CN113884705A - Monitoring method and system of cluster fan anemometer and computer readable storage medium - Google Patents
Monitoring method and system of cluster fan anemometer and computer readable storage medium Download PDFInfo
- Publication number
- CN113884705A CN113884705A CN202111142539.7A CN202111142539A CN113884705A CN 113884705 A CN113884705 A CN 113884705A CN 202111142539 A CN202111142539 A CN 202111142539A CN 113884705 A CN113884705 A CN 113884705A
- Authority
- CN
- China
- Prior art keywords
- fan
- cluster
- wind
- wind speed
- speed data
- 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
- 238000000034 method Methods 0.000 title claims abstract description 60
- 238000012544 monitoring process Methods 0.000 title claims abstract description 59
- 230000002159 abnormal effect Effects 0.000 claims abstract description 14
- 238000012790 confirmation Methods 0.000 claims description 7
- 238000012549 training Methods 0.000 claims description 6
- 238000004140 cleaning Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000010200 validation analysis Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 2
- 230000015654 memory Effects 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 101100182247 Caenorhabditis elegans lat-1 gene Proteins 0.000 description 2
- 101100182248 Caenorhabditis elegans lat-2 gene Proteins 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000000717 retained effect Effects 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 1
- 101150061388 LON1 gene Proteins 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P21/00—Testing or calibrating of apparatus or devices covered by the preceding groups
- G01P21/02—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers
- G01P21/025—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers for measuring speed of fluids; for measuring speed of bodies relative to fluids
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Indicating Or Recording The Presence, Absence, Or Direction Of Movement (AREA)
- Wind Motors (AREA)
Abstract
The embodiment of the invention provides a monitoring method and a system of cluster fan anemometers and a computer readable storage medium. The method comprises the following steps: acquiring wind speed data of an anemometer of a fan in a wind power plant and spatial position information of the fan; determining a fan cluster group based on the spatial position information of every two fans in the wind power plant; and monitoring the anemometers of all the fans in the cluster of fans for anomalies based on the wind speed data of the anemometers of all the fans in the cluster of fans. The embodiment of the invention can easily determine the fan cluster group based on the spatial position information, and can carry out abnormal monitoring on the anemometers of all fans in one fan cluster group, thereby greatly improving the monitoring efficiency.
Description
Technical Field
The embodiment of the invention relates to the technical field of wind power generation, in particular to a monitoring method and a monitoring system for cluster fan anemometers and a computer readable storage medium.
Background
With the gradual depletion of energy sources such as coal and petroleum, human beings increasingly pay more attention to the utilization of renewable energy sources. Wind energy is increasingly gaining attention as a clean renewable energy source in all countries of the world. With the continuous development of wind power technology, the application of fans in power systems is increasing day by day. The wind turbine is a large-scale device for converting wind energy into electric energy, and is usually arranged in areas with rich wind energy resources.
At present, the abnormity early warning of the wind turbine anemometer judges similar wind turbines by utilizing the correlation among data of different wind turbines, and judges whether abnormity occurs or not based on the data statistical difference among the similar wind turbines. However, in the actual process, the phenomenon that similar fans change constantly occurs, which is not beneficial to the selection of reference fans, and different statistical methods and means also have great influence on the abnormal early warning.
Disclosure of Invention
Embodiments of the present invention provide a method and a system for monitoring wind anemometers of cluster fans, and a computer readable storage medium, which can easily determine a fan cluster based on spatial location information, and can monitor wind anemometers of all fans in a fan cluster, thereby greatly improving monitoring efficiency.
One aspect of an embodiment of the present invention provides a method for monitoring wind anemometers of cluster wind turbines. The method comprises the following steps: acquiring wind speed data of an anemometer of a fan in a wind power plant and spatial position information of the fan; determining a fan cluster group based on the spatial position information of every two fans in the wind power plant; and monitoring the anemometers of all the fans in the cluster of fans for anomalies based on the wind speed data of the anemometers of all the fans in the cluster of fans.
In another aspect of the embodiment of the invention, a monitoring system of the cluster wind turbine anemometer is further provided. The monitoring system of the cluster wind turbine anemometer comprises one or more processors and is used for realizing the monitoring method of the cluster wind turbine anemometer.
Yet another aspect of an embodiment of the present invention also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a program which, when executed by a processor, implements a method of monitoring cluster wind turbine anemometers as described above.
The monitoring method and the system of the cluster fan anemometer and the computer readable storage medium of the embodiment of the invention can determine the fan cluster based on the space position information of the fan, the fan cluster is simple and reliable to select, and the random selection of the fan cluster is greatly reduced.
In addition, the monitoring method, the system and the computer readable storage medium of the cluster group fan anemometer in the embodiment of the invention can monitor the anemometers of all fans in the whole fan cluster in an abnormal way, thereby greatly improving the efficiency of monitoring the abnormal situation.
Drawings
FIG. 1 is a flow chart of a method of monitoring cluster fan anemometers in accordance with one embodiment of the present invention;
FIG. 2 is a schematic diagram of determining a cluster of fans, in accordance with one embodiment of the present invention;
FIG. 3 illustrates the specific steps of anomaly monitoring of the anemometers of all of the wind turbines in a cluster of wind turbines based on the wind speed data of the anemometers of all of the wind turbines in the cluster of wind turbines, in accordance with an embodiment of the present invention;
FIG. 4 is a schematic view of a VAE model according to one embodiment of the present invention;
FIG. 5 is a schematic block diagram of a monitoring system for cluster fan anemometers in accordance with one embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Unless otherwise defined, technical or scientific terms used in the embodiments of the present invention should have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. The use of "first," "second," and similar terms in the description and in the claims does not indicate any order, quantity, or importance, but rather is used to distinguish one element from another. Also, the use of the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one. "plurality" or "a number" means two or more. Unless otherwise indicated, "front", "rear", "lower" and/or "upper" and the like are for convenience of description and are not limited to one position or one spatial orientation. The word "comprising" or "comprises", and the like, means that the element or item listed as preceding "comprising" or "includes" covers the element or item listed as following "comprising" or "includes" and its equivalents, and does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
The embodiment of the invention provides a monitoring method of cluster fan anemometers. FIG. 1 discloses a flow chart of a method for monitoring cluster wind turbine anemometers in accordance with one embodiment of the present invention. As shown in fig. 1, the monitoring method of the cluster wind turbine anemometer according to one embodiment of the present invention may include steps S11 to S13.
In step S11, wind speed data of anemometers of wind turbines 10 (shown in fig. 2) and spatial position information of the wind turbines 10 in the wind farm 1 are acquired.
A plurality of wind turbines 10 are included in the wind farm 1. The wind turbine 10 may generate a large amount of status Data during the operation process, And an SCADA (Supervisory Control And Data Acquisition) system of the wind farm 1 may collect And store the Data, for example: wind speed data, power data and the like of the anemometer so as to develop intelligent operation and maintenance data mining work. The spatial position information, the impeller diameter, the owner information, and the like of the wind turbine 10 are also typically recorded in the configuration information table of the wind turbine 10 and stored in the database of the SCADA system as the record information of the wind farm 1.
Thus, wind speed data of the anemometers of the wind turbines 10 and spatial position information of the wind turbines 10 in the wind farm 1 may be obtained from the SCADA system of the wind farm 1. The anemometer is typically mounted at the rear end of the nacelle for each wind turbine 10.
In step S12, a wind turbine cluster G may be determined based on the spatial location information of two wind turbines 10 in the wind farm 1. There is a relatively large correlation between the wind turbines 10 in a wind turbine cluster G.
In some embodiments, the spatial location information of the wind turbine 10 may include longitude and latitude coordinates of the wind turbine 10. The determining of the wind turbine cluster G based on the spatial position information of two wind turbines 10 in the wind farm 1 in step S12 may further include step S121 and step S122.
In step S121, a distance D (as shown in fig. 2) between two wind turbines 10 may be calculated based on the longitude and latitude coordinates of the two wind turbines 10 in the wind farm 1.
For any two wind turbines 10 in the wind farm 1, for example, the wind turbine 10A and the wind turbine 10B, the longitude and latitude coordinates of the wind turbine 10A are (lat1, lon1) and the longitude and latitude coordinates of the wind turbine 10B are (lat2, lon 2).
An example of a calculation formula of Distance (abbreviated as D) between every two fans 10 (for example, the fan 10A and the fan 10B) is shown as follows:
Distance＝Re×(x+Ob/8×(C1-C2))
wherein, ObIs the oblateness of the earth, the oblateness of the earth ObThe calculation formula of (a) is as follows:
Ob＝(Re-Rb)/Re
wherein R iseThe equatorial radius of the earth, its value is 6378137 m; rbIs the polar radius of the earth, with a value of 6356752 m;
x＝arccos(sin(Pa)×sin(Pb)+cos(Pa)×cos(Pb)×cos(lon1–lon2))
C1＝(sin(x)-x)×(sin(Pa)+sin(Pb))^2/cos(x/2)^2
C2＝(sin(x)+x)×(sin(Pa)-sin(Pb))^2/sin(x/2)^2
Pa＝arctan(Rb/Re×tan(lat1))
Pb＝arctan(Rb/Re×tan(lat2))
therefore, when the longitude and latitude coordinates of the two fans 10 are obtained, the distance D between the two fans 10 can be calculated by the above formula. By analogy, the distance D between every two fans 10 in the wind farm 1 can be calculated according to the longitude and latitude coordinates between every two fans 10 in the wind farm 1.
After the distance D between every two fans 10 is calculated, the relationship between the fans 10 can be determined. Then, in step S122, the fan cluster G may be determined based on the distance D between the two fans 10 calculated in step S121.
How to determine the wind turbine cluster G based on the distance D between each two wind turbines 10 will be described in detail below in connection with FIG. 2.
As shown in fig. 2, a plurality of wind turbines 10 are included in the wind farm 1. After the distance D between every two fans 10 in the wind farm 1 is calculated, a target fan 11 to be monitored may be selected from the wind farm 1, the distance D between the target fan 11 and other fans 10 in the wind farm 1 is extracted from the calculated distance D between every two fans 10 in the wind farm 1, and a neighboring fan 12 of the target fan 11 may be determined based on the distance D between the target fan 11 and other fans 10 in the wind farm 1.
In some embodiments, when the distance D between the target fan 11 and any other fan 10 in the wind farm 1 is less than or equal to a predetermined distance threshold, then that fan 10 is determined to be a neighboring fan 12 of the target fan 11.
In one embodiment, the distance threshold may be determined at a predetermined multiple of the impeller diameter of the wind turbine 10. The method is characterized in that a preset multiple of the diameter of an impeller is used as a distance threshold value, based on the position relation between fans and consideration of terrain, climate factors and the like, and in order to eliminate wake flow influence between the fans, the distance between the fans is usually larger than the preset multiple of the diameter of the impeller; in addition, if the distance is too far away, the wind speed data is easy to be interfered by terrain and weather factors, so that the difference of the wind speed data is too large, and modeling is difficult. Thus, for example, the distance threshold may be determined at 5 impeller diameters, thereby both reducing wake effects between fans and facilitating modeling. Specifically, a circle may be drawn with a radius of 5 times the diameter of the impeller centered on the target fan 11, and all fans drawn within the circle may be considered as neighboring fans 12 of the target fan 11. As shown in fig. 2, four fans in the four directions of the front, rear, left, and right of the target fan 11 are respectively identified as the adjacent fans 12 of the target fan 11.
In another embodiment, the distance threshold may be determined according to the layout of the wind turbines 10 in the wind farm 1, for example, for a wind farm 1 arranged in a matrix, the maximum of the row spacing and the column spacing of the wind turbines 10 in the wind farm 1.
Then, the target fan 11 and all neighboring fans 12 of the target fan 11 are selected as one fan cluster G.
According to the monitoring method of the cluster fan anemometer, the fan cluster G can be determined according to the spatial position information among the fans 10, and the fans 10 with high correlation are divided into the fan cluster G according to the spatial position information, so that the determination method of the fan cluster G is simple and convenient, and the difficulty in fan selection is reduced; moreover, once the fan cluster group G is determined, the fan cluster group G is relatively reliable and fixed, and the phenomenon that the existing similar fan 10 is changed constantly is avoided.
In the whole wind farm 1, the whole wind farm 1 can be divided into a plurality of wind turbine clusters G according to the correlation between the wind turbines 10 by reasonably selecting a plurality of target wind turbines 11, so that all the wind turbines 10 of the whole wind farm 1 can be covered. In some embodiments, to reduce the number of fan clusters and improve the operation efficiency, the number of fans in the fan cluster G is preferably greater than or equal to 3.
Referring back to fig. 1, after preliminarily determining a fan cluster G based on the spatial position information between two fans 10 in step S12, in order to further determine the correlation between adjacent fans 12 in the fan cluster G, the monitoring method of the cluster fan anemometer according to the embodiment of the present invention may further include step S21. In step S21, the wind turbine cluster G in step S12 may be secondarily confirmed based on the correlation of the wind speed data of the wind turbine anemometers in the wind turbine cluster G to obtain a secondarily confirmed wind turbine cluster G'. The fan cluster group is comprehensively determined based on the spatial position information and the operation data information, and the monitoring efficiency is further improved.
In some embodiments, the secondary validation of fan cluster G based on the correlation of the wind speed data of the fan anemometers in fan cluster G in step S21 may further include steps S211 and S212.
In step S211, a correlation coefficient between the wind speed data of the target fan 11 and the anemometers of the neighboring fans 12 in the fan cluster G is calculated.
In one embodiment, the correlation coefficient between the wind speed data of the anemometer of the target wind turbine 11 and the adjacent wind turbine 12 may be a Pearson correlation coefficient (Pearson correlation coefficient). Therefore, in step S211, a pearson correlation coefficient between the wind speed data of the target wind turbine 11 and the anemometers of the neighboring wind turbines 12 in the wind turbine cluster G may be calculated.
The calculation formula of the pearson correlation coefficient between the wind speed data of the target fan 11 and the anemometer adjacent to the fan 12 is as follows:
where r represents the Pearson correlation coefficient, n represents the number of samples, XiWind speed data, Y, of an anemometer representing the target wind turbine 11iRepresenting wind speed data for an anemometer adjacent to wind turbine 12,
represents the mean value of the wind speed of the anemometer of the target fan 11,
representing the mean value, σ, of the wind speed of the anemometers adjacent to wind turbine 12XRepresents the standard deviation of the wind speed of the anemometer of the target fan 11,σYrepresenting the standard deviation of the wind speed of the anemometer adjacent to wind turbine 12.
In step S212, the fan cluster G obtained in step S12 may be secondarily confirmed based on the correlation coefficient, for example, the pearson correlation coefficient r, between the wind speed data of the anemometers of the target fan 11 and the neighboring fans 12 in the fan cluster G calculated in step S211 to obtain a secondarily confirmed fan cluster G'.
When the calculated pearson correlation coefficient between the wind speed data of the anemometer of the target wind turbine 11 and any one of the neighboring wind turbines 12 in the wind turbine cluster G is greater than or equal to a predetermined value, for example, greater than 0.9, the neighboring wind turbine 12 is retained in the wind turbine cluster G; when the calculated pearson correlation coefficient between the wind speed data of the anemometer of the target wind turbine 11 and any neighboring wind turbine 12 is smaller than a predetermined value, for example, smaller than 0.9, the neighboring wind turbine 12 is deleted from the wind turbine cluster G. Accordingly, the fan cluster group G' after the secondary confirmation can be obtained, the correlation between the fans 10 in the fan cluster group G can be further ensured, and the division accuracy of the fan cluster group G can be further improved. For example, as shown in fig. 2, if the pilson correlation coefficient between the wind speed data of the anemometers of the target fan 11 and the adjacent fans 12 in front of, behind, and on the right of the target fan 11 is found to be greater than 0.9 and the pilson correlation coefficient between the wind speed data of the anemometers of the target fan 11 and the adjacent fans 12 on the left of the target fan 11 is found to be less than 0.9 by the above-described calculation of the pilson correlation coefficient, the three adjacent fans 12 in front of, behind, and on the right of the target fan 11 are retained in the fan cluster G, and the adjacent fan 12 on the left of the target fan 11 is deleted from the fan cluster G, thereby obtaining a fan cluster G' after secondary confirmation.
In some embodiments, if the anemometer of the selected target fan 11 is abnormal, the pearson correlation coefficient between the wind speed data of the target fan 11 and the wind speed data of the anemometer of any one of the neighboring fans 12 in the fan cluster G is very poor, at this time, it may be directly determined that the anemometer of the selected target fan 11 is abnormal, and then the target fan needs to be additionally selected to determine the fan cluster for subsequent abnormality monitoring.
Continuing back to fig. 1, in step S13, the anemometers of all of the wind turbines 10 in the wind turbine cluster G may be abnormally monitored based on the determined wind speed data of the anemometers of all of the wind turbines 10 in the wind turbine cluster G in step S12.
In the case that the monitoring method of the cluster wind turbine anemometer according to the embodiment of the present invention includes step S21, in step S13, the anemometers of all wind turbines 10 in the wind turbine cluster G 'after the secondary confirmation in step S21 may be monitored for abnormality based on the wind speed data of the anemometers of all wind turbines 10 in the wind turbine cluster G' after the secondary confirmation in step S21.
How the anemometers of all wind turbines 10 of a wind turbine cluster G/G 'are monitored for anomalies based on the wind speed data of the anemometers of all wind turbines 10 of the cluster G/G' will be described in detail below in connection with FIG. 3.
The wind speed data of the wind turbine anemometer in the wind farm 1 acquired in step S11 includes historical wind speed data and current wind speed data. From the wind speed data of the anemometers of the wind turbines in the wind farm 1, historical wind speed data and current wind speed data for a predetermined period of time (e.g. within one year) for the anemometers of all wind turbines 10 in the cluster G/G' of wind turbines may be extracted.
The step S13 of monitoring the anemometers of all wind turbines 10 in the wind turbine cluster G/G 'for anomalies based on the wind speed data of the anemometers of all wind turbines 10 in the wind turbine cluster G/G' may comprise the steps S31 to S33.
As shown in fig. 3, in step S31, the acquired historical wind speed data of the anemometers of all the wind turbines 10 in the wind turbine cluster G/G' within a predetermined time period (for example, within one year) is input into a VAE (Variational Auto-Encoder) model for training, and parameter information of the VAE model is generated, so that the trained VAE model can be obtained. FIG. 4 discloses a schematic diagram of a VAE model according to an embodiment of the present invention. The VAE model includes an input layer, an output layer, and a plurality of hidden layers between the input layer and the output layer. In the embodiment of the invention, the data input by the input layer is the real-time wind speed of all the fans 10 in the fan cluster group G/G 'at a certain moment, and the data output by the output layer is the predicted wind speed of all the fans 10 in the fan cluster group G/G' corresponding to the moment. It should be noted that the VAE model graph shown in fig. 4 is only one of many VAEs, however, the VAE model of the embodiment of the present invention is not limited to the limited form shown in fig. 4. The trained VAE model can be used for judging the abnormity of the wind turbine anemometer at the later time.
In some embodiments, in order to improve the quality of the acquired wind speed data, before step S31, the step S13 of the embodiment of the present invention of monitoring the anemometers of all the wind turbines 10 in the wind turbine cluster G/G 'based on the wind speed data of the anemometers of all the wind turbines 10 in the wind turbine cluster G/G' may further include step S30. In step S30, the acquired historical wind speed data for a predetermined period of time (e.g., within one year) of the anemometers of all of the wind turbines 10 in the wind turbine cluster G/G' is preprocessed to obtain preprocessed historical wind speed data.
Preprocessing the historical wind speed data over the predetermined time period may include at least one of:
treatment 1: and performing data cleaning on the historical wind speed data.
Wherein, the data cleaning of the historical wind speed data may further comprise processing 1.1 and/or processing 1.2. In process 1.1, wind speed data that is less than a predetermined first wind speed threshold and greater than a predetermined second wind speed threshold, which is greater than the first wind speed threshold, may be deleted from the historical wind speed data in view of the size of the amount of data available. For example, wind speed data less than 3 meters/second and greater than 20 meters/second are removed from the historical wind speed data. In process 1.2, wind speed data with data missing may be deleted from the historical wind speed data.
And (3) treatment 2: and carrying out normalization processing on the cleaned historical wind speed data.
Therefore, after the preprocessing, higher-quality historical wind speed data can be obtained.
In the case where step S13 includes step S30, the inputting of the historical wind speed data into the VAE model for training in step S31 includes: inputting the preprocessed historical wind speed data in the step S30 into the VAE model for training, so as to obtain a trained VAE model.
After the trained VAE model is obtained, the process may proceed to step S32. In step S32, the acquired current wind speed data of the anemometers of all the wind turbines 10 in the wind turbine cluster G/G 'may be input into the trained VAE model obtained in step S31, so that the predicted wind speed data corresponding to each wind turbine 10 in the wind turbine cluster G/G' output by the trained VAE model may be obtained. Then, the process may proceed to step S33.
In step S33, it may be determined whether the anemometer of each of the wind turbines 10 in the wind turbine cluster G/G 'is abnormal based on the current wind speed data of each of the wind turbines 10 in the wind turbine cluster G/G' and the corresponding predicted wind speed data obtained in step S32.
The step S33 of determining whether the anemometer of each wind turbine 10 in the wind turbine cluster G/G 'is abnormal based on the current wind speed data and the corresponding predicted wind speed data of each wind turbine 10 in the wind turbine cluster G/G' may further include steps S331 to S334.
Continuing with fig. 3, in step S331, a difference between the current wind speed data and the corresponding predicted wind speed data of each wind turbine 10 in the wind turbine cluster G/G 'is calculated to obtain a wind speed residual of each wind turbine 10 in the wind turbine cluster G/G'.
In step S332, it is determined whether the wind speed residual of each fan 10 in the fan cluster G/G' obtained in step S331 exceeds a predetermined wind speed threshold. In the case of yes, the process further proceeds to step S333. Otherwise, the process returns to step S331, and the wind speed residuals of the wind turbines 10 in the wind turbine cluster G/G' at the next sampling time are calculated.
In step S333, if it is determined in step S332 that the wind speed residual of each fan 10 in the fan cluster G/G' exceeds the predetermined wind speed threshold, it is continuously determined whether the predetermined wind speed threshold continues for a predetermined period. In the case of yes judgment, the process proceeds to step S334. Otherwise, the process returns to step S331.
In step S334, when the wind speed residual of a certain wind turbine 10 in the wind turbine cluster G exceeds a predetermined wind speed threshold and lasts for a predetermined period, it is determined that the anemometer of the wind turbine 10 is abnormal.
Referring back to fig. 1, in some embodiments, the monitoring method of the cluster wind turbine anemometer according to the embodiments of the present invention may further include step S14. In step S14, when it is detected that an anemometer of a certain wind turbine 10 in the wind turbine cluster G is abnormal, a fault alarm of the anemometer is triggered.
The monitoring method of the cluster fan anemometer provided by the embodiment of the invention can determine the fan cluster G based on the spatial position information of the fan 10, the fan cluster G is simple to select, and the difficulty in selecting the fan cluster G is greatly reduced.
In addition, the monitoring method of the cluster group fan anemometer of the embodiment of the invention can carry out abnormity monitoring and early warning on the anemometers of all the fans 10 in the whole fan cluster group G, thereby greatly improving the efficiency of abnormity monitoring and early warning. In addition, the monitoring method of the cluster group fan anemometer provided by the embodiment of the invention can also be used for carrying out abnormity monitoring and early warning on the fan anemometer in each fan cluster group G in a plurality of fan cluster groups G in the whole wind power plant, so that the abnormity monitoring and early warning efficiency is improved to the greatest extent.
The embodiment of the invention also provides a monitoring system 200 of the cluster fan anemometer. FIG. 5 discloses a schematic block diagram of a monitoring system 200 for cluster fan anemometers in accordance with one embodiment of the present invention. As shown in fig. 5, the monitoring system 200 of the cluster wind turbine anemometer may include one or more processors 201 for implementing the monitoring method of the cluster wind turbine anemometer according to any of the above embodiments. In some embodiments, the cluster wind turbine anemometer monitoring system 200 may include a computer readable storage medium 202, and the computer readable storage medium 202 may store a program that may be invoked by the processor 201 and may include a non-volatile storage medium. In some embodiments, the cluster wind turbine anemometer monitoring system 200 may include a memory 203 and an interface 204. In some embodiments, the monitoring system 200 of the cluster wind turbine anemometer according to the embodiments of the present invention may further include other hardware according to practical applications.
The monitoring system 200 of the cluster fan anemometer according to the embodiment of the present invention has similar beneficial technical effects to the monitoring method of the cluster fan anemometer described above, and therefore, the details are not repeated herein.
The embodiment of the invention also provides a computer readable storage medium. The computer readable storage medium has stored thereon a program which, when executed by a processor, implements the method for monitoring cluster wind turbine anemometers according to any of the embodiments described above.
Embodiments of the invention may take the form of a computer program product embodied on one or more storage media including, but not limited to, disk storage, CD-ROM, optical storage, and the like, in which program code is embodied. Computer-readable storage media include permanent and non-permanent, removable and non-removable media and may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer readable storage media include, but are not limited to: phase change memory/resistive random access memory/magnetic memory/ferroelectric memory (PRAM/RRAM/MRAM/FeRAM) and like new memories, Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, may be used to store information that may be accessed by a computing device.
The monitoring method, the system and the computer readable storage medium of the cluster wind turbine anemometer provided by the embodiment of the invention are described in detail above. The monitoring method, the system and the computer readable storage medium of the cluster wind turbine anemometer according to the embodiments of the present invention are described herein by using specific examples, and the above descriptions of the embodiments are only used to help understanding the core idea of the present invention, and are not intended to limit the present invention. It should be noted that, for those skilled in the art, various improvements and modifications can be made without departing from the spirit and principle of the present invention, and these improvements and modifications should fall within the scope of the appended claims.
Claims (18)
1. A monitoring method of a cluster fan anemometer is characterized in that: it includes:
acquiring wind speed data of an anemometer of a fan in a wind power plant and spatial position information of the fan;
determining a fan cluster group based on the spatial position information of every two fans in the wind power plant; and
the wind anemometers of all of the wind turbines in the cluster of wind turbines are monitored for anomalies based on the wind speed data of the wind anemometers of all of the wind turbines in the cluster of wind turbines.
2. The method of claim 1, wherein: the acquiring of the wind speed data of the anemometer of the wind turbine and the spatial position information of the wind turbine in the wind power plant comprises the following steps:
and acquiring the wind speed data of an anemometer of a fan and the spatial position information of the fan in the wind power plant from the SCADA system of the wind power plant.
3. The method of claim 1, wherein: the spatial position information of the fans comprises longitude and latitude coordinates of the fans, and the determination of the fan cluster group based on the spatial position information of every two fans in the wind power plant comprises the following steps:
calculating the distance between every two fans based on the longitude and latitude coordinates of every two fans in the wind power plant; and
determining the cluster of fans based on a distance between two fans.
4. The method of claim 3, wherein: the determining the cluster of fans based on a distance between two fans comprises:
selecting a target fan;
determining neighboring fans of the target fan based on distances between the target fan and other fans in the wind farm; and
selecting the target fan and all neighboring fans of the target fan as one fan cluster group.
5. The method of claim 4, wherein: the determining neighboring fans of the target fan based on distances between the target fan and other fans in the wind farm includes:
and when the distance between the target fan and any other fan in the wind power plant is smaller than or equal to a preset distance threshold value, determining that the fan is a neighboring fan of the target fan.
6. The method of claim 5, wherein: the distance threshold is determined as a predetermined multiple of the impeller diameter of the wind turbine or as the maximum of the row and column spacing of the wind turbines in the wind farm.
7. The method of claim 4, wherein: further comprising:
performing secondary confirmation on the fan cluster group based on the correlation of the wind speed data of the fan anemometers in the fan cluster group to obtain a secondarily confirmed fan cluster group,
wherein the monitoring anemometers of all wind turbines in the cluster of wind turbines based on wind speed data of anemometers of all wind turbines in the cluster of wind turbines comprises:
and monitoring the anemometers of all the fans in the fan cluster group after secondary confirmation based on the wind speed data of the anemometers of all the fans in the fan cluster group after secondary confirmation.
8. The method of claim 7, wherein: the secondary validation of the cluster of wind turbines based on the correlation of wind speed data of the wind turbine anemometers in the cluster of wind turbines comprises:
calculating a correlation coefficient between wind speed data of the target wind turbine and anemometers of adjacent wind turbines in the wind turbine cluster; and
performing secondary validation on the cluster of wind turbines based on the calculated correlation coefficient between the wind speed data of the target wind turbine and the anemometers of the neighboring wind turbines in the cluster of wind turbines.
9. The method of claim 8, wherein: and the correlation coefficient between the wind speed data of the target fan and the wind speed data of the anemometers adjacent to the target fan adopts a Pearson correlation coefficient.
10. The method of claim 9, wherein: the secondarily confirming the fan cluster based on the calculated correlation coefficient between the wind speed data of the target fan and the wind speed data of the anemometers of the adjacent fans in the fan cluster comprises:
when the Pearson correlation coefficient between the wind speed data of the anemometers of the target fan and any adjacent fan is larger than or equal to a preset value, keeping the adjacent fan in the fan cluster group; and when the Pearson correlation coefficient between the wind speed data of the anemometers of the target fan and any adjacent fan is smaller than the preset value, deleting the adjacent fan from the fan cluster.
11. The method of claim 1, wherein: the wind speed data comprises historical wind speed data and current wind speed data in a preset time period, and the monitoring of the wind speed meters of all the fans in the fan cluster for the abnormity based on the wind speed data of the wind speed meters of all the fans in the fan cluster comprises:
inputting the acquired historical wind speed data of the anemometers of all the fans in the fan cluster in the preset time period into a VAE model for training to obtain a trained VAE model;
inputting the acquired current wind speed data of the anemometers of all the fans in the fan cluster group into the trained VAE model to obtain predicted wind speed data, output by the trained VAE model, corresponding to each fan in the fan cluster group; and
determining whether anemometers of each of the fans in the cluster of fans are abnormal based on the current wind speed data and the corresponding predicted wind speed data for each of the fans in the cluster of fans.
12. The method of claim 11, wherein: the determining whether an anemometer of each fan in the cluster of fans is abnormal based on the current wind speed data and the corresponding predicted wind speed data for each fan in the cluster of fans comprises:
calculating a difference value between the current wind speed data and the corresponding predicted wind speed data of each fan in the fan cluster group to obtain a wind speed residual error of each fan in the fan cluster group; and
and when the wind speed residual error of a certain fan in the fan cluster exceeds a preset wind speed threshold value and lasts for a preset period, determining that an anemometer of the fan is abnormal.
13. The method of claim 11, wherein: it still includes:
preprocessing the acquired historical wind speed data of all wind turbines in the wind turbine cluster within the preset time period to obtain preprocessed historical wind speed data,
wherein inputting the historical wind speed data into a VAE model for training comprises: inputting the preprocessed historical wind speed data into the VAE model for training.
14. The method of claim 13, wherein: preprocessing the historical wind speed data over the predetermined time period includes at least one of:
performing data cleaning on the historical wind speed data; and
and carrying out normalization processing on the cleaned historical wind speed data.
15. The method of claim 14, wherein: the data cleaning of the historical wind speed data comprises at least one of the following processes:
deleting wind speed data which are smaller than a preset first wind speed threshold value and larger than a preset second wind speed threshold value from the historical wind speed data, wherein the second wind speed threshold value is larger than the first wind speed threshold value; and
and deleting the wind speed data with data missing from the historical wind speed data.
16. The method of claim 1, wherein: it still includes:
and triggering a fault alarm of the anemometer when the anemometer of a certain fan in the fan cluster is monitored to be abnormal.
17. A monitoring system of cluster fan anemometer, its characterized in that: comprising one or more processors for implementing a method of monitoring cluster wind turbine anemometers according to any of the claims 1-16.
18. A computer readable storage medium having stored thereon a program which, when executed by a processor, carries out a method of monitoring cluster fan anemometers according to any of claims 1-16.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111142539.7A CN113884705B (en) | 2021-09-28 | 2021-09-28 | Cluster fan anemometer monitoring method and system and computer readable storage medium thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111142539.7A CN113884705B (en) | 2021-09-28 | 2021-09-28 | Cluster fan anemometer monitoring method and system and computer readable storage medium thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113884705A true CN113884705A (en) | 2022-01-04 |
| CN113884705B CN113884705B (en) | 2024-10-01 |
Family
ID=79007341
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111142539.7A Active CN113884705B (en) | 2021-09-28 | 2021-09-28 | Cluster fan anemometer monitoring method and system and computer readable storage medium thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113884705B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115434878A (en) * | 2022-11-09 | 2022-12-06 | 东方电气风电股份有限公司 | Wind generating set temperature cluster control method, device, equipment and medium |
Citations (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150355215A1 (en) * | 2014-06-05 | 2015-12-10 | State Grid Corporation Of China | Approach to assess available wind resource distribution based on interpolation method |
| CN105628977A (en) * | 2015-12-31 | 2016-06-01 | 新疆金风科技股份有限公司 | Calibration method, device and system of anemometer correction coefficient |
| WO2016086360A1 (en) * | 2014-12-02 | 2016-06-09 | Abb Technology Ltd | Wind farm condition monitoring method and system |
| CN107392304A (en) * | 2017-08-04 | 2017-11-24 | 中国电力科学研究院 | A kind of Wind turbines disorder data recognition method and device |
| CN107843745A (en) * | 2017-09-20 | 2018-03-27 | 新疆金风科技股份有限公司 | The method for diagnosing faults and device of the anemobiagraph of wind power generating set, storage medium |
| CN108596229A (en) * | 2018-04-13 | 2018-09-28 | 北京华电智慧科技产业有限公司 | Online abnormal monitoring, diagnosing method and system |
| CN109458305A (en) * | 2018-10-23 | 2019-03-12 | 北京金风科创风电设备有限公司 | Anemometer fault-tolerant control method and device and wind power plant controller |
| CN110020000A (en) * | 2017-12-22 | 2019-07-16 | 北京金风慧能技术有限公司 | Determine the method and apparatus of abnormal air speed data |
| CN111159900A (en) * | 2019-12-30 | 2020-05-15 | 国核电力规划设计研究院有限公司 | Method and device for predicting wind speed of fan |
| CN111324936A (en) * | 2018-11-29 | 2020-06-23 | 北京金风慧能技术有限公司 | Fan wind speed prediction method, computer readable storage medium and computing device |
| CN111414354A (en) * | 2020-03-12 | 2020-07-14 | 广西电网有限责任公司 | Wind power plant data processing method and device and computer readable storage medium |
| CN112000654A (en) * | 2020-08-25 | 2020-11-27 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | High-speed railway strong wind monitoring data quality control method and device |
| US20200379454A1 (en) * | 2019-05-31 | 2020-12-03 | Panasonic Intellectual Property Management Co., Ltd. | Machine learning based predictive maintenance of equipment |
| CN112081716A (en) * | 2019-06-14 | 2020-12-15 | 新疆金风科技股份有限公司 | Operation control method, device and equipment of wind generating set and storage medium |
| CN112149757A (en) * | 2020-10-23 | 2020-12-29 | 新华三大数据技术有限公司 | Abnormity detection method and device, electronic equipment and storage medium |
| CN112240267A (en) * | 2019-07-17 | 2021-01-19 | 成都信息工程大学 | Fan monitoring method based on wind speed correlation and wind power curve |
| CN112348050A (en) * | 2020-09-30 | 2021-02-09 | 中国铁路上海局集团有限公司 | Anemograph arrangement method based on wind characteristics along high-speed rail |
| CN112417734A (en) * | 2020-11-25 | 2021-02-26 | 国能日新科技股份有限公司 | Wind speed correction method and device based on geographic information of wind power plant |
| CN112580900A (en) * | 2021-02-23 | 2021-03-30 | 国能日新科技股份有限公司 | Short-term power prediction method and system based on single fan modeling |
| CN112596126A (en) * | 2020-06-30 | 2021-04-02 | 北京金风慧能技术有限公司 | Wind speed prediction model training method, prediction method and device and electronic equipment |
| CN112836180A (en) * | 2021-04-19 | 2021-05-25 | 北京瑞莱智慧科技有限公司 | Sensing data determination method and device, electronic equipment and readable storage medium |
| US20210172421A1 (en) * | 2017-06-02 | 2021-06-10 | Beijing Goldwind Science & Creation Windpower Equipment Co., Ltd. | Correction method and apparatus for predicted wind speed of wind farm |
| CN113030516A (en) * | 2019-12-24 | 2021-06-25 | 华能如东八仙角海上风力发电有限责任公司 | Anemometer fault detection method, device, equipment and storage medium |
| CN113297728A (en) * | 2021-04-30 | 2021-08-24 | 东方电气风电有限公司 | Single-fan virtual wind speed calculation method and system based on wind field wind speed correlation |
| US20210272704A1 (en) * | 2020-02-27 | 2021-09-02 | Kabushiki Kaisha Toshiba | Condition monitoring device and method |
| CN113361100A (en) * | 2021-06-04 | 2021-09-07 | 哈电风能有限公司 | Wind speed estimation method, device, equipment and computer readable storage medium |
-
2021
- 2021-09-28 CN CN202111142539.7A patent/CN113884705B/en active Active
Patent Citations (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150355215A1 (en) * | 2014-06-05 | 2015-12-10 | State Grid Corporation Of China | Approach to assess available wind resource distribution based on interpolation method |
| WO2016086360A1 (en) * | 2014-12-02 | 2016-06-09 | Abb Technology Ltd | Wind farm condition monitoring method and system |
| CN105628977A (en) * | 2015-12-31 | 2016-06-01 | 新疆金风科技股份有限公司 | Calibration method, device and system of anemometer correction coefficient |
| US20210172421A1 (en) * | 2017-06-02 | 2021-06-10 | Beijing Goldwind Science & Creation Windpower Equipment Co., Ltd. | Correction method and apparatus for predicted wind speed of wind farm |
| CN107392304A (en) * | 2017-08-04 | 2017-11-24 | 中国电力科学研究院 | A kind of Wind turbines disorder data recognition method and device |
| CN107843745A (en) * | 2017-09-20 | 2018-03-27 | 新疆金风科技股份有限公司 | The method for diagnosing faults and device of the anemobiagraph of wind power generating set, storage medium |
| CN110020000A (en) * | 2017-12-22 | 2019-07-16 | 北京金风慧能技术有限公司 | Determine the method and apparatus of abnormal air speed data |
| CN108596229A (en) * | 2018-04-13 | 2018-09-28 | 北京华电智慧科技产业有限公司 | Online abnormal monitoring, diagnosing method and system |
| CN109458305A (en) * | 2018-10-23 | 2019-03-12 | 北京金风科创风电设备有限公司 | Anemometer fault-tolerant control method and device and wind power plant controller |
| CN111324936A (en) * | 2018-11-29 | 2020-06-23 | 北京金风慧能技术有限公司 | Fan wind speed prediction method, computer readable storage medium and computing device |
| US20200379454A1 (en) * | 2019-05-31 | 2020-12-03 | Panasonic Intellectual Property Management Co., Ltd. | Machine learning based predictive maintenance of equipment |
| CN112081716A (en) * | 2019-06-14 | 2020-12-15 | 新疆金风科技股份有限公司 | Operation control method, device and equipment of wind generating set and storage medium |
| CN112240267A (en) * | 2019-07-17 | 2021-01-19 | 成都信息工程大学 | Fan monitoring method based on wind speed correlation and wind power curve |
| CN113030516A (en) * | 2019-12-24 | 2021-06-25 | 华能如东八仙角海上风力发电有限责任公司 | Anemometer fault detection method, device, equipment and storage medium |
| CN111159900A (en) * | 2019-12-30 | 2020-05-15 | 国核电力规划设计研究院有限公司 | Method and device for predicting wind speed of fan |
| US20210272704A1 (en) * | 2020-02-27 | 2021-09-02 | Kabushiki Kaisha Toshiba | Condition monitoring device and method |
| CN111414354A (en) * | 2020-03-12 | 2020-07-14 | 广西电网有限责任公司 | Wind power plant data processing method and device and computer readable storage medium |
| CN112596126A (en) * | 2020-06-30 | 2021-04-02 | 北京金风慧能技术有限公司 | Wind speed prediction model training method, prediction method and device and electronic equipment |
| CN112000654A (en) * | 2020-08-25 | 2020-11-27 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | High-speed railway strong wind monitoring data quality control method and device |
| CN112348050A (en) * | 2020-09-30 | 2021-02-09 | 中国铁路上海局集团有限公司 | Anemograph arrangement method based on wind characteristics along high-speed rail |
| CN112149757A (en) * | 2020-10-23 | 2020-12-29 | 新华三大数据技术有限公司 | Abnormity detection method and device, electronic equipment and storage medium |
| CN112417734A (en) * | 2020-11-25 | 2021-02-26 | 国能日新科技股份有限公司 | Wind speed correction method and device based on geographic information of wind power plant |
| CN112580900A (en) * | 2021-02-23 | 2021-03-30 | 国能日新科技股份有限公司 | Short-term power prediction method and system based on single fan modeling |
| CN112836180A (en) * | 2021-04-19 | 2021-05-25 | 北京瑞莱智慧科技有限公司 | Sensing data determination method and device, electronic equipment and readable storage medium |
| CN113297728A (en) * | 2021-04-30 | 2021-08-24 | 东方电气风电有限公司 | Single-fan virtual wind speed calculation method and system based on wind field wind speed correlation |
| CN113361100A (en) * | 2021-06-04 | 2021-09-07 | 哈电风能有限公司 | Wind speed estimation method, device, equipment and computer readable storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115434878A (en) * | 2022-11-09 | 2022-12-06 | 东方电气风电股份有限公司 | Wind generating set temperature cluster control method, device, equipment and medium |
| CN115434878B (en) * | 2022-11-09 | 2023-02-03 | 东方电气风电股份有限公司 | Wind generating set temperature cluster control method, device, equipment and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113884705B (en) | 2024-10-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP2940296B1 (en) | Systems and methods for optimizing operation of a wind farm | |
| CA2997644C (en) | Simulation method and system | |
| Maatallah et al. | Wind power assessment and evaluation of electricity generation in the Gulf of Tunis, Tunisia | |
| CN106779200A (en) | Based on the Wind turbines trend prediction method for carrying out similarity in the historical data | |
| CN113884705B (en) | Cluster fan anemometer monitoring method and system and computer readable storage medium thereof | |
| CN113530769B (en) | Ice-throwing prevention control method and system for wind generating set and computer readable storage medium | |
| Kazor et al. | The role of regimes in short‐term wind speed forecasting at multiple wind farms | |
| CN113586336B (en) | Control method and control device of wind generating set and computer readable storage medium | |
| Xiong et al. | An ultra-short-term wind speed correction method based on the fluctuation characteristics of wind speed | |
| CN110188939B (en) | Wind power prediction method, system, equipment and storage medium of wind power plant | |
| CN116579386A (en) | Wind turbine generator system state monitoring method, electronic equipment and storage medium | |
| Kim et al. | Accuracy of a short-term wind power forecasting model based on deep learning using LiDAR-SCADA integration: A case study of the 400-MW Anholt offshore wind farm. | |
| Varughese et al. | Prediction Of Solar Power Generation Based On Machine Learning Algorithm | |
| CN113775481A (en) | CMS vibration protection method and device and computer readable storage medium | |
| CN112950403A (en) | Method and device for processing abnormal data of multiple working conditions of fan and computer readable storage medium | |
| Kehe et al. | Research of wind power prediction model based on RBF neural network | |
| CN114113683B (en) | Monitoring method and system for fan anemoscope in wind farm and computer readable storage medium | |
| CN114154567A (en) | Wind power plant station operation data anomaly identification method based on machine learning | |
| Saroha et al. | Wind power forecasting | |
| CN113449985B (en) | Gearbox monitoring method and system and computer readable storage medium | |
| Nemes et al. | Reliability consideration on wind farms energy production | |
| CN115855500A (en) | Bearing monitoring method and system and computer readable storage medium | |
| CN118690669B (en) | Offshore wind field inversion method based on improved CNN-LSTM network | |
| CN114781711A (en) | Method and device for predicting residual life of rotating part and computer readable storage medium | |
| CN113486473A (en) | State monitoring method and system of wind generating set and computer readable storage medium |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |