阅读说明：本技术 一种风电机组偏航频繁检测预警算法 (Wind turbine generator yaw frequent detection early warning algorithm ) 是由 徐树彪 魏敏 陈克锐 杜成康 刘军 姚剑平 于 2020-12-18 设计创作，主要内容包括：本发明涉及风电机组异常检测技术领域,具体地说,涉及一种风电机组偏航频繁检测预警算法。包括数据采集、风机偏航次数统计、偏航次数离群点检测、风电机组偏航频繁预标记、偏航频繁概率计算及风电机组偏航频繁预警等步骤。本发明设计不需要风机复杂结构的设计参数,无需建模仿真,也不需要加装传感器,可以快速计算分析数据,方便部署,并且识别精度较高,及时发现风电机组异常,并降低由于摩擦片过度磨损带可能来的设备故障风险,可以有效提高风电场的后期运维效率,降低使用及运维成本。(The invention relates to the technical field of wind turbine generator abnormity detection, in particular to a wind turbine generator yaw frequent detection early warning algorithm. The method comprises the steps of data acquisition, fan yaw frequency statistics, yaw frequency outlier detection, frequent pre-marking of wind turbine yaw, frequent probability calculation of yaw, frequent early warning of wind turbine yaw and the like. The design of the invention does not need the design parameters of a complex structure of the fan, does not need modeling simulation, does not need to be additionally provided with a sensor, can quickly calculate and analyze data, is convenient to deploy, has higher identification precision, finds the abnormality of the wind turbine generator in time, reduces the equipment fault risk possibly caused by excessive wear of the friction plate, can effectively improve the later-stage operation and maintenance efficiency of the wind power plant, and reduces the use and operation and maintenance cost.)

1. The utility model provides a wind turbine generator system driftage frequent detection early warning algorithm which characterized in that: the method comprises the following steps:

s1, data acquisition;

s2, counting the yawing times of the fan;

s3, detecting the yaw frequency outliers;

s4, pre-marking the yaw frequency of the wind turbine generator;

s5, calculating the yaw frequent probability;

and S6, performing frequent yaw early warning on the wind turbine generator.

2. The wind turbine generator yaw frequent detection early warning algorithm according to claim 1, characterized in that: in S1, the method for acquiring data includes the following steps:

s1.1, firstly compiling and constructing an algorithm system for yaw detection and early warning on a computer, and then deploying the algorithm system on an independent computer or a cloud or a wind turbine system;

s1.2, connecting an algorithm system with a scada system of the wind turbine generator through wireless transmission and big data cloud platform technology, and reading and receiving recorded data of the yaw condition of the fan in the system;

s1.3, writing a registration program of maintenance records on a computer, providing a platform for recording maintenance conditions of each time for maintenance workers, and sharing data with an algorithm program;

and S1.4, connecting sensors deployed on all groups of fans when the wind turbine generator is installed to an algorithm system through a wireless communication technology, and uploading acquired data to the algorithm system in time.

3. The wind turbine generator yaw frequent detection early warning algorithm according to claim 2, characterized in that: in the S1.1, when the wind turbine generator is deployed in a wind field and the hardware and software load capacity of the wind turbine generator is sufficient, the algorithm system can be directly loaded on a wind turbine generator system; when the wind turbine generator is deployed outside a wind farm and the communication technology is not stable enough, independent computer equipment can be arranged in the wind farm, and the algorithm system is loaded on an independent computer system; when the wind turbine generator is deployed outside a far-away wind field and the communication technology is stable, the algorithm system can be loaded at the cloud end, and remote detection and early warning operation on wind turbine generator yaw is achieved.

4. The wind turbine generator yaw frequent detection early warning algorithm according to claim 1, characterized in that: in the step S2, the method for counting the fan yaw number includes the following steps:

s2.1, respectively reading the data collected by each channel, extracting data related to the fan yaw record through an algorithm for extracting key words, and converting the data extracted by each channel into a uniform format;

s2.2, combining all groups of data, comparing and screening the data, and screening out invalid and repeated data;

s2.3, based on the processed data, defining different time periods, and dividing K time windows according to the size of the time periods;

s2.4, respectively counting the yawing times N of each fan in the same window according to the divided time periods_ij(where i 1, 2., M, j 1, 2., K, M is the number of all fans in the wind farm);

and S2.5, respectively storing the statistical results into corresponding files so as to read the statistical results.

5. The wind turbine generator yaw frequent detection early warning algorithm according to claim 4, characterized in that: in S2.3, the divided time periods include, but are not limited to, hours, days, weeks, months, seasons, half years, one year, two years, and the like.

6. The wind turbine generator yaw frequent detection early warning algorithm according to claim 1, characterized in that: in S3, the method for detecting the number of drifts from the outlier includes the following steps:

S3.1、taking the yaw times of all the fans in the same time window as a data Set_ij(wherein i 1, 2.., M, j 1, 2.., K), performing multi-stage outlier detection;

and S3.2, on the basis of the S3.1, adopting different outlier detection methods to respectively carry out 1-n times of outlier detection on each data set.

7. The wind turbine generator yaw frequent detection early warning algorithm according to claim 6, characterized in that: in S3.2, the method for detecting the outlier includes, but is not limited to, a local abnormal factor detection algorithm (LOF), an isolated forest (TForest), a one-close SVM algorithm, and the like, and the algorithm and the priority of the algorithm are not limited in the calculation process, and one or more detection methods are randomly adopted by the system.

8. The wind turbine generator yaw frequent detection early warning algorithm according to claim 1, characterized in that: in S4, the method for pre-marking the yaw frequency of the wind turbine generator includes the following steps:

s4.1, aiming at the operation process of multiple outlier detection in the same time window, marking the fan with one detected outlier in multiple detections as primary yaw frequency;

s4.2, aiming at the operation process of multiple outlier detection in the same time window, marking fans with outliers detected in any two times in multiple detections as secondary yaw frequency;

s4.3, aiming at the multiple outlier detection operation processes in the same time window, marking the fan with outlier detected in any three times in the multiple detections as three-level yaw frequency;

and S4.4, according to the flow, by analogy, the yaw frequency grade of each fan in the same selected time can be marked.

9. The wind turbine generator yaw frequent detection early warning algorithm according to claim 1, characterized in that: in S5, the method for calculating the yaw frequency probability includes the following steps:

s5.1, systemCounting the primary abnormal mark number L of each fan in K time windows_ij ¹And respectively calculating the probability value P of the primary abnormality of each fan_i ¹＝L_ij ¹K (where i 1, 2., M, j 1, 2., K);

s5.2, counting the number L of secondary abnormal marks of each fan in K time windows_ij ²And respectively calculating the probability value P of the secondary abnormality of each fan_i ²＝L_ij ²K (where i 1, 2., M, j 1, 2., K);

s5.3, counting the number L of three-level abnormal marks of each fan in K time windows_ij ³And respectively calculating the probability value P of three-level abnormality of each fan_i ³＝L_ij ³K (where i 1, 2., M, j 1, 2., K);

and S5.4, according to the flow, by analogy, calculating one or more abnormal probability values of each fan in the selected time.

10. The wind turbine generator yaw frequent detection early warning algorithm according to claim 1, characterized in that: in the S6, the method for wind turbine yaw frequent early warning includes the following steps:

s6.1, setting primary, secondary and tertiary thresholds of frequent yawing probability aiming at fans of the same type respectively, wherein the primary, secondary and tertiary thresholds are increased in sequence;

s6.2, sequentially improving the early warning strength according to the sequence of the primary early warning, the secondary early warning and the tertiary early warning, and respectively marking the corresponding urgency degrees by adopting different colors or message prompt tones;

s6.3, comparing a plurality of yaw frequency probability values calculated by each fan in a selected time region with primary, secondary and tertiary thresholds in sequence;

