CN105046367A - Wind power budgeting method based on high-precision real-time database - Google Patents
Wind power budgeting method based on high-precision real-time database Download PDFInfo
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- CN105046367A CN105046367A CN201510460899.XA CN201510460899A CN105046367A CN 105046367 A CN105046367 A CN 105046367A CN 201510460899 A CN201510460899 A CN 201510460899A CN 105046367 A CN105046367 A CN 105046367A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The invention discloses a wind power budgeting method based on a high-precision real-time database. The budgeting method comprises the following steps of: by measurement of a watt-hour meter, obtaining day-degree actual power production; obtaining historical wind speed data; by using a wind speed power theoretical model provided by a manufacturer, using the historical wind speed data for calculating day-degree power; by using a trend of the day-degree power, calculating day-degree theoretical power generation; calculating day-degree generating rate; calculating day-degree average generating rate; by using a wind-measuring device of a meteorological department or a wind park, predicting a wind speed trend in a future time; according to the predicted wind speed, by combination with the wind speed power theoretical model provided by the manufacturer, calculating a day-degree predicting power trend; by using the day-degree predicting power trend, calculating day-degree predicting theoretical power generation; and calculating day-degree predicting power generation. The method is convenient and quick in data calculation, high in calculating precision and small in error, adapts to various fans, and meanwhile, has no requirements on geographical distribution of the fans.
Description
Technical field
Based on the wind power budget model algorithm of high precision real-time data base, belong to field of computer technology.
Background technology
Current wind generating is more prevailing, and in power field, proportion is expanding year by year.So, for how precisely to predict wind-power electricity generation situation, to reduce the impact to network load, more and more become distinct issues.The wind speed power prediction model of current existence, the basic data needed for computation model, precision is not high, causes the model precision calculating gained not high.Meanwhile, due to the difference of the weather conditions of each region, cause the error result calculated just larger.
Summary of the invention
Technical matters to be solved by this invention is the defect overcoming prior art, and provide a kind of wind power budget approach based on high precision real-time data base, computational accuracy is high, and error is little.
For solving the problems of the technologies described above, the invention provides a kind of wind power budget approach based on high precision real-time data base, it is characterized in that, comprise the following steps:
Step 1) calculate acquisition day degree actual power generation by electricity-measuring meter;
Step 2) obtain historical wind speed data;
Step 3) the wind speed Power Theory model that provided by producer, utilize historical wind speed data, calculate day degree power tendency;
Step 4) utilize day degree power tendency, by integral algorithm or power estimation algorithm, calculate day topology degree generated energy;
Step 5) according to day topology degree generated energy and day degree actual power generation, calculate day degree Generation Rate;
Step 6) according to day degree Generation Rate, calculate the average Generation Rate of day degree;
Step 7) survey wind devices by meteorological department or wind field self, dope the wind speed tendency situation of following a period of time;
Step 8) according to the wind speed predicted, in conjunction with the wind speed Power Theory model that producer provides, calculate day degree predicted power tendency;
Step 9) utilize day degree predicted power tendency, by integral algorithm or power estimation algorithm, calculate day degree prediction theory generated energy;
Step 10) according to day degree prediction theory generated energy and the average Generation Rate of day degree, calculate day degree prediction generated energy.
Historical wind speed data are obtained by high precision real-time data base.
Step 5) in, through type (1), calculates day degree Generation Rate:
Step 6) in, through type (3) calculates the average Generation Rate of day degree:
Step 10) in, through type (5) calculates day degree prediction generated energy:
The day degree prediction generated energy=day degree prediction theory generated energy average Generation Rate of * day degree (5).
Step 4) in, day the step of integral algorithm of topology degree generated energy be:
Adopt integral algorithm, accuracy requirement as required, calculates the electric quantity data of each Precision Time unit, and the electricity that all unit interval calculate is added up, and draws the generated energy of corresponding time period.
Step 4) in, day the computing formula of power estimation algorithm of topology degree generated energy be:
Day topology degree generated energy=day degree power * day degree time (2)
The performance number in each second in wanted computing time section is added up, the corresponding charge value calculated in timing statistics section.
The beneficial effect that the present invention reaches:
Wind power budget approach based on high precision real-time data base of the present invention, data convenience of calculation is quick, and computational accuracy is high, and error is little, adapts to all kinds blower fan, meanwhile, to the no requirement (NR) of blower fan Regional Distribution.
Accompanying drawing explanation
M-power corresponding diagram when Fig. 1 is;
Fig. 2 is wind speed-power corresponding diagram.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described.Following examples only for technical scheme of the present invention is clearly described, and can not limit the scope of the invention with this.
Wind power prediction model is many, main is all carry out data fitting around the wind speed of corresponding time, power data, form certain corresponding relation, in this algorithm, statistics wind speed and power corresponding data, this is more crucial, but this data statistics is got up, difficulty is larger, and the method precision calculated is not high yet.
Below, describe the brand-new computing method of the application in detail, the application is summarized as Generation Rate computing method.
1, Generation Rate
In these computing method, the computing formula of Generation Rate is defined as follows:
These data of actual power generation, obtain than being easier to, and for separate unit blower fan, every Fans has oneself metering ammeter, by metering ammeter, just can calculate day degree, monthly, annual actual power generation.
So, how to obtain the key that theoretical generated energy is problem.
2, generated energy calculates
Generated energy, except ammeter calculating correction values, can also be calculated by Power x Time integration: the time period of specifying, and carries out integral and calculating draw power data.Temporal power corresponding relation as shown in Figure 1.
X-axis is the time, and y-axis is power, to hatched area summation in figure, just can calculate electricity.
To dash area summation, two kinds of algorithms can be adopted:
1) integral algorithm
Adopt differentiate integral algorithm, be accurate to the power data of each second correspondence, if accuracy requirement is higher, can be as accurate as the performance number of each nanosecond or millisecond correspondence, by differentiate integration, calculate each second (or nanosecond, millisecond) electric quantity data, the electricity that all unit interval calculate is added up, just can draw the charge value of corresponding time period, this algorithm, electric quantity data is more accurate.
2) power estimation algorithm
Electricity estimation algorithm, the performance number in each second, can simply can be regarded as the electricity in this second.
Generated energy computing formula: generated energy=power * time (2)
Because the time is 1 second, so power just as electricity, can will notice that electric quantity unit converts here, then the performance number in each second in wanted computing time section is added up, just can correspondingly calculate, the charge value in timing statistics section.This algorithm, relative simple and fast, more convenient to estimation.
3, theoretical generated energy
The computing method of generated energy, before by analysis, calculate theoretical generated energy below.
The calculating of theoretical generated energy needs the condition of three aspects to form: 1) the theoretical wind speed power module of blower fan; 2) high precision (second, nanosecond or grade) air speed data in real time; 3) above-mentioned electricity computing method are adopted.
1) the theoretical wind speed power module of blower fan
Every Fans, when dispatching from the factory, is all furnished with the blower fan of this kind of model, under desired operation, and corresponding computing formula, as shown in Figure 2.In figure, be wind speed-power corresponding relation figure that blower fan producer provides, and corresponding computing formula.X-axis is wind speed, and y-axis is power
The effective wind speed of general blower fan is 3m/s to 25m/s, dissimilar unit, can be slightly different.
2) acquisition level second or millimetre-sized real-time air speed data
Outside the wind speed that You Liao producer provides-power relation model, also need to obtain high-precision air speed data, generally can be obtained by high-precision real-time data base, current existing best high precision real-time data base comprises PI database, the air speed value of second level corresponding time can be obtained from the inside, after having had air speed value, just can be calculated by the theoretical wind speed power module of blower fan and put the theoretical power data of this wind speed sometime, the time m-power diagram in corresponding timing statistics section will be formed, as shown in Figure 1.
3) above-mentioned electricity computing method are adopted.
After formation time-power diagram, just can adopt the account form mentioned in electricity computing method, be no matter integral algorithm or power estimation algorithm can, key sees the degree of accuracy to data demand.Calculate corresponding electric quantity data, claim the electric quantity data now calculated to be called: theoretical generated energy, so far, obtains theoretical generated energy data.
4, the average Generation Rate of day degree
Had theoretical generated energy and actual power generation, through type (1), just can calculate Generation Rate, in order to improve the availability of Generation Rate, needs to average algorithm to the Generation Rate of continuous 5 years or 10 year same period:
By this algorithm, the average Generation Rate numerical value of the day degree that just can calculate in a year any a day.
5, electricity is predicted
Can find out from formula (1),
Actual power generation=theoretical generated energy * Generation Rate (4)
For prediction electricity, be exactly:
Prediction generated energy=average the Generation Rate of prediction theory generated energy * day degree (5)
Generation Rate data, can pass through historical data, calculate the average Generation Rate of day degree.
Wind speed-power module that prediction theory generated energy can be passed through prediction of wind speed (these data can be installed voluntarily by meteorological department or wind field forecasting wind speed device obtain) and blower fan producer and provide is calculated by integral algorithm or power estimation algorithm, there are the average Generation Rate of day degree and prediction theory generated energy, then general formula (5), just can dope day degree generated energy.
Therefore, the wind power budget approach based on high precision real-time data base of the present invention can be adopted, a computation model is set up by software programming, realize the automatic Prediction to day degree generated energy data, data convenience of calculation is quick, and computational accuracy is high, error is little, adapt to all kinds blower fan, meanwhile, to the no requirement (NR) of blower fan Regional Distribution.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite not departing from the technology of the present invention principle; can also make some improvement and distortion, these improve and distortion also should be considered as protection scope of the present invention.
Claims (7)
1., based on a wind power budget approach for high precision real-time data base, it is characterized in that, comprise the following steps:
Step 1) calculate acquisition day degree actual power generation by electricity-measuring meter;
Step 2) obtain historical wind speed data;
Step 3) the wind speed Power Theory model that provided by producer, utilize historical wind speed data, calculate day degree power tendency;
Step 4) utilize day degree power tendency, by integral algorithm or power estimation algorithm, calculate day topology degree generated energy;
Step 5) according to day topology degree generated energy and day degree actual power generation, calculate day degree Generation Rate;
Step 6) according to day degree Generation Rate, calculate the average Generation Rate of day degree;
Step 7) survey wind devices by meteorological department or wind field self, dope the wind speed tendency situation of following a period of time;
Step 8) according to the wind speed predicted, in conjunction with the wind speed Power Theory model that producer provides, calculate day degree predicted power tendency;
Step 9) utilize day degree predicted power tendency, by integral algorithm or power estimation algorithm, calculate day degree prediction theory generated energy;
Step 10) according to day degree prediction theory generated energy and the average Generation Rate of day degree, calculate day degree prediction generated energy.
2. the wind power budget approach based on high precision real-time data base according to claim 1, is characterized in that, obtains historical wind speed data by high precision real-time data base.
3. the wind power budget approach based on high precision real-time data base according to claim 1, is characterized in that, step 5) in, through type (1), calculates day degree Generation Rate:
4. the wind power budget approach based on high precision real-time data base according to claim 1, is characterized in that, step 6) in, through type (3) calculates the average Generation Rate of day degree:
5. the wind power budget approach based on high precision real-time data base according to claim 1, is characterized in that, step 10) in, through type (5) calculates day degree prediction generated energy:
The day degree prediction generated energy=day degree prediction theory generated energy average Generation Rate of * day degree (5).
6. the wind power budget approach based on high precision real-time data base according to claim 1, is characterized in that, step 4) in, day the step of integral algorithm of topology degree generated energy be:
Adopt integral algorithm, accuracy requirement as required, calculates the electric quantity data of each Precision Time unit, and the electricity that all unit interval calculate is added up, and draws the generated energy of corresponding time period.
7. the wind power budget approach based on high precision real-time data base according to claim 1, is characterized in that, step 4) in, day the computing formula of power estimation algorithm of topology degree generated energy be:
Day topology degree generated energy=day degree power * day degree time (2)
The performance number in each second in wanted computing time section is added up, the corresponding charge value calculated in timing statistics section.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106022543A (en) * | 2016-06-28 | 2016-10-12 | 华电国际宁夏新能源发电有限公司 | Wind power and electric quantity diagnosing system |
| CN115408445A (en) * | 2022-09-01 | 2022-11-29 | 中国长江电力股份有限公司 | Method for calculating and visually processing daily electric quantity data of cascade power station in real time |
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| US20130060472A1 (en) * | 2011-09-02 | 2013-03-07 | Onsemble, Llc | Systems, methods and apparatus for indexing and predicting wind power output from virtual wind farms |
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| CN106022543A (en) * | 2016-06-28 | 2016-10-12 | 华电国际宁夏新能源发电有限公司 | Wind power and electric quantity diagnosing system |
| CN115408445A (en) * | 2022-09-01 | 2022-11-29 | 中国长江电力股份有限公司 | Method for calculating and visually processing daily electric quantity data of cascade power station in real time |
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