CN106570790A - Wind farm output power data restoration method considering segmental characteristics of wind speed data - Google Patents
Wind farm output power data restoration method considering segmental characteristics of wind speed data Download PDFInfo
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Abstract
The invention discloses a wind farm output power data restoration method considering the segmental characteristics of wind speed data. The method includes the following steps that: S1, repeated, missing and unreasonable abnormal data are screened out from obtained data, and the abnormal data are divided into continuous abnormal type data and local abnormal type data according to the lengths of continuous time sequences corresponding to the abnormal data; S2, as for the local abnormal data, an interpolation method is adopted to obtain restored wind farm output power data; S3, as for the continuous abnormal type data, whether the continuous abnormal type data contain segmentation points is judged through using normal data before and after the abnormal data and based on a maximum posterior probability, and an ARMA model is obtained through the normal data or a mode recognition method and based on the wind speed characteristics of each segment, and restored wind speed is generated through using the ARMA model based on the wind speed characteristics, and therefore, restored wind farm output power data can be obtained; and S4, the validity of the restored data is verified, and a restoration report is outputted. With the method adopted, the decision-making accuracy of auxiliary services in a power grid can be improved, and unnecessary system backup can be reduced.
Description
Technical field
The invention belongs to new energy power station goes out force data repairs field, more particularly to one kind is counted and air speed data is segmented characterizing
Output of wind electric field data recovery method.
Background technology
Because the wind energy amount of accumulateing is huge, widely distributed, it is cleaning, pollution-free, current wind-power electricity generation is in the world
To fast development.But the randomness, undulatory property and intermittent feature due to wind energy, wind-powered electricity generation is accessed on a large scale will be to power system
Produce tremendous influence, it is therefore necessary to the output of wind electric field historical data of access system is analyzed, extraction wind-powered electricity generation is carried out
Field power producing characteristics, the scheduling for operation of power networks provides important decision-making foundation.
However, wind energy turbine set general position is more remote, communication condition is poor, and its detection data is logical in real time with data center
Letter is not sufficiently stable, and the problems such as shortage of data, repetition, mistake often occurs in Jing, has a strong impact on the quality of output of wind electric field data, limits
The application of the data.Therefore, for the reparation of the abnormal datas such as these disappearance, repetition, mistakes, it appears particularly significant.
Existing data recovery technique, is the feature for going out data by extracting wind energy turbine set, then using interpolation or prediction mostly
Etc. mode, the correction value of abnormal data is obtained.This kind of data recovery technique, can preferably guarantee the system of output of wind electric field data
The concordance of meter characteristic, eliminates impact of the abnormal data item to output of wind electric field data characteristicses.
Although this method can largely eliminate interference of the abnormal data to output of wind electric field statistical property,
This kind of recovery technique often only can guarantee that the system that the data of reparation are consistent in a longer time section with normal data
Meter characteristic.Even if considering the difference on Various Seasonal or daytime and night wind-powered electricity generation statistical property in some recovery techniques, but still
Cannot effectively portray the characteristic that day with wind of gale force and little wind day etc. are determined by real-time weather information, and the day of these days with wind of gale force and little wind day
Number and distribution character are particularly significant for the planning and operation of power system, it is impossible to which repairing these characteristics will have a strong impact on anomaly ratio
The application of the larger output of wind electric field data of example.
Accordingly, it would be desirable to a kind of new output of wind electric field data recovery technique is avoiding the generation of drawbacks described above.
The content of the invention
For the deficiencies in the prior art, it is an object of the invention to provide the segmented characterizing wind energy turbine set of a kind of meter and air speed data
Exert oneself data recovery method, determination and abnormal data pair of the reparation with wind speed as core, by the waypoint to wind series
The determination of the statistics feature answered, realizes the output of wind electric field data recovery technique of meter and short-term wind speed statistical property difference,
So that in repair data, not only acting as " roguing ", moreover it is possible to introduce valuable effective information, for raising output of wind electric field number
There is very significant meaning according in Power System Planning and operating application.
The segmented characterizing output of wind electric field data recovery method of a kind of meter and air speed data, the restorative procedure includes following
Step：
S1, the screening from the data for obtaining repeats, lacks and irrational abnormal data, and according to corresponding to abnormal data
The length of continuous time series, is divided into continuous abnormal type and the class of local ectype two；
S2, for local anomaly type data, the output of wind electric field data for obtaining repairing using interpolation method；
S3, for continuous abnormal type data, based on maximum a posteriori probability, is judged using the normal data before and after abnormal data
Whether abnormal data contains waypoint, and the wind speed characteristics for being then based on per section are obtained by normal data or mode identification method, base
In the wind speed characteristics, the wind speed repaired is generated using arma modeling, further obtain the output of wind electric field data repaired；
S4, verifies the effectiveness of repair data, exports recovery report.
Preferably, the S1 is specially：
The data record that many datas of same time point correspondence are exerted oneself is searched, these data records are duplicate data；Search
Without the time point for going out force data in data window repair time, corresponding to these time points for missing data, screen wind energy turbine set
Go out continuous 4 time points data above identical data record in force data, go out data record of the force data more than start capacity
There is the data record exerted oneself with night, these data records are unreasonable data；If continuously no less than 5 time point correspondences
Data record is abnormal data, then the data record corresponding to these time points is continuous abnormal type data record, and remaining is different
Regular data is local anomaly type data record.
Preferably, the S2 is specially：
Note local anomaly type data amount check is N, takes data [N/2] item before local anomaly type data, afterwards data [N/2]
, the corresponding abscissa value of these data is denoted as 1,2 ..., [N/2], N+ [N/2], N+ [N/2]+1 ..., 2N；With above-mentioned N
Individual point fitting obtains N rank multinomial fitting functions；Digital simulation function in [N/2]+1, make by the value of [N/2]+2 ..., [N/2]+N
Go out force data for what is repaired.
Preferably, the S3 specifically includes following steps：
The normal data before and after this group of abnormal data is chosen, the normal data length before and after abnormal data is 1 day；It is based on
KS is checked, and judges whether abnormal data contains waypoint using the normal data before and after abnormal data；If in this group of abnormal data
Containing waypoint, sampling waypoint position, data are utilized respectively the affiliated normal data being segmented and obtain ARMA moulds before and after waypoint
Type, then obtains seasonal effect in time series wind speed corresponding to abnormal data using arma modeling；If not containing in this group of abnormal data point
Duan Dian, then directly obtain arma modeling using the normal data before and after abnormal data, then obtains abnormal number using arma modeling
According to corresponding seasonal effect in time series wind speed；Based on the power producing characteristics formula of blower fan, going out for all types of blower fans of wind energy turbine set is obtained by wind speed
Power sequence；The blower fan started shooting in wind energy turbine set sequence of exerting oneself is asked into each, the output of wind electric field data repaired are obtained.
Technical scheme has the advantages that：
The segmented characterizing output of wind electric field data recovery method of a kind of meter for providing of the invention and air speed data, the method can
To consider wind speed non-stationary property in a short time, by segmentation in a short time, the essence of output of wind electric field data reparation is improve
Degree.The present invention can more effectively correct the exception record in output of wind electric field data, improve the matter of output of wind electric field data
Amount；This is conducive to improving the level of decision-making of Electric Power Network Planning and operation, improves the decision accuracy of assistant service in electrical network, and reduction need not
The system reserve wanted, so as to improve the economy of power grid construction and operation.
Description of the drawings
Below by drawings and Examples, technical scheme is described in further detail.
Fig. 1 is the overall flow of the segmented characterizing output of wind electric field data recovery method of a kind of meter of the present invention and air speed data
Figure；
Fig. 2 is the AC event of the segmented characterizing output of wind electric field data recovery method of a kind of meter of the present invention and air speed data
Commutation voltage area schematic diagram during barrier.
Specific embodiment
In order to have a clear understanding of technical scheme, its detailed structure will be set forth in the description that follows.Obviously, originally
Simultaneously deficiency is limited to the specific details that those skilled in the art is familiar with for the concrete execution of inventive embodiments.The preferred reality of the present invention
Apply example to be described in detail as follows, except these for describing in detail implement exception, there can also be other embodiment.
The present invention is described in further details with reference to the accompanying drawings and examples.
With reference to Fig. 1, the entirety of the segmented characterizing output of wind electric field data recovery method of a kind of meter of the present invention and air speed data
Flow chart, the screening comprising abnormal data and classification, Weather information and photovoltaic are exerted oneself, and parameter attribute is extracted, abnormal data group is repaiied
The key step such as multiple, specially：
1) screening is obtained repeating in data, lacked and irrational abnormal data, and continuous according to corresponding to abnormal data
Seasonal effect in time series length, is divided into continuous abnormal type and the class of local ectype two；
2) for local anomaly type data, the output of wind electric field data for obtaining repairing using interpolation method；
3) for continuous abnormal type data, based on maximum a posteriori probability, judged using the normal data before and after abnormal data
Whether abnormal data contains waypoint, and the wind speed characteristics for being then based on per section are obtained by normal data or mode identification method, base
In the wind speed characteristics, the wind speed repaired is generated using arma modeling, further obtain the output of wind electric field data repaired；
4) effectiveness of repair data is verified, recovery report is exported.
The step 1) specifically comprise the steps of：The data record that many datas of same time point correspondence are exerted oneself is searched,
These data records are duplicate data；Without the time point for going out force data, these time point institutes in searching data window repair time
It is corresponding for missing data, continuous 4 time points data above identical data record in screening output of wind electric field data, exert oneself
Data are more than the data record of start capacity and night has the data record exerted oneself, and these data records are unreasonable data；
If being continuously abnormal data no less than 5 time point corresponding data records, the data record corresponding to these time points
For continuous abnormal type data record, remaining abnormal data is local anomaly type data record.
The step 2) specifically comprise the steps of：Note local anomaly type data amount check is N, take local anomaly type data it
Front data [N/2] item, data [N/2] item afterwards, the corresponding abscissa value of these data is denoted as 1,2 ..., [N/2], N+
[N/2], N+ [N/2]+1 ..., 2N；N rank multinomial fitting functions are obtained with the fitting of above-mentioned N number of point；Digital simulation function is in [N/
2]+1, [N/2]+2 ..., the value of [N/2]+N goes out force data as what is repaired.
The step 3) specifically comprise the steps of：The normal data before and after this group of abnormal data is chosen, before abnormal data
Normal data length afterwards is 1 day；Checked based on KS, whether abnormal data is judged using the normal data before and after abnormal data
Containing waypoint；If containing waypoint in this group of abnormal data, waypoint position of sampling, data are utilized respectively institute before and after waypoint
The normal data of category segmentation obtains arma modeling, then obtains seasonal effect in time series wind corresponding to abnormal data using arma modeling
Speed；If not containing waypoint in this group of abnormal data, directly using the normal data before and after abnormal data, ARMA moulds are obtained
Type, then obtains seasonal effect in time series wind speed corresponding to abnormal data using arma modeling；Based on the power producing characteristics formula of blower fan, by
Wind speed obtains the sequence of exerting oneself of all types of blower fans of wind energy turbine set；The blower fan started shooting in wind energy turbine set sequence of exerting oneself is asked into each, is repaired
Output of wind electric field data.
Wherein, the screening of abnormal data and classification：Abnormal data refers mainly to duplicate data, missing data and unreasonable data
Three kinds.As shown in Fig. 2 duplicate data refers to that a plurality of different photovoltaic plant corresponding to a certain moment is exerted oneself data record；Disappearance
Data refer to that corresponding incomplete photovoltaic plant of a certain moment is exerted oneself data record, and herein " incomplete " refers to out that force data is remembered
The physical significance that each data item having in record is not enough to by between is mutually derived；Unreasonable data are referred to and do not meet physics
Actual photovoltaic plant is exerted oneself data record.
According to above-mentioned classification, three class abnormal datas are searched and screened successively：Search many datas of same time point correspondence to go out
The data record of power, these data records are duplicate data；Without the time point for going out force data in searching data window repair time,
Corresponding to these time points for missing data, it is identical that screening photovoltaic plant goes out continuous 4 time point data above in force data
Data record, go out force data more than start capacity data record and night there is the data record exerted oneself, these data notes
Record as unreasonable data.
When unreasonable data are screened, it is contemplated that the precision and error of data recording.When start capacity is PonWhen, can be with
Photovoltaic plant is exerted oneself in [- α Pon,(1+α)Pon] it is interval when start shooting capacity without departing from photovoltaic plant, α can according to the quality of data
Select 0.03～0.1 numerical value.
It is abnormal data group by the adjacent abnormal data merger of time point, if the element number of abnormal data group is no less than
5, then the data record in this group of abnormal data group is continuous abnormal type data record, is otherwise local anomaly type data record.
The reparation of local anomaly type data：Local anomaly type data are different with the property of continuous abnormal type data, Qian Zhesuo
Corresponding time interval is shorter, and change of the meteorological data in this time interval be not obvious, now affects the master that photovoltaic is exerted oneself
Want the characteristic that factor is regional area, it is not necessary to repaired using meteorological data.So, the present invention is for local anomaly type number
Different restorative procedures is adopted according to continuous abnormal type data.
For local anomaly type data, directly repaired using polynomial interpolation.If local anomaly type data amount check
For N, data [N/2] item before local anomaly type data is taken, afterwards data [N/2] item, the corresponding abscissa value point of these data
It is not designated as 1,2 ..., [N/2], N+ [N/2], N+ [N/2]+1 ..., 2N；N rank multinomials fitting letter is obtained with the fitting of above-mentioned N number of point
Number；In [N/2]+1, the value of [N/2]+2 ..., [N/2]+N goes out force data to digital simulation function as what is repaired.
The waypoint checked based on KS is judged：The air speed data of adjacent one day is respectively taken before and after abnormal data group, is remembered respectively
For S1 and S2.Present invention KS checks (Kolmogorov-Smirnove test) and checks whether with distribution, step come S1 and S2
It is rapid as follows：
1) assume that S1 and S2 obeys same distribution；
2) cumulative probability of two groups of air speed datas is counted, F1, n (x) and F1 is designated as respectively, n (x), wherein n are S1's and S2
Data volume, cumulative probability is defined as follows：
Wherein, I [- ∞, x] is (Xi) indicator function, i.e. Xi<X is 1, is otherwise 0；
3) KS statistics is calculated：
Wherein, sup is supreum operation；
4) check whether that refusal is assumed
If meeting following formula, (under 0.05 level) refusal is it is assumed that S1 and S2 obeys different distributions, i.e., this group abnormal data
There is waypoint, otherwise there is no waypoint.
If there is section point in abnormal data, using being uniformly distributed, waypoint position of sampling.
The parameter estimation of 4.ARMA models
Arma modeling (autoregressive moving-average model) is the typical method of Wind speed model, the present invention using ARMA (3,3)
Model adopts 3 order mode types simulating wind speed, i.e. autoregression component and moving averages component.The time serieses shape of ARMA (3,3)
Formula is as follows：
Wherein c is constant, and ε t are white noise (obeying the stochastic variable for being desired for that 0, variance is the Gauss distribution of δ 2), φ i
With the parameter that θ i are model.
The present invention carries out parameter estimation using autoregression approximatioss.The normal wind series that note parameter estimation is used are X,
Length is n, needs the parameter estimated to be φ and θ, δ 2, c.
1) by the expectation estimation constant c of wind series
2) parameter phi of wind series corresponding A R (3) model is estimated
Note
Reach s (φ) minimizingφ least-squares estimations as.If note
Then s (φ) can be written as
Then the least-squares estimation of φ is
3) calculation of wind speed series error
Based on step 2) obtainThe residual error for seeking wind series is
4) calculate ARMA (3,3) in parameter phi, θ and δ 2
Note
Then reach Q (φ, θ) minimizingWithThe least-squares estimation of φ and θ as.
The least-squares estimation of δ 2 is
The segmented characterizing output of wind electric field data recovery method of a kind of meter for providing of the invention and air speed data, the method can
To consider wind speed non-stationary property in a short time, by segmentation in a short time, the essence of output of wind electric field data reparation is improve
Degree.The present invention can more effectively correct the exception record in output of wind electric field data, improve the matter of output of wind electric field data
Amount；This is conducive to improving the level of decision-making of Electric Power Network Planning and operation, improves the decision accuracy of assistant service in electrical network, and reduction need not
The system reserve wanted, so as to improve the economy of power grid construction and operation.
Finally it should be noted that：Above example is most only to illustrate technical scheme rather than a limitation
Pipe has been described in detail with reference to above-described embodiment to the present invention, and those of ordinary skill in the art still can be to this
Bright specific embodiment is modified or equivalent, these without departing from spirit and scope of the invention any modification or
Equivalent, is applying within pending claims.
Claims (4)
1. a kind of meter and the segmented characterizing output of wind electric field data recovery method of air speed data, it is characterised in that the reparation side
Method is comprised the following steps：
S1, the screening from the data for obtaining repeats, lacks and irrational abnormal data, and continuous according to corresponding to abnormal data
Seasonal effect in time series length, is divided into continuous abnormal type and the class of local ectype two；
S2, for local anomaly type data, the output of wind electric field data for obtaining repairing using interpolation method；
S3, for continuous abnormal type data, based on maximum a posteriori probability, judges abnormal using the normal data before and after abnormal data
Whether data contain waypoint, and the wind speed characteristics for being then based on per section are obtained by normal data or mode identification method, based on this
Wind speed characteristics, the wind speed repaired is generated using arma modeling, further obtains the output of wind electric field data repaired；
S4, verifies the effectiveness of repair data, exports recovery report.
2. a kind of meter and the segmented characterizing output of wind electric field data recovery method of air speed data according to claim 1, it is special
Levy and be, the S1 is specially：
The data record that many datas of same time point correspondence are exerted oneself is searched, these data records are duplicate data；Searching data
Without the time point for going out force data in repair time window, corresponding to these time points for missing data, screen output of wind electric field
Continuous 4 time points data above identical data record in data, go out data record of the force data more than start capacity and night
Between there is the data record exerted oneself, these data records be unreasonable data；If being continuously no less than 5 time point corresponding datas
Record be abnormal data, then the data record corresponding to these time points be continuous abnormal type data record, remaining abnormal number
According to for local anomaly type data record.
3. a kind of meter and the segmented characterizing output of wind electric field data recovery method of air speed data according to claim 1, it is special
Levy and be, the S2 is specially：
Note local anomaly type data amount check is N, takes data [N/2] item before local anomaly type data, afterwards data [N/2] item,
The corresponding abscissa value of these data is denoted as 1,2 ..., [N/2], N+ [N/2], N+ [N/2]+1 ..., 2N；With above-mentioned N number of
Point fitting obtains N rank multinomial fitting functions；Digital simulation function in [N/2]+1, [N/2]+2 ..., the value conduct of [N/2]+N
That what is repaired goes out force data.
4. a kind of meter and the segmented characterizing output of wind electric field data recovery method of air speed data according to claim 1, it is special
Levy and be, the S3 specifically includes following steps：
The normal data before and after this group of abnormal data is chosen, the normal data length before and after abnormal data is 1 day；Examined based on KS
Test, judge whether abnormal data contains waypoint using the normal data before and after abnormal data；If containing in this group of abnormal data
Waypoint, sampling waypoint position, data are utilized respectively the affiliated normal data being segmented and obtain arma modeling before and after waypoint, so
Obtain seasonal effect in time series wind speed corresponding to abnormal data using arma modeling afterwards；If not containing waypoint in this group of abnormal data,
Then directly arma modeling is obtained using the normal data before and after abnormal data, then obtain abnormal data institute using arma modeling
Correspondence seasonal effect in time series wind speed；Based on the power producing characteristics formula of blower fan, the sequence of exerting oneself of all types of blower fans of wind energy turbine set is obtained by wind speed
Row；The blower fan started shooting in wind energy turbine set sequence of exerting oneself is asked into each, the output of wind electric field data repaired are obtained.
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| CN110513252A (en) * | 2019-08-30 | 2019-11-29 | 湘电风能有限公司 | A kind of wind power plant SCADA system data abnormality alarming repair system and method |
| CN112067908A (en) * | 2020-08-20 | 2020-12-11 | 国网山东省电力公司电力科学研究院 | Fitting method and system for distortion electric field when transformer substation robot measures power frequency electric field |
| CN112067908B (en) * | 2020-08-20 | 2023-06-16 | 国网山东省电力公司电力科学研究院 | Method and system for fitting distorted electric field during power frequency electric field measurement by substation robot |
| CN118604717A (en) * | 2024-08-07 | 2024-09-06 | 保定市兆微软件科技有限公司 | Electric energy meter operation error monitoring method and system |
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