s6.4, in S6.3, when any yaw frequency probability value exceeds a three-level threshold value, sending out a three-level early warning aiming at the typhoon;

s6.5, in S6.3, when the yaw frequency probability values do not exceed the three-level threshold value but any one yaw frequency probability value exceeds the two-level threshold value, sending out a two-level early warning aiming at the typhoon;

s6.6, in S6.3, when the yaw frequency probability values do not exceed the secondary threshold value but any one yaw frequency probability value exceeds the primary threshold value, a primary early warning is sent to the typhoon;

s6.7, when the system sends out an early warning, the user can perform early warning confirmation through the user terminal, and when the system detects that the user terminal does not receive a feedback signal for confirming the early warning message by the user within a period of time, the system sends the early warning message to the mobile terminal of the on-duty user;

s6.8, the user confirms early warning on the terminal, and carries out overhauling and deviation rectifying operations on the fan with frequent yawing in sequence from high to low according to the early warning urgency degree in time, and after overhauling is completed, the maintenance record is updated on the mobile terminal or the user terminal.

Technical Field

The invention relates to the technical field of wind turbine generator abnormity detection, in particular to a wind turbine generator yaw frequent detection early warning algorithm.

Background

In recent years, wind power installations have increased year by year, and large wind turbine generators are the core of wind power generation. The yaw system is the most important subsystem of a large-scale wind generating set, and the performance of the yaw system greatly influences the performance of the whole generating set. The yaw system has a complex structure, is a functional system integrating mechanical, electrical and control, and has abnormal performance caused by reasons of mechanical transmission deviation, sensor error, unreasonable control parameter setting and the like. Due to the abnormality of the yaw system, for example, the control system is abnormal, and yaw hardware failure (such as serious abrasion of a yaw friction plate and failure of a yaw caliper) can cause frequent yaw of the yaw system of the wind turbine generator, so that the power generation stability of the wind turbine generator is greatly influenced, and more serious hardware failure, even safety accidents, can be caused. Therefore, the wind turbine generator with frequent yawing is identified by carrying out statistical analysis on the yawing frequency of the wind turbine generator in advance, and the method has very important significance for safe and stable power generation of the wind turbine generator.

At present, some algorithms and methods for frequently detecting yaw are not available, and two methods of establishing a thermodynamic coupling contact model of a brake disc and a friction plate and installing a sensor are generally adopted for solving the problem of a yaw friction plate, but the two methods still have great problems, such as: the method of physical model simulation needs detailed structural parameters of the wind turbine generator, and the structural parameters are often relatively confidential structural design parameters and are difficult to obtain; the method of adding the sensor is adopted, and the method of adding the sensor on the built wind turbine generator is troublesome to implement and high in cost.

Disclosure of Invention

The invention aims to provide a wind turbine generator yaw frequent detection early warning algorithm to solve the problems in the background technology.

In order to solve the technical problem, one of the objectives of the present invention is to provide a wind turbine yaw frequent detection early warning algorithm, which includes the following steps:

s1, data acquisition;

s2, counting the yawing times of the fan;

s3, detecting the yaw frequency outliers;

s4, pre-marking the yaw frequency of the wind turbine generator;

s5, calculating the yaw frequent probability;

and S6, performing frequent yaw early warning on the wind turbine generator.

As a further improvement of the technical solution, in S1, the method flow of data acquisition includes the following steps:

s1.1, firstly compiling and constructing an algorithm system for yaw detection and early warning on a computer, and then deploying the algorithm system on an independent computer or a cloud or a wind turbine system;

s1.2, connecting an algorithm system with a scada system of the wind turbine generator through wireless transmission and big data cloud platform technology, and reading and receiving recorded data of the yaw condition of the fan in the system;

s1.3, writing a registration program of maintenance records on a computer, providing a platform for recording maintenance conditions of each time for maintenance workers, and sharing data with an algorithm program;

and S1.4, connecting sensors deployed on all groups of fans when the wind turbine generator is installed to an algorithm system through a wireless communication technology, and uploading acquired data to the algorithm system in time.

As a further improvement of the technical scheme, in S1.1, when the wind turbine is deployed in a wind farm and the hardware and software load capacities thereof are sufficient, the algorithm system may be directly loaded on the wind turbine system; when the wind turbine generator is deployed outside a wind farm and the communication technology is not stable enough, independent computer equipment can be arranged in the wind farm, and the algorithm system is loaded on an independent computer system; when the wind turbine generator is deployed outside a far-away wind field and the communication technology is stable, the algorithm system can be loaded at the cloud end, and remote detection and early warning operation on wind turbine generator yaw is achieved.

As a further improvement of the technical solution, in S2, the method for counting the yaw number of the wind turbine includes the following steps:

s2.1, respectively reading the data collected by each channel, extracting data related to the fan yaw record through an algorithm for extracting key words, and converting the data extracted by each channel into a uniform format;

s2.2, combining all groups of data, comparing and screening the data, and screening out invalid and repeated data;

s2.3, based on the processed data, defining different time periods, and dividing K time windows according to the size of the time periods;

s2.4, respectively counting the yawing times N of each fan in the same window according to the divided time periods_ij(where i 1, 2., M, j 1, 2., K, M is the number of all fans in the wind farm);

and S2.5, respectively storing the statistical results into a file corresponding to each fan so as to read the statistical results.

As a further improvement of the present technical solution, in S2.3, the divided time periods include, but are not limited to, hours, days, weeks, months, seasons, half years, one year, two years, and the like.

As a further improvement of the present invention, in S3, the method for detecting the number of drifts outlier includes the steps of:

s3.1, taking the yawing times of all fans in the same time window as a data Set_ij(wherein i 1, 2.., M, j 1, 2.., K), performing multi-stage outlier detection;

and S3.2, on the basis of the S3.1, adopting different outlier detection methods to respectively carry out 1-n times of outlier detection on each data set.

As a further improvement of the technical solution, in S3.2, the method for detecting the outlier includes, but is not limited to, a local abnormal factor detection algorithm (LOF), an isolated forest (TForest), a one-close SVM algorithm, and the like, and the algorithm and the priority of the algorithm are not limited in the calculation process, and the system randomly adopts one or more detection methods.

As a further improvement of the technical solution, in S4, the method for pre-marking the frequent yaw of the wind turbine generator includes the following steps:

s4.1, aiming at the operation process of multiple outlier detection in the same time window, marking the fan with one detected outlier in multiple detections as primary yaw frequency;

s4.2, aiming at the operation process of multiple outlier detection in the same time window, marking fans with outliers detected in any two times in multiple detections as secondary yaw frequency;

s4.3, aiming at the multiple outlier detection operation processes in the same time window, marking the fan with outlier detected in any three times in the multiple detections as three-level yaw frequency;

and S4.4, according to the flow, by analogy, the yaw frequency grade of each fan in the same selected time can be marked.

As a further improvement of the present technical solution, in S5, the method for calculating the frequent yaw probability includes the following steps:

s5.1, counting the primary abnormal mark number L of each fan in K time windows_ij ¹And respectively calculating the probability value P of the primary abnormality of each fan_i ¹＝L_ij ¹K (where i 1, 2., M, j 1, 2., K);

s5.2, counting the number L of secondary abnormal marks of each fan in K time windows_ij ²And respectively calculating the probability value P of the secondary abnormality of each fan_i ²＝L_ij ²K (where i 1, 2., M, j 1, 2., K);

s5.3, counting the number L of three-level abnormal marks of each fan in K time windows_ij ³And respectively calculating the probability value P of three-level abnormality of each fan_i ³＝L_ij ³K (where i 1, 2., M, j 1, 2., K);

and S5.4, according to the flow, by analogy, calculating one or more abnormal probability values of each fan in the selected time.

As a further improvement of the technical solution, in S6, the method for frequent yaw early warning of the wind turbine includes the following steps:

s6.1, setting primary, secondary and tertiary thresholds of frequent yawing probability aiming at fans of the same type respectively, wherein the primary, secondary and tertiary thresholds are increased in sequence;

s6.2, sequentially improving the early warning strength according to the sequence of the primary early warning, the secondary early warning and the tertiary early warning, and respectively marking the corresponding urgency degrees by adopting different colors or message prompt tones;

s6.3, comparing a plurality of yaw frequency probability values calculated by each fan in a selected time region with primary, secondary and tertiary thresholds in sequence;

s6.4, in S6.3, when any yaw frequency probability value exceeds a three-level threshold value, sending out a three-level early warning aiming at the typhoon;

s6.5, in S6.3, when the yaw frequency probability values do not exceed the three-level threshold value but any one yaw frequency probability value exceeds the two-level threshold value, sending out a two-level early warning aiming at the typhoon;

s6.6, in S6.3, when the yaw frequency probability values do not exceed the secondary threshold value but any one yaw frequency probability value exceeds the primary threshold value, a primary early warning is sent to the typhoon;

s6.7, when the system sends out an early warning, the user can perform early warning confirmation through the user terminal, and when the system detects that the user terminal does not receive a feedback signal for confirming the early warning message by the user within a period of time, the system sends the early warning message to the mobile terminal of the on-duty user;

s6.8, the user confirms early warning on the terminal, and carries out overhauling and deviation rectifying operations on the fan with frequent yawing in sequence from high to low according to the early warning urgency degree in time, and after overhauling is completed, the maintenance record is updated on the mobile terminal or the user terminal.

The invention also aims to provide a wind turbine generator yaw frequent detection early warning algorithm system, which comprises a data acquisition unit, a data processing unit, a yaw early warning unit and a human-computer interaction unit; the data acquisition unit is used for acquiring historical yaw data of each wind turbine; the data processing unit is used for screening, counting and calculating historical yaw data so as to predict the yaw condition of the fan; the yaw early warning unit is used for prejudging whether the fan yaws and carrying out early warning according to a data calculation result; the man-machine interaction unit is used for establishing an interaction channel between a user and the algorithm system.

As a further improvement of the technical scheme, the data processing unit comprises a data screening module, a data statistics module, an outlier detection module and a probability calculation module; the data cleaning and screening module is used for unifying the format of the data and screening out invalid data; the data statistics module is used for respectively counting the historical yaw frequency of each fan according to different time periods; the outlier detection module is used for judging whether the fan has frequent yawing through an outlier detection algorithm; the probability calculation module is used for calculating the probability of frequent yaw of each fan so as to give an early warning in time

As a further improvement of the technical scheme, the human-computer interaction unit comprises an interface display module, a signal mutual transmission module and an information input module; the interface display module is used for displaying early warning information to a user on a user terminal; the signal mutual transmission module is used for providing a channel of feedback information for a user; the information entry module is used for providing a way for a user to enter new data so as to update the historical database.

The invention also aims to provide a wind turbine generator yaw frequent detection early warning algorithm device, which comprises a processor, a memory and a computer program stored in the memory and running on the processor, wherein the processor is used for realizing any one of the steps of the wind turbine generator yaw frequent detection early warning algorithm when executing the computer program.

The fourth objective of the present invention is that the computer readable storage medium stores a computer program, and the computer program, when executed by the processor, implements any of the above steps of the wind turbine generator yaw frequent detection early warning algorithm.

Compared with the prior art, the invention has the beneficial effects that: in the wind turbine yaw frequent detection early warning algorithm, calculation is carried out based on existing historical operating data of the wind turbine, whether the wind turbine has a yaw frequent problem or not is identified, whether the wind turbine has a yaw friction plate abrasion problem or not is further predicted, design parameters of a fan complex structure are not needed, modeling simulation is not needed, a sensor is not needed, data can be calculated and analyzed quickly, deployment is convenient, identification precision is high, abnormality of the wind turbine is found timely, equipment failure risks possibly caused by excessive abrasion of the friction plate are reduced, use and operation and maintenance costs are reduced, and later operation and maintenance efficiency of a wind power plant can be effectively improved.

Drawings

FIG. 1 is an exemplary product architecture diagram in the present invention;

FIG. 2 is an overall flow chart of the algorithm of the present invention;

FIG. 3 is a partial flow chart of an algorithm in the present invention;

FIG. 4 is a second partial flow chart of the algorithm of the present invention;

FIG. 5 is a third partial flowchart of the algorithm of the present invention;

FIG. 6 is a fourth partial flowchart of the algorithm of the present invention;

FIG. 7 is a fifth partial flow chart of the algorithm of the present invention;

FIG. 8 is a sixth partial flowchart of the algorithm of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Embodiments of the Algorithm

As shown in fig. 1 to 8, an object of the present embodiment is to provide a wind turbine yaw frequent detection early warning algorithm, which includes the following steps:

s1, data acquisition;

s2, counting the yawing times of the fan;

s3, detecting the yaw frequency outliers;

s4, pre-marking the yaw frequency of the wind turbine generator;

s5, calculating the yaw frequent probability;

and S6, performing frequent yaw early warning on the wind turbine generator.

In this embodiment, in S1, the method flow of data acquisition includes the following steps:

s1.1, firstly compiling and constructing an algorithm system for yaw detection and early warning on a computer, and then deploying the algorithm system on an independent computer or a cloud or a wind turbine system;

s1.2, connecting an algorithm system with a scada system of the wind turbine generator through wireless transmission and big data cloud platform technology, and reading and receiving recorded data of the yaw condition of the fan in the system;

s1.3, writing a registration program of maintenance records on a computer, providing a platform for recording maintenance conditions of each time for maintenance workers, and sharing data with an algorithm program;

and S1.4, connecting sensors deployed on all groups of fans when the wind turbine generator is installed to an algorithm system through a wireless communication technology, and uploading acquired data to the algorithm system in time.

Specifically, in S1.1, when the wind turbine is deployed in a wind farm and the hardware and software load capacity of the wind turbine is sufficient, the algorithm system may be directly loaded on the wind turbine system; when the wind turbine generator is deployed outside a wind farm and the communication technology is not stable enough, independent computer equipment can be arranged in the wind farm, and the algorithm system is loaded on an independent computer system; when the wind turbine generator is deployed outside a far-away wind field and the communication technology is stable, the algorithm system can be loaded at the cloud end, and remote detection and early warning operation on wind turbine generator yaw is achieved.

In this embodiment, in S2, the method for counting the fan yaw number includes the following steps:

s2.1, respectively reading the data collected by each channel, extracting data related to the fan yaw record through an algorithm for extracting key words, and converting the data extracted by each channel into a uniform format;

s2.2, combining all groups of data, comparing and screening the data, and screening out invalid and repeated data;

s2.3, based on the processed data, defining different time periods, and dividing K time windows according to the size of the time periods;

s2.4, respectively counting the yawing times N of each fan in the same window according to the divided time periods_ij(where i 1, 2., M, j 1, 2., K, M is the number of all fans in the wind farm);

and S2.5, respectively storing the statistical results into corresponding files so as to read the statistical results.

In S2.3, the divided time periods include, but are not limited to, hours, days, weeks, months, seasons, half years, one year, two years, and the like.

In S2.3, the division manner of the time period may adopt a method of dividing a fixed time point, or may also adopt a method of calculating a time period forward by using an algorithm, and in this embodiment, the second method is preferred, so that the accuracy and the instantaneity of the early warning measurement and calculation can be improved.

In this embodiment, in S3, the method for detecting the number of drifts from the outlier includes the following steps:

s3.1, taking the yawing times of all fans in the same time window as a data Set_ij(wherein i 1, 2.., M, j 1, 2.., K), performing multi-stage outlier detection;

and S3.2, on the basis of the S3.1, adopting different outlier detection methods to respectively carry out 1-n times of outlier detection on each data set.

Wherein, n is 1, 2.. times, m, wherein, an algorithm of one-time outlier detection is adopted, so that the working efficiency of detection and early warning can be improved, but the precision is not high; the algorithm of multiple outlier detection is adopted, the detection and early warning accuracy can be improved, the consumed time is long, and a user can select and set the outlier according to the actual operation condition of a wind field in the using process.

Specifically, in S3.2, the method for detecting the outlier includes, but is not limited to, a local abnormal factor detection algorithm (LOF), an isolated forest (TForest), a one-clean SVM algorithm, and the like, and the algorithm and the priority of the algorithm are not limited in the calculation process, and the system randomly adopts one or more detection methods.

The LOF has the algorithm formula as follows:

wherein, the formula (1) is the local reachable density of the point p, and is expressed as the reciprocal of the average reachable distance from the point in the kth neighborhood of the point p to the point p; equation (2) is a local outlier factor for point p, denoted as neighborhood point N of point p_K(p) an average of a ratio of the local achievable density of (p) to the local achievable density of point p.

The algorithm formula of TForest is as follows:

wherein the content of the first and second substances,

wherein, S (x, ψ) is a formula for calculating an abnormal value score, specifically expressed as: for each data point x_iTraverse each orphan tree (iTree) to compute point x_iAverage height h (x) in forest_i) And normalizing the average height of all the points.

Wherein, the algorithm formula of the one-clas SVM is as follows:

wherein the above equation is a distance function of two data points, and whether a new data point z is in the class is judged by the distance from z to the center being less than or equal to the radius.

In this embodiment, in S4, the method for pre-marking the frequent yaw of the wind turbine generator includes the following steps:

s4.1, aiming at the operation process of multiple outlier detection in the same time window, marking the fan with one detected outlier in multiple detections as primary yaw frequency;

s4.2, aiming at the operation process of multiple outlier detection in the same time window, marking fans with outliers detected in any two times in multiple detections as secondary yaw frequency;

s4.3, aiming at the multiple outlier detection operation processes in the same time window, marking the fan with outlier detected in any three times in the multiple detections as three-level yaw frequency;

and S4.4, according to the flow, by analogy, the yaw frequency grade of each fan in the same selected time can be marked.

In addition, it should be noted that the above-mentioned marking level definition method for frequent yawing is only an exemplary method, and other methods may be used for defining the marking level definition method in a specific application process, and need to be preset and written in an algorithm system.

In this embodiment, in S5, the method for calculating the yaw frequency probability includes the following steps:

s5.1, counting the primary abnormal mark number L of each fan in K time windows_ij ¹And respectively calculating the probability value P of the primary abnormality of each fan_i ¹＝L_ij ¹K (where i 1, 2., M, j 1, 2., K);

s5.2, counting the number L of secondary abnormal marks of each fan in K time windows_ij ²And respectively calculating the probability value P of the secondary abnormality of each fan_i ²＝L_ij ²K (where i 1, 2., M, j 1, 2., K);

s5.3, counting the number L of three-level abnormal marks of each fan in K time windows_ij ³And respectively calculating the probability value P of three-level abnormality of each fan_i ³＝L_ij ³K (where i 1, 2., M, j 1, 2., K);

and S5.4, according to the flow, by analogy, calculating one or more abnormal probability values of each fan in the selected time.

In this embodiment, in S6, the method for wind turbine yaw frequent early warning includes the following steps:

s6.1, setting primary, secondary and tertiary thresholds of frequent yawing probability aiming at fans of the same type respectively, wherein the primary, secondary and tertiary thresholds are increased in sequence;

s6.2, sequentially improving the early warning strength according to the sequence of the primary early warning, the secondary early warning and the tertiary early warning, and respectively marking the corresponding urgency degrees by adopting different colors or message prompt tones;

s6.3, comparing a plurality of yaw frequency probability values calculated by each fan in a selected time region with primary, secondary and tertiary thresholds in sequence;

s6.4, in S6.3, when any yaw frequency probability value exceeds a three-level threshold value, sending out a three-level early warning aiming at the typhoon;

s6.5, in S6.3, when the yaw frequency probability values do not exceed the three-level threshold value but any one yaw frequency probability value exceeds the two-level threshold value, sending out a two-level early warning aiming at the typhoon;

s6.6, in S6.3, when the yaw frequency probability values do not exceed the secondary threshold value but any one yaw frequency probability value exceeds the primary threshold value, a primary early warning is sent to the typhoon;

s6.7, when the system sends out an early warning, the user can perform early warning confirmation through the user terminal, and when the system detects that the user terminal does not receive a feedback signal for confirming the early warning message by the user within a period of time, the system sends the early warning message to the mobile terminal of the on-duty user;

s6.8, the user confirms early warning on the terminal, and carries out overhauling and deviation rectifying operations on the fan with frequent yawing in sequence from high to low according to the early warning urgency degree in time, and after overhauling is completed, the maintenance record is updated on the mobile terminal or the user terminal.

System embodiment

The embodiment aims to provide a wind turbine generator yaw frequent detection early warning algorithm system, which comprises a data acquisition unit, a data processing unit, a yaw early warning unit and a human-computer interaction unit; the data acquisition unit is used for acquiring historical yaw data of each wind turbine; the data processing unit is used for cleaning, counting and calculating historical yaw data so as to predict the yaw condition of the fan; the yaw early warning unit is used for prejudging whether the fan yaws and carrying out early warning according to the data calculation result; and the man-machine interaction unit is used for establishing an interaction channel between the user and the algorithm system.

In this embodiment, the data processing unit includes a data screening module, a data statistics module, an outlier detection module and a probability calculation module; the data cleaning and screening module is used for unifying the format of the data and screening out invalid data; the data statistics module is used for respectively counting the historical yaw frequency of each fan according to different time periods; the outlier detection module is used for judging whether the fan has frequent yawing through an outlier detection algorithm; the probability calculation module is used for calculating the probability of frequent yaw of each fan so as to give an early warning in time

In the embodiment, the man-machine interaction unit comprises an interface display module, a signal mutual transmission module and an information input module; the interface display module is used for displaying the early warning information to the user on the user terminal; the signal mutual transmission module is used for providing a channel of feedback information for a user; the information entry module is used for providing a way for a user to enter new data so as to update the historical database.

Product and electronic device embodiments

As shown in fig. 1, an exemplary product structure of the present invention is shown, and includes a computer, and a user terminal and a mobile terminal which are matched with the computer, where the computer is connected with a scada system of a wind turbine generator through a wireless communication technology to implement data sharing, and meanwhile, the computer may also enter maintenance records and sensor data.

It should be noted that the functions of the graph coding module, the cloud model building module, and the perception detection module are described in detail with reference to the description of the method portion corresponding to each module, and are not described herein again.

Further, the embodiment further includes an early warning algorithm device for frequent yaw detection of a wind turbine generator, which includes a processor, a memory, and a computer program stored in the memory and running on the processor.

The processor comprises one or more than one processing core, the processor is connected with the processor through a bus, the memory is used for storing program instructions, and the yaw frequent detection early warning algorithm of the wind turbine generator is realized when the processor executes the program instructions in the memory.

Alternatively, the memory may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

In addition, the invention also provides a computer readable storage medium, wherein a computer program is stored in the computer readable storage medium, and when the computer program is executed by a processor, the steps of the wind turbine generator yaw frequent detection early warning algorithm are realized.

Optionally, the present invention further provides a computer program product containing instructions, which when run on a computer, causes the computer to execute the steps of the wind turbine yaw frequent detection early warning algorithm in the above aspects.

It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by hardware related to instructions of a program, and the program may be stored in a computer readable storage medium, where the above mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.