CN103675356A - Anemometer fault detection method and system on the basis of particle swarm optimization - Google Patents

The application discloses an anemometer fault detection method and a system on the basis of particle swarm optimization. According to the method, wind speed data of at least two wind turbines to be measured with close geographic positions within a certain period of time are respectively acquired, and multiple groups of local wind speed difference values corresponding to each time period are acquired after segmented extraction and comparison according to the arranged same time period; a probability distribution curve is established for the local wind speed difference value of each time period, and shape parameter data and proportion parameter data corresponding to each time period are acquired simultaneously; location and size parameters of multiple fault judgment thresholds are selected at will in within a preset numerical range by utilizing a particle swarm optimization mode, correctness of fault judgment is tested on each judgment threshold, traversal iteration is performed on the whole particle swarms and the optimal judgment threshold with minimum error is acquired via convergence so that the optimal judgment threshold is applied to perform fault judgment on the wind speed data of the wind turbines to be measured within the certain period of time. A problem that faults of anemometers of the wind turbines are difficult to accurately and timely detect is solved by the anemometer fault detection method and the system.

Anemometer fault detection method and system based on particle swarm optimization

Technical Field

The application relates to the field of anemometer monitoring of wind turbines, in particular to an anemometer fault detection method and system based on particle swarm optimization.

Background

PSO (Particle Swarm Optimization), a population-based random Optimization technique that mimics the clustering behavior of insects, herds, birds, and fish, etc., that find food in a cooperative manner, with each member of the population constantly changing its search pattern by learning its own experience and that of other members.

In the prior art, wind power plants are built in geographical areas with wide terrain and rich wind energy resources, but natural wind is uncontrollable and changes in real time, sometimes the wind speed is less than the cut-in wind speed of a wind generating set, so that the wind power generation is difficult to start; sometimes, the wind speed is larger than the bearable limit wind speed of the wind generating set, and the wind generating set is damaged. Therefore, the wind generating set is provided with an anemoscope to monitor the wind speed in real time so as to control the start or stop of the fan under different conditions.

The anemoscope of the wind turbine needs to monitor the wind speed in real time, namely, the anemoscope is always in a working state, so that the probability of failure of the anemoscope is high. At some time, the wind power resource is larger than the cut-in wind speed of the fan, main components of the fan also run normally, but the fan has poor performance and even stops, and the wind power resource is just caused by the fault of an anemometer. Errors caused by anemoscope faults are always small at first and are therefore often ignored, and the wind speed value monitored after the anemoscope faults can be wrong, so that the control system carries out wrong control according to wrong data of the anemoscope, and finally the wind turbine is poor in performance and even stops. Once the wind turbine is stopped, the cost of repairing the wind turbine is very huge, and the stop of the wind turbine also causes great loss of the generated energy.

Due to the characteristic that natural wind in a wind field is random and changeable, whether the data monitored by a certain anemoscope is wrong or not is difficult to judge, and even if historical data of the anemoscope is collected, the rule and the state of the data cannot be effectively judged, so that whether the anemoscope is in fault or not cannot be accurately reflected.

In summary, the defect of the anemometer fault determination is an urgent technical problem to be solved.

Disclosure of Invention

In view of this, the technical problem to be solved by the present application is to provide a particle swarm optimization-based anemometer fault detection method and system, so as to solve the problem that it is difficult to accurately and timely detect the anemometer fault of a wind turbine.

In order to solve the technical problem, the application discloses an anemometer fault detection method based on particle swarm optimization, which comprises the following steps:

respectively acquiring wind speed data of at least two wind turbines to be tested in similar geographical positions within a certain time, extracting the wind speed data in a segmented manner according to time periods with the same time duration, and comparing the wind speed data to obtain a plurality of groups of local wind speed difference values corresponding to the time periods;

establishing a corresponding probability distribution curve for the local wind speed difference value of each time period, correspondingly obtaining shape parameter data and proportion parameter data corresponding to each time period, and generating coordinates consisting of the shape parameter data and the proportion parameter data corresponding to different time periods;

randomly selecting a plurality of groups of position and size parameters of a fault judgment threshold in a preset numerical range by utilizing a particle swarm optimization mode, wherein each group of parameters represents a particle and generates a corresponding data range as the judgment threshold, checking the error of the fault judgment of each judgment threshold according to coordinates formed by shape parameter data and proportion parameter data corresponding to each time period, traversing and iterating the whole particle swarm, converging all the particles into an optimal judgment threshold with the minimum error, and performing fault judgment on the wind speed data of the wind turbine to be tested within a certain time by using the optimal judgment threshold.

Further, randomly selecting multiple groups of position and size parameters of a fault judgment threshold in a preset numerical range by using a particle swarm optimization mode, wherein each group of parameters represents a particle and generates a corresponding data range as the judgment threshold, checking each judgment threshold for the mismatching of the fault judgment according to coordinates formed by shape parameter data and proportion parameter data corresponding to each time period, then performing traversal iteration processing on the whole particle swarm, converging all the particles into an optimal judgment threshold with the minimum error, and performing fault judgment on the wind speed data of the wind turbine to be tested within a certain time by using the optimal judgment threshold, and further comprising the following steps of:

randomly selecting a plurality of groups of position and size parameters of a fault judgment threshold in a preset numerical range by utilizing a particle swarm optimization mode, assuming that the judgment threshold is a circle, taking the position parameter as a circle center coordinate, taking the size parameter as a radius length value, generating a corresponding circle judgment threshold by each group of parameters, checking the error of fault judgment of each circle judgment threshold according to a coordinate formed by shape parameter data and proportion parameter data corresponding to each time period, traversing and iterating the whole particle swarm, converging all the particles into an optimal circle judgment threshold with the minimum error, and performing fault judgment on the wind speed data of the wind turbine to be tested within a certain time by using the optimal circle judgment threshold.

Wherein, further, the data range is further composed of a non-failure area containing the total non-failure data and a failure area containing the total failure data.

Wherein, further, the data range with the minimum error is further: and the total number of the fault data contained in the fault-free area of the data range and the fault-free data contained in the fault-free area of the data range is the minimum data range.

Wherein, further, the certain time further comprises: weeks, months and/or a time period of not less than 7 days.

Wherein, further, the wind speed data is segmented and extracted according to a set time period, and further comprises the following steps: and carrying out segmented extraction on the wind speed data according to a set time period taking week, month or whole days as a fixed value.

In order to solve the technical problem, the present application further discloses an anemometer fault detection system based on particle swarm optimization, including: the system comprises a wind speed difference acquisition module, a parameter processing module and a threshold optimization module; wherein,

the wind speed difference acquisition module is used for respectively acquiring wind speed data of at least two wind turbines to be detected in similar geographical positions within a certain time, extracting the wind speed data in sections according to time periods with the same time duration, comparing the wind speed data, acquiring a plurality of groups of local wind speed difference values corresponding to the time periods, and sending the local wind speed difference values to the parameter processing module;

the parameter processing module is used for establishing a corresponding probability distribution curve for the local wind speed difference value of each time period, correspondingly obtaining shape parameter data and proportion parameter data corresponding to each time period, generating coordinates formed by the shape parameter data and the proportion parameter data corresponding to different time periods, and sending the coordinates to the threshold optimization module;

the threshold optimization module is used for randomly selecting a plurality of groups of position and size parameters of the fault judgment threshold in a preset numerical range by utilizing a particle swarm optimization mode, wherein each group of parameters represents a particle and generates a corresponding data range as the judgment threshold, the mismatching of the fault judgment of each judgment threshold is checked according to coordinates formed by shape parameter data and proportion parameter data corresponding to each time period, then the traversal iteration processing is carried out on the whole particle swarm, finally, all the particles are converged into the optimal judgment threshold with the minimum error, and the optimal judgment threshold is used for carrying out the fault judgment on the wind speed data of the wind turbine to be tested in a certain time.

Wherein, further, the threshold optimization module is further configured to:

randomly selecting a plurality of groups of position and size parameters of a fault judgment threshold in a preset numerical range by utilizing a particle swarm optimization mode, assuming that the judgment threshold is a circle, taking the position parameter as a circle center coordinate, taking the size parameter as a radius length value, generating a corresponding circle judgment threshold by each group of parameters, checking the error of fault judgment of each circle judgment threshold according to a coordinate formed by shape parameter data and proportion parameter data corresponding to each time period, traversing and iterating the whole particle swarm, converging all the particles into an optimal circle judgment threshold with the minimum error, and performing fault judgment on the wind speed data of the wind turbine to be tested within a certain time by using the optimal circle judgment threshold.

Wherein, further, the data range is further composed of a non-fault area containing the total non-fault data and a fault area containing the total fault data.

Wherein, further, the data range with the minimum error is further: and the total number of the fault data contained in the fault-free area of the data range and the fault-free data contained in the fault-free area of the data range is the minimum data range.

Wherein, further, the certain time further comprises: weeks, months and/or a time period of not less than 7 days.

The wind speed difference acquisition module is further used for extracting the wind speed data in a segmented manner according to a set time period taking week, month or whole days as a fixed value.

Compared with the prior art, the anemoscope fault detection method and system based on particle swarm optimization achieve the following effects:

1) according to the particle swarm optimization-based anemoscope fault detection method and system, difference value processing can be performed on wind speed data in an anemoscope of a wind turbine in a long-time multi-grouping mode, after relevant data of probability statistics of wind speed difference are obtained, accurate threshold values are extracted from historical data in an optimized mode through a bionic PSO technology, the anemoscope with abnormal data can be effectively identified according to the threshold values, and therefore the anemoscope with faults and the time of occurrence of the faults are further accurately determined.

2) According to the particle swarm optimization-based anemometer fault detection method and system, the acquired data in the anemometers on different wind turbines can be divided into detailed time periods, and a plurality of data points are respectively set in each time period, so that the fault time of the anemometer can be effectively detected in a wide time region.

Of course, it is not necessary for any product to achieve all of the above-described technical effects simultaneously.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a flow chart block diagram of an anemometer fault detection method based on particle swarm optimization according to the first embodiment and the second embodiment of the present application.

Fig. 2 is a schematic diagram of the circular data range selected as the threshold in the particle swarm optimization-based anemometer fault detection method according to the third embodiment of the present application.

Fig. 3 is a schematic structural diagram of an anemometer fault detection system based on particle swarm optimization according to the fourth embodiment of the present application.

Detailed Description

As used in the specification and in the claims, certain terms are used to refer to particular components. As one skilled in the art will appreciate, manufacturers may refer to a component by different names. This specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. "substantially" means within an acceptable error range, and a person skilled in the art can solve the technical problem within a certain error range to substantially achieve the technical effect. Furthermore, the term "coupled" is intended to encompass any direct or indirect electrical coupling. Thus, if a first device couples to a second device, that connection may be through a direct electrical coupling or through an indirect electrical coupling via other devices and couplings. The description which follows is a preferred embodiment of the present application, but is made for the purpose of illustrating the general principles of the application and not for the purpose of limiting the scope of the application. The protection scope of the present application shall be subject to the definitions of the appended claims.

As shown in fig. 1, a method for detecting a fault of an anemometer based on particle swarm optimization according to a first embodiment of the present application includes:

step 101, respectively acquiring wind speed data of at least two wind turbines to be tested in similar geographical positions within a certain time, extracting the wind speed data in a segmented manner according to time periods with the same time duration, and comparing the wind speed data to obtain a plurality of groups of local wind speed difference values corresponding to the time periods;

102, establishing a corresponding probability distribution curve for the local wind speed difference value of each time period, correspondingly obtaining shape parameter data and proportion parameter data corresponding to each time period, and generating coordinates formed by the shape parameter data and the proportion parameter data corresponding to different time periods;

103, randomly selecting a plurality of groups of position and size parameters of the fault judgment threshold in a preset numerical range by using a particle swarm optimization mode, wherein each group of parameters represents a particle and generates a corresponding data range as the judgment threshold, checking the error of the fault judgment of each judgment threshold according to coordinates formed by shape parameter data and proportion parameter data corresponding to each time period, traversing and iterating the whole particle swarm, converging all the particles into an optimal judgment threshold with the minimum error, and performing fault judgment on the wind speed data of the wind turbine to be tested within a certain time by using the optimal judgment threshold.

It should be noted that, in the above steps of this embodiment, it is difficult to determine whether the anemoscope is faulty from the wind speed data recorded by the anemoscope on one wind turbine, so that two or more anemoscopes are selected to record the wind speed data at the same time, and if the data recorded by the anemoscopes of two adjacent wind turbines are very different within a certain time (for example, the data of the two anemoscopes are greatly different), it can be roughly determined that one of the anemoscopes is faulty, and the time period during which the faulty anemoscope is faulty can be determined according to the time period in the above steps, so as to find out the fault of the anemoscope as soon as possible.

Referring to fig. 2, a method for detecting a fault of an anemometer based on particle swarm optimization according to a second embodiment of the present application includes:

the method comprises the steps of firstly, respectively obtaining wind speed data of at least two wind turbines to be tested in close geographical positions within a certain time, extracting the wind speed data in a segmented mode according to time periods with the same time duration, comparing the wind speed data, and obtaining multiple groups of local wind speed difference values corresponding to the time periods.

For the first step, the wind turbines to be tested at different geographic positions are affected by different natural wind field states, and particularly for the wind turbines to be tested at very far intervals, the obtained wind speed data are likely to have larger differences due to the different wind field states, so that the wind turbines to be tested at close geographic positions are selected in the application.

In this embodiment, each set of values in each time period may be obtained by setting a data point for the wind speed data in each time period.

It should be noted that sufficient wind speed data can be obtained by acquiring wind speed data for a long enough time, but the grouping of the acquired wind speed data will affect the overall subsequent steps because: the time periods are preset, after the time periods are grouped, data points are set for the wind speed data in each time period, the number of the data points is related to the time periods, for the wind speed data distributed in a long time period, if the set time period is short, the number of the data points in each time period is small, and the small number of the data points can cause inaccuracy of probability distribution calculation, so that the probability distribution curve obtained in the second step cannot accurately represent the data point condition in the corresponding time interval; if the set time interval is longer, the time interval is not suitable, because each time interval is an observation unit for judging whether a fault occurs, and the long time interval is selected, so that even if the anemometer in which time interval the fault occurs is judged in the subsequent step III, the prediction of the fault loses significance due to the overlong time interval.

Establishing a corresponding probability distribution curve for the local wind speed difference value of each time period, correspondingly obtaining shape parameter data and proportion parameter data corresponding to each time period, and generating coordinates formed by the shape parameter data and the proportion parameter data corresponding to different time periods;

in step two, the coordinates formed by the shape parameter data and the proportion parameter data corresponding to the different time periods can be correspondingly reflected in a coordinate system, and all coordinate points are distributed in the coordinate system to form a certain range.

And step three, randomly selecting a plurality of groups of position and size parameters of the fault judgment threshold in a preset numerical range by utilizing a particle swarm optimization mode, wherein each group of parameters represents a particle and generates a corresponding data range as the judgment threshold, checking the error of the fault judgment of each judgment threshold according to coordinates formed by shape parameter data and proportion parameter data corresponding to each time period, traversing and iterating the whole particle swarm, converging all the particles into the optimal judgment threshold with the minimum error, and performing fault judgment on the wind speed data of the wind turbine to be tested within a certain time by using the optimal judgment threshold.

It should be noted that the preset value range may be selected from a range in which the coordinates formed by the shape parameter data and the scale parameter data are reflected in a coordinate system, and specifically, the values of the shape parameter data and the scale parameter data have a maximum value and a minimum value, and the values of the scale parameter data also have a maximum value and a minimum value, so that the preset value range may be selected from a range of the maximum value and the minimum value of the shape parameter data and the scale parameter data, respectively. Of course, the selection of the preset numerical range does not limit the application.

In step three, the generation of the data range specifically includes: within a preset numerical range, randomly selecting 200 groups of position and size parameters of a fault judgment threshold, assuming that the judgment threshold is a circle, taking the position parameter as a circle center coordinate, taking the size parameter as a radius length value, wherein each group of parameters represents a particle and generates a corresponding circle judgment threshold, checking each judgment threshold for error of fault judgment according to coordinates formed by shape parameter data and proportion parameter data corresponding to each time period, performing traversal iteration processing on the whole particle swarm, converging all the particles into an optimal judgment threshold with the minimum error, and performing fault judgment on the wind speed data of the wind turbine to be tested within a certain time by using the optimal judgment threshold.

The data range is specifically composed of a non-failure area containing total non-failure data and a failure area containing total failure data, in this embodiment, a circular area of the data range is a non-failure area, and areas outside the circular area are failure areas. All the non-fault areas of the data range should be non-fault data, all the fault areas should be fault data, if fault data occurs in the non-fault areas or non-fault data occurs in the fault areas, the data range is regarded as an error, and the data range in which the total number of the fault data included in the non-fault areas and the non-fault data included in the fault areas of the data range is the minimum in all the time periods is set as a threshold.

With reference to fig. 1 to 2, a particle swarm optimization-based anemometer fault detection method according to a third embodiment of the present application is specifically applied as follows:

firstly, wind speed data of anemometers of at least two wind turbines adjacent to each other in geographic position within a certain time are respectively obtained, and natural wind conditions monitored by the wind turbines adjacent to each other in geographic position are the same or similar, so that accuracy of data analysis is improved_AAnd WS_BOf course, in practical application, the anemometers on a plurality of wind turbines can be selected. The wind speed data WS_AAnd WS_BPerforming group extraction according to set time periods, and extracting the wind speed data WS of the two wind turbines in each time period_AAnd WS_BThe difference processing is performed to generate a wind speed difference WSD for the time period, that is,

wsd=|ws_A-ws_B|

in this embodiment, the certain period of time may be a week, a month and/or a time period of not less than 7 daysIn order to obtain sufficient wind speed data, it is necessary to acquire the wind speed data for a sufficient time, and preferably, the certain time is 70 weeks. In addition, the wind speed data may be extracted in groups according to a set time period with a fixed value of week, month or whole day, and preferably, the wind speed data of the wind turbines a and B are grouped according to a week time period, that is, the time of one week. Setting data points in 10 minutes, 1008 data points can be set in one week, that is, 1008 WS data points are set in each week_AAnd WS_BTherefore, there are 1008 wind speed differences WSD in each week period.

Secondly, establishing a corresponding probability distribution curve for the wind speed difference WSD of each time period, and obtaining shape parameter data k and proportion parameter data lambda corresponding to each time period according to the probability distribution curve corresponding to the wind speed difference WSD of each time period.

In this embodiment, the probability distribution curve is specifically a weber probability distribution curve, and the probability distribution curve may be represented as pdf:

<math> <mrow> <mi>pdf</mi> <mrow> <mo>(</mo> <mi>wsd</mi> <mo>;</mo> <mi>&lambda;</mi> <mo>,</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mi>k</mi> <mi>&lambda;</mi> </mfrac> <msup> <mrow> <mo>(</mo> <mfrac> <mi>wsd</mi> <mi>&lambda;</mi> </mfrac> <mo>)</mo> </mrow> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mi>wsd</mi> <mo>/</mo> <mi>&lambda;</mi> <mo>)</mo> </mrow> <mi>k</mi> </msup> </mrow> </msup> </mrow> </math>

and extracting corresponding shape parameter data k and proportion parameter data lambda from the Weber probability distribution curve formed by the wind speed difference values in each cycle time period, and extracting 70 groups (corresponding to 70 cycles of total time) of the shape parameter data k and the proportion parameter data lambda. For each of the weber probability distribution curves, a coordinate point form (λ, k) is used to obtain 70 sets (λ, k), each of which is (λ, k)₁，k₁）、（λ₂，k₂）…（λ₇₀，k₇₀) As shown in fig. 2, the 70 sets of coordinate points are distributed in the spatial coordinate system, and meanwhile, according to the operation report of the wind field, it can be known which of the 70 sets of data are normal operation data (i.e. which weeks have no fault), and which of the 70 sets of data are fault operation data (i.e. which weeks have fault).

Thirdly, the optimization mode of PSO is adopted, 200 groups of position and size parameters of the fault judgment threshold are randomly selected in a preset numerical range, the judgment threshold is assumed to be a circle, the position parameter is used as a circle center coordinate, the size parameter is used as a radius length value, and each group of parameters represents a particle and generates a corresponding circle judgment threshold. Each particle is represented by the formula:

{P_i:x_i,y_i,r_i}

wherein P is_iRepresents the ith particle, (x)_i，y_i) Represents the center of a circle of the threshold, r_iRepresenting the threshold radius.

Judging 70 groups (lambda, k) according to a data range formed by each circular threshold, wherein the data in the circular data range are coordinate points formed by shape parameter data k and proportion parameter data lambda corresponding to the normal operation of the anemometer, and the coordinate points can be called fault-free areas; the data outside the circular data range is a coordinate point formed by the shape parameter data k and the scale parameter data λ corresponding to the occurrence of the anemometer problem, and may be referred to as a fault area. However, in practical applications, fault data may occur in a fault-free area, and fault-free data may occur in a fault-free area, which is an error.

And traversing and iterating a plurality of circular fault judgment thresholds generated by the whole particle swarm, converging all particles into an optimal judgment threshold with the minimum error, and judging the fault of the wind speed data of the wind turbine to be detected within a certain time by using the optimal judgment threshold.

In the PSO optimization method, each particle in the particle group has memory, and the optimal position where the particle has traveled can be stored and is denoted as P_best(ii) a Each particle can also memorize the optimal position, denoted G, experienced by the entire population of particles_best. Thus, the PSO optimization mode is based on P at each iterative transformation of the particle_bestAnd G_bestThe center (x, y) and radius r are adjusted.

Similarly, the PSO optimization method traverses all 200 groups of particles to obtain different circular data ranges, and may select the circular data range with the smallest error value as the threshold according to the actual fault state of the acquired data in the report of the wind farm.

In fig. 2, the abscissa represents a proportional parameter value, the ordinate represents a shape parameter value, the hollow dots represent non-failure data, the asterisks represent failure data, and the circular area surrounded by black bold lines is the circular data range with the smallest error value, i.e., the threshold. The fault-free area of the circular data range still contains a small amount of fault data, but the fault area outside the circular data range only contains fault data, the error value is already reduced to the minimum, and meanwhile, the PSO optimization mode continuously selects a more appropriate data range along with continuous recording and updating.

Therefore, the working states of the wind turbines A and B in a certain time in the future are judged by taking the circular data range obtained in the steps as a threshold.

The wind speed difference acquisition module is further used for carrying out segmented extraction on the wind speed data according to a set time period taking week, month or whole days as a fixed value.

In addition, the certain time further includes: weeks, months and/or a time period of not less than 7 days.

Further, the threshold optimization module is specifically configured to arbitrarily select multiple sets of position and size parameters of the fault determination threshold within a preset numerical range by using a particle swarm optimization method, assume that the determination threshold is a circle, use the position parameter as a circle center coordinate, use the size parameter as a radius length value, use each set of parameter as a particle and generate a corresponding circle determination threshold, check each circle determination threshold for a fault determination error according to coordinates formed by shape parameter data and proportion parameter data corresponding to each time period, then perform traversal iteration processing on the whole particle swarm, finally converge all the particles into an optimal circle determination threshold with a minimum error, and perform fault determination on the wind speed data of the wind turbine to be determined within a certain time by using the optimal circle determination threshold.

Since the method has already been described in detail in the embodiments of the present application, the expanded description of the corresponding parts of the system and the method related in the embodiments is omitted here, and will not be described again. The description of the specific contents in the system may refer to the contents of the method embodiments, which are not limited in detail herein.

Compared with the prior art, the anemoscope fault detection method and system based on particle swarm optimization achieve the following effects:

1) according to the particle swarm optimization-based anemoscope fault detection method and system, difference value processing can be performed on wind speed data in an anemoscope of a wind turbine in a long-time multi-grouping mode, after relevant data of probability statistics of wind speed difference are obtained, accurate threshold values are extracted from historical data in an optimized mode through a bionic PSO technology, the anemoscope with abnormal data can be effectively identified according to the threshold values, and therefore the anemoscope with faults and the time of occurrence of the faults are further accurately determined.

2) According to the particle swarm optimization-based anemometer fault detection method and system, the acquired data in the anemometers on different wind turbines can be divided into detailed time periods, and a plurality of data points are respectively set in each time period, so that the fault time of the anemometer can be effectively detected in a wide time region.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.