CN113027698B - Detection method and device for abnormity of variable pitch control loop of wind generating set - Google Patents

Detection method and device for abnormity of variable pitch control loop of wind generating set Download PDF

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Publication number
CN113027698B
CN113027698B CN201911353081.2A CN201911353081A CN113027698B CN 113027698 B CN113027698 B CN 113027698B CN 201911353081 A CN201911353081 A CN 201911353081A CN 113027698 B CN113027698 B CN 113027698B
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pitch angle
sampling
value
generating set
wind generating
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CN113027698A (en
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卡瓦尔·阿力
周杰
魏蒙
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Jinfeng Technology Co ltd
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Xinjiang Goldwind Science and Technology Co Ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/304—Spool rotational speed
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/328—Blade pitch angle
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Abstract

The detection method and the detection device for the abnormity of the variable pitch control loop of the wind generating set are provided, and the detection method comprises the following steps: acquiring sampling values of a pitch angle of a wind generating set at a plurality of sampling moments within a preset time period; determining predicted values of pitch angles at the plurality of sampling instants according to a prediction function; analyzing the deviation condition of the sampling value of the pitch angle relative to the predicted value of the pitch angle; if the sampling value of the pitch angle deviates relative to the predicted value of the pitch angle, judging whether the change of the sampling value of the pitch angle meets the abnormal characteristic; and outputting a detection result aiming at the variable pitch control loop according to the judgment result of the abnormal characteristic. By adopting the method and the device for detecting the abnormity of the variable pitch control circuit of the wind generating set, the abnormity state of the variable pitch control circuit of the wind generating set can be effectively identified, the abnormity state is processed timely, and the stability of the wind generating set is ensured.

Description

Detection method and device for abnormity of variable pitch control loop of wind generating set
Technical Field
The present invention relates generally to the field of wind power generation technologies, and in particular, to a method and an apparatus for detecting an abnormality of a pitch control loop of a wind turbine generator system.
Background
A variable pitch system of the wind generating set can stabilize the rotating speed and power output or carry out pneumatic brake shutdown by adjusting the pitch angle. The principle of the variable pitch control loop is as follows: the wind speed measuring device comprises an anemoscope, a tower bottom control cabinet, a hub variable pitch system, a hub, 3 axle boxes and wind speed information transmitting devices, wherein the anemoscope is arranged on an engine room and used for measuring wind speed, transmitting wind speed information to the tower bottom control cabinet, determining variable pitch information by analyzing the wind speed information, transmitting the variable pitch information to the center boxes of the hub variable pitch system, transmitting the variable pitch information to the 3 axle boxes by the center boxes, and adjusting the variable pitch angle of a blade by the 3 axle boxes through variable pitch driving.
The normal operation of a variable pitch control loop of the wind generating set is related to the safety and stability and the power generation performance of the whole machine, and a reasonable protection strategy needs to be formulated, a protection algorithm needs to be developed and protection needs to be implemented in the device.
The abnormity judgment of the variable pitch system in the control program of the existing wind generating set is generally at fault level, namely, fault shutdown is executed when the variable pitch system has clear fault characteristics. The abnormal judgment method cannot identify abnormal states from early stage, cannot recognize deep abnormal states of the variable pitch control loop, and can cause the output reduction of the wind generating set and the damage of key components such as a variable pitch motor if the wind generating set runs in a sub-health state for a long time.
One abnormal pattern of the pitch control loop is shown in fig. 1, with time on the abscissa and pitch angle on the ordinate. Such an abnormal pattern is characterized in that the pitch angle is slowly decreased due to the limitation of the lower limit of the pitch angle, and mainly means that the pitch angle is not decreased to an expected target value within a predetermined time, that is, the decrease width or the decrease rate within the predetermined time is insufficient.
The reasons for this may be: the control parameter setting of the variable pitch control loop is not appropriate; and the control function for reducing the load of the wind generating set adopted aiming at the worse working condition is abnormal. The influence of such abnormal mode on the wind generating set mainly lies in: and the control curve of the actual operation of the wind generating set deviates from the design curve at the expense of generating capacity. The detection difficulty of such abnormal patterns is: generally, the obvious abnormal stability of the whole machine cannot be caused, and faults are reported to realize protection.
Disclosure of Invention
An object of an exemplary embodiment of the present invention is to provide a method and an apparatus for detecting an abnormality of a pitch control loop of a wind turbine generator system, so as to overcome at least one of the above-mentioned disadvantages.
In one general aspect, there is provided a method of detecting an abnormality in a pitch control loop of a wind turbine generator system, the method comprising: acquiring sampling values of a pitch angle of a wind generating set at a plurality of sampling moments within a preset time period; determining predicted values of pitch angles at the plurality of sampling instants according to a prediction function; analyzing the deviation condition of the sampling value of the pitch angle relative to the predicted value of the pitch angle; if the sampling value of the pitch angle deviates relative to the predicted value of the pitch angle, judging whether the change of the sampling value of the pitch angle meets the abnormal characteristic; and outputting a detection result aiming at the variable pitch control loop according to the judgment result of the abnormal characteristic.
Alternatively, the prediction function may be constructed by: by fitting the sample values of the pitch angle at said plurality of sample instants, a prediction function is obtained reflecting a linear change of the pitch angle over time.
Optionally, the step of analyzing the deviation of the sampled value of the pitch angle from the predicted value of the pitch angle may comprise: at each sampling moment, respectively calculating the difference value between the sampling value of the pitch angle and the predicted value of the pitch angle; and determining the deviation condition of the sampling value of the pitch angle relative to the predicted value of the pitch angle according to the comparison result of the difference value calculated at each sampling moment and the set threshold value.
Alternatively, the step of determining the deviation of the sampled value of the pitch angle from the predicted value of the pitch angle based on the comparison of the difference calculated at each sampling instant with the set threshold value may comprise: counting the number of the difference values smaller than the set threshold value in the difference values calculated at the plurality of sampling moments; if the counted number is smaller than the preset value, determining that the sampling value of the pitch angle deviates relative to the predicted value of the pitch angle; and if the counted number is not less than a preset value, determining that the sampling value of the pitch angle does not deviate relative to the predicted value of the pitch angle, wherein the set threshold value is a negative value.
Alternatively, the set threshold may be determined by: calculating the standard deviation of the difference values smaller than zero in the difference values calculated at the plurality of sampling moments; and determining the opposite number of the standard deviation of the preset multiple as the set threshold.
Alternatively, the step of determining whether the change in the sampled value of pitch angle satisfies an anomaly characteristic may comprise: extracting a slope of a prediction function for reflecting linear change of the pitch angle along with time; and determining whether the change of the sampling value of the pitch angle meets the abnormal characteristic by judging whether the slope of the prediction function can represent the abnormal change characteristic of the pitch angle.
Optionally, the step of determining whether the slope of the prediction function is characteristic of an abnormal change in pitch angle may comprise: judging whether the slope of the prediction function is within a preset slope range or not; if the slope of the prediction function is in the preset slope range, determining that the slope of the prediction function can represent the abnormal change characteristic of the pitch angle, and determining that the change of the sampling value of the pitch angle meets the abnormal characteristic; and if the slope of the prediction function is not in the preset slope range, determining that the slope of the prediction function cannot represent the abnormal change characteristic of the pitch angle, and determining that the change of the sampling value of the pitch angle does not meet the abnormal characteristic.
Optionally, the detecting may further include: determining whether the sampled values of the pitch angle acquired satisfy an anomaly detection condition, wherein if the anomaly detection condition is satisfied, predicted values of the pitch angle at the plurality of sampling instants may be determined according to a prediction function.
Alternatively, the step of determining whether the acquired sample values of the pitch angle satisfy an anomaly detection condition may comprise: determining the current running state of the wind generating set; if the current running state of the wind generating set is in a grid-connected power generation state, determining that the sampling value of the acquired pitch angle meets an abnormal detection condition; and if the current running state of the wind generating set is not in a grid-connected power generation state, determining that the acquired sampling value of the pitch angle does not meet the abnormal detection condition.
Optionally, the step of determining the current operating state of the wind park may comprise: acquiring sampling values of the impeller rotating speed of the wind generating set at the plurality of sampling moments; comparing sampling values of the pitch angles of the wind generating set at the plurality of sampling moments with a preset pitch angle threshold value, and comparing the sampling values of the impeller rotating speed of the wind generating set at the plurality of sampling moments with a preset impeller rotating speed threshold value; if the maximum value in the sampling values of the pitch angles of the wind generating set at the multiple sampling moments is smaller than a preset pitch angle threshold value, and the minimum value in the sampling values of the impeller rotating speeds of the wind generating set at the multiple sampling moments is larger than a preset impeller rotating speed threshold value, determining that the current operating state of the wind generating set is in a grid-connected power generation state; if the maximum value in the sampling values of the pitch angle of the wind generating set is not less than the preset pitch angle threshold value, and/or the minimum value in the sampling values of the impeller rotating speed of the wind generating set is not more than the preset impeller rotating speed threshold value, determining that the current operating state of the wind generating set is not in a grid-connected power generation state, or determining the current operating state of the wind generating set by obtaining a state word for indicating the current operating state of the wind generating set.
In another general aspect, there is provided a pitch control loop abnormality detection apparatus of a wind turbine generator system, the detection apparatus including: the sampling value acquisition module is used for acquiring sampling values of the pitch angles of the wind generating set at a plurality of sampling moments within a preset time period; a predicted value determining module that determines predicted values of the pitch angles at the plurality of sampling times according to a prediction function; the deviation condition analysis module is used for analyzing the deviation condition of the sampling value of the pitch angle relative to the predicted value of the pitch angle; the characteristic abnormity determining module is used for judging whether the change of the sampling value of the pitch angle meets the abnormity characteristic or not if the situation that the sampling value of the pitch angle deviates from the predicted value of the pitch angle is determined; and the detection result determining module outputs a detection result aiming at the variable pitch control loop according to the judgment result of the abnormal characteristic.
In another general aspect, there is provided a controller comprising: a processor; a memory for storing a computer program which, when executed by the processor, implements the above described method of detecting an anomaly in a pitch control loop of a wind turbine generator set.
In another general aspect, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, implements a method of detecting a pitch control loop anomaly of a wind park as described above.
By adopting the method and the device for detecting the abnormity of the variable pitch control circuit of the wind generating set, the abnormity state of the variable pitch control circuit of the wind generating set can be identified as early as possible, the fault caused by the maintenance of the abnormity state for a period of time can be effectively avoided, and operation and maintenance personnel can timely process the abnormity state, thereby being beneficial to improving the overall performance of the wind generating set.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings which illustrate exemplary embodiments.
FIG. 1 shows a schematic view of an abnormal pattern of a pitch control loop of a wind park;
FIG. 2 illustrates a flow chart of a method of detecting a pitch control loop anomaly of a wind park according to an exemplary embodiment of the invention;
FIG. 3 shows a block diagram of a detection arrangement of a pitch control loop anomaly of a wind park according to an exemplary embodiment of the invention;
fig. 4 illustrates a block diagram of a controller according to an exemplary embodiment of the present invention.
Detailed Description
Various example embodiments will now be described more fully with reference to the accompanying drawings, in which some example embodiments are shown.
The invention provides a method for detecting abnormity of a variable pitch control loop of a wind generating set, wherein the abnormity belongs to the appearance before the fault, namely, the abnormity state is maintained for a period of time, the fault is possibly formed, and the early judgment of the fault needs to identify the abnormity state so as to avoid the abnormity state from continuing or becoming serious to cause the fault.
A method of identifying a pitch control loop anomaly of a wind park is described below with reference to FIG. 2.
FIG. 2 shows a flow chart of a method of detecting an anomaly in a pitch control loop of a wind park according to an exemplary embodiment of the invention.
Referring to fig. 2, in step S10, sampled values of the pitch angle of the wind park at a plurality of sampling instants within a predetermined time period are obtained.
In one case, the data may be obtained from a SCADA system of the wind turbine generator system.
Here, the scada (supervisory Control And Data acquisition) system refers to a Data acquisition And monitoring Control system, And is used for generating And returning relevant Data of the wind turbine generator system, generally second-level Data.
As an example, the data acquired from the SCADA system may include, but is not limited to, sampled values of the pitch angle of the wind park and a time stamp (the sampling time for each sampled value).
For example, data may be acquired from the SCADA system at intervals of a predetermined period of time for subsequent pitch control loop anomaly analysis. Or the data acquired from the SCADA system can be segmented according to the duration of a preset time period, so that the abnormal analysis of the pitch control loop can be performed according to the data in the preset time period after the segmentation.
In one example, assuming a sampling period of 1s for the data in the SCADA system and a duration of the predetermined period of time of 10 minutes, the data acquired from the SCADA system is a sampled value of the pitch angle at 600 sampling instants, i.e., 600 data points.
In another case, the sampled values of the pitch angle of the wind turbine may be obtained online. In other words, the sampling value of the pitch angle of the wind generating set can be obtained in real time, so that the abnormity of the pitch control loop can be analyzed in real time.
For example, a sliding window may be defined that includes sampled values of pitch angle over a predetermined period of time, the sliding window being moved over time to perform pitch control loop anomaly analysis on the data within the sliding window.
In a preferred example, after step S10, the method for detecting abnormality of a control circuit of a wind turbine generator system according to an exemplary embodiment of the present invention may further include: it is determined whether the acquired sample values of the pitch angle satisfy an anomaly detection condition.
If the abnormality detection condition is satisfied, step S20 is executed, and if the abnormality detection condition is not satisfied, the acquired sampling value of the operation parameter is not analyzed.
For example, it may be determined whether the acquired sampled values of the pitch angle satisfy an anomaly detection condition by identification of an operational state of the wind park.
In this case, the step of determining whether the acquired sample values of the pitch angle satisfy the abnormality detection condition may include: determining the current operation state of the wind generating set; if the current running state of the wind generating set is in a grid-connected power generation state, determining that the sampling value of the acquired pitch angle meets an abnormal detection condition; and if the current running state of the wind generating set is not in a grid-connected power generation state, determining that the acquired sampling value of the pitch angle does not meet the abnormal detection condition.
In one example, a current operating state of the wind park may be determined based on a pitch angle of the wind park and an impeller speed. At the moment, sampling values of the wheel rotating speed of the wind generating set at a plurality of adopting moments are obtained.
For example, the step of determining the current operating state of the wind park may comprise: comparing sampling values of the pitch angles of the wind generating set at a plurality of sampling moments with a preset pitch angle threshold value, and comparing the sampling values of the impeller rotating speed of the wind generating set at a plurality of sampling moments with a preset impeller rotating speed threshold value; if the maximum value in the sampling values of the pitch angles of the wind generating set at the multiple sampling moments is smaller than a preset pitch angle threshold value, and the minimum value in the sampling values of the impeller rotating speed of the wind generating set at the multiple sampling moments is larger than a preset impeller rotating speed threshold value, determining that the current operating state of the wind generating set is in a grid-connected power generation state; and if the maximum value of the sampling values of the pitch angle of the wind generating set at the plurality of sampling moments is not less than (greater than or equal to) the preset pitch angle threshold value, and/or the minimum value of the sampling values of the impeller rotating speed of the wind generating set at the plurality of sampling moments is not greater than (less than or equal to) the preset impeller rotating speed threshold value, determining that the current operating state of the wind generating set is not in a grid-connected power generation state.
That is, the anomaly analysis is performed for the sampling values of the pitch angle acquired in the grid-connected power generation state in the exemplary embodiment of the present invention.
For example, the expression for determining the current operating state of the wind turbine may be as follows:
max(PA)＜PA0 AND min(GS)＞GS0 (1)
in equation (1), PA represents the sampled value of the pitch angle, max (PA) represents the maximum value of the sampled values of the pitch angle, PA0Representing a preset pitch angle threshold value, GS representing sampled values of the impeller rotational speed, min (GS) representing a minimum of the sampled values of the impeller rotational speed, GS0Representing a preset impeller speed threshold. As an example, PA may be selected0At 45 degrees, GS03 rpm, but the invention is not limited thereto, and the PA can be adjusted by those skilled in the art according to the actual requirement0And GS0The value of (2).
In another example, the current operating state of the wind park may be determined by obtaining a state word indicating the current operating state of the wind park.
At this time, in step S10, the sampling values of the pitch angle are acquired at a plurality of sampling moments while acquiring the status word, so as to determine the current operating state of the wind turbine generator system based on the acquired status word.
For example, if the state words acquired at the multiple sampling moments indicate that the wind generating set is in a grid-connected power generation state, it is determined that the current operating state of the wind generating set is in the grid-connected power generation state, and at this time, subsequent abnormal analysis of the pitch control loop is continued, if at least one state word exists in the state words acquired at the multiple sampling moments and does not indicate that the wind generating set is in the grid-connected power generation state, it is determined that the current operating state of the wind generating set is not in the grid-connected power generation state, and at this time, abnormal analysis is not performed on the sampling value of the pitch angle within the predetermined time period.
In step S20, predicted values of the pitch angle at a plurality of sampling times are determined from the prediction function.
Preferably, the prediction function may refer to a straight line reflecting a linear change of the pitch angle with time. Here, the prediction function may be constructed in various ways. For example, the prediction function may be constructed using the acquired sample values of the pitch angle of the wind park at a plurality of sample instants within the predetermined time period.
In a preferred example, the prediction function for reflecting the linear change in pitch angle over time may be obtained by fitting sampled values of the pitch angle at a plurality of sampling instants.
As an example, the sample values of the pitch angle at a plurality of sample instants may be fitted by a linear regression method to obtain the prediction function.
For example, assuming that the independent variable vector X is a vector of 1 to 600, corresponding to the time points of 600 sampling instants within the predetermined period of 10 minutes in step S10, the dependent variable vector Y is a sampled value of the pitch angle acquired at each sampling instant within the predetermined period of 10 minutes. As an example, taking Python environment as an example, a linear regression analysis can be performed in Python environment according to the following formula (2):
model＝LinearRegression()
model.fit(X,Y) (2)
after linear regression analysis, the resulting prediction function can be as follows:
y＝k×x+b (3)
in equation (3), y represents the pitch angle, x represents the time point at the sampling time, and the slope k and the constant b are obtained by the above-described linear regression analysis.
After the prediction function is determined, the time point X of each sampling time within the predetermined time period is substituted into the prediction function, and a predicted value Y1 of the operating parameter corresponding to the time point of each sampling time is obtained.
For example, in a Python environment, the following equation (4) may be used:
Y1＝model.predict(X) (4)
it should be understood that the above is described by taking the implementation of linear regression analysis in Python environment as an example, but the present invention is not limited thereto, and may also be implemented in R environment or matlab environment. In addition, the expression of formula (2) may take other forms, for example, linear regression analysis may also be implemented using a piewise () linear regression function.
It should be understood that the way of determining the prediction function listed above is only a preferred example, and the present invention is not limited thereto, and the prediction function for reflecting the linear change of the pitch angle with time may be obtained by other ways.
In step S30, the deviation of the sampled value of pitch angle from the predicted value of pitch angle is analyzed to determine whether there is a deviation of the sampled value of pitch angle from the predicted value of pitch angle.
For example, at each sampling moment, the difference value between the sampling value of the pitch angle and the predicted value of the pitch angle is calculated respectively; and determining deviation of the sampling value of the pitch angle relative to the predicted value of the pitch angle according to the comparison result of the difference value obtained by calculation at each sampling moment and the set threshold value.
Specifically, the number of the differences smaller than the set threshold among the differences calculated at the plurality of sampling moments is counted; if the counted number is smaller than a preset value, determining that the sampling value of the pitch angle deviates from the predicted value of the pitch angle; and if the counted number is not less than the preset value, determining that the sampling value of the pitch angle does not deviate relative to the predicted value of the pitch angle. Here, the threshold is set to a negative value.
Taking the example in step S20 as an example, the dependent variable vector Y may be subtracted from the predicted dependent variable Y1 as follows:
deltaY＝Y1-Y (5)
in equation (5), deltaY represents a result vector (i.e., a difference result), and the number of elements whose value is smaller than a set threshold value in the statistical result vector deltaY is counted. As an example, the value of the set threshold may be-0.5, but the present invention is not limited thereto, and a person skilled in the art may adjust the value of the set threshold according to actual requirements.
The purpose of analyzing the deviation condition in step S30 is to determine whether more sampled values of the pitch angle exist within a predetermined time period are lower than the predicted value of the pitch angle, and if so, it indicates that the research on the fact that the lower limit of the abnormal pattern pitch angle is limited, which causes the slow decline of the pitch angle is not true. That is, the number of abnormal points is small (the counted number is smaller than the preset value) to further determine whether the abnormal feature is satisfied (i.e., whether the abnormal feature is slowly decreased).
In a preferred example, the set threshold may be determined by: calculating the standard deviation of the difference value which is less than zero in the difference values calculated at a plurality of sampling moments; and determining the opposite number of the standard deviation of the preset multiple as the set threshold value.
By the processing mode of the follow-up threshold, the set threshold changes along with the change of the sampling value of the pitch angle, namely, the set thresholds corresponding to different preset time periods are different, and the adaptivity of the algorithm is improved.
And if the sampled value of the pitch angle is determined not to deviate relative to the predicted value of the pitch angle, the abnormity analysis of the pitch control loop is not continued.
If it is determined that the sampled value of pitch angle has a deviation from the predicted value of pitch angle, step S40 is executed: and judging whether the change of the sampling value of the pitch angle meets the abnormal characteristic.
For example, the step of determining whether the change in the sampled value of pitch angle satisfies an anomaly characteristic may comprise: extracting a slope of a prediction function for reflecting linear change of the pitch angle along with time; and determining whether the change of the sampling value of the pitch angle meets the abnormal characteristic or not by judging whether the slope of the prediction function can represent the abnormal change characteristic of the pitch angle or not.
Specifically, whether the slope of the prediction function is within a preset slope range or not is judged, if the slope of the prediction function is within the preset slope range, the slope of the prediction function is determined to be capable of representing the abnormal change characteristic of the pitch angle, at the moment, the change of the sampling value of the pitch angle is indicated to meet the abnormal characteristic, if the slope of the prediction function is not within the preset slope range, the slope of the prediction function is determined not to be capable of representing the abnormal change characteristic of the pitch angle, the change of the sampling value of the pitch angle is indicated not to meet the abnormal characteristic, and at the moment, the abnormal analysis is not performed on the sampling value of the pitch angle.
In an exemplary embodiment of the invention, it is determined whether a change in the sampled value of the pitch angle satisfies an anomaly characteristic by determining whether the slope of the prediction function can characterize a slow decline in pitch angle over time. For example, if the slope of the prediction function is within a preset slope range, the change of the sampled value of the pitch angle belongs to a slow descent.
Taking the example in the above step S20 as an example, the slope k of the prediction function y ═ k × x + b may be extracted, and it may be determined whether the slope k is within the preset slope range [ γ ″1,γ2]Within. As an example, γ1And gamma2The values of (a) may be respectively 0.0008 and 0.008, it should be understood that the present invention is not limited thereto, and those skilled in the art may adjust the value of the preset slope range according to actual needs.
In step S50, a detection result for the pitch control circuit is output based on the determination result of the abnormal characteristic.
For example, if the change in the sampled value of the pitch angle does not satisfy the anomaly characteristic, it is determined that there is no anomaly in the pitch control loop of the wind turbine generator set. If the change of the sampling value of the pitch angle meets the abnormal characteristic, the fact that the pitch control loop of the wind generating set is abnormal is determined, at the moment, an early warning signal for indicating that the pitch control loop detects the abnormality can be output, and a protection action can be triggered or an operation and maintenance suggestion can be pushed.
The method for detecting the abnormity of the pitch control loop can realize the detection of the abnormity mode of the slow decline of the pitch angle caused by the limitation of the lower limit of the pitch angle. After the early warning signal is output and the abnormity is solved by the troubleshooting of operation and maintenance personnel, the loss of the generated energy when the wind generating set is in an abnormal state for a long time can be avoided, the deviation of an actual operation control curve of the wind generating set from a design curve is avoided, and the performance of the wind generating set can be integrally improved.
According to the detection method for the abnormity of the variable pitch control loop of the wind generating set, disclosed by the invention, the early warning signal can be output in time when the abnormity occurs in the variable pitch control loop. The early warning signal can be used for guiding operation and maintenance planning personnel, so that the influence of the abnormal pitch control loop on the output of the wind generating set is avoided, and the stability and the power generation performance of the wind generating set are guaranteed to be optimally balanced.
FIG. 3 shows a block diagram of an apparatus for detecting an anomaly in a pitch control loop of a wind park according to an exemplary embodiment of the invention.
As shown in fig. 3, the apparatus for detecting an abnormality in a pitch control loop of a wind turbine generator system according to an exemplary embodiment of the present invention includes: the system comprises a sampling value acquisition module 101, a predicted value determination module 102, a deviation condition analysis module 103, a characteristic abnormity determination module 104 and a detection result determination module 105.
Specifically, the sampling value acquisition module 101 acquires sampling values of a pitch angle of the wind turbine generator set at a plurality of sampling moments within a predetermined time period.
In a preferred example, the apparatus for detecting an abnormality in a pitch control loop of a wind turbine generator system according to an exemplary embodiment of the present invention may further include: and an anomaly detection condition determining module (not shown in the figure) for determining whether the acquired sampling values of the pitch angle satisfy an anomaly detection condition.
If the anomaly detection condition determination module determines that the anomaly detection condition is satisfied, the predicted value determination module 102 determines the predicted value of the pitch angle at the plurality of sampling instants according to a prediction function. If the anomaly detection condition determination module determines that the anomaly detection condition is not satisfied, the predicted value determination module 102 does not determine the predicted value of the pitch angle.
In a preferred example, the anomaly detection condition determination module may determine whether the acquired sampled values of the pitch angle satisfy the anomaly detection condition by identification of an operational state of the wind park.
For example, the abnormality detection condition determination module may determine whether the abnormality detection condition is satisfied by: determining the current operating state of the wind generating set, if the current operating state of the wind generating set is in a grid-connected power generation state, determining that the sampling value of the acquired pitch angle meets an abnormal detection condition, and if the current operating state of the wind generating set is not in the grid-connected power generation state, determining that the sampling value of the acquired pitch angle does not meet the abnormal detection condition.
In one example, the anomaly detection condition determination module may determine a current operating state of the wind generating set based on a pitch angle of the wind generating set and an impeller rotational speed.
For example, the sampling value obtaining module 101 further obtains sampling values of the impeller rotation speed of the wind turbine generator system at the plurality of sampling moments. In this case, the abnormality detection condition determination module compares sampling values of pitch angles of the wind turbine generator set at a plurality of sampling times with a preset pitch angle threshold value, and compares sampling values of impeller rotation speeds of the wind turbine generator set at a plurality of sampling times with a preset impeller rotation speed threshold value.
And if the maximum value in the sampling values of the pitch angles of the wind generating set at the multiple sampling moments is smaller than a preset pitch angle threshold value and the minimum value in the sampling values of the impeller rotating speed of the wind generating set at the multiple sampling moments is larger than a preset impeller rotating speed threshold value, the abnormality detection condition determining module determines that the current running state of the wind generating set is in a grid-connected power generation state.
And if the maximum value in the sampling values of the pitch angle of the wind generating set is not less than the preset pitch angle threshold value and/or the minimum value in the sampling values of the impeller rotating speed of the wind generating set is not more than the preset impeller rotating speed threshold value, the abnormity detection condition determining module determines that the current running state of the wind generating set is not in a grid-connected power generation state.
In another example, the anomaly detection condition determination module may determine the current operating state of the wind park by obtaining a state word indicating the current operating state of the wind park.
The predicted value determination module 102 determines predicted values for the pitch angle at a plurality of sampling instants according to a prediction function.
For example, the predictor determination module 102 may construct the prediction function by: by fitting the sampling values of the pitch angle at a plurality of sampling instants, a prediction function is obtained reflecting a linear change of the pitch angle over time.
The deviation analysis module 103 analyzes the deviation of the sampling value of the pitch angle from the predicted value of the pitch angle.
For example, the deviation analysis module 103 calculates a difference between the sampling value of the pitch angle and the predicted value of the pitch angle at each sampling time, and determines the deviation of the sampling value of the pitch angle from the predicted value of the pitch angle based on the comparison result between the difference calculated at each sampling time and the set threshold.
Specifically, the deviation condition analysis module 103 may count the number of difference values smaller than the set threshold value among the difference values calculated at the plurality of sampling moments, determine that the sampling value of the pitch angle deviates from the predicted value of the pitch angle if the counted number is smaller than the preset value, and determine that the sampling value of the pitch angle does not deviate from the predicted value of the pitch angle if the counted number is not smaller than the preset value. Here, the threshold is set to a negative value.
In a preferred example, the deviation scenario analysis module 103 may determine the set threshold by: and calculating the standard deviation of the difference value which is less than zero in the difference values calculated at a plurality of sampling moments, and determining the opposite number of the standard deviations of the preset multiple as a set threshold value.
If it is determined that the sampled value of the pitch angle does not deviate from the predicted value of the pitch angle, the characteristic anomaly determination module 104 does not make the determination.
If the sampled value of the pitch angle is determined to deviate from the predicted value of the pitch angle, the characteristic anomaly determination module 104 determines whether the variation of the sampled value of the pitch angle satisfies the anomaly characteristic.
For example, the characteristic anomaly determination module 104 may extract a slope of a prediction function that reflects a linear change of the pitch angle over time, and determine whether a change in the sampled value of the pitch angle satisfies an anomaly characteristic by determining whether the slope of the prediction function is capable of characterizing an anomalous change characteristic of the pitch angle.
Specifically, the feature anomaly determination module 104 may determine whether the slope of the prediction function is within a preset slope range. If the slope of the prediction function is within the preset slope range, the characteristic anomaly determination module 104 determines that the slope of the prediction function can represent the anomaly change characteristic of the pitch angle, and if the slope of the prediction function is not within the preset slope range, the characteristic anomaly determination module 104 determines that the slope of the prediction function cannot represent the anomaly change characteristic of the pitch angle.
The detection result determination module 105 outputs a detection result for the pitch control loop according to the determination result of the abnormal characteristic.
For example, if the change in the sampled value of the pitch angle does not satisfy the anomaly characteristic, the detection result determination module 105 determines that there is no anomaly in the pitch control loop of the wind turbine generator set. If the change of the sampling value of the pitch angle meets the abnormal characteristic, the detection result determining module 105 determines that the pitch control loop of the wind generating set is abnormal, and at this time, an early warning signal for indicating that the pitch control loop detects the abnormality can be output, and a protection action can be triggered or an operation and maintenance suggestion can be pushed.
Fig. 4 illustrates a block diagram of a controller according to an exemplary embodiment of the present invention.
As shown in fig. 4, the controller 200 according to an exemplary embodiment of the present invention includes: a processor 201 and a memory 202.
In particular, the memory 202 is used for storing a computer program which, when being executed by the processor 201, implements the above described method for detecting an abnormality of a pitch control loop of a wind park.
Here, the method for detecting an abnormality of the pitch control loop of the wind turbine generator system shown in fig. 2 may be executed in the processor 201 shown in fig. 4. That is, each module shown in fig. 3 may be implemented by a general-purpose hardware processor such as a digital signal processor or a field programmable gate array, may be implemented by a special-purpose hardware processor such as a special chip, and may be implemented completely by a computer program in a software manner, for example, may be implemented as each module in the processor 201 shown in fig. 4.
There is also provided, in accordance with an exemplary embodiment of the present invention, a computer-readable storage medium storing a computer program. The computer readable storage medium stores a computer program which, when executed by a processor, causes the processor to perform the above described method of detecting an abnormality in a pitch control loop of a wind park. The computer readable recording medium is any data storage device that can store data read by a computer system. Examples of the computer-readable recording medium include: read-only memory, random access memory, read-only optical disks, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission through the internet via wired or wireless transmission paths).
According to the method and the device for detecting the abnormity of the variable pitch control loop of the wind generating set, the abnormity of the variable pitch control loop can be recognized as early as possible, and an early warning signal is given out in time, so that the method and the device can be used for guiding the planned operation and maintenance of field personnel, avoiding the influence of the abnormity of the variable pitch control loop on the output of the wind generating set, and ensuring the stability and the power generation performance of the wind generating set to be in optimal balance.
In addition, according to the method and the device for detecting the abnormity of the variable pitch control loop of the wind generating set, the loss of the generated energy when the wind generating set is in an abnormal state for a long time can be avoided, and the deviation of the actual operation control curve of the wind generating set from the design curve can be avoided.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims.

Claims (13)

1. A detection method for abnormity of a variable pitch control loop of a wind generating set is characterized by comprising the following steps:
acquiring sampling values of a pitch angle of a wind generating set at a plurality of sampling moments within a preset time period;
determining predicted values of pitch angles at the plurality of sampling instants according to a prediction function;
analyzing the deviation condition of the sampling value of the pitch angle relative to the predicted value of the pitch angle;
if the sampling value of the pitch angle deviates relative to the predicted value of the pitch angle, judging whether the change of the sampling value of the pitch angle meets the abnormal characteristic;
and outputting a detection result aiming at the variable pitch control loop according to the judgment result of the abnormal characteristic.
2. The detection method according to claim 1, characterized in that the prediction function is constructed by:
by fitting the sample values of the pitch angle at said plurality of sample instants, a prediction function is obtained reflecting a linear change of the pitch angle over time.
3. Method for detecting according to claim 1, wherein the step of analyzing the deviation of sampled values of the pitch angle from predicted values of the pitch angle comprises:
at each sampling moment, respectively calculating the difference value between the sampling value of the pitch angle and the predicted value of the pitch angle;
and determining the deviation condition of the sampling value of the pitch angle relative to the predicted value of the pitch angle according to the comparison result of the difference value calculated at each sampling moment and the set threshold value.
4. A method of detecting according to claim 3, wherein the step of determining the deviation of the sampled value of the pitch angle from the predicted value of the pitch angle based on the comparison of the difference calculated at each sampling instant with the set threshold value comprises:
counting the number of the difference values smaller than the set threshold value in the difference values calculated at the plurality of sampling moments;
if the counted number is smaller than the preset value, determining that the sampling value of the pitch angle deviates relative to the predicted value of the pitch angle;
if the counted number is not less than the preset value, determining that the sampling value of the pitch angle does not deviate from the predicted value of the pitch angle,
wherein the set threshold is a negative value.
5. The detection method according to claim 4, wherein the set threshold is determined by:
calculating the standard deviation of the difference values smaller than zero in the difference values calculated at the plurality of sampling moments;
and determining the opposite number of the standard deviation of the preset multiple as the set threshold.
6. The method for detecting according to claim 2, wherein the step of determining whether a change in the sampled value of pitch angle satisfies an anomaly characteristic comprises:
extracting a slope of a prediction function for reflecting linear change of the pitch angle along with time;
and determining whether the change of the sampling value of the pitch angle meets the abnormal characteristic by judging whether the slope of the prediction function can represent the abnormal change characteristic of the pitch angle.
7. The method for detecting according to claim 6, wherein the step of determining whether the slope of the prediction function is characteristic of an abnormal change in pitch angle comprises:
judging whether the slope of the prediction function is within a preset slope range or not;
if the slope of the prediction function is in the preset slope range, determining that the slope of the prediction function can represent the abnormal change characteristics of the pitch angle, and determining that the change of the sampling value of the pitch angle meets the abnormal change characteristics;
and if the slope of the prediction function is not in the preset slope range, determining that the slope of the prediction function cannot represent the abnormal change characteristic of the pitch angle, and determining that the change of the sampling value of the pitch angle does not meet the abnormal characteristic.
8. The detection method according to claim 1, wherein the detecting further comprises: determining whether the acquired sample values of the pitch angle satisfy an anomaly detection condition,
wherein if an anomaly detection condition is satisfied, the predicted values of the pitch angles at the plurality of sampling instants are determined according to a prediction function.
9. The method of inspection according to claim 8, wherein the step of determining whether the acquired sample values of the pitch angle satisfy an anomaly detection condition comprises:
determining the current operation state of the wind generating set;
if the current running state of the wind generating set is in a grid-connected power generation state, determining that the sampling value of the acquired pitch angle meets an abnormal detection condition;
and if the current running state of the wind generating set is not in a grid-connected power generation state, determining that the acquired sampling value of the pitch angle does not meet the abnormal detection condition.
10. The detection method according to claim 9, wherein the step of determining the current operating state of the wind park comprises:
acquiring sampling values of the impeller rotating speed of the wind generating set at the plurality of sampling moments;
comparing sampling values of the pitch angles of the wind generating set at the plurality of sampling moments with a preset pitch angle threshold value, and comparing the sampling values of the impeller rotating speed of the wind generating set at the plurality of sampling moments with a preset impeller rotating speed threshold value;
if the maximum value in the sampling values of the pitch angles of the wind generating set at the multiple sampling moments is smaller than a preset pitch angle threshold value, and the minimum value in the sampling values of the impeller rotating speeds of the wind generating set at the multiple sampling moments is larger than a preset impeller rotating speed threshold value, determining that the current operating state of the wind generating set is in a grid-connected power generation state;
if the maximum value in the sampling values of the pitch angle of the wind generating set is not less than the preset pitch angle threshold value and/or the minimum value in the sampling values of the impeller rotating speed of the wind generating set is not more than the preset impeller rotating speed threshold value, determining that the current running state of the wind generating set is not in a grid-connected power generation state,
or determining the current operation state of the wind generating set by acquiring a state word for indicating the current operation state of the wind generating set.
11. A detection device for detecting abnormity of a variable pitch control loop of a wind generating set is characterized by comprising:
the sampling value acquisition module is used for acquiring sampling values of the pitch angles of the wind generating set at a plurality of sampling moments within a preset time period;
a predicted value determining module that determines predicted values of the pitch angles at the plurality of sampling times according to a prediction function;
the deviation condition analysis module is used for analyzing the deviation condition of the sampling value of the pitch angle relative to the predicted value of the pitch angle;
the characteristic abnormity determining module is used for judging whether the change of the sampling value of the pitch angle meets the abnormity characteristic or not if the situation that the sampling value of the pitch angle deviates relative to the predicted value of the pitch angle is determined;
and the detection result determining module outputs a detection result aiming at the variable pitch control loop according to the judgment result of the abnormal characteristic.
12. A controller, comprising:
a processor;
a memory for storing a computer program which, when executed by the processor, implements a method of detecting a pitch control loop anomaly of a wind park according to any of claims 1 to 10.
13. A computer-readable storage medium having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, carries out a method of detecting a pitch control loop anomaly of a wind park according to any one of claims 1 to 10.
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Publication number Priority date Publication date Assignee Title
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Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010116663A1 (en) * 2009-04-06 2010-10-14 ナブテスコ株式会社 Pitch control device for windmill
CN103629048A (en) * 2013-12-20 2014-03-12 济南轨道交通装备有限责任公司 Intelligent pitch control system of wind turbine generator and pitch control method thereof
CN104807644A (en) * 2015-04-14 2015-07-29 北京中恒博瑞数字电力科技有限公司 Fault early warning method and system for wind generation set variable-pitch system
CN105134510A (en) * 2015-09-18 2015-12-09 北京中恒博瑞数字电力科技有限公司 State monitoring and failure diagnosis method for wind generating set variable pitch system
CN105464912A (en) * 2016-01-27 2016-04-06 国电联合动力技术有限公司 Method and device for detecting freezing of wind generating set blades
EP3133282A1 (en) * 2015-08-19 2017-02-22 Senvion GmbH Method and system for monitoring an individual blade adjustment of a wind power system
CN107327367A (en) * 2017-06-30 2017-11-07 北京金风科创风电设备有限公司 The abnormal recognition methods of wind generating set pitch control and device
CN107355342A (en) * 2017-06-30 2017-11-17 北京金风科创风电设备有限公司 The abnormal recognition methods of wind generating set pitch control and device
WO2017211367A1 (en) * 2016-06-07 2017-12-14 Vestas Wind Systems A/S Adaptive control of a wind turbine by detecting a change in performance
CN108443065A (en) * 2018-03-06 2018-08-24 浙江运达风电股份有限公司 A kind of Large-scale Wind Turbines independent feathering control optimization method
CN109209765A (en) * 2017-06-29 2019-01-15 北京金风科创风电设备有限公司 The pitch control method and system of wind power generating set
CN109723609A (en) * 2017-10-31 2019-05-07 中国电力科学研究院有限公司 A kind of fault early warning method and system of paddle change system of wind turbines

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DK177434B1 (en) * 2010-06-18 2013-05-21 Vestas Wind Sys As Method for controlling a wind turbine
DK2886856T3 (en) * 2013-12-20 2020-01-02 Siemens Gamesa Renewable Energy As Detection of pitch angle adjustment error
CN105222742A (en) * 2014-05-26 2016-01-06 通用电气公司 Slurry is apart from fault detection system and method
EP3221582B1 (en) * 2014-11-21 2021-04-21 Vestas Wind Systems A/S A method for estimating a wind speed including calculating a pitch angle adjusted for blade torsion

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010116663A1 (en) * 2009-04-06 2010-10-14 ナブテスコ株式会社 Pitch control device for windmill
CN103629048A (en) * 2013-12-20 2014-03-12 济南轨道交通装备有限责任公司 Intelligent pitch control system of wind turbine generator and pitch control method thereof
CN104807644A (en) * 2015-04-14 2015-07-29 北京中恒博瑞数字电力科技有限公司 Fault early warning method and system for wind generation set variable-pitch system
EP3133282A1 (en) * 2015-08-19 2017-02-22 Senvion GmbH Method and system for monitoring an individual blade adjustment of a wind power system
CN105134510A (en) * 2015-09-18 2015-12-09 北京中恒博瑞数字电力科技有限公司 State monitoring and failure diagnosis method for wind generating set variable pitch system
CN105464912A (en) * 2016-01-27 2016-04-06 国电联合动力技术有限公司 Method and device for detecting freezing of wind generating set blades
WO2017211367A1 (en) * 2016-06-07 2017-12-14 Vestas Wind Systems A/S Adaptive control of a wind turbine by detecting a change in performance
CN109209765A (en) * 2017-06-29 2019-01-15 北京金风科创风电设备有限公司 The pitch control method and system of wind power generating set
CN107327367A (en) * 2017-06-30 2017-11-07 北京金风科创风电设备有限公司 The abnormal recognition methods of wind generating set pitch control and device
CN107355342A (en) * 2017-06-30 2017-11-17 北京金风科创风电设备有限公司 The abnormal recognition methods of wind generating set pitch control and device
CN109723609A (en) * 2017-10-31 2019-05-07 中国电力科学研究院有限公司 A kind of fault early warning method and system of paddle change system of wind turbines
CN108443065A (en) * 2018-03-06 2018-08-24 浙江运达风电股份有限公司 A kind of Large-scale Wind Turbines independent feathering control optimization method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
吴定会等.基于辨识算法的风力机桨距执行器故障诊断.《控制工程》.2016,第23卷(第06期),全文. *
唐新安等.风电机组故障诊断方法研究.《风能》.2015,(第03期),全文. *
肖成等.基于SCADA系统的风电变桨故障预测方法研究.《可再生能源》.2017,第35卷(第02期),全文. *

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