CN105205564A - Wind power plant wind curtailment electric quantity statistical system and method based on anemometer tower neural network - Google Patents
Wind power plant wind curtailment electric quantity statistical system and method based on anemometer tower neural network Download PDFInfo
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- CN105205564A CN105205564A CN201510631893.4A CN201510631893A CN105205564A CN 105205564 A CN105205564 A CN 105205564A CN 201510631893 A CN201510631893 A CN 201510631893A CN 105205564 A CN105205564 A CN 105205564A
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Abstract
The invention discloses a wind power plant wind curtailment electric quantity statistical system and method based on an anemometer tower neural network and belongs to the technical field of wind power plant wind curtailment electric quantity statistics. The wind curtailment electric quantity statistical method is provided based on the fact that the neural network is adopted for wind power prediction and is simple and practical. Wind speed and wind direction data at different heights can be acquired by means of an anemometer tower to serve as a data source, and a wind curtailment electric quantity statistical module can be used for singular point calibration and correction on data which are acquired in real time; besides, due to the fact that input data and output data of different fans are different more or less, an independent neural network model is established for each fan so as to improve the calculation precision of expected output of a wind power plant.
Description
Technical field
The invention belongs to wind energy turbine set and abandon wind-powered electricity generation amount statistical technique field, be specifically related to a kind of wind energy turbine set based on anemometer tower neural network and abandon wind-powered electricity generation volume statistic system and method.
Background technology
Along with wind power through-transmission technique is more and more ripe, country more and more payes attention to wind-resources development, and large-scale wind power field is grid-connected gradually; Up to now, wind-powered electricity generation has become the trend of China's generation of electricity by new energy technology; But put into operation due to the extensive unordered fast of wind-powered electricity generation, power grid construction is delayed, the instability of peak load regulation network scarce capacity, wind power output, and the restriction of fast tunable joint power supply capacity in electrical network, causing electrical network to receiving wind-powered electricity generation limited in one's ability, causing abandoning landscape condition more and more serious;
Due to instability and the randomness of wind-powered electricity generation, cause and the increase of wind-powered electricity generation quantitative statistics difficulty is abandoned to wind-powered electricity generation, make to abandon wind-powered electricity generation amount and there is offset issue, the accuracy of abandoning wind-powered electricity generation amount statistics will produce a very large impact following wind-powered electricity generation research and development, be unfavorable for the optimum use of wind power resources;
Abandoning wind-powered electricity generation quantitative statistics is the basis carrying out wind-powered electricity generation and Electric Power Network Planning, is also to formulate the important evidence that wind-powered electricity generation abandons wind principle and specification; At present, domestic wind-powered electricity generation quantitative statistics method of abandoning mainly comprises four kinds: template processing machine method, prediction curve method, Plan Curve method, powertrace method; The most frequently used method is template processing machine method, but the shortcoming of this method is that wind energy turbine set template processing machine is not easily chosen, and by the impact of wind energy turbine set geographic position and physical features distribution, simultaneously the overall Wind turbines related coefficient of exerting oneself with template processing machine of exerting oneself not easily is determined; Prediction curve method, it is simple, convenient to implement, but requires that the precision of wind power prediction is higher, and the power error that prediction curve method produces mainly affects larger by precision of prediction; Plan Curve method, implement the convenient information not needing wind energy turbine set to provide extra, cost is low, but it is higher to wind power prediction accuracy requirement, generation schedule requirement a few days ago formulated to staff planners higher, affect by wind power prediction precision and the rationality of planning a few days ago, it is large to abandon wind-powered electricity generation amount statistics deviation; Powertrace method, it is higher that statistics abandons wind-powered electricity generation amount accuracy rate, but implement more complicated, and need wind field to provide wind speed and power data, calculated amount is larger;
Therefore, seek a kind of effective wind-powered electricity generation amount statistical method of abandoning and become the important topic that contemporary power system power supply company and wind-powered electricity generation enterprise carry out statistical work.
Summary of the invention
For the deficiencies in the prior art, the present invention proposes a kind of wind energy turbine set based on anemometer tower neural network and abandons wind-powered electricity generation volume statistic system and method, solve existing wind energy turbine set abandon the difficult problem of wind-powered electricity generation amount statistics to reach, and realize electrical network and more reasonably dispatch the object more stably run with system.
Wind energy turbine set based on anemometer tower neural network abandons a wind-powered electricity generation volume statistic system, and this system comprises real-time data acquisition device and abandons wind-powered electricity generation amount statistical server;
Described real-time data acquisition device comprises anemometer tower and blower fan data monitoring device, wherein,
Anemometer tower: for gathering the weather information of wind energy turbine set differing heights, and gathered weather information is sent to abandons in wind-powered electricity generation amount statistical server;
Described blower fan data monitoring device: for gather each blower fan in wind energy turbine set actual go out force value, and by each blower fan actual go out force value be sent to and abandon in wind-powered electricity generation amount statistical server;
Described wind-powered electricity generation amount of abandoning statistical server: for using the input of the history weather information of wind energy turbine set differing heights as neural network, the history of each blower fan of correspondence is gone out the output of force value as neural network, what adopt neural network algorithm to build each blower fan should send power neural network model, again according to the Practical Meteorological Requirements information of the wind energy turbine set differing heights of statistics moment collection, what obtain each blower fan should send force value, according to obtain should send force value with actual go out the difference of force value, acquisition wind energy turbine set abandon wind-powered electricity generation amount.
Described weather information comprises wind speed and direction.
Described wind-powered electricity generation amount of abandoning statistical server comprises abandons wind-powered electricity generation amount statistical module and staqtistical data base.
Adopt and abandon based on the wind energy turbine set of anemometer tower neural network the statistical method that wind-powered electricity generation volume statistic system carries out, comprise the following steps:
Step 1, adopt anemometer tower to gather the weather information of wind energy turbine set differing heights, and gathered weather information is sent to abandons in wind-powered electricity generation amount statistical server, be saved to staqtistical data base as history weather information; Adopt blower fan data monitoring device gather each blower fan in wind energy turbine set actual go out force value, and by each blower fan actual go out force value be sent to and abandon in wind-powered electricity generation amount statistical server, be saved to staqtistical data base and go out force value as history;
Step 2, the history weather data extracting differing heights from staqtistical data base and corresponding blower fan history go out force value, are normalized;
Step 3, using the input of the history weather information of the wind energy turbine set differing heights after normalized as neural network, the history of each blower fan of the correspondence after normalized is gone out the output of force value as neural network, what adopt neural network algorithm to build each blower fan should send power neural network model;
Step 4, according to the Practical Meteorological Requirements information of wind energy turbine set differing heights that the statistics moment gathers, what obtain each blower fan should send force value;
Step 5, according to obtain should send force value and actual go out the difference of force value, that above-mentioned difference and time are carried out integral operation obtains wind energy turbine set abandons wind-powered electricity generation amount.
Advantage of the present invention:
The present invention proposes a kind of wind energy turbine set based on anemometer tower neural network and abandons wind-powered electricity generation volume statistic system and method, carry out on the basis of wind power prediction in use neural network, propose and abandon wind-powered electricity generation amount statistical method, and method is simple, practical, the wind speed and direction data at differing heights place can be obtained as data source by anemometer tower, abandon wind-powered electricity generation amount statistical module simultaneously and can carry out singular point demarcation and correction to the data of real-time data acquisition, in addition, because different blower fans input data and output data all have either large or small difference, so set up independently neural network model to every Fans, the computational accuracy that wind energy turbine set should send power can be improved.
Accompanying drawing explanation
Fig. 1 is that the wind energy turbine set based on anemometer tower neural network of an embodiment of the present invention abandons wind-powered electricity generation volume statistic system structured flowchart;
Fig. 2 is that the wind energy turbine set based on anemometer tower neural network of an embodiment of the present invention abandons wind-powered electricity generation amount statistical method process flow diagram;
Fig. 3 is that the anemometer tower neural network of an embodiment of the present invention abandons wind-powered electricity generation amount statistic algorithm schematic diagram;
Fig. 4 be an embodiment of the present invention abandon based on anemometer tower neural network the Comparative result figure that wind-powered electricity generation amount statistical method and traditional template processing machine abandon wind-powered electricity generation amount statistical method, wherein, 1 represents that wind energy turbine set should send force curve, 2 represent the actual power curve of wind energy turbine set, 3 represent template processing machine method curve, and 4 represent anemometer tower neural network curve.
Embodiment
Below in conjunction with accompanying drawing, an embodiment of the present invention is described further.
As shown in Figure 1, in the embodiment of the present invention, for the large Beishan Mountain, the Dalian wind energy turbine set of a day, the wind energy turbine set based on anemometer tower neural network is abandoned wind-powered electricity generation volume statistic system and is comprised real-time data acquisition device and abandon wind-powered electricity generation amount statistical server; Described real-time data acquisition device comprises anemometer tower and blower fan data monitoring device, and described wind-powered electricity generation amount of abandoning statistical server comprises abandons wind-powered electricity generation amount statistical module and staqtistical data base;
As shown in Figure 1, in the embodiment of the present invention, in the embodiment of the present invention, the model of wind energy turbine set apoplexy machine is UP82-1500 and UP96-2000, above-mentioned blower fan self is with blower fan data monitoring device, an anemometer tower is provided with in wind energy turbine set, each blower fan carries blower fan data monitoring device, and the output terminal of anemometer tower, the output terminal of blower fan data monitoring device connect the input end abandoning wind-powered electricity generation amount statistical server, and the output terminal abandoning wind-powered electricity generation amount statistical server connects grid dispatching center;
In the embodiment of the present invention, gathered weather information for gathering the weather information (wind speed and direction) of wind energy turbine set differing heights, and is sent to and abandons in wind-powered electricity generation amount statistical server by anemometer tower; Blower fan data monitoring device for gather each blower fan in wind energy turbine set actual go out force value, and by each blower fan actual go out force value be sent to and abandon in wind-powered electricity generation amount statistical server; Abandon wind-powered electricity generation amount statistical server for using the input of the history weather information of wind energy turbine set differing heights as neural network, the history of each blower fan of correspondence is gone out the output of force value as neural network, what adopt neural network algorithm to build each blower fan should send power neural network model, again according to the Practical Meteorological Requirements information of the wind energy turbine set differing heights of statistics moment collection, what obtain each blower fan should send force value, according to obtain should send force value with actual go out the difference of force value, acquisition wind energy turbine set abandon wind-powered electricity generation amount;
In the embodiment of the present invention, the wind energy turbine set based on anemometer tower neural network abandons wind-powered electricity generation amount statistical method, and method flow diagram as shown in Figure 2, comprises the following steps:
Step 1, adopt anemometer tower to gather the weather information of wind energy turbine set differing heights, and gathered weather information is sent to abandons in wind-powered electricity generation amount statistical server, be saved to staqtistical data base as history weather information; Adopt blower fan data monitoring device gather each blower fan in wind energy turbine set actual go out force value, and by each blower fan actual go out force value be sent to and abandon in wind-powered electricity generation amount statistical server, be saved to staqtistical data base and go out force value as history;
In the embodiment of the present invention, the all blower fans of wind energy turbine set are numbered, number is respectively 1, and 2,3, wind energy turbine set differing heights weather data is obtained, in the embodiment of the present invention, using the data of large for Dalian Beishan Mountain wind energy turbine set as sample data by the anemometer tower of real-time data acquisition device, gather the wind speed and direction of 10m, 30m, 50m and axial fan hub At The Height, blower fan data monitoring system obtains that every Fans is actual exerts oneself;
In the embodiment of the present invention, abandon wind-powered electricity generation amount statistical module and realize carrying out singular point demarcation and correction to the data of real-time data acquisition; Singular point comprises lacking surveys data, ill data etc., by studying the history weather data of wind energy turbine set and going out the statistical law of force data, determine the base region of every data target and the wind speed power curve of blower fan, carry out judgement and the correction of singular point, and be stored in staqtistical data base;
Step 2, the history weather data extracting differing heights from staqtistical data base and corresponding blower fan history go out force value, are normalized;
In the embodiment of the present invention, extract from staqtistical data base 10m, 30m, 50m, the wind speed and direction data of axial fan hub At The Height and corresponding blower fan actual go out force data, be normalized, wherein wind direction data acquisition its sine value and cosine value replaces;
Step 3, using the input of the history weather information of the wind energy turbine set differing heights after normalized as neural network, the history of each blower fan of the correspondence after normalized is gone out the output of force value as neural network, what adopt neural network algorithm to build each blower fan should send power neural network model;
In the embodiment of the present invention, as shown in Figure 3, matlab programming language is adopted to carry out data analysis and model emulation, 12 groups of weather datas that normalized is crossed, the i.e. wind speed of 10m, 30m, 50m, axial fan hub At The Height, the wind direction sine value of 10m, 30m, 50m, axial fan hub At The Height, the wind direction cosine value of 10m, 30m, 50m, axial fan hub At The Height, as input, blower fan is actual exerts oneself as output, and what utilize neural network algorithm to set up separate unit blower fan should send power neural network model;
Step 4, according to the Practical Meteorological Requirements information of wind energy turbine set differing heights that the statistics moment gathers, what obtain each blower fan should send force value;
Step 5, according to obtain should send force value with actual go out the difference of force value, that above-mentioned difference and time are carried out integral operation obtains wind energy turbine set abandons wind-powered electricity generation amount, then the air quantity of abandoning of acquisition is sent to grid dispatching center, points out staff.
In the embodiment of the present invention, as shown in Figure 4, by the separate unit blower fan obtained should send Li Jia and rear with wind energy turbine set actual go out masterpiece difference obtain adding up the moment and abandon wind-powered electricity generation amount, concrete formula is as follows:
Wherein, W
1represent t
1~ t
2time period wind field abandons wind-powered electricity generation amount, W
2represent t
3~ t
4time period wind field abandons wind-powered electricity generation amount, W
3represent t
5-t
6time period wind field abandons wind-powered electricity generation amount, P
1t () represents t
1~ t
2time period wind field is abandoned wind and is gone out force function, P
2t () represents t
3~ t
4time period wind field is abandoned wind and is gone out force function, P
3t () represents t
5-t
6time period wind field is abandoned wind and is gone out force function;
In the embodiment of the present invention, model training parameter in whole step, abandon the blower fan produced in wind-powered electricity generation amount computation process and should send force data and be all stored in staqtistical data base;
Fig. 4 is that the large Beishan Mountain, the Dalian wind field of a day should send power and actual curve map of exerting oneself, clear for showing, tightly choose 96, this wind field sky sample point to show, can find out and should send power and actual curve map of exerting oneself, and the power curve that the power curve that anemometer tower neural network of the present invention obtains obtains with traditional template processing machine method is compared, should send force curve closer to wind energy turbine set, therefore the anemometer tower neural network statistics of the present invention effectiveness comparison of abandoning wind-powered electricity generation amount is good;
The advantage that method proposed by the invention is compared with the prior art, as shown in table 1:
The impact of table 1 anemometer tower neural network and template processing machine method is compared
Provided by the present invention is that a kind of wind energy turbine set based on anemometer tower neural network abandons wind-powered electricity generation volume statistic system and method, good degree solves China's wind energy turbine set and abandons the difficult problem of wind-powered electricity generation amount statistics, for peak load regulation network, adjust trend and scheduling to provide guarantee and foundation.
Claims (4)
1. the wind energy turbine set based on anemometer tower neural network abandons a wind-powered electricity generation volume statistic system, it is characterized in that, this system comprises real-time data acquisition device and abandons wind-powered electricity generation amount statistical server;
Described real-time data acquisition device comprises anemometer tower and blower fan data monitoring device, wherein,
Anemometer tower: for gathering the weather information of wind energy turbine set differing heights, and gathered weather information is sent to abandons in wind-powered electricity generation amount statistical server;
Described blower fan data monitoring device: for gather each blower fan in wind energy turbine set actual go out force value, and by each blower fan actual go out force value be sent to and abandon in wind-powered electricity generation amount statistical server;
Described wind-powered electricity generation amount of abandoning statistical server: for using the input of the history weather information of wind energy turbine set differing heights as neural network, the history of each blower fan of correspondence is gone out the output of force value as neural network, what adopt neural network algorithm to build each blower fan should send power neural network model, again according to the Practical Meteorological Requirements information of the wind energy turbine set differing heights of statistics moment collection, what obtain each blower fan should send force value, according to obtain should send force value with actual go out the difference of force value, acquisition wind energy turbine set abandon wind-powered electricity generation amount.
2. the wind energy turbine set based on anemometer tower neural network according to claim 1 abandons wind-powered electricity generation volume statistic system, it is characterized in that, described weather information comprises wind speed and direction.
3. the wind energy turbine set based on anemometer tower neural network according to claim 1 abandons wind-powered electricity generation volume statistic system, it is characterized in that, described wind-powered electricity generation amount of abandoning statistical server comprises abandons wind-powered electricity generation amount statistical module and staqtistical data base.
4. the employing wind energy turbine set based on anemometer tower neural network according to claim 1 abandons the statistical method that wind-powered electricity generation volume statistic system carries out, and it is characterized in that, comprises the following steps:
Step 1, adopt anemometer tower to gather the weather information of wind energy turbine set differing heights, and gathered weather information is sent to abandons in wind-powered electricity generation amount statistical server, be saved to staqtistical data base as history weather information; Adopt blower fan data monitoring device gather each blower fan in wind energy turbine set actual go out force value, and by each blower fan actual go out force value be sent to and abandon in wind-powered electricity generation amount statistical server, be saved to staqtistical data base and go out force value as history;
Step 2, the history weather data extracting differing heights from staqtistical data base and corresponding blower fan history go out force value, are normalized;
Step 3, using the input of the history weather information of the wind energy turbine set differing heights after normalized as neural network, the history of each blower fan of the correspondence after normalized is gone out the output of force value as neural network, what adopt neural network algorithm to build each blower fan should send power neural network model;
Step 4, according to the Practical Meteorological Requirements information of wind energy turbine set differing heights that the statistics moment gathers, what obtain each blower fan should send force value;
Step 5, according to obtain should send force value and actual go out the difference of force value, that above-mentioned difference and time are carried out integral operation obtains wind energy turbine set abandons wind-powered electricity generation amount.
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| C06 | Publication | ||
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| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20151230 |
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| RJ01 | Rejection of invention patent application after publication |