CN108763584B - Method and system for filtering scattered points of wind power curve - Google Patents
Method and system for filtering scattered points of wind power curve Download PDFInfo
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Abstract
The invention belongs to the technical field of wind driven generators, data analysis and data processing, and discloses a method for filtering scattered points of a wind power curve, which comprises the following steps: s001, acquiring a SCADA data set M01 data packet of the target fan by adopting an SCADA system; step S002, inputting the M01 data packet into a data filter, and subpackaging to obtain a data packet M011 and a data packet M012; step S003, obtain the renewal data packet M011a and renewal data packet M012 b; step S004, combining and acquiring the updated power data packet M02; and step S005, performing data correction on the updated power data packet M02 acquired in the step S004, and removing distortion data to acquire a filtered power data set M03.
Description
Technical Field
The invention belongs to the technical field of wind driven generators, data analysis and data processing, particularly relates to the field of a method or a system for processing discrete data or distorted data in SCADA (supervisory control and data acquisition) data acquired by wind power, and particularly relates to a method and a system for filtering scattered points of a wind power curve.
Background
A wind power curve obtained by a wind generating set SCADA system in actual production often has a large number of scattered points or abnormal shapes, and is mostly caused by different control strategies, different working conditions and abnormal values in data. In the data analysis of the wind turbine, scattered points of a wind power curve need to be cleaned, so that a smooth power curve is obtained for subsequent analysis and modeling.
The existing technology for filtering the scattered points of the wind power curve comprises the following steps:
the method needs the working condition identification variable, but in most cases, the SCADA system does not have the variable or record loss, and scattered points in a wind power curve can still exist widely even if only the data of normal operating conditions are screened;
working condition filtering is performed by using variables in an SCADA system such as blade angles and rotating speeds, the method is an alternative method when the working condition identification of the method 1 is deficient, but working condition identification based on other variables is limited, and all working conditions cannot be covered;
the method divides a power curve into grid systems by two variables of wind speed and power, calculates the density of data in each grid, and judges whether the data are sparse scattered points to remove. The method has the defects that the effect is obviously reduced by adopting a power quantile method in the Chinese patent application No. CN201110432282.9 under the condition that scattered points are extremely dispersed, and the problem that the effect is reduced under the condition that the scattered points of the power curve are excessively dispersed in the method 3 is solved to overcome the defects of the method and a system for filtering the scattered points tracked by the power curve.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention aims to provide a method and a system for filtering scattered points of a wind power curve, which are used for solving the problem that distorted discrete points still exist when filtering is performed by using an operating condition identification variable in SCADA data in the prior art, so that an expected smooth power curve cannot be obtained; and the problem that the working condition filtration is carried out by using the variables in the SCADA system such as the angle of the blade, the rotating speed and the like, so that the applicable range is limited and the full working condition cannot be covered is solved. The method divides the M01 data packets collected from the SCADA system according to the set threshold value by using a filter to realize the sub-packaging of the data; windowing is carried out on successfully-packetized data through a power curve tracker, iterative traversal calculation is carried out on the windowed data in each window, all scattered point data which do not meet the conditions are removed, and finally a data set is reserved to obtain a smooth power curve for subsequent analysis and modeling.
The technical scheme adopted by the invention is as follows:
a method for filtering scattered points of a wind power curve comprises the following steps:
s001, acquiring a SCADA data set M01 data packet of the target fan by adopting an SCADA system;
step S002, inputting the M01 data packet into a data filter, setting a power threshold value h to be 0.1 by the data filter, comparing and filtering all power data in the M01 data packet, and filtering to obtain a data packet M011 with a power less than or equal to 0.1 and a data packet M012 with a power greater than 0.1;
step S003, importing the data packet M011 and the data packet M012 in step S002 into the first filtering unit G1 and the second filtering unit G2 of the power curve tracker with initialized parameters for data cleaning and filtering, so as to obtain an update data packet M011a and an update data packet M012b, respectively;
step S004, merging the update packet M011a and the update packet M012b obtained in step S003 to obtain an updated power packet M02;
and step S005, performing data correction on the updated power data packet M02 acquired in the step S004, and removing distortion data to acquire a filtered power data set M03.
In order to achieve more accurate data processing, the data type in the SCADA data set M01 data packet in step S001 preferably corresponds to the initialization parameter type in the power curve tracker in step S003.
Preferably, the initialization parameters include an upper boundary position value of wind speed, a lower boundary position value of wind speed, a size value of a power window, a standard deviation threshold value of wind speed, a width threshold value of the upper and lower boundaries, allowable upper and lower boundaries of a difference value of a front and rear window tracking points, a deviation value of a window tracking point, a rated power initial wind speed value and a rated power floating value.
It is worth mentioning that: the SCADA system is a DCS and electric power automatic monitoring system based on a computer; the method has wide application field, and can be applied to a plurality of fields such as data acquisition and monitoring control, process control and the like in the fields of electric power, metallurgy, petroleum, chemical industry, gas, railways and the like. Therefore, the data acquisition in the corresponding field or the target data types required to be acquired for different purposes are partially or completely different, and based on the requirement of subsequent calculation of the invention, the purpose of corresponding the acquired data types in the SCADA system to the initialization parameter types of the power curve tracker for subsequently realizing data windowing is to realize uniform connection and acquisition of data. For example: if the power curve tracker relies on the wind speed standard deviation threshold as the basic data for cleaning or filtering basic calculation during the calculation process, but the corresponding collection in the SCADA data set M01 data packet does not result in the interruption/suspension of calculation directly.
In order to further refine the present invention, in step S003, the process of the first filtering unit G1 obtaining the update packet M011a through the packet M011 includes the following steps:
step S0031, determining a power curve starting point: selecting the average value of all wind speed data with power within the interval of 1-10;
step S0032, invalid data is filtered: and selecting and deleting all data with power less than 5 and wind speed greater than the starting point of the power curve to obtain an updated data packet M011 a. The first filtering unit G1 is one of the operation modules in the power curve tracker, and the operation rules burned in the operation module can be implemented by a method including but not limited to custom logic operation. This implementation of the setting for the currently existing power curve tracker is a prior art technology for a person skilled in the art, and will not be described in detail here. Indeed, it should be particularly noted that, although the implementation of the technical problem is well known, the method for performing operations/calculations included in, but not limited to, the setting and calculation logic program is not known, that is, the invention is described.
On the basis of the above method, it is further preferable that the process of the second filter unit G2 obtaining the update data packet M012b by the data packet M012 in step S003 includes the following steps:
step S0033 all the data in the data packet M012 are windowed with a fixed width to obtain a window W1、W2、W3.......WnSequentially extracting data in each divided window in a circulating mode;
step S0034, judging the current window WnWhether data exists therein; if no data is present, loop to the next window Wn+1The above judgment is carried out; if there is data, the window W is calculatedn+1A description value of (a); the descriptive values comprise upper and lower boundary values, wind speed standard deviation and upper and lower boundary widths of wind speed data;
the upper and lower boundary values of the wind speed data are as follows: calculating by taking the upper and lower boundary positions of the wind speed data as quantiles, and expressing by upper and lower in the following;
wind speed standard deviation: denoted as ws _ sd below; width of upper and lower boundaries: calculating the absolute value of the difference value of the upper maximum value and the lower maximum value minus the tracking point, and expressing the absolute value as upper _ width and lower _ width below; calculation window Wn+1Comparing the median of all the reserved data in the window with the final tracking point of the previous window Wn, and determining the tracking point of the current window, wherein the tracking point is expressed by tracker; the updating of the tracking point is realized by adopting a cross-window tracking point updating method;
the descriptive value calculation formula is as follows:
1) standard deviation of wind speed
Wherein ws represents a wind speed data value, μ is an arithmetic mean of the wind speed data, N represents a data amount, wsiRepresenting the ith wind speed data value, i ∈ {1,2, … …, N };
2) width of upper and lower boundaries
upper_width＝|upper-tracker|
lower_width＝|lower-tracker|
If no data exists, the wind speed standard deviation and the width of the upper boundary and the lower boundary are set to be 0; if the window wind speed standard deviation and the width of the upper boundary and the lower boundary are larger than the corresponding threshold, and the current iteration in the window has data and circularly traverses all the reserved data in the window;
step S0035, traversing and deleting until the wind speed standard deviation of the current window and the widths of the upper boundary and the lower boundary accord with set parameters:
3) if the upper boundary width is larger than the lower boundary width and the absolute value of the difference between the two values is larger than 1, deleting the data;
4) deleting if the lower boundary width is greater than the upper boundary width and the absolute value of the difference between the two values is greater than 1;
5) if the absolute value of the difference between the widths of the upper boundary and the lower boundary is less than 1, and if the current wind speed data point is greater than the upper boundary value or the current wind speed data point is less than the lower boundary value, deleting the data point;
6) updating all description values of the current window and the tracking point of the current window, wherein the tracking point is calculated as the current window wn+1Keeping the wind speed median of the data; the update data packet M012b is obtained by data filtering until all windows are updated circularly.
In order to better implement the present invention, the method for performing data modification on the updated power packet M02 in step S005 is performed as follows:
7) judging the relation between the power data in the power data packet M02 and the power corresponding to the rated power initial wind speed;
8) judging whether the power data in the power data packet M02 is in a range of a rated power plus or minus a maximum power floating value;
if the 7) and the 8) are both satisfied, reserving the power data, otherwise, deleting the corresponding power data which do not satisfy the 7) and the 8) at the same time, and obtaining a final filtered power data set M03.
In the present invention, the cross-window tracking point updating method is preferably implemented by using the following scheme:
such as the current window Wn+1Data size greater than 2, previous window WnIf the absolute value of the difference between the final tracking point and the current median is smaller than the lower bound of the allowable value, the tracking point is updated to be the current window Wn+1A median; as in the previous window WnThe absolute value of the difference between the final tracking point and the current median is greater than the lower bound of the allowable value, and the absolute value of the difference between the final tracking point and the current median is less than the allowable valueIf the value is in the upper bound, updating the tracking point to be the average value of the two values; as in the previous window WnThe absolute value of the difference value between the final tracking point and the current median is larger than the upper bound of the allowable value, and the tracking point is updated to be the previous window WnFinal tracking point plus current window Wn+1Tracking point offset values;
such as the current window Wn+1The data volume is less than or equal to 2, and the tracking point is updated to be the previous window WnAdding the final tracking point to the window tracking point offset value;
such as the current window Wn+1For the first window W with data1Current window Wn+1Tracking point and previous window WnThe tracking point is synchronously set to the window Wn+1Median wind speed data.
A system for wind power curve scatter filtering, comprising a SCADA system: the SCADA data set M01 data packet is used for collecting a SCADA data set M01 data packet of a target fan, and comprises wind speed upper boundary position data, wind speed lower boundary position data, power window size data, wind speed standard difference threshold value data, upper and lower boundary width threshold value data, front and rear window tracking point difference value allowable upper and lower boundary data, window tracking point offset value data, rated power initial wind speed data and rated power floating value data;
a screening device: dividing the SCADA data set M01 data packets into different target data sets by setting a power critical value h;
power curve tracker: the method is used for calculating and acquiring smooth power curve data from different target data sets screened by the screener according to any one of the wind power curve scatter filtering methods in the invention.
The invention has the beneficial effects that:
the method adopts a mode of window-division iterative traversal calculation, filtering and screening, and can comprehensively filter and eliminate scattered point problems caused by recording loss, abnormal working conditions and working condition marking loss in various SCADA systems to obtain a smooth power curve; meanwhile, the invention sequentially adopts the data error judgment, and then carries out logic processing on the data to obtain the data meeting the set requirement and carries out window data updating, thus avoiding the problems of limited application range, data distortion screening or excessive scattered point mixing in the prior art.
Drawings
FIG. 1 is a flow chart of example 1 of the present invention;
FIG. 2 is a flow chart of a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram of a wind power curve before scatter filtering;
FIG. 4 is a schematic view of a wind power curve after scatter filtering;
Detailed Description
The invention is further described with reference to the following figures and specific embodiments. Before the embodiments of the present invention are explained, in order to facilitate the understanding of the aspects of the present invention, the following terms/nouns are first explained:
1. SCADA (Supervisory Control And Data acquisition) system
Namely a data acquisition and supervisory control system. The SCADA system is a DCS and electric power automatic monitoring system based on a computer; the method has wide application field, and can be applied to a plurality of fields such as data acquisition and monitoring control, process control and the like in the fields of electric power, metallurgy, petroleum, chemical industry, gas, railways and the like.
2. Wind speed standard deviation threshold
The average of the distances that each wind speed xi deviates from the average wind speed is the square root of the sum of the squared deviations.
3. Median number
The Median (also called Median, English: media), a term in statistics, represents a value in a sample, population or probability distribution, which can divide a set of values into two equal parts, upper and lower.
For a finite number set, the median can be found by ranking all observations high and low. If there are an even number of observations, the median is usually taken as the average of the two most intermediate values.
Example 1:
as shown in fig. 1, 3 and 4, the present embodiment provides a method for wind power curve scatter filtering, which includes the following steps:
and S001, acquiring a SCADA data set M01 data packet of the target fan by adopting SCADA system acquisition.
Step S002, inputting the M01 data packet into a data filter, setting a power threshold value h to be 0.1 by the data filter, comparing and filtering all power data in the M01 data packet, and filtering to obtain a data packet M011 with a power less than or equal to 0.1 and a data packet M012 with a power greater than 0.1; here, it should be noted that, by filtering the data according to the method, specific values of the power threshold h may be set for different fans or different actual target data requirements, and one or more of the power threshold h may be set, so as to divide the M01 data packet into a plurality of data packets M011 and M012 … … M01n divided by the power threshold h.
Step S003, the data packet M011 and the data packet M012 in step S002 are simultaneously imported into the first filtering unit G1 and the second filtering unit G2 of the power curve tracker that has been initialized with parameters for data cleaning and filtering, so as to obtain the update data packet M011a and the update data packet M012b, respectively.
In step S004, the update packet M011a and the update packet M012b obtained in step S003 are merged to obtain the updated power packet M02.
And step S005, performing data correction on the updated power data packet M02 acquired in the step S004, and removing distortion data to acquire a filtered power data set M03.
Example 2:
in order to achieve further accurate data processing, in embodiment 1, the data type in the SCADA data set M01 data packet in step S001 corresponds to the initialization parameter type in the power curve tracker in step S003; the initialization parameters comprise an upper boundary position value of the wind speed, a lower boundary position value of the wind speed, a size value of a power window, a standard deviation threshold value of the wind speed, a width threshold value of the upper boundary and the lower boundary, a difference tolerance upper boundary and lower boundary of a front window tracking point and a rear window tracking point, a window tracking point deviant value, a rated power initial wind speed value and a rated power floating value. It is worth mentioning that: the SCADA system is a DCS and electric power automatic monitoring system based on a computer; the method has wide application field, and can be applied to a plurality of fields such as data acquisition and monitoring control, process control and the like in the fields of electric power, metallurgy, petroleum, chemical industry, gas, railways and the like. Therefore, the data acquisition in the corresponding field or the target data types required to be acquired for different purposes are partially or completely different, and based on the requirement of subsequent calculation of the invention, the purpose of corresponding the acquired data types in the SCADA system to the initialization parameter types of the power curve tracker for subsequently realizing data windowing is to realize uniform connection and acquisition of data. For example: if the power curve tracker relies on the wind speed standard deviation threshold as the basic data for cleaning or filtering basic calculation during the calculation process, but the corresponding collection in the SCADA data set M01 data packet does not result in the interruption/suspension of calculation directly.
Example 3:
in order to further refine the present invention, on the basis of any of the above embodiments, with reference to fig. 2, in this embodiment, the process that the first filtering unit G1 obtains the update packet M011a through the packet M011 in step S003 includes the following steps:
step S0031, determining a power curve starting point: selecting the average value of all wind speed data with power within the interval of 1-10;
step S0032, invalid data is filtered: and selecting and deleting all data with power less than 5 and wind speed greater than the starting point of the power curve to obtain an updated data packet M011 a. The first filtering unit G1 is one of the operation modules in the power curve tracker, and the operation rules burned in the operation module can be implemented by a method including but not limited to custom logic operation. This implementation of the setting for the currently existing power curve tracker is a prior art technology for a person skilled in the art, and will not be described in detail here. Indeed, it should be particularly noted that, although the implementation of the technical problem is well known, the method for performing operations/calculations included in, but not limited to, the setting and calculation logic program is not known, that is, the invention is described.
On the basis of the above method, it is further preferable that the process of the second filter unit G2 obtaining the update data packet M012b by the data packet M012 in step S003 includes the following steps:
step S0033, performing windowing on all data in the data packet M012 with a fixed width to obtain a window W1、W2、W3.......WnSequentially extracting data in each divided window in a circulating mode;
step S0034, judging the current window WnWhether data exists therein; if no data is present, loop to the next window Wn+1The above judgment is carried out; if there is data, the window W is calculatedn+1A description value of (a); the descriptive values comprise upper and lower boundary values, wind speed standard deviation and upper and lower boundary widths of wind speed data;
the upper and lower boundary values of the wind speed data are as follows: calculating by taking the upper and lower boundary positions of the wind speed data as quantiles, and expressing by upper and lower in the following;
wind speed standard deviation: denoted as ws _ sd below; width of upper and lower boundaries: calculating the absolute value of the difference value of the upper maximum value and the lower maximum value minus the tracking point, and expressing the absolute value as upper _ width and lower _ width below; calculation window Wn+1Median of all reserved data, and previous window WnComparing the final tracking points, and determining the tracking point of the current window, wherein the tracking point is expressed by tracker; the updating of the tracking point is realized by adopting a cross-window tracking point updating method;
the descriptive value calculation formula is as follows:
1) standard deviation of wind speed
Wherein ws represents a wind speed data value, μ is an arithmetic mean of the wind speed data, N represents a data amount, wsiRepresenting the ith wind speed data value, i ∈ {1,2, … …, N };
2) width of upper and lower boundaries
upper_width＝|upper-tracker|
lower_width＝|lower-tracker|
If no data exists, the wind speed standard deviation and the width of the upper boundary and the lower boundary are set to be 0; if the window wind speed standard deviation and the width of the upper boundary and the lower boundary are larger than the corresponding threshold, and the current iteration in the window has data and circularly traverses all the reserved data in the window;
step S0035, traversing and deleting until the wind speed standard deviation of the current window and the widths of the upper boundary and the lower boundary accord with set parameters:
3) if the upper boundary width is larger than the lower boundary width and the absolute value of the difference between the two values is larger than 1, deleting the data;
4) deleting if the lower boundary width is greater than the upper boundary width and the absolute value of the difference between the two values is greater than 1;
5) if the absolute value of the difference between the widths of the upper boundary and the lower boundary is less than 1, and if the current wind speed data point is greater than the upper boundary value or the current wind speed data point is less than the lower boundary value, deleting the data point;
6) updating all description values of the current window and the tracking point of the current window, wherein the tracking point is calculated as the current window Wn+1Keeping the wind speed median of the data; the update data packet M012b is obtained by data filtering until all windows are updated circularly.
In this embodiment, the method for performing data correction on the updated power packet M02 in step S005 is performed as follows:
7) judging the relation between the power data in the power data packet M02 and the power corresponding to the rated power initial wind speed;
8) judging whether the power data in the power data packet M02 is in a range of a rated power plus or minus a maximum power floating value;
if the 7) and the 8) are both satisfied, reserving the power data, otherwise, deleting the corresponding power data which do not satisfy the 7) and the 8) at the same time, and obtaining a final filtered power data set M03. As shown in fig. 3 and 4, power scatter plots before and after filtering for blower No. 1-8 months in a certain zone H1-01F. Of course, the power scatter diagrams collected in different fan models and within a specified period range are different, but a smooth curve with a certain width can be obtained by the method, so that the aim of filtering discrete points is fulfilled.
Example 4:
in this embodiment, the cross-window tracking point updating method is preferably implemented by using the following scheme:
such as the current window Wn+1Data size greater than 2, previous window WnIf the absolute value of the difference between the final tracking point and the current median is smaller than the lower bound of the allowable value, the tracking point is updated to be the current window Wn+1A median; as in the previous window WnIf the absolute value of the difference between the final tracking point and the current median is greater than the lower bound of the allowable value and the absolute value of the difference between the final tracking point and the current median is less than the upper bound of the allowable value, updating the tracking point to be the mean value of the two values; as in the previous window WnThe absolute value of the difference value between the final tracking point and the current median is larger than the upper bound of the allowable value, and the tracking point is updated to be the previous window WnFinal tracking point plus current window Wn+1Tracking point offset values;
such as the current window Wn+1The data volume is less than or equal to 2, and the tracking point is updated to be the previous window WnAdding the final tracking point to the window tracking point offset value;
such as the current window Wn+1For the first window W with data1Current window Wn+1Tracking point and previous window WnThe tracking point is synchronously set to the window Wn+1Median wind speed data.
Example 5:
a system for wind power curve scatter filtering, comprising a SCADA system: the SCADA data set M01 data packet is used for collecting a SCADA data set M01 data packet of a target fan, and comprises wind speed upper boundary position data, wind speed lower boundary position data, power window size data, wind speed standard difference threshold value data, upper and lower boundary width threshold value data, front and rear window tracking point difference value allowable upper and lower boundary data, window tracking point offset value data, rated power initial wind speed data and rated power floating value data;
a screening device: dividing the SCADA data set M01 data packets into different target data sets by setting a power critical value h;
power curve tracker: the method is used for calculating and acquiring smooth power curve data from different target data sets screened by the screener according to any one of the wind power curve scatter filtering methods in the invention.
The invention is not limited to the above alternative embodiments, and any other various forms of products can be obtained by anyone in the light of the present invention, but any changes in shape or structure thereof, which fall within the scope of the present invention as defined in the claims, fall within the scope of the present invention.
Claims (6)
1. A method for filtering the dispersion point of a wind power curve is characterized by comprising the following steps: the method comprises the following steps:
s001, acquiring a SCADA data set M01 data packet of the target fan by adopting an SCADA system;
step S002, inputting the M01 data packet into a data filter, setting a power threshold value h to be 0.1 by the data filter, comparing and filtering all power data in the M01 data packet, and filtering to obtain a data packet M011 with a power less than or equal to 0.1 and a data packet M012 with a power greater than 0.1;
step S003, importing the data packet M011 and the data packet M012 in step S002 into the first filtering unit G1 and the second filtering unit G2 of the power curve tracker with initialized parameters for data cleaning and filtering, so as to obtain an update data packet M011a and an update data packet M012b, respectively;
step S004, merging the update packet M011a and the update packet M012b obtained in step S003 to obtain an updated power packet M02;
step S005, performing data correction on the updated power data packet M02 acquired in the step S004, and eliminating distortion data to acquire a filtered power data set M03;
the process of the first filter unit G1 obtaining the update packet M011a through the packet M011 in step S003 includes the following steps:
step S0031, determining a power curve starting point: selecting the average value of all wind speed data with power within the interval of 1-10;
step S0032, invalid data is filtered: selecting and deleting all data with power less than 5 and wind speed greater than the starting point of the power curve to obtain an updated data packet M011 a;
the process of the second filter unit G2 obtaining the update data packet M012b by the data packet M012 in step S003 includes the following steps:
step S0033, performing windowing on all data in the data packet M012 with a fixed width to obtain a window W1、W2、W3.......WnSequentially extracting data in each divided window in a circulating mode;
step S0034, judging the current window WnWhether data exists therein; if no data is present, loop to the next window Wn+1The above judgment is carried out; if there is data, the window W is calculatedn+1A description value of (a); the descriptive values comprise upper and lower boundary values, wind speed standard deviation and upper and lower boundary widths of wind speed data;
the upper and lower boundary values of the wind speed data are as follows: calculating by taking the upper and lower boundary positions of the wind speed data as quantiles, and expressing by upper and lower in the following;
wind speed standard deviation: denoted as ws _ sd below; width of upper and lower boundaries: calculating the absolute value of the difference value of the upper maximum value and the lower maximum value minus the tracking point, and expressing the absolute value as upper _ width and lower _ width below; calculation window Wn+1Comparing the median of all the reserved data in the window with the final tracking point of the previous window Wn, and determining the tracking point of the current window, wherein the tracking point is expressed by tracker; the updating of the tracking point is realized by adopting a cross-window tracking point updating method;
the descriptive value calculation formula is as follows:
1) standard deviation of wind speed
Where μ is the arithmetic mean of the wind speed data and N represents the dataMeasuring, wsiRepresenting the ith wind speed data value, i ∈ {1,2, … …, N };
2) width of upper and lower boundaries
upper_width＝|upper-tracker|
lower_width＝|lower-tracker|
If no data exists, the wind speed standard deviation and the width of the upper boundary and the lower boundary are set to be 0; if the window wind speed standard deviation and the width of the upper boundary and the lower boundary are larger than the corresponding threshold, and the current iteration in the window has data and circularly traverses all the reserved data in the window;
step S0035, traversing and deleting until the wind speed standard deviation of the current window and the widths of the upper boundary and the lower boundary accord with set parameters:
3) if the upper boundary width is larger than the lower boundary width and the absolute value of the difference between the two values is larger than 1, deleting the data;
4) deleting if the lower boundary width is greater than the upper boundary width and the absolute value of the difference between the two values is greater than 1;
5) if the absolute value of the difference between the widths of the upper boundary and the lower boundary is less than 1, and if the current wind speed data point is greater than the upper boundary value or the current wind speed data point is less than the lower boundary value, deleting the data point;
6) updating all description values of the current window and the tracking point of the current window, wherein the tracking point is calculated as the current window Wn+1Keeping the wind speed median of the data; the update data packet M012b is obtained by data filtering until all windows are updated circularly.
2. The method of claim 1, wherein the wind power curve scatter filtering comprises: the data type in the SCADA data set M01 data packet in step S001 corresponds to the initialization parameter type in the power curve tracker in step S003.
3. A method of wind power curve scatter filtering according to claim 2, characterized by: the initialization parameters comprise an upper boundary position value of the wind speed, a lower boundary position value of the wind speed, a size value of a power window, a standard deviation threshold value of the wind speed, a width threshold value of the upper boundary and the lower boundary, a difference tolerance upper boundary and lower boundary of a front window tracking point and a rear window tracking point, a window tracking point deviant value, a rated power initial wind speed value and a rated power floating value.
4. A method of wind power curve scatter filtering according to any one of claims 1-3, characterized by: the method for performing data correction on the updated power packet M02 in step S005 is performed as follows:
7) judging the relation between the power data in the power data packet M02 and the power corresponding to the rated power initial wind speed;
8) judging whether the power data in the power data packet M02 is in a range of a rated power plus or minus a maximum power floating value;
if the 7) and the 8) are both satisfied, reserving the power data, otherwise, deleting the corresponding power data which do not satisfy the 7) and the 8) at the same time, and obtaining a final filtered power data set M03.
5. The method of claim 1, wherein the wind power curve scatter filtering comprises: the cross-window tracking point updating method comprises the following steps:
such as the current window Wn+1Data size greater than 2, previous window WnIf the absolute value of the difference between the final tracking point and the current median is smaller than the lower bound of the allowable value, the tracking point is updated to be the current window Wn+1A median; as in the previous window WnIf the absolute value of the difference between the final tracking point and the current median is greater than the lower bound of the allowable value and the absolute value of the difference between the final tracking point and the current median is less than the upper bound of the allowable value, updating the tracking point to be the mean value of the two values; as in the previous window WnThe absolute value of the difference value between the final tracking point and the current median is larger than the upper bound of the allowable value, and the tracking point is updated to be the previous window WnFinal tracking point plus current window Wn+1Tracking point offset values;
such as the current window Wn+1The data volume is less than or equal to 2, and the tracking point is updated to be the previous window WnAdding the final tracking point to the window tracking point offset value;
such as the current window Wn+1For the first window W with data1Current window Wn+1TrackingDot and previous window WnThe tracking point is synchronously set to the window Wn+1Median wind speed data.
6. A wind power curve scatter filtering system is characterized in that:
the system comprises an SCADA system: the SCADA data set M01 data packet is used for collecting a SCADA data set M01 data packet of a target fan, and comprises wind speed upper boundary position data, wind speed lower boundary position data, power window size data, wind speed standard difference threshold value data, upper and lower boundary width threshold value data, front and rear window tracking point difference value allowable upper and lower boundary data, window tracking point offset value data, rated power initial wind speed data and rated power floating value data;
a screening device: dividing the SCADA data set M01 data packets into different target data sets by setting a power critical value h;
power curve tracker: the method for achieving the scattered point filtering of the wind power curve of the different target data sets after being screened by the screen is used for obtaining smooth power curve data through calculation according to the method for the scattered point filtering of the wind power curve of claim 5.
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