CN116557230A - Wind power plant unit power abnormality online assessment method and system - Google Patents
Wind power plant unit power abnormality online assessment method and system Download PDFInfo
- Publication number
- CN116557230A CN116557230A CN202310572102.XA CN202310572102A CN116557230A CN 116557230 A CN116557230 A CN 116557230A CN 202310572102 A CN202310572102 A CN 202310572102A CN 116557230 A CN116557230 A CN 116557230A
- Authority
- CN
- China
- Prior art keywords
- wind speed
- power
- active power
- fan
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 230000005856 abnormality Effects 0.000 title claims abstract description 23
- 230000002159 abnormal effect Effects 0.000 claims abstract description 37
- 238000011156 evaluation Methods 0.000 claims abstract description 15
- 238000012216 screening Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000007781 pre-processing Methods 0.000 claims description 10
- 230000007812 deficiency Effects 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 8
- 238000012163 sequencing technique Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 4
- 230000008030 elimination Effects 0.000 claims 1
- 238000003379 elimination reaction Methods 0.000 claims 1
- 238000010248 power generation Methods 0.000 abstract description 10
- 238000012423 maintenance Methods 0.000 abstract description 5
- 230000015556 catabolic process Effects 0.000 abstract description 4
- 238000006731 degradation reaction Methods 0.000 abstract description 4
- 238000007689 inspection Methods 0.000 abstract description 2
- 238000011158 quantitative evaluation Methods 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 description 8
- 238000012935 Averaging Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Combustion & Propulsion (AREA)
- Mathematical Optimization (AREA)
- Algebra (AREA)
- Computational Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Mathematical Analysis (AREA)
- Mechanical Engineering (AREA)
- Sustainable Energy (AREA)
- Pure & Applied Mathematics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Sustainable Development (AREA)
- Wind Motors (AREA)
Abstract
The invention discloses a wind farm unit power abnormality online assessment method and system, comprising the following steps: dividing wind speed steady-state working condition data, taking the intermediate value of a wind speed interval as the active power value of a fan, and obtaining a power curve of the active power of each single fan through the divided wind speed interval; further obtaining a power curve of active power of the full-field cluster unit; and judging the relation between the relative deviation of the active power of the single fan and the active power of the cluster unit in each wind speed interval and the threshold value, and judging the abnormal level of the fan power. According to the invention, on the basis of not adding additional sensors, fan power abnormality alarm, quantitative evaluation of the degradation degree of the power generation performance and identification of the power generation low-efficiency unit are realized, and the period of fan power abnormality evaluation and low-efficiency unit identification is shortened. The wind turbine generator system with abnormal power can be positioned, so that maintenance personnel can conveniently overhaul the wind turbine generator system, and the operation and maintenance cost of the wind turbine generator system for fixed inspection is reduced.
Description
Technical Field
The invention belongs to the technical field of wind power generation, and relates to a method and a system for evaluating power abnormality of a wind power plant unit on line.
Background
After the wind generating set is put into operation for a period of time, the power generation performance of the wind generating set is affected by the change of surrounding environment conditions, the self body design structure, the service life of parts and the like, and the power generation performance of the wind generating set is deteriorated to different degrees. The method mainly relies on comparing the difference between the actual power curve of the wind turbine and the designed wind power curve to evaluate the generating performance of the wind turbine. The actual power curve of the wind turbine generator is generally obtained by testing the power curve of the wind turbine generator on site. In the power curve testing process of the wind turbine generator, a large number of electrical measuring devices such as an airborne radar, a power transmitter and a current sensor are required to acquire real-time meteorological data and fan power data around a fan, a measuring result is influenced by meteorological condition changes, long time is required to be spent for measuring a full wind speed section, and purchase and installation costs of the large number of measuring devices are high. Obviously, the power curve test of the wind turbine generator has long test period and high test cost, and cannot meet the requirement of real-time online evaluation of the power generation performance of the wind turbine generator.
Disclosure of Invention
The invention aims to solve the problems that in the prior art, the power curve test of a wind turbine generator system has long test period and high test cost and cannot meet the real-time online evaluation requirement of the power generation performance of the wind turbine generator system, and provides a wind turbine generator system power abnormality online evaluation method and system.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
an online evaluation method for power anomalies of a wind farm unit comprises the following steps:
acquiring operation data of the full-farm wind turbine, preprocessing the operation data of the full-farm wind turbine, and acquiring steady-state working condition data of wind speed;
dividing wind speed steady-state working condition data to obtain a plurality of wind speed intervals;
based on the active power of the fan in the wind speed interval, acquiring an active power value of the fan corresponding to the intermediate value of the wind speed interval;
based on the divided wind speed intervals, acquiring active power values of fans of the full-field n fans at each wind speed in a full wind speed section, and further acquiring power curves of active power of each single fan;
obtaining a power curve of active power of the full-field cluster unit based on the average value of active power values of n fans in all the same wind speed intervals;
judging whether the relative deviation between the active power of a single fan and the active power of the cluster unit in each wind speed interval is larger than a threshold value, and if so, judging that the relative deviation is normal; if the power of the fan is smaller than the preset value, judging the abnormal level of the power of the fan based on the magnitude of the relative deviation.
The invention further improves that:
further, the full-farm wind turbine operation data includes: the wind speed, wind direction, active power of a fan, rotation speed of a generator, rotation speed of an impeller and yaw angle in unit time are used as indexes, and operation data of all measuring points are indexed in time sequence to form a multi-dimensional time sequence matrix.
Further, the operation data of the full-farm wind turbine generator is preprocessed to obtain the steady-state working condition data of the wind speed, specifically: removing outliers and screening steady-state working condition data;
outliers and outliers are removed, specifically: and eliminating operation data of the wind turbine generator set below cut-in wind speed and above cut-out wind speed based on a direct screening method, and eliminating operation data of active power of a fan, rotating speed of a generator and rotating speed of an impeller which are smaller than zero.
Further, steady state working condition data is screened, specifically: screening steady-state working condition data based on a quartile method and a 3 sigma criterion method; the calculation method for processing the data by the quartile method comprises the following steps:
dividing a multidimensional time sequence matrix according to a wind speed minimum value to a wind speed maximum value at intervals of 0.1m/s, and sequencing active power values of fans in each interval from small to large to obtain a power value sequence [ p ] 1 ，p 2 ，p 3 ，…，p n ]；
Calculate the lower quartile Q 1 Upper quartile Q 3 Tetrad difference I QR ：
Setting the range of normal values of the operation data as [ N ] 1 ，N 2 ]Normal value interval lower bound N 1 Upper boundary N of normal value interval 2 Calculated according to the formula (4):
if the operation data is not in the normal value range, the operation data is considered as an abnormal value and is removed;
the steady-state working condition data is screened based on a 3 sigma criterion method, and specifically comprises the following steps: dividing the wind speed between the minimum value and the maximum value by 0.1m/s, and assuming that the fan operation data x obeys normal distribution for the operation data in each wind speed interval
R(|x-μ|>3σ)≤0.003 (5)
Wherein μ and σ represent the mathematical expectation and standard deviation of the normal population, respectively; data values greater than μ+3σ or less than μ -3σ are culled as outliers.
Further, the steady-state working condition data of wind speed is divided, and a plurality of wind speed intervals are obtained, specifically:
dividing wind speed steady-state working condition data according to wind speed minimum value to wind speed maximum value at fixed interval, and sequencing fan active power values in each interval from small to large to obtain a power value sequence [ P ] 1 ，P 2 ，P 3 ，…，P n ]。
Further, based on the active power of the fan in the wind speed interval, the active power value of the fan corresponding to the intermediate value of the wind speed interval is obtained, and specifically: and taking the average value of the active power of the fan in the wind speed interval as the active power value of the fan corresponding to the intermediate value of the wind speed interval.
Further, based on the active power of the fan in the wind speed interval, acquiring the active power value of the fan corresponding to the intermediate value of the wind speed interval, and further comprising: for the situation that active power data of a fan are missing in certain wind speed interval sections, acquiring an active power value of the fan corresponding to a central wind speed value of the wind speed interval section based on an interpolation method; the calculation formula is as follows:
wherein P is c For interpolating the obtained active power value of the fan, P 2 、P 1 Respectively the active power values of fans above and below the wind speed interval corresponding to the active power deficiency value, v 2 、v 1 、v c The wind speed interval upper limit value, the wind speed interval lower limit value and the wind speed interval central value which correspond to the active power deficiency value respectively.
Further, the threshold is specifically:
wherein P is i Is the active power of a single fan,the power of the cluster of the full-field n fans in the same wind field, the same model and the same wind speed is obtained.
Further, based on the magnitude of the relative deviation, judging the abnormal level of the fan power, specifically: setting the relative deviation threshold value of active power as a; setting the power abnormality of the fan to be three-stage; the method comprises an abnormal third stage, an abnormal second stage and an abnormal first stage respectively; the abnormal third-level, abnormal second-level and abnormal first-level alarm thresholds are that the relative deviation between the power of a single unit and the active power of a cluster unit is respectively lower than a, b and c; wherein a > b > c;
an online evaluation system for power anomalies of a wind farm unit, comprising:
the pretreatment module is used for obtaining the running data of the whole-field wind turbine, and carrying out pretreatment on the running data of the whole-field wind turbine to obtain the steady-state working condition data of wind speed;
the division module divides the wind speed steady-state working condition data to obtain a plurality of wind speed intervals;
the first acquisition module is used for acquiring the active power value of the fan corresponding to the intermediate value of the wind speed interval based on the active power of the fan in the wind speed interval;
the second acquisition module acquires the active power values of fans of the full-field n fans at all wind speeds in the full-wind speed section based on the divided wind speed intervals, and further acquires the power curves of the active power of all the single fans;
the third acquisition module is used for acquiring a power curve of the active power of the full-field cluster unit based on the average value of the active power values of the n fans in the same wind speed interval;
the judging module judges whether the relative deviation between the active power of the single fan and the active power of the cluster unit under each wind speed is larger than a threshold value or not, and if so, the relative deviation is normal; if the power of the fan is smaller than the preset value, judging the abnormal level of the power of the fan based on the magnitude of the relative deviation.
Compared with the prior art, the invention has the following beneficial effects:
dividing wind speed steady-state working condition data, taking a wind speed interval intermediate value as a fan active power value, and obtaining a power curve of active power of each single fan through the divided wind speed interval; further obtaining a power curve of active power of the full-field cluster unit; and judging the relation between the relative deviation of the active power of the single fan and the active power of the cluster unit in each wind speed interval and the threshold value, and judging the abnormal level of the fan power. The invention has lower cost, realizes the fan power abnormality alarm, the quantitative evaluation of the degradation degree of the power generation performance and the identification of the power generation low-efficiency unit on the basis of not adding additional sensors, has the advantages of real-time on-line, rapidness and accuracy, and greatly shortens the period of the fan power abnormality evaluation and the identification of the low-efficiency unit. The wind turbine generator system with abnormal power can be positioned, so that operation and maintenance personnel can be conveniently and accurately guided to conduct investigation and maintenance of the wind turbine in time, and the operation and maintenance cost of unit fixed inspection is effectively reduced.
Drawings
For a clearer description of the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a wind farm unit power anomaly online assessment method of the present invention;
FIG. 2 is another flow chart of the wind farm unit power anomaly online assessment method of the present invention;
FIG. 3 is a comparison of the pre-processing of operational data of the present invention;
FIG. 4 is a graph of power curve anomaly set versus cluster power for the present invention; wherein, fig. 4 (a) is a graph comparing the organic power of the fan No. 3 with the organic power of the cluster; FIG. 4 (b) is a graph comparing the organic power of fan number 7 with the organic power of the clusters; FIG. 4 (c) is a graph comparing the organic power of fan number 11 to the organic power of the clusters; FIG. 4 (d) is a graph comparing the organic power of fan number 21 with the organic power of the clusters; FIG. 4 (e) is a graph comparing the organic power of the No. 51 fan with the organic power of the clusters; FIG. 4 (f) is a graph comparing the organic power of the fan 62 to the organic power of the clusters;
FIG. 5 is a schematic structural diagram of an online evaluation system for power anomalies of a wind farm unit.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the embodiments of the present invention, it should be noted that, if the terms "upper," "lower," "horizontal," "inner," and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present invention and simplifying the description, and does not indicate or imply that the apparatus or element to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the term "horizontal" if present does not mean that the component is required to be absolutely horizontal, but may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" should be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The invention is described in further detail below with reference to the attached drawing figures:
referring to fig. 1, the invention discloses an online evaluation method for power abnormality of a wind farm unit, which comprises the following steps:
s101, acquiring operation data of the full-farm wind turbine generator, preprocessing the operation data of the full-farm wind turbine generator, and acquiring steady-state working condition data of wind speed.
The operation data of the full-farm wind turbine generator set comprises: the wind speed, wind direction, active power of a fan, rotation speed of a generator, rotation speed of an impeller and yaw angle in unit time are used as indexes, and operation data of all measuring points are indexed in time sequence to form a multi-dimensional time sequence matrix.
Preprocessing operation data of the full-farm wind turbine to obtain steady-state working condition data of wind speed, wherein the data specifically comprises: removing outliers and screening steady-state working condition data;
outliers and outliers are removed, specifically: and eliminating operation data of the wind turbine generator set below cut-in wind speed and above cut-out wind speed based on a direct screening method, and eliminating operation data of active power of a fan, rotating speed of a generator and rotating speed of an impeller which are smaller than zero.
Screening steady-state working condition data, specifically: screening steady-state working condition data based on a quartile method and a 3 sigma criterion method; the calculation method for processing data by the quartile method comprises the following steps:
dividing a multidimensional time sequence matrix according to a wind speed minimum value to a wind speed maximum value at intervals of 0.1m/s, and sequencing active power values of fans in each interval from small to large to obtain a power value sequence [ p ] 1 ，p 2 ，p 3 ，…，p n ]；
Calculate the lower quartile Q 1 Upper quartile Q 3 Tetrad difference I QR ：
I QR ＝Q 3 -Q 1 (3)
Setting the range of normal values of the operation data as [ N ] 1 ，N 2 ]Normal value interval lower bound N 1 Upper boundary N of normal value interval 2 Calculated according to the formula (4):
if the operation data is not in the normal value range, the operation data is considered as an abnormal value and is removed;
the steady-state working condition data is screened based on a 3 sigma criterion method, and specifically comprises the following steps: dividing the wind speed between the minimum value and the maximum value by 0.1m/s, and assuming that the fan operation data x obeys normal distribution for the operation data in each wind speed interval
R(|x-μ|>3σ)≤0.003 (5)
Wherein μ and σ represent the mathematical expectation and standard deviation of the normal population, respectively; data values greater than μ+3σ or less than μ -3σ are culled as outliers.
S102, dividing the steady-state working condition data of the wind speed to obtain a plurality of wind speed intervals.
Dividing wind speed steady-state working condition data according to wind speed minimum value to wind speed maximum value at fixed interval, and sequencing fan active power values in each interval from small to large to obtain a power value sequence [ P ] 1 ，P 2 ，P 3 ，…，P n ]。
S103, based on the active power of the fan in the wind speed interval, acquiring the active power value of the fan corresponding to the intermediate value of the wind speed interval.
And taking the average value of the active power of the fan in the wind speed interval as the active power value of the fan corresponding to the intermediate value of the wind speed interval.
For the situation that active power data of a fan are missing in certain wind speed interval sections, acquiring an active power value of the fan corresponding to a central wind speed value of the wind speed interval section based on an interpolation method; the calculation formula is as follows:
wherein P is c For interpolating the obtained active power value of the fan, P 2 、P 1 Respectively the active power values of fans above and below the wind speed interval corresponding to the active power deficiency value, v 2 、v 1 、v c The wind speed interval upper limit value, the wind speed interval lower limit value and the wind speed interval central value which correspond to the active power deficiency value respectively.
S104, based on the divided wind speed intervals, acquiring active power values of fans of the full-field n fans at all wind speeds in the full wind speed section, and further acquiring power curves of active power of all the single fans.
Sequentially connecting the active power values of the fans of each wind speed interval of the single fan to obtain a power curve of the active power of the single fan; and repeating the steps to obtain the power curve of the active power of the full-field n fans.
S105, obtaining a power curve of active power of the full-field cluster unit based on the average value of the active power values of n fans in the same wind speed interval.
And superposing the active power values of the n fans in the same wind speed interval, averaging, and taking the average value as the active power value of the cluster base group in the wind speed interval. Repeating the steps to obtain the average active power value of n fans in each same wind speed interval; and then acquiring a power curve of the active power of the full-field cluster unit.
S106, judging whether the relative deviation between the active power of the single fan and the active power of the cluster unit under each wind speed is larger than a threshold value, and if so, judging that the relative deviation is normal; if the power of the fan is smaller than the preset value, judging the abnormal level of the power of the fan based on the magnitude of the relative deviation.
The threshold is specifically:
wherein P is i Is the active power of a single fan,the power of the cluster of the full-field n fans in the same wind field, the same model and the same wind speed is obtained.
Based on the magnitude of the relative deviation, judging the abnormal level of the fan power, specifically: setting the relative deviation threshold value of active power as a; setting the power abnormality of the fan to be three-stage; the method comprises an abnormal third stage, an abnormal second stage and an abnormal first stage respectively; the abnormal third-level, abnormal second-level and abnormal first-level alarm thresholds are that the relative deviation between the power of a single unit and the active power of a cluster unit is respectively lower than a, b and c; wherein a > b > c;
examples:
as shown in FIG. 2, the invention discloses an online evaluation method for power anomalies of a wind farm unit, which comprises the following steps:
A. and acquiring and preprocessing operation data of the full-farm wind turbine.
B. And (5) fitting and calculating the power curves of the whole-field single unit and the whole-field cluster unit.
C. And determining a power abnormality alarm threshold value of the wind turbine generator and identifying the low-efficiency turbine generator.
Preferably, in the step A, operation data of measuring points such as wind speed, wind direction, active power of a fan, rotation speed of a generator, rotation speed of an impeller, yaw angle and the like in a period of time are read through a SCADA (data acquisition and monitoring control system) of the wind turbine, and the operation data of each measuring point takes time sequence as an index to jointly form a multi-dimensional time sequence matrix.
Preferably, in the step a, after acquiring operation data of a SCADA (data acquisition and monitoring control system) of a wind turbine, data preprocessing needs to be performed on the extracted operation data, including: and preprocessing such as outlier removal, steady-state working condition data screening and the like.
Referring to fig. 3, preferably, the outlier and outlier rejection in the operation data fetched in the step a mainly includes: removing operation data of the wind turbine generator below cut-in wind speed and above cut-out wind speed by adopting a direct screening method, and removing operation data of active power of a fan, rotating speed of a generator and rotating speed of an impeller which are smaller than zero;
preferably, the operation data extracted in the step A is subjected to steady-state working condition data screening treatment after outliers and abnormal values are removed. The method comprises the following steps: quartile and 3σ criteria. The calculation method for processing data by using the quartile method comprises the following steps:
dividing wind speed intervals: dividing a multidimensional time sequence matrix according to a wind speed minimum value to a wind speed maximum value at intervals of 0.1m/s, and sequencing active power values of fans in each interval from small to large to obtain a power value sequence [ p ] 1 ，p 2 ，p 3 ，…，p n ]。
Calculate the lower quartile Q 1 Upper quartile Q 3 Tetrad difference I QR :
I QR ＝Q 3 -Q 1 (3)
Setting the range of normal values of the operation data as [ N ] 1 ，N 2 ]: normal value interval lower bound N 1 Upper boundary N of normal value interval 2 The method is calculated according to the following formulas:
3 sigma criterion method filters steady state working condition data: dividing the wind speed between the minimum value and the maximum value by 0.1m/s, and assuming that the fan operation data x obeys normal distribution for the operation data in each wind speed interval
R(|x-μ|>3σ)≤0.003 (5)
Where μ and σ represent the mathematical expectation and standard deviation, respectively, of a normal population. At this time, the probability of occurrence of data values greater than μ+3σ or less than μ -3σ in the running data values is small. Thus, data values greater than μ+3σ or less than μ -3σ are culled as outliers according to the above formula.
Preferably, in the step B, the fitting calculation of the power curve of the full-field single unit and the full-field cluster unit is performed. The step of calculating the power curves of n fans in the whole wind field is as follows: the data obtained after data preprocessing and steady-state working condition screening are subjected to wind speed minimum value v min To a maximum value v max Equally dividing the wind speed intervals by 0.2m/s to obtain m wind speed intervals, wherein the ith wind speed interval is [ v ] min +0.1×(i-1)，v min +0.1×(i+1)]. Calculating the average value of the active power of the fan in each wind speed interval as the intermediate value v of the wind speed interval i The corresponding active power value of the fan. Wind speed minimum v min Intermediate value v of wind speed interval i Maximum wind velocity v max The three satisfy the following formula (6):
preferably, in the step B, the fitting calculation of the power curve of the full-field single unit and the full-field cluster unit is performed. And calculating the active power value of the fan corresponding to the central wind speed value of the wind speed interval by adopting an interpolation method under the condition that the active power data of the fan is missing in some wind speed intervals. As shown in formula (7):
wherein P is c For interpolating the obtained active power value of the fan, P 2 、P 1 Respectively the active power values of fans above and below the wind speed interval corresponding to the active power deficiency value, v 2 、v 1 、v c The wind speed interval upper limit value, the wind speed interval lower limit value and the wind speed interval central value which correspond to the active power deficiency value respectively.
Preferably, in the step B, the fitting calculation of the power curve of the full-field single unit and the full-field cluster unit is performed. And after calculating the active power values of the fans of the n fans of the full wind field at each wind speed in the full wind speed section, drawing power curves of the n fans of the full wind field. And averaging the active power values of the full-field n fans in the same wind field, the same wind model and the same wind speed to obtain the active power value of the full-field cluster unit, and further drawing a power curve of the full-field cluster unit. Referring to fig. 4, fig. 4 (a) is a graph comparing the organic power of fan No. 3 with the organic power of the cluster; FIG. 4 (b) is a graph comparing the organic power of fan number 7 with the organic power of the clusters; FIG. 4 (c) is a graph comparing the organic power of fan number 11 to the organic power of the clusters;
FIG. 4 (d) is a graph comparing the organic power of fan number 21 with the organic power of the clusters; FIG. 4 (e) is a graph comparing the organic power of the No. 51 fan with the organic power of the clusters; fig. 4 (f) is a graph comparing the organic power of the fan No. 62 with the organic power of the clusters.
Preferably, in the step C, the power abnormality alarm threshold value of the wind turbine generator system is determined and the low-efficiency turbine generator system is identified. Calculating the relative deviation delta of active power of a single unit and a cluster unit at each wind speed i And the power abnormality judgment condition is used as a judging condition of the power abnormality of the wind turbine.
Wherein P is i Is the active power of a single fan,is a set of n fans in the whole field under the same wind field, the same model and the same wind speedGroup power.
Preferably, in the step C, the power abnormality alarm threshold value of the wind turbine generator system is determined and the low-efficiency turbine generator system is identified. The active power relative deviation threshold is set to be-5%. When the relative deviation between the active power of a single fan and the active power of the cluster unit is less than-5%, the unit is judged to be in an abnormal state, and the unit is positioned and identified as a low-efficiency generating unit for alarming. The three-stage, the two-stage and the one-stage alarming thresholds of abnormal fan power are set to be that the relative deviation between the power of a single unit and the active power of a cluster unit is respectively lower than-5%, 10% and 15%.
Preferably, in the step C, the power abnormality alarm threshold value of the wind turbine generator system is determined and the low-efficiency turbine generator system is identified. Further, in order to quantify the degradation degree of the power generation performance of the wind turbine generator, the degradation degree is graded as follows according to the relative deviation value of the active power of the single unit and the cluster unit:
referring to fig. 5, the invention discloses an online evaluation system for power abnormality of a wind farm unit, comprising:
the pretreatment module is used for obtaining the running data of the whole-field wind turbine, and carrying out pretreatment on the running data of the whole-field wind turbine to obtain the steady-state working condition data of wind speed;
the division module divides the wind speed steady-state working condition data to obtain a plurality of wind speed intervals;
the first acquisition module is used for acquiring the active power value of the fan corresponding to the intermediate value of the wind speed interval based on the active power of the fan in the wind speed interval;
the second acquisition module acquires the active power values of fans of the full-field n fans at all wind speeds in the full-wind speed section based on the divided wind speed intervals, and further acquires the power curves of the active power of all the single fans;
the third acquisition module is used for acquiring a power curve of the active power of the full-field cluster unit based on the average value of the active power values of the n fans in the same wind speed interval;
the judging module judges whether the relative deviation between the active power of the single fan and the active power of the cluster unit under each wind speed is larger than a threshold value or not, and if so, the relative deviation is normal; if the power of the fan is smaller than the preset value, judging the abnormal level of the power of the fan based on the magnitude of the relative deviation.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The method for evaluating the power abnormality of the wind power plant unit on line is characterized by comprising the following steps of:
acquiring operation data of the full-farm wind turbine, preprocessing the operation data of the full-farm wind turbine, and acquiring steady-state working condition data of wind speed;
dividing wind speed steady-state working condition data to obtain a plurality of wind speed intervals;
based on the active power of the fan in the wind speed interval, acquiring an active power value of the fan corresponding to the intermediate value of the wind speed interval;
based on the divided wind speed intervals, acquiring active power values of fans of the full-field n fans at each wind speed in a full wind speed section, and further acquiring power curves of active power of each single fan;
obtaining a power curve of active power of the full-field cluster unit based on the average value of active power values of n fans in all the same wind speed intervals;
judging whether the relative deviation between the active power of a single fan and the active power of the cluster unit in each wind speed interval is larger than a threshold value, and if so, judging that the relative deviation is normal; if the power of the fan is smaller than the preset value, judging the abnormal level of the power of the fan based on the magnitude of the relative deviation.
2. The method for online assessment of wind farm unit power anomalies according to claim 1, wherein the full farm wind farm unit operational data comprises: the wind speed, wind direction, active power of a fan, rotation speed of a generator, rotation speed of an impeller and yaw angle in unit time are used as indexes, and operation data of all measuring points are indexed in time sequence to form a multi-dimensional time sequence matrix.
3. The method for online assessment of power anomalies of a wind farm set according to claim 2, wherein the preprocessing of the operation data of the full farm wind farm set to obtain steady state wind speed condition data is specifically as follows: removing outliers and screening steady-state working condition data;
the outlier and abnormal value elimination is specifically as follows: and eliminating operation data of the wind turbine generator set below cut-in wind speed and above cut-out wind speed based on a direct screening method, and eliminating operation data of active power of a fan, rotating speed of a generator and rotating speed of an impeller which are smaller than zero.
4. The method for online assessment of power anomalies of a wind farm set according to claim 3, wherein the screening of steady-state condition data is specifically: screening steady-state working condition data based on a quartile method and a 3 sigma criterion method; the calculation method for processing the data by the quartile method comprises the following steps:
dividing a multidimensional time sequence matrix according to a wind speed minimum value to a wind speed maximum value at intervals of 0.1m/s, and sequencing active power values of fans in each interval from small to large to obtain a power value sequence [ p ] 1 ，p 2 ，p 3 ，…，p n ]；
Calculate the lower quartile Q 1 Upper quartile Q 3 Tetrad difference I QR ：
I QR ＝Q 3 -Q 1 (3)
Setting the range of normal values of the operation data as [ N ] 1 ，N 2 ]Normal value interval lower bound N 1 Upper boundary N of normal value interval 2 Calculated according to the formula (4):
N 1 ＝Q 1 -1.5I QR (4)
N 2 ＝Q 2 +1.5I QR
if the operation data is not in the normal value range, the operation data is considered as an abnormal value and is removed;
the steady-state working condition data is screened based on a 3 sigma criterion method, and specifically comprises the following steps: dividing the wind speed between the minimum value and the maximum value by 0.1m/s, and assuming that the fan operation data x obeys normal distribution for the operation data in each wind speed interval
P(|x-μ|>3σ)≤0.003 (5)
Wherein μ and σ represent the mathematical expectation and standard deviation of the normal population, respectively; data values greater than μ+3σ or less than μ -3σ are culled as outliers.
5. The method for online assessment of power anomalies of a wind farm set according to claim 1, wherein the dividing of wind speed steady state condition data is performed to obtain a plurality of wind speed intervals, specifically:
dividing wind speed steady-state working condition data according to wind speed minimum value to wind speed maximum value at fixed interval, and sequencing fan active power values in each interval from small to large to obtain a power value sequence [ P ] 1 ，P 2 ，P 3 ，…，P n ]。
6. The method for online evaluation of power anomalies of wind power plants according to claim 5, wherein the obtaining the fan active power value corresponding to the intermediate value of the wind speed interval based on the fan active power in the wind speed interval specifically comprises: and taking the average value of the active power of the fan in the wind speed interval as the active power value of the fan corresponding to the intermediate value of the wind speed interval.
7. The method for online evaluation of power anomalies of wind farm units according to claim 6, wherein the obtaining the fan active power value corresponding to the intermediate value of the wind speed interval based on the fan active power in the wind speed interval further comprises: for the situation that active power data of a fan are missing in certain wind speed interval sections, acquiring an active power value of the fan corresponding to a central wind speed value of the wind speed interval section based on an interpolation method; the calculation formula is as follows:
wherein P is c For interpolating the obtained active power value of the fan, P 2 、P 1 Respectively the active power values of fans above and below the wind speed interval corresponding to the active power deficiency value, v 2 、v 1 、v c The wind speed interval upper limit value, the wind speed interval lower limit value and the wind speed interval central value which correspond to the active power deficiency value respectively.
8. The method for online evaluation of power anomalies of wind farm units according to claim 7, wherein the threshold is specifically:
wherein P is i Is the active power of a single fan,the power of the cluster of the full-field n fans in the same wind field, the same model and the same wind speed is obtained.
9. The method for online assessment of power abnormality of a wind farm unit according to claim 8, wherein the determining of the abnormality level of the fan power based on the magnitude of the relative deviation is specifically as follows: setting the relative deviation threshold value of active power as a; setting the power abnormality of the fan to be three-stage; the method comprises an abnormal third stage, an abnormal second stage and an abnormal first stage respectively; the abnormal third-level, abnormal second-level and abnormal first-level alarm thresholds are that the relative deviation between the power of a single unit and the active power of a cluster unit is respectively lower than a, b and c; wherein a > b > c;
10. an online evaluation system for power anomalies of a wind farm unit, comprising:
the pretreatment module is used for obtaining the running data of the whole-field wind turbine, and carrying out pretreatment on the running data of the whole-field wind turbine to obtain the steady-state working condition data of wind speed;
the division module divides the wind speed steady-state working condition data to obtain a plurality of wind speed intervals;
the first acquisition module is used for acquiring the active power value of the fan corresponding to the intermediate value of the wind speed interval based on the active power of the fan in the wind speed interval;
the second acquisition module acquires the active power values of fans of the full-field n fans at all wind speeds in the full-wind speed section based on the divided wind speed intervals, and further acquires the power curves of the active power of all the single fans;
the third acquisition module is used for acquiring a power curve of the active power of the full-field cluster unit based on the average value of the active power values of the n fans in the same wind speed interval;
the judging module judges whether the relative deviation between the active power of the single fan and the active power of the cluster unit under each wind speed is larger than a threshold value or not, and if so, the relative deviation is normal; if the power of the fan is smaller than the preset value, judging the abnormal level of the power of the fan based on the magnitude of the relative deviation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310572102.XA CN116557230A (en) | 2023-05-19 | 2023-05-19 | Wind power plant unit power abnormality online assessment method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310572102.XA CN116557230A (en) | 2023-05-19 | 2023-05-19 | Wind power plant unit power abnormality online assessment method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116557230A true CN116557230A (en) | 2023-08-08 |
Family
ID=87497926
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310572102.XA Pending CN116557230A (en) | 2023-05-19 | 2023-05-19 | Wind power plant unit power abnormality online assessment method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116557230A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117555892A (en) * | 2024-01-10 | 2024-02-13 | 江苏省生态环境大数据有限公司 | Atmospheric pollutant multimode fusion accounting model post-treatment method |
-
2023
- 2023-05-19 CN CN202310572102.XA patent/CN116557230A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117555892A (en) * | 2024-01-10 | 2024-02-13 | 江苏省生态环境大数据有限公司 | Atmospheric pollutant multimode fusion accounting model post-treatment method |
| CN117555892B (en) * | 2024-01-10 | 2024-04-02 | 江苏省生态环境大数据有限公司 | Atmospheric pollutant multimode fusion accounting model post-treatment method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US6587737B2 (en) | Method for the monitoring of a plant | |
| CN102758655B (en) | For the protection of the method for rotating machinery | |
| CN116557230A (en) | Wind power plant unit power abnormality online assessment method and system | |
| CN112228290B (en) | Intelligent early warning method for faults of variable pitch system of wind turbine | |
| CN112065668A (en) | Wind turbine generator state abnormity assessment method and system | |
| CN118243200B (en) | Gas turbine flowmeter fault diagnosis method and system | |
| CN116624341A (en) | Wind turbine generator power abnormality diagnosis method, system and equipment | |
| CN117131321A (en) | Electric energy data acquisition method | |
| CN113468728B (en) | Variable pitch system fault prediction method based on neural network | |
| CN117989472A (en) | Gas pipeline pressure monitoring and analyzing system and method | |
| CN116878886B (en) | Method for monitoring abrasion of water pump bearing of cooling tower | |
| CN117972600A (en) | Wind turbine generator set key component abnormality detection method based on multidimensional fault feature learning | |
| US20240018940A1 (en) | Method and system for performing condition monitoring of wind turbine components | |
| CN115929569B (en) | Wind turbine generator system variable pitch system fault diagnosis method | |
| CN112326246A (en) | Bearing safety state online monitoring method based on periodic data and nuclear density estimation | |
| CN116465627A (en) | Mechanical equipment fault monitoring system | |
| CN107218180B (en) | A kind of wind power generating set driving unit fault alarm method based on vibration acceleration measurement | |
| US20180087489A1 (en) | Method for windmill farm monitoring | |
| Souza et al. | Evaluation of data based normal behavior models for fault detection in wind turbines | |
| CN116756644B (en) | Early warning method for icing fault of anemoclinograph of wind turbine generator | |
| CN114135449B (en) | Wind turbine generator blade fault early warning method | |
| CN113898528B (en) | Abnormality detection method, model training method and related device for fan variable pitch bearing | |
| CN117128143A (en) | Blade fault detection method and related device | |
| CN118032118B (en) | Wireless acceleration sensor based on wifi-mesh technology | |
| CN116754794A (en) | Wind turbine generator anemometer fault identification method and system based on least square method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |