CN108087210B - Wind generating set blade abnormity identification method and device - Google Patents
Wind generating set blade abnormity identification method and device Download PDFInfo
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Abstract
The invention relates to the field of wind power, in particular to a wind generating set blade abnormity identification method, which is used for monitoring the running state of a wind generating set, firstly obtaining running parameter data of the wind generating set, wherein the running parameter data at least comprises a Y-direction acceleration value, then determining abnormity detection data based on the running parameter data, then determining an abnormity detection data section in which the abnormity detection data continuously appear, then counting the total times of the abnormity detection data section, and if the total times is greater than or equal to a preset abnormity time threshold value, judging that the wind generating set blade is abnormal. Otherwise, the blade is not considered abnormal. When the performance of the blade is reduced due to cracking and other problems, the acceleration value in the Y direction in the running process of the fan is abnormal, the abnormal data continuously appear for many times in a certain time, the abnormal data indicate that the blade is abnormal, the abnormal data can be timely monitored when the blade is abnormal, and the timeliness and the accuracy of abnormal blade identification are improved.
Description
Technical Field
The invention relates to the technical field of wind power, in particular to a method and a device for identifying abnormity of a blade of a wind generating set.
Background
Wind power generation refers to converting kinetic energy of wind into electric energy, and wind energy is a clean renewable energy source, so that more and more attention is paid to wind power generation. The devices required for wind power generation are called wind generating sets. The wind generating set comprises a wind wheel and a generator, wherein the wind wheel comprises blades, a hub, a reinforcing member and the like, the generator is arranged in a cabin, and the blades rotate by wind force to generate electricity. It can be seen that the blades are a core component of the process of wind power generation.
During the process of rotating and generating electricity, the blades are exposed to the natural environment for a long time, and the conditions of cracking, breaking and the like can occur after long-term operation, so that the normal operation of the wind generating set is seriously influenced. The process from crack to crack and fracture of the blade of the wind generating set is a process which gradually deteriorates along with the accumulation of time. When the blade appears tiny crackle, it is not big to the influence of fan whole operation, but along with the increase of fracture degree, when the fracture condition even appears, will seriously influence the performance of blade. At present, the blade can be observed by human eyes only when the blade has large cracks or breaks, but the operation of the unit is seriously influenced. When the number of the blades is changed into 2 or 2.5 after the blades are broken, the generated power can be obviously reduced, and the condition that part of the blades are failed can be known by monitoring the change of the generated power. In addition, when the blade appears cracking or fracture by a wide margin, the stationarity of fan operation receives the influence, can lead to the fault rate of fan operation to increase, when frequent trouble appears, also can discover the problem that the blade became invalid. However, the above modes can be found only when the blade cracks seriously or even causes a fault, and certain hysteresis exists, so that not only is a great risk, but also the economic benefit of power generation is affected.
Disclosure of Invention
In view of this, the embodiment of the invention provides a method and a device for identifying blade abnormality of a wind turbine generator system, so as to solve the problems that the blade problem is found to be relatively delayed and the blade abnormality cannot be found in time in the prior art.
According to a first aspect, an embodiment of the present invention provides a method for identifying an abnormality of a blade of a wind turbine generator system, which is used for monitoring an operation state of the wind turbine generator system, and first obtaining operation parameter data of the wind turbine generator system, where the operation parameter data at least includes a Y-direction acceleration value, such as an amplitude change of an acceleration signal of a nacelle; and determining abnormal detection data based on the operation parameter data, wherein the abnormal detection data refers to data which is greatly changed compared with other data under the working condition. Then, determining an abnormal detection data segment continuously presenting the abnormal detection data, and representing the adjacent data with relevance in a period of time by the abnormal detection data segment; and then counting the total times of the abnormal detection data segment, obtaining the frequency of abnormal detection data, finally judging whether the total times is greater than or equal to a preset abnormal time threshold value, and if the total times is greater than or equal to the preset abnormal time threshold value, judging that the blade of the wind generating set is abnormal. When the performance of the blade is reduced due to cracking and other problems, the acceleration value in the Y direction in the running process of the fan is abnormal, and abnormal data continuously appear for many times within a certain time, so that the blade is abnormal, and measures need to be taken in advance to avoid faults.
In one embodiment combined with the first aspect, determining the abnormality detection data based on the operating parameter data includes: acquiring operation parameter data of the wind generating set during normal operation power generation from the operation parameter data; extracting target detection parameter data from the operation parameter data during normal operation power generation; determining anomaly detection data based on the target detection parameter data. Target detection parameter data are extracted from the operation parameter data of the generator set in the normal power generation state, so that abnormal data caused by the starting and stopping state or the fault state of the generator can be filtered, parameters of the generator set in the normal power generation state are selected, and the problems caused by blade cracking and the like can be identified.
