under the wind speed box;
in the step (7), circularly calculating data in all wind speed boxes, and keeping a wind angle-maximum wind energy utilization index envelope curve and corresponding wind deviation angles, if the standard deviation of all the wind deviation angles is larger than a set value, carrying out dispersion control on all the wind deviation angles, and outputting the envelope curve which needs to be kept; if the standard deviation of all the wind deviation angles is less than or equal to the set value, directly entering the next step;
in step (8), the reliability of the result is determined based on the number of retained envelopes and the deviation angle dispersion by a method in which the greater the number of retained envelopes, the smaller the deviation angle dispersion, the more the result conforms to the model assumption, and a high reliability level is given.
5. The method for identifying the wind power generator yaw wind incorrectness is characterized in that if the obtained reliability level is low and the iteration count does not exceed the set iteration number threshold, the next iteration is continuously operated, and the outlier proportion is set to be accumulated on the basis of the previous iteration;
and if the iteration is terminated, judging the final result and the reliability grade to be output according to the result and the reliability grade of each iteration in the result collector.
6. The method for identifying the yaw wind incorrectness of the wind driven generator as claimed in claim 4, wherein the flow of the wind deviation angle dispersion control method is as follows: (a) calculating median of all deviation angles, and recording as M; (b) calculating the distance between all deviation angle values Xi and M, wherein the distance is calculated as abs (Xi-M); (c) deleting the points farthest from M; (d) recalculating the standard deviation and median of the retained deviation angles; (e) and (d) repeatedly executing the steps (b) to (d) until the standard deviation is smaller than the set value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910395581.6A CN110094310B (en) | 2019-05-13 | 2019-05-13 | Method for identifying wind power generator yaw wind disharmony |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910395581.6A CN110094310B (en) | 2019-05-13 | 2019-05-13 | Method for identifying wind power generator yaw wind disharmony |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110094310A CN110094310A (en) | 2019-08-06 |
| CN110094310B true CN110094310B (en) | 2020-05-19 |
Family
ID=67447926
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910395581.6A Active CN110094310B (en) | 2019-05-13 | 2019-05-13 | Method for identifying wind power generator yaw wind disharmony |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110094310B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110985309B (en) | 2019-12-09 | 2022-03-11 | 远景智能国际私人投资有限公司 | Yaw wind anomaly detection method, device, equipment and storage medium |
| CN110886681B (en) * | 2019-12-13 | 2021-04-27 | 北京三力新能科技有限公司 | Yaw angle positioning control method based on time partition and yaw sector |
| CN111882833B (en) * | 2020-07-21 | 2021-09-21 | 华润电力唐山丰润有限公司 | Equipment fault early warning method, device, equipment and medium based on outlier parameters |
| CN112730877A (en) * | 2020-12-18 | 2021-04-30 | 云南滇能智慧能源有限公司 | Wind turbine generator yaw frequent detection early warning algorithm |
| CN113236507B (en) * | 2021-05-28 | 2022-08-19 | 中南大学 | Yaw static error diagnosis method and system for wind turbine generator |
| CN116502543B (en) * | 2023-06-21 | 2023-09-22 | 宇动源(北京)信息技术有限公司 | Wind turbine generator set wind deviation recognition method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103629044A (en) * | 2013-12-18 | 2014-03-12 | 中国科学院工程热物理研究所 | Blade root structure of horizontal-axis wind turbine blade |
| CN104481804A (en) * | 2014-12-05 | 2015-04-01 | 北京金风科创风电设备有限公司 | Wind power generator group wind correction control method, device and system |
| CN105508148A (en) * | 2015-12-31 | 2016-04-20 | 北京金风科创风电设备有限公司 | Method and system for capturing maximum wind energy based on wind energy distribution |
| CN106014858A (en) * | 2016-07-21 | 2016-10-12 | 浙江运达风电股份有限公司 | Automatic calibration method and device for air aligning errors of wind generation set |
| CN109139371A (en) * | 2018-02-28 | 2019-01-04 | 北京金风科创风电设备有限公司 | Method, device and system for determining deviation of wind angle and correcting wind angle |
-
2019
- 2019-05-13 CN CN201910395581.6A patent/CN110094310B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103629044A (en) * | 2013-12-18 | 2014-03-12 | 中国科学院工程热物理研究所 | Blade root structure of horizontal-axis wind turbine blade |
| CN104481804A (en) * | 2014-12-05 | 2015-04-01 | 北京金风科创风电设备有限公司 | Wind power generator group wind correction control method, device and system |
| CN105508148A (en) * | 2015-12-31 | 2016-04-20 | 北京金风科创风电设备有限公司 | Method and system for capturing maximum wind energy based on wind energy distribution |
| CN106014858A (en) * | 2016-07-21 | 2016-10-12 | 浙江运达风电股份有限公司 | Automatic calibration method and device for air aligning errors of wind generation set |
| CN109139371A (en) * | 2018-02-28 | 2019-01-04 | 北京金风科创风电设备有限公司 | Method, device and system for determining deviation of wind angle and correcting wind angle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110094310A (en) | 2019-08-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110094310B (en) | Method for identifying wind power generator yaw wind disharmony | |
| CN110067708B (en) | Method for identifying yaw wind disharmony by using power curve | |
| CN103291544B (en) | Digitizing Wind turbines power curve method for drafting | |
| CN107798462A (en) | A kind of wind power plant wind power generating set operation exception monitor and performance evaluation system | |
| CN110985309B (en) | Yaw wind anomaly detection method, device, equipment and storage medium | |
| CN111260503B (en) | Wind turbine generator power curve outlier detection method based on cluster center optimization | |
| CN110362045B (en) | Marine doubly-fed wind turbine generator fault discrimination method considering marine meteorological factors | |
| KR20170084955A (en) | Failure diagnosing method and apparatus of wind turbine | |
| CN110578659B (en) | System and method for processing SCADA data of wind turbine generator | |
| CN114165392A (en) | Wind turbine generator set power abnormity diagnosis method and device and storage medium | |
| CN116771610A (en) | Method for adjusting fault evaluation value of variable pitch system of wind turbine | |
| CN112145373B (en) | Wind speed correction method for wind meter of wind driven generator | |
| CN112855458B (en) | Anemometer fault diagnosis method, system and medium for wind generating set | |
| CN116085212B (en) | Method and system for monitoring running state of new energy wind turbine generator in real time | |
| CN111209914A (en) | Method for eliminating abnormal wind power data | |
| CN114912807A (en) | Method and system for evaluating generated energy improving effect of technically improved wind turbine generator | |
| CN113848347A (en) | Health state detection method for wind meter of wind driven generator | |
| Qiao et al. | Research on SCADA data preprocessing method of Wind Turbine | |
| CN111141382B (en) | Vibration-based multi-dimensional fan blade self-diagnosis device and method | |
| CN114033631B (en) | Online identification method for wind energy utilization coefficient of wind turbine generator | |
| CN112459970B (en) | Wind turbine generator anomaly detection method using data counterstudy | |
| CN111878326B (en) | Method and system for detecting freezing fault of fan, electronic equipment and medium | |
| CN116861219B (en) | Wind turbine generator pitch-variable fault diagnosis method | |
| CN112800103B (en) | System and method for acquiring power curve of wind turbine generator based on SCADA data mining | |
| CN116816615A (en) | Online monitoring method and system for power of wind driven generator |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Xie Peng Inventor after: Jin Chao Inventor after: Jin Wenjing Inventor after: Shi Zhe Inventor after: Li Jie Inventor before: Xie Peng Inventor before: Jin Chao Inventor before: Wen Jing Inventor before: Shi Zhe Inventor before: Li Jie |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |