ULTRA-SHORT-TERM FORECASTING METHOD INCLUDING REAL-TIME MONITORING OF THE EFFECT OF UPPER AND LOWER COURSES

• US 20150039228A1
• Filed: 02/06/2013
• Published: 02/05/2015
• Est. Priority Date: 02/13/2012
• Status: Abandoned Application

• Current Assignee: Gansu Electric Power Corporation Wind Power Technology Center, Gansu Electric Power Corporation (State Grid Corporation of China), State Grid Corporation of China
• Original Assignee: Gansu Electric Power Corporation Wind Power Technology Center, Gansu Electric Power Corporation (State Grid Corporation of China), State Grid Corporation of China
• Inventors: Wang, Ningbo, Lu, Liang, Li, Zhaorong, Liu, Guangtu, Zhao, Long, Zhang, Tiejun, Wang, Dingmei, Ma, Ming, Ma, Yanhong, Li, Xiaoxia
• Application Number:
  US14/378,358
• Publication Number:
  US 20150039228A1
• Time in Patent Office: Days
• US Class Current: 702/3
• CPC Class Codes: G01W 1/00 Meteorology
• CPC Class Codes: G01W 1/02 Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed G01W1/10 takes precedence
• CPC Class Codes: G01W 1/10 Devices for predicting weather conditions computers per se G06; display devices G09
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

An ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses, comprising: obtaining ultra-short-term model forecast results through the model lattices based on the T639 global spectral model course library data source, the CALMET wind field diagnostic model and static data; establishing statistical equations on the effect of upper and lower courses between the corresponding reference index station and each target wind tower for obtaining the effect of upper and lower courses of the target wind towers based on the wind tower database of the target wind power base and combined with the wind direction and speed real-time monitoring data of the reference index stations in the upper and lower courses, forecasting the ultra-short-term wind speed changes of each target wind tower and correct combined with the ultra-short-term model forecast results to form forecasting of the ultra-short-term wind speed changes of the wind towers in the target wind power base; after repeated cycling, obtaining forecasting of the future ultra-short-term wind speed changes of the wind farms in the target wind power base at all altitudes in the target area. The forecasting method has high forecasting precision, good prediction accuracy and wide application range.

Claims

1. An ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses, comprising the following steps:

(a) obtaining ultra-short-term model forecast results through model lattices using the WRF-RUC (Weather Research and Forecasting-Rapid Update Cycle) system and the WRF3DVAR (Weather Research and Forecasting Three Dimensional Variational) variational assimilation technique based on T639 global spectral model course library data source, the CALMET (California Meteorological Model) wind field diagnostic model and static data;

wherein the step (a) specifically comprises the following substeps;

(a1) processing static data, downscaling the WRF mesoscale numerical forecasting model and generating model lattices based on the CALMET wind field diagnostic model;

(a2) reading the T639 global spectral model assimilated data, analyzing the meteorological field data in GRIB format in the T639 global spectral model assimilated data and interpolating into the corresponding model lattices based on the T639 global spectral model course library data source;

(a3) generating initial field and boundary conditions based on the meteorological field information on the model lattices;

establishing main model program for cyclic integral forecast computation through analysis using the WRF-RUC system and the WRF3DVAR variational assimilation technique; and

(a4) starting the main model program for cycle operation to achieve ultra-short-term forecasting and obtain ultra-short-term model forecast results;

(b) carrying out numerical analysis and statistics and establishing statistical equations on the effect of upper and lower courses between the corresponding reference index station and each target wind tower for computing the effect of upper and lower courses of the target wind towers based on the wind tower database of the target wind power base and combined with the wind direction and speed real-time monitoring data of the reference index stations in the upper and lower courses;

(c) forecasting the future ultra-short-term wind speed changes of each target wind tower based on the computed results on the effect of upper and lower courses of each target wind tower and correcting combined with the ultra-short-term model forecast results to form forecasting of the ultra-short-term wind speed changes of the wind towers in the target wind power base; and

(d) obtaining forecasting of the future ultra-short-term wind speed changes of wind farms in the target wind power base at all altitudes in the target area after repeated cycling of the above operations.

2. The ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses as claimed in claim 1 is characterized in that step (a) further comprises the following substep:

(a5) using plotting equipment to output model product, and outputting ultra-short-term model forecast results.

3. The ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses as claimed in claim 1 is characterized in that, in step (a2), the operation to interpolate the analytical results of the meteorological field data in GRIB (General Regularly-distributed Information in Binary) format in the T639 global spectral model assimilated data into the corresponding model lattices specifically comprises the following substeps:

(a21) interpolating the analytical results of the meteorological field data in GRIB format in the T639 global spectral model assimilated data into the corresponding model lattices horizontally;

(a22) interpolating the analytical results of the meteorological field data in GRIB format in the T639 global spectral model assimilated data into the corresponding model lattices vertically.

4. The ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses as claimed in claim 3 is characterized in that, before step (a21), it further comprises the following substep:

preparing for lattice formulation or carrying out model lattice formulation after obtaining the analytical results of the meteorological field data in GRIB format in the T639 global spectral model assimilated data.

5. The ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses as claimed in claim 1 is characterized in that, in step (a4), the cycle operation is running 4 cycles a day;

in the 4 cycles, the cycles starting from 12UTC and 0OUTC are cold start and the others are hot start.

6. The ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses as claimed in claim 1 is characterized in that step (b) specifically comprises the following substeps:

(b1) obtaining the live monitoring data of the reference wind towers based on the wind tower database of the target wind power base;

(b2) for each target wind tower, screening for reference index station with best correlation concerning the effect of upper and lower courses in different wind directions through the optimal subset method;

(b3) establishing statistical equations on the effect of upper and lower courses between the corresponding reference index station and each target wind tower through numerical analysis and statistics based on the live monitoring data of the reference wind towers;

(b4) computing the effect of upper and lower courses of the target wind towers through the statistical equations on the effect of upper and lower courses between the corresponding reference index station and each target wind tower based on the wind direction and speed real-time monitoring data of the reference index stations in the upper and lower courses; and

(b5) forecasting the future ultra-short-term wind speed changes of each target wind tower based on the computed results on the effect of upper and lower courses of each target wind tower.

7. The ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses as claimed in claim 6 is characterized in that, in substep (b4), the wind direction and speed real-time monitoring data of the reference index stations in the upper and lower courses includes the effect of upper and lower courses and high and low altitude effect of wind speed.

8. The ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses as claimed in claim 6 is characterized in that, in step (b3), the statistical equations on the effect of upper and lower courses between the corresponding reference index station and each target wind tower include forecast equation on the wind speed of the wind power base in the future 0-3 hours;

in substep (b5), the set time of the future ultra short term includes 5-10 minutes.

9. The ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses as claimed in claim 1 is characterized in that, in step (d), the target area includes areas in 10 meters spacing within 10-120 minutes and the altitudes include 10 meters altitude, 70 meters altitude and 100 meters altitude;

in the forecasting of the future ultra-short-term wind speed changes of the wind farms in the target wind power base at all altitudes in the target area, the forecast efficiency is 60 hours and the forecast interval is 15 minutes.

10. The ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses as claimed in claim 2 is characterized in that, in step (a2), the operation to interpolate the analytical results of the meteorological field data in GRIB (General Regularly-distributed Information in Binary) format in the T639 global spectral model assimilated data into the corresponding model lattices specifically comprises the following substeps:

(a21) interpolating the analytical results of the meteorological field data in GRIB format in the T639 global spectral model assimilated data into the corresponding model lattices horizontally;

(a22) interpolating the analytical results of the meteorological field data in GRIB format in the T639 global spectral model assimilated data into the corresponding model lattices vertically.

11. The ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses as claimed in claim 2 is characterized in that, in step (a4), the cycle operation is running 4 cycles a day;

in the 4 cycles, the cycles starting from 12UTC and 0OUTC are cold start and the others are hot start.