In another embodiment combined with the first aspect, the determining abnormal detection data based on the target detection parameter data in this embodiment includes first calculating a mean value of accelerations in Y direction in all target detection parameter data; then judging whether the difference value between the acceleration value in the Y direction in each target detection parameter data and the average value of the acceleration in the Y direction exceeds a preset difference value threshold value or not; when the difference is larger than or equal to a preset difference threshold value, judging that the target detection parameter data are abnormal detection data; and when the difference is smaller than a preset difference threshold value, judging that the target detection parameter data are non-abnormal detection data. According to the scheme, the Y-direction acceleration value is compared with the Y-direction acceleration mean value, and the data is determined to be abnormal only when the Y-direction acceleration value exceeds the normal range, so that abnormal data can be identified more accurately by the method, and the method has higher accuracy. Through the non-abnormal detection data, whether the interval between the abnormal detection data sections is in a normal operation state or a processing stop operation state can be better judged, and the detection precision is improved.
In another embodiment with reference to the first aspect, the preset difference threshold is determined according to a standard deviation of the target detection parameter data. The value of the cabin acceleration signal within one standard deviation from the mean value accounts for 68% of the total value, the value within two standard deviations accounts for 95%, and the value within three standard deviations accounts for 99%. The k-time value of the standard deviation of the cabin acceleration signal in the Y direction is set as a target difference threshold value Y _ amplitude of the acceleration amplitude anomaly, namely, most of the values of the cabin acceleration signal with non-abnormal amplitude are within the threshold value range, and the cabin acceleration signal with abnormal amplitude exceeding the threshold value can be defined as abnormal amplitude of the cabin acceleration signal.
In another embodiment with reference to the first aspect, the obtaining of the operation parameter data during normal operation power generation of the wind turbine generator system from the operation parameter data includes screening the operation parameter data during normal operation power generation of the wind turbine generator system based on the status flag, the active power, the pitch angle and/or the generator speed.
In another embodiment combined with the first aspect, determining an abnormality detection data segment in which the abnormality detection data continuously appears includes: determining an interval between adjacent ones of the anomaly detection data; judging whether the interval between the abnormal detection data exceeds a preset interval threshold value or not; and recording an abnormal detection data segment once when the interval does not exceed a preset interval threshold. If the interval between the abnormal detection data segments is long, the correlation between the two abnormal data segments is small, so that the abnormal data segments are not suitable for data combination and need to be eliminated, and the judgment precision is improved.
In another embodiment combined with the first aspect, determining an abnormality detection data segment in which the abnormality detection data continuously appears includes: determining the number of continuous non-abnormal detection data between adjacent abnormal detection data; judging whether the number of the continuous non-abnormal detection data exceeds a preset number threshold value or not; and when the number of the continuous non-abnormal detection data between the adjacent abnormal detection data does not exceed the preset number threshold, recording an abnormal detection data segment once. The number of the non-abnormal detection data can better show the normal operation state of the generator set, and the data of the fan in a shutdown state or long time intervals are removed, so that the accuracy of the method is improved.
In another embodiment with reference to the first aspect, the preset abnormal number threshold and/or the preset difference threshold are determined through training according to the type of target detection parameter data and the operation condition of the wind turbine generator system. The operation condition of the wind generating set comprises at least one of wind speed, rotating speed and power. Considering different wind speeds, rotating speeds and powers in a normal power generation state, the threshold values need to be obtained through a multi-training mode according to different working conditions and different selected detection parameters, so that the threshold values have better pertinence, and the identification efficiency is improved.
According to a second aspect, an embodiment of the present invention provides a wind turbine generator system blade abnormality identification apparatus, including a parameter obtaining unit, configured to obtain operation parameter data of the wind turbine generator system, where the operation parameter data at least includes a Y-direction acceleration value; a first processing unit for determining anomaly detection data based on the operating parameter data; a second processing unit for determining an abnormal detection data segment in which the abnormal detection data continuously appears; the third processing unit is used for counting the total times of the abnormal detection data segments; and the abnormity judging unit is used for judging whether the total times is greater than or equal to a preset abnormity time threshold value or not, and judging that the blade of the wind generating set is abnormal if the total times is greater than or equal to the preset abnormity time threshold value.
With reference to one embodiment of the second aspect, the first processing unit includes: the data selection subunit is used for acquiring the operation parameter data of the wind generating set during normal operation and power generation from the operation parameter data; the data extraction subunit is used for extracting target detection parameter data from the operation parameter data during normal operation power generation; an abnormality detection data determination subunit operable to determine abnormality detection data based on the target detection parameter data. .
In combination with another embodiment of the second aspect, the abnormality detection data determination subunit includes: the first calculation subunit is used for calculating the average value of the accelerations in the Y direction in all the target detection parameter data; the comparison subunit is used for judging whether the difference value between the Y-direction acceleration value in each target detection parameter data and the Y-direction acceleration average value exceeds a preset difference value threshold value or not; an anomaly detection data generation subunit, configured to determine that the target detection parameter data is anomaly detection data when the difference is greater than or equal to a preset difference threshold; and the non-abnormal detection data generation subunit is used for judging that the target detection parameter data are non-abnormal detection data when the difference is smaller than a preset difference threshold. .
In combination with another embodiment of the second aspect, the second processing unit includes: an interval determination subunit configured to determine an interval between adjacent ones of the abnormality detection data; an interval judgment subunit, configured to judge whether an interval between the abnormal detection data exceeds a preset interval threshold; and the first abnormal detection data segment recording subunit is used for recording the abnormal detection data segment once when the interval does not exceed the preset interval threshold.
In combination with another embodiment of the second aspect, the second processing unit includes: a number determining subunit, configured to determine the number of consecutive non-abnormal detection data between adjacent abnormal detection data; a number judgment subunit, configured to judge whether the number of the continuous non-abnormal detection data exceeds a preset number threshold; and the second abnormal detection data segment recording subunit is used for recording an abnormal detection data segment once when the number of the continuous non-abnormal detection data between the adjacent abnormal detection data does not exceed the preset number threshold.
According to a third aspect, the embodiment of the present invention provides a wind generating set control device, which is characterized by comprising a memory and a processor, wherein the memory and the processor are connected in communication with each other, the memory stores computer instructions, and the processor executes the computer instructions, so as to implement the wind generating set blade abnormality identification method in the first aspect and the optional modes thereof.
According to a fourth aspect, the embodiment of the present invention provides a computer-readable storage medium, which stores computer instructions for causing the computer to execute the wind turbine generator system blade abnormality identification method in the first aspect and the optional modes thereof.
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The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 shows a flow chart of a wind park blade anomaly identification method according to an embodiment of the invention;
FIG. 2 shows a flow chart of a wind park blade anomaly identification method according to another embodiment of the invention;
FIG. 3 shows a flow diagram of a method of obtaining anomaly detection data according to another embodiment of the present invention;
FIG. 4 is a diagram illustrating the effect of the wind generating set blade abnormality identification method according to another embodiment of the invention;
FIG. 5 shows a schematic view of a wind turbine generator system blade anomaly identification device according to another embodiment of the invention;
fig. 6 shows a schematic view of a wind generating set control according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
Example 1
The embodiment provides a wind generating set blade abnormity identification method, which is used for monitoring the running state safety of a wind generating set, and the method can be operated in a controller of the wind generating set and is used for timely finding whether blades in the wind generating set crack and influence the pneumatic performance of the blades or the running balance of an impeller, so that abnormal blades can be timely found.
The method for identifying the abnormality of the blade of the wind generating set in the embodiment is shown in fig. 1 through a flow chart, and comprises the following steps:
and S11, obtaining operation parameter data of the wind generating set, wherein the operation parameter data at least comprise a Y-direction acceleration value.
In this embodiment, the historical data for selecting the operating parameters of the wind turbine generator system may be historical data for recording an operating condition of the wind turbine generator system over a period of time, where the historical data at least includes a Y-direction acceleration value, and in this embodiment, an acceleration signal in the Y-direction of the blade is selected. As other alternative embodiments, the Y-direction acceleration value may also be selected from the Y-direction acceleration values of the amplitude signal and the displacement signal corresponding to the impeller, the nacelle, or the tower.
And S12, determining abnormal detection data based on the operation parameter data.
In this step, the detection data in which the abnormality occurs is found out among all the operation parameter data. The average value of the acceleration values of the blades in the Y direction under the normal power generation state can be obtained, when the difference value between the acceleration values of the blades in the Y direction and the average value exceeds a certain amplitude range, the data are considered to be abnormal detection data, and the amplitude range can be determined according to the operation condition of the wind generating set.
And S13, determining abnormal detection data segments of the abnormal detection data which continuously appear.
In general, the abnormal detection data may occur occasionally due to an emergency, and the interval between the abnormal detection data and other abnormal detection data is relatively long, and in this case, the data is not considered to have a correlation. Whether or not there is a correlation between the anomaly data may be determined according to the interval between adjacent anomaly detection data. The specific mode is as follows:
first, an interval between adjacent ones of the abnormality detection data is determined.
And secondly, judging whether the interval between the abnormal detection data exceeds a preset interval threshold value. The duration threshold is set to be 0.5 hour, and is obtained through statistics of algorithm model training results, but in other embodiments, the duration threshold can be reasonably set as required, and the duration within 15 minutes to 1 hour can be generally selected. If the preset interval threshold is exceeded, the fact that no correlation exists between the two abnormal data is shown, the blade abnormality in a time period cannot be reflected at the same time, and the data are not considered to be related abnormality detection data. And when the interval does not exceed a preset interval threshold, considering that the association exists between the two abnormal detection data, and merging and recording the two abnormal detection data into a primary abnormal detection data segment.
And S14, counting the total times of the abnormal detection data segments.
Since the adjacent abnormality detection data satisfying the condition is recorded as the one-time abnormality detection data segment by the judgment for the adjacent abnormality detection data in step S13, the number of times of abnormality detection data can be accumulated by counting the number of times of these abnormality detection data segments.
S15, judging whether the total times is larger than or equal to a preset abnormal time threshold value or not, if the total times is larger than or equal to the preset abnormal time threshold value, judging that the wind generating set blade is abnormal, otherwise, not considering that the wind generating set blade is abnormal.
When the total times exceed a preset abnormal time threshold value, it is indicated that multiple continuous abnormal data occur during the operation of the unit within a certain time, the abnormal data are greatly related to the operation of the blades, the blades have a large problem, and the abnormal data are judged to exist in the blades at the moment, and need to be processed in time to avoid the occurrence of faults.
According to the wind generating set blade abnormity identification method provided by the embodiment, whether the wind generating set blade is abnormal or not is judged according to whether the Y-direction acceleration value in the operation parameter data of the wind generating set is abnormal or not and the continuous abnormal condition occurs. When the performance of the blade is reduced due to cracking and other problems, the acceleration value in the Y direction in the operation process of the wind generating set is abnormal, abnormal data continuously appear for a plurality of times within a certain time, the abnormal data indicate that the blade is abnormal and need to be paid attention to in time to avoid the occurrence of failure of the blade, and therefore risks existing in the operation process of the wind generating set are reduced.
Example 2
The embodiment provides a method for identifying an abnormality of a blade of a wind generating set, which has the same use scenario as that of embodiment 1, and the flowchart is shown in fig. 2, and the method includes the following steps:
and S21, obtaining operation parameter data of the wind generating set, wherein the operation parameter data at least comprise a Y-direction acceleration value.
The input operation parameter data of the wind generating set is site transient operation data, which can be real-time data or pre-stored historical data. For example, the data sampling frequency is 1/7Hz, and the data is called 7 seconds data or 7s data, namely, the central monitoring system records the instantaneous values of different variable signals in the operation process of the unit as required and stores the instantaneous values as real-time or historical data every 7 seconds. The basic variable signals contained in the field detection data comprise: on-site code number [1 ]]Code of machine set [1 ]]Time [ ymd _ hms ]]Wind speed [ m/s ]]Rotational speed [ r/min ]]Active power [ kW ]]X direction cabin acceleration [ g ]]And acceleration of cabin in Y direction [ g ]]Number 1-3 blade pitch angle [ ° ]]No. 1-3 blade variable pitch speed [ °/s]Data available status flag bit [1 ]]Where g denotes the acceleration of gravity in m/s2. For example, in the embodiment, the acceleration of the nacelle in the Y direction is selected as the target detection parameter data, and the change of other variable signals can be directly or indirectly used as the basis for various judgments in the algorithm, such as the signals of the rotating speed, the time, the wind speed, the power, the pitch angle and the like for determining different working conditions. In other embodiments, abnormal changes in the x-direction nacelle acceleration and the amplitude of the effective value of the nacelle acceleration of the unit may be detected (or combined), and the detection algorithm is the same, but the specific threshold value may be different from the detection algorithm for abnormal changes in the amplitude of the Y-direction nacelle acceleration. In this embodiment, 7s data of the detected unit is extracted according to the detection start-stop time. The time can be reasonably set, more second-level data are generally used for time domain analysis, less useful information is stored in longer data, and the use efficiency is not high. The operation parameter data in this embodiment may be compatible with other data at different sampling time intervals, such as 20ms, 1s, 10s, 1min, 10min, and the like, in addition to the generally common transient operation data of the unit 7s, and because the sampling frequencies of different data are different, it is necessary to retrain each threshold in the model algorithm according to different sampling time intervals to obtain the optimal detection effect. And (4) extracting 7s data of the detected unit within 30 days before the detection time by considering the data quantity and the data processing duration which are beneficial to identifying the failure characteristics. The data extraction process is operated in the background through a fan operation platformAt present, data to be detected can also be accessed from a big data cloud platform, such as a golden wind KMX big data cloud platform, an amazon AWS cloud platform, and the like, and data can also be imported in an offline mode, and even can be detected by an offline operation scheme model.
And S22, determining abnormal detection data based on the operation parameter data.
Firstly, obtaining the operation parameter data of the wind generating set during normal operation and power generation from the operation parameter data. For starting and stopping and in a fault state, the operating parameters of the fan can be changed greatly, and the variation of the acceleration of the engine room is large when starting and stopping and faults occur. In order to ensure the validity of the data, the target detection parameter data is extracted from the operation parameters of the generator set in the normal power generation state, and the data in the abnormal power generation state is not considered.
For example, if the rotational speed of the generator exceeds the rated rotational speed, the operational stability of the fan is greatly affected, and it is difficult to identify whether the rotational speed of the generator exceeds the rated rotational speed or the rotational speed of the fan is caused by blade failure, so that data obtained when the generator set is in a normal power generation state, that is, when the rotational speed is less than or equal to the rated rotational speed, needs to be identified.
In this embodiment, the 7s data in the normal power generation state of the unit is screened out by using the state flag bit, and the 7s data are merged line by line according to the time sequence, where the screening condition is that the available state flag bit of the data is equal to 1, and the data of each variable at the time is recorded in each line of the merged 7s data. In the embodiment, data is screened by using the status flag bit, and the data in the normal power generation state of the unit is screened out for detection. In other embodiments, the active power, the pitch angle and/or the generator speed may also be used to screen the data of the operating parameters of the wind turbine generator during normal operation of power generation, that is, the data corresponding to the range of the pitch angle and the power range is selected to screen the data of the operating parameters during normal operation of power generation, for example, the data of the pitch angle of the wind turbine generator is less than 30 ° and the active power is greater than or equal to 20kW, so as to approximate the data of the wind turbine generator during normal power generation.
And secondly, extracting target detection parameter data from the operation parameter data during normal operation power generation.
In the embodiment, the amplitude change of the acceleration signal of the engine room in the Y direction is selected as a detection object, mainly, the aerodynamic characteristics of blades are influenced after the blades of the unit fail (such as cracking and breaking of the blades), dynamic characteristics of an impeller are unbalanced, vibration inside and outside the surface of the impeller is generated under the action of centrifugal force, the vibration can be more or less superposed on the acceleration signal of the engine room according to the failure degree of the blades, the amplitude change of the acceleration signal of the engine room in the Y direction is more obvious than that in the X direction in statistics of operation data of the unit with the failed blades on site, and failure characteristics are easier to identify and extract.
Thirdly, determining abnormal detection data based on the target detection parameter data, and the flow chart is shown in fig. 3.
First, the Y-direction acceleration mean value Y _ mean is calculated among all Y-direction cabin acceleration signals.
Then, whether the difference value between the Y-direction acceleration value in each target detection parameter data and the Y-direction acceleration mean value Y _ mean exceeds a preset difference value threshold value is judged.
The preset difference threshold value is determined according to the standard deviation of the mean value of the acceleration in the Y direction. As a specific implementation manner of this embodiment, the standard deviation of the Y-direction cabin acceleration signal is calculated, and according to the definition of normal distribution, the value of the cabin acceleration signal within less than one standard deviation from the mean value Y _ mean accounts for 68% of the total value, the value ratio within two standard deviations accounts for 95%, and the value ratio within three standard deviations accounts for 99%. The k-time value of the standard deviation of the cabin acceleration signal in the Y direction is set as a target difference threshold value Y _ amplitude of the acceleration amplitude anomaly, namely, most of the values of the cabin acceleration signal with non-abnormal amplitude are within the threshold value range, and the cabin acceleration signal with abnormal amplitude exceeding the threshold value can be defined as abnormal amplitude of the cabin acceleration signal. The k value is obtained by training and counting a large amount of field data of each model according to an algorithm model. K is equal to 5, the system is more sensitive and easier to misreport when k is smaller, and the system is less easy to misreport when k is larger, so that a proper value needs to be obtained through statistics to improve the alarm accuracy.
And when the difference is larger than or equal to a preset difference threshold value, judging that the target detection parameter data are abnormal detection data. And when the difference is smaller than a preset difference threshold value, judging that the target detection parameter data are non-abnormal detection data. The larger the difference value is, the larger the possibility that the acceleration in the Y direction is abnormal is, when the difference value is larger than a certain degree, the data is considered to be abnormal, otherwise, the acceleration in the Y direction is judged to be non-abnormal detection data.
And S23, determining abnormal detection data segments of the abnormal detection data which continuously appear.
If the overall time span of the operation parameter data is large, for example, the time span is one month or more, and if the time interval between the abnormal detection data is long, it indicates that the occurrence relevance of the two abnormal data is small, the two abnormal data should be treated separately rather than being merged, and the abnormal detection data segment here may be determined by the following method, specifically including:
first, the number of continuous non-abnormal detection data between adjacent abnormal detection data is determined.
Considering that the interval time between the abnormal detection data is longer, which may be caused by the reason that the unit is not started for a long time, and the abnormal detection data appears in the unit after the unit is started, in order to eliminate the situation, the interval of the abnormal detection data in the running state of the unit is determined by the number of the continuous non-abnormal detection data.
And secondly, judging whether the number of the continuous non-abnormal detection data exceeds a preset number threshold value.
And if the non-abnormal detection data between the adjacent abnormal detection data exceeds certain data, the relevance of the two abnormal occurrences is considered to be poor, and the combination processing is not carried out. The number threshold value is reasonably set according to the working condition.
In the embodiment, the non-abnormal detection data is selected for statistics aiming at the detection data with large time span, and the data with small relevance is removed, so that better identification precision is achieved.
And thirdly, recording an abnormal detection data segment once when the number of the continuous non-abnormal detection data between the adjacent abnormal detection data does not exceed the preset number threshold.
When the number of non-abnormal data between adjacent abnormal detection data is small and does not exceed a preset number threshold, the two abnormal occurrences are associated, and the data is considered to belong to the abnormal detection data segment, and the integral operation state of the blade can be reflected through combination.
And S24, counting the total times of the abnormal detection data segments. The same as S13 in embodiment 1, and the description thereof is omitted.
S25, judging whether the total times is larger than or equal to a preset abnormal time threshold value or not, if the total times is larger than or equal to the preset abnormal time threshold value, judging that the blade of the wind generating set is abnormal, otherwise, not considering that the blade is abnormal. The same as S15 in embodiment 1, and the description thereof is omitted.
In this embodiment, the preset abnormal number threshold and/or the preset difference threshold and/or the preset number prediction is determined by training according to the type of target detection parameter data and the operation condition of the wind turbine generator system. Considering different wind speeds, rotating speeds and power sections under the normal power generation state, the above threshold values of the model need to be retrained according to different working conditions. A feasible scheme aiming at the megawatt direct-drive wind generating set is to select a small wind speed section (5-8m/s) to compare and analyze abnormal change of the acceleration amplitude of the cabin under each sub-cabin according to 0.5r/min sub-cabin of the rotating speed of the generating set (or 50kW sub-cabin of power).
The specific implementation mode comprises the following steps:
1) selecting small wind speed section (5-8m/s) data of the unit, dividing the bins according to the wind speed per 0.5m/s, selecting the bins with abnormal and obvious cabin acceleration signal amplitude through data training, detecting the bin position data by applying the scheme, and determining a target parameter reference value and the target difference value threshold value in the data training process.
2) Selecting data of a high-power section (more than or equal to (rated power-200 kW)) of the unit, dividing bins every 50kW according to power, and selecting the bins with abnormal and obvious cabin acceleration signal amplitude through data training.
3) Selecting data of large rotating speed (greater than or equal to rated rotating speed-2 r/min)) of the unit, dividing bins according to rotating speed every 0.5r/min, selecting the bins with abnormal and obvious cabin acceleration signal amplitude through data training, detecting the bin position data by applying the algorithm, and determining a target parameter reference value and the target difference threshold value in the algorithm in the data training process.
According to the wind generating set blade abnormity identification method in the embodiment, the detection parameters are obtained under the condition that the generator normally operates, the condition that the time interval is long due to the fact that the generator does not operate is eliminated through non-abnormal data, and when the abnormity detection data exceed the normal range, the data are determined to be abnormal, so that the method can accurately identify the abnormal data and has higher accuracy.
The field test result is shown in fig. 4, and the 15# unit of a certain wind power plant in Gansu province on 4 months and 3 days in 2015 has a blade cracking condition. In the 7s real-time data of 4, 2 and 4 days before the failure, the Y-direction cabin acceleration signal has abnormal large vibration for a long time from 18:03:53 to 20:38:02 of the day, and the amplitude exceeds the threshold value Y _ amplitude of the acceleration amplitude abnormality for 170 times. The process, the data access and the result output in the embodiment can be realized in an R language environment, the functions of the scheme can also be realized in compiling environments such as Python, Matlab, Scala and the like, and different model development platforms, such as a jinfeng MD4X model development platform, can be implanted for operation.
Example 3
A wind generating set blade abnormity identification device is shown in a structural block diagram in FIG. 5, and comprises:
the parameter acquiring unit 31 is configured to acquire operation parameter data of the wind turbine generator system, where the operation parameter data at least includes a Y-direction acceleration value; the specific implementation manner is the same as S11 in embodiment 1 or S21 in embodiment 2, and is not described herein again.
A first processing unit 32 for determining abnormality detection data based on the operational parameter data; the specific implementation manner is the same as S12 in embodiment 1 or S22 in embodiment 2, and is not described herein again.
A second processing unit 33 for determining an abnormality detection data segment in which the abnormality detection data continuously appears; the specific implementation manner is the same as S13 in embodiment 1 or S23 in embodiment 2, and is not described herein again.
A third processing unit 34, configured to count the total number of times of the anomaly detection data segments; the specific implementation manner is the same as S14 in embodiment 1 or S24 in embodiment 2, and is not described herein again.
And the abnormality judgment unit 35 is configured to judge whether the total number of times is greater than or equal to a preset abnormal number threshold, and if the total number of times is greater than or equal to the preset abnormal number threshold, judge that the blade of the wind turbine generator system is abnormal. The specific implementation manner is the same as S15 in embodiment 1 or S25 in embodiment 2, and is not described herein again.
Wherein the first processing unit 32 comprises:
the data selection subunit is used for acquiring the operation parameter data of the wind generating set during normal operation and power generation from the operation parameter data;
the data extraction subunit is used for extracting target detection parameter data from the operation parameter data during normal operation power generation;
an abnormality detection data determination subunit operable to determine abnormality detection data based on the target detection parameter data.
Wherein the abnormality detection data determination subunit includes:
the first calculation subunit is used for calculating the average value of the accelerations in the Y direction in all the target detection parameter data;
the comparison subunit is used for judging whether the difference value between the Y-direction acceleration value in each target detection parameter data and the Y-direction acceleration average value exceeds a preset difference value threshold value or not;
an anomaly detection data generation subunit, configured to determine that the target detection parameter data is anomaly detection data when the difference is greater than or equal to a preset difference threshold;
and the non-abnormal detection data generation subunit is used for judging that the target detection parameter data are non-abnormal detection data when the difference is smaller than a preset difference threshold.
As a specific implementation manner, the second processing unit 33 includes:
an interval determination subunit configured to determine an interval between adjacent ones of the abnormality detection data;
an interval judgment subunit, configured to judge whether an interval between the abnormal detection data exceeds a preset interval threshold;
and the first abnormal detection data segment recording subunit is used for recording the abnormal detection data segment once when the interval does not exceed the preset interval threshold.
As another specific implementation manner, the second processing unit 33 includes:
a number determining subunit, configured to determine the number of consecutive non-abnormal detection data between adjacent abnormal detection data;
a number judgment subunit, configured to judge whether the number of the continuous non-abnormal detection data exceeds a preset number threshold;
and the second abnormal detection data segment recording subunit is used for recording an abnormal detection data segment once when the number of the continuous non-abnormal detection data between the adjacent abnormal detection data does not exceed the preset number threshold.
The wind generating set blade abnormity identification device in this embodiment can identify the problem of blade failure at the initial stage that the blade cracks to influence the aerodynamic performance of the blade, find the problem in time, predict potential risks, effectively monitor the operation of the fan, and have better blade abnormity identification precision.
Example 4
In the present embodiment, a wind generating set control device is provided, as shown in fig. 6, which includes a memory 42 and a processor 41, where the memory 42 and the processor 41 are communicatively connected to each other, and may be connected through a bus or in another manner, and fig. 6 takes the example of connection through a bus as an example.
The processor 41 may be a Central Processing Unit (CPU). The Processor 41 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 42, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the wind turbine generator system blade abnormality identification method in the embodiment of the present invention (for example, the parameter obtaining unit 31, the first processing unit 32, the second processing unit 33, the third processing unit 34, and the abnormality determination unit 35 shown in fig. 5). The processor 41 executes various functional applications and data processing of the processor by executing the non-transitory software programs, instructions and modules stored in the memory 42, namely, implements the wind turbine generator system blade abnormality identification method in the above method embodiment 1 or 2.
The memory 42 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 41, and the like. Further, the memory 42 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 42 may optionally include memory located remotely from processor 41, which may be connected to processor 41 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 42 and when executed by the processor 41 perform a wind park blade anomaly identification method as in the embodiment of fig. 1-2.
The specific details of the wind turbine generator system blade abnormality identification method may be understood by referring to the corresponding relevant description and effects in the embodiments shown in fig. 1 to fig. 2, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (12)
1. A wind generating set blade abnormity identification method is characterized by comprising the following steps:
acquiring operation parameter data of the wind generating set, wherein the operation parameter data at least comprises a Y-direction acceleration value;
determining anomaly detection data based on the operational parameter data;
determining an abnormal detection data segment in which the abnormal detection data continuously appears;
counting the total times of the abnormal detection data segments;
judging whether the total times is greater than or equal to a preset abnormal time threshold value or not, and if the total times is greater than or equal to the preset abnormal time threshold value, judging that the blades of the wind generating set are abnormal;
the determining of the abnormal detection data segment in which the abnormal detection data continuously appears includes:
determining an interval between adjacent ones of the anomaly detection data;
judging whether the interval between the abnormal detection data exceeds a preset interval threshold value or not;
when the interval does not exceed a preset interval threshold, recording an abnormal detection data segment once;
or, determining the number of continuous non-abnormal detection data between adjacent abnormal detection data;
judging whether the number of the continuous non-abnormal detection data exceeds a preset number threshold value or not;
and when the number of the continuous non-abnormal detection data between the adjacent abnormal detection data does not exceed the preset number threshold, recording an abnormal detection data segment once.
2. The method of claim 1, wherein said determining anomaly detection data based on said operational parameter data comprises:
acquiring operation parameter data of the wind generating set during normal operation power generation from the operation parameter data;
extracting target detection parameter data from the operation parameter data during normal operation power generation;
determining anomaly detection data based on the target detection parameter data.
3. The method of claim 2, wherein said determining anomaly detection data based on said target detection parameter data comprises:
calculating the average value of the acceleration in the Y direction in all the target detection parameter data;
judging whether the difference value between the Y-direction acceleration value in each target detection parameter data and the Y-direction acceleration mean value exceeds a preset difference value threshold value or not;
when the difference is larger than or equal to a preset difference threshold value, judging that the target detection parameter data are abnormal detection data;
and when the difference is smaller than a preset difference threshold value, judging that the target detection parameter data are non-abnormal detection data.
4. The method of claim 3, wherein the predetermined difference threshold is determined based on a standard deviation of the target detection parameter data.
5. The method according to any one of claims 2-4, wherein the obtaining of the operational parameter data of the wind turbine during normal operation of the wind turbine generator system for power generation from the operational parameter data comprises: and screening the operation parameter data of the wind generating set during normal operation power generation based on the state flag bit, the active power, the pitch angle and/or the rotating speed of the generator.
6. Method according to claim 3 or 4, characterized in that the preset anomaly number threshold value and/or the preset difference threshold value is determined by training according to the type of target detection parameter data and the operating condition of the wind turbine generator set.
7. The method of claim 6, wherein the operating conditions of the wind turbine generator system include at least one of wind speed, rotational speed, and power.
8. A wind generating set blade abnormity recognition device is characterized by comprising:
the parameter acquisition unit is used for acquiring operation parameter data of the wind generating set, and the operation parameter data at least comprises a Y-direction acceleration value;
a first processing unit for determining anomaly detection data based on the operating parameter data;
a second processing unit for determining an abnormal detection data segment in which the abnormal detection data continuously appears;
the third processing unit is used for counting the total times of the abnormal detection data segments;
the abnormality judgment unit is used for judging whether the total times is greater than or equal to a preset abnormal time threshold value or not, and if the total times is greater than or equal to the preset abnormal time threshold value, judging that the wind generating set blade is abnormal;
the second processing unit includes:
an interval determination subunit configured to determine an interval between adjacent ones of the abnormality detection data;
an interval judgment subunit, configured to judge whether an interval between the abnormal detection data exceeds a preset interval threshold;
the first abnormal detection data segment recording subunit is used for recording an abnormal detection data segment once when the interval does not exceed a preset interval threshold;
or, comprising:
a number determining subunit, configured to determine the number of consecutive non-abnormal detection data between adjacent abnormal detection data;
a number judgment subunit, configured to judge whether the number of the continuous non-abnormal detection data exceeds a preset number threshold;
and the second abnormal detection data segment recording subunit is used for recording an abnormal detection data segment once when the number of the continuous non-abnormal detection data between the adjacent abnormal detection data does not exceed the preset number threshold.
9. The apparatus of claim 8, wherein the first processing unit comprises:
the data selection subunit is used for acquiring the operation parameter data of the wind generating set during normal operation and power generation from the operation parameter data;
the data extraction subunit is used for extracting target detection parameter data from the operation parameter data during normal operation power generation;
an abnormality detection data determination subunit operable to determine abnormality detection data based on the target detection parameter data.
10. The apparatus of claim 9, wherein the anomaly detection data determination subunit comprises:
the first calculation subunit is used for calculating the average value of the accelerations in the Y direction in all the target detection parameter data;
the comparison subunit is used for judging whether the difference value between the Y-direction acceleration value in each target detection parameter data and the Y-direction acceleration average value exceeds a preset difference value threshold value or not;
an anomaly detection data generation subunit, configured to determine that the target detection parameter data is anomaly detection data when the difference is greater than or equal to a preset difference threshold;
and the non-abnormal detection data generation subunit is used for judging that the target detection parameter data are non-abnormal detection data when the difference is smaller than a preset difference threshold.
11. A wind generating set control device, characterized by comprising a memory and a processor, wherein the memory and the processor are connected in communication with each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the wind generating set blade abnormality identification method according to any one of claims 1 to 7.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions for causing the computer to execute the wind turbine generator system blade abnormality identification method according to any one of claims 1 to 7.
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| CN110578659B (en) * | 2018-06-07 | 2021-12-03 | 华北电力大学 | System and method for processing SCADA data of wind turbine generator |
| CN111749854B (en) * | 2019-03-27 | 2022-04-19 | 北京金风科创风电设备有限公司 | Protection method and equipment of wind generating set |
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| CN113494428B (en) * | 2020-03-20 | 2022-11-22 | 新疆金风科技股份有限公司 | Fault detection method and device of wind generating set |
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| CN112016249A (en) * | 2020-08-31 | 2020-12-01 | 华北电力大学 | SCR denitration system bad data identification method based on optimized BP neural network |
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| CN113606098B (en) * | 2021-08-23 | 2022-07-05 | 哈电风能有限公司 | Method and system for detecting aerodynamic abnormality of blades of wind turbine generator and storage medium |
| CN114190916B (en) * | 2021-12-07 | 2023-05-23 | 河南省儿童医院郑州儿童医院 | Fabric sensor-based child respiration monitoring method and system |
| CN114215706B (en) * | 2021-12-27 | 2024-02-20 | 南京邮电大学 | Early warning method and device for cracking faults of wind turbine generator blades |
| CN114810652B (en) * | 2022-06-28 | 2022-10-28 | 潍柴动力股份有限公司 | Method, device, equipment, medium and product for detecting control abnormality of supercharger |
| CN116292133B (en) * | 2022-12-30 | 2023-11-17 | 北京金风科创风电设备有限公司 | Method and device for detecting clearance abnormality of wind generating set |
| CN116369874B (en) * | 2023-03-27 | 2023-12-12 | 浙江想能睡眠科技股份有限公司 | Human health detection method, system and storage medium based on mattress data |
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