CN105447770A - Assessment method for applying power grid monitoring data to refined weather forecast - Google Patents
Assessment method for applying power grid monitoring data to refined weather forecast Download PDFInfo
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Abstract
The present invention discloses an assessment method for applying power grid monitoring data to refined weather forecast. The method comprises the following steps: S1. designing an experimental scheme; and S2. performing refined weather forecast accuracy assessment: performing comparison and assessment on a measured value of a measured site along a power grid and a simulation value of the designed experimental scheme, so as to select a numerical simulation scheme with the smallest deviation and the most accurate prediction. The beneficial effect of the method disclosed by the present invention is as follows: in one aspect, power grid weather data acquired by a transmission and substation device state monitoring system is applied to a refined weather forecast technology as basic assimilated data of weather forecast, and in the other aspect, the power grid weather monitoring data is used to verify accuracy of weather forecast.
Description
Technical field
The present invention relates to electric power monitoring technical field, particularly, relate to the appraisal procedure that electrical network weather data is applied to the weather forecast that becomes more meticulous.
Background technology
At present, electrical network has tentatively built up the power transmission and transformation equipment state monitoring system covering the whole network, the important line in the multiple district of disaster is achieved to the monitoring of the environmental parameters such as temperature, humidity, wind speed, rainfall.Meanwhile, various places Utilities Electric Co. cooperates with weather bureau, scientific research department, establishes multiple meteorological movie & video information platform.But mostly the meteorological movie & video platform that various places are set up, be that the data only utilizing meteorological department to grasp carry out weather forecast, a large amount of electrical network periphery weather environment market demands power transmission and transformation equipment state monitoring system do not accumulated are to forecast.
Publication number is the Chinese patent " Electric Power Capital Construction meteorological disaster method for early warning and system based on the Weather Forecast Information that becomes more meticulous " of CN102982658A, disclose a kind of Electric Power Capital Construction meteorological disaster method for early warning based on the Weather Forecast Information that becomes more meticulous, comprising: receive the Weather Forecast Information that becomes more meticulous; According to the geographic position in capital construction place, from the described Weather Forecast Information that becomes more meticulous, calculate corresponding weather forecast value; The parameter threshold of taking precautions against natural calamities of more described weather environment value and construction project, generates meteorological disaster information; From meteorological disaster anticipation database, policy information of taking precautions against natural calamities is inquired about according to meteorological disaster information; Send meteorological disaster early warning information and policy information of taking precautions against natural calamities.The advantage of this invention is, achieves the automatic identification to disaster, Auto-matching emergency plan and automatically issues early warning.
Meso-scale meteorology develops an important branch rapidly in modern weather science, the air Mesoscale Motion that it is studied, and is related to discovery and prevention that Regional Gravity wants diastrous weather.One side is wherein by meso-scale model, carries out deep modeling effort and forecast experiments to Study of Meso Scale Weather process.Along with developing rapidly of computer technology in recent years, meso-scale model reaches its maturity.Mesoscale Numerical weather forecasting pattern has many, and such as WRF computing system (MesoscaleModel5) is current domestic and international application pattern comparatively widely.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of electrical network weather data by the collection of power transmission and transformation equipment state monitoring system and is applied to the appraisal procedure become more meticulous in weather forecasting techniques.
The present invention's adopted technical scheme that solves the problem is:
Electrical network weather data is applied to the appraisal procedure of the weather forecast that becomes more meticulous, and comprises the following steps:
S1, experimental designs: comprise following 4 schemes:
Scheme 1: do ambient field with NCEP forecast fields data, does not do the assimilation of any observational data, utilizes numerical weather prediction model simulating grid meteorological along the line;
Scheme 2: do ambient field with NCEP forecast fields data, and assimilate meteorological sounding data, utilizes numerical weather prediction model simulating grid meteorological along the line, and the assimilation period is 96 hours;
Scheme 3: do ambient field with NCEP forecast fields data, and assimilate meteorological sounding data and electrical network weather observation data, utilize numerical weather prediction model simulating grid meteorological along the line, the assimilation period is 24 hours;
Scheme 4: do ambient field with NCEP forecast fields data, and assimilate meteorological sounding data and electrical network weather observation data, utilize numerical weather prediction model simulating grid meteorological along the line, the assimilation period is 96 hours.
Described NCEP forecast fields data refers to FNL whole world analysis of data (FinalOperationalGlobalAnalysis) that Environmental forecasting centre (NCEP)/American National Center for Atmospheric Research (NCAR) provides, and downloads and obtain from the data file store of American National Center for Atmospheric Research management.
Described electrical network weather observation data is the electrical network weather data that electrical network surveys the power transmission and transformation equipment state monitoring system collection of website along the line.
Described numerical weather prediction model is WRF pattern.
Specifically being set to of WFR pattern:
Adopt triple nested, its outermost layer grid lattice point number is 90 × 60, and HORIZONTAL PLAID distance is 27km, middle layer grid lattice point number is 72 × 72, and HORIZONTAL PLAID is apart from being 9km, and innermost layer grid lattice point number is 90 × 90, HORIZONTAL PLAID is apart from being 3km, and vertical direction is delamination 37 layers all, and top of model air pressure is 100hpa.
S2, become more meticulous weather forecast accuracy evaluation: the analogue value of electrical network being surveyed the experimental program of website measured value and above-mentioned design along the line carries out comparative evaluation, thus choose that deviation is minimum, forecast numerical simulation scheme the most accurately.
In described step S2, concrete quantum evaluation comprises:
S21, wind speed: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE, standardization mean deviation NMB and standardization average error NME;
S22, wind direction: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE;
S23, temperature: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE, standardization mean deviation NMB and standardization average error NME;
S24, relative humidity: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE, standardization mean deviation NMB and standardization average error NME;
S25, the result obtained according to S21 ~ S24, analysis and evaluation numerical simulation result and electrical network survey the extent of deviation of website measured value along the line, thus choose that deviation is minimum, forecast numerical simulation scheme the most accurately.
In above-mentioned S21 ~ S24, the computing method of each parameter value adopt existing conventional Calculation Method.
Evaluating quality is exactly compare deviation size, coefficient R is maximum, index of coincidence IOA is maximum, root-mean-square error RMSE is minimum, standardization mean deviation NMB is minimum, standardization average error NME is minimum can be used as evaluation criteria, usual standardization average error NMB is minimum as criterion, minimum just best.
The electrical network weather data that power transmission and transformation equipment state monitoring system gathers by the present invention, as the basis assimilation data of weather forecast, be applied to WRF pattern (WeatherResearchandForecastingModel, weather research and Forecast Mode) region become more meticulous in weather forecasting techniques, improve forecast accuracy; For numerical model Optimization Work have accumulated certain experiences, mesoscale model is better utilized to provide the service of electrical network microclimate.
To sum up, the invention has the beneficial effects as follows:
The electrical network weather data that power transmission and transformation equipment state monitoring system gathers by the present invention, be applied to and become more meticulous in weather forecasting techniques, on the one hand as the basis assimilation data of weather forecast, on the other hand, electrical network weather data is utilized to check the weather forecast accuracy that becomes more meticulous.
Accompanying drawing explanation
Fig. 1 is assimilation experiments scheme of the present invention;
To be that the WRF grid 3 layers of the embodiment of the present invention is nested arrange schematic diagram to Fig. 2;
Fig. 3 is the electrical network microclimate eyeball information of the embodiment of the present invention;
Fig. 4 is that the pattern simulation wind speed result of the embodiment of the present invention and the statistical indicator of measured value contrast;
Fig. 5 is that the pattern simulation wind direction result of the embodiment of the present invention and the statistical indicator of measured value contrast;
Fig. 6 is that the pattern simulation temperature results of the embodiment of the present invention and the statistical indicator of measured value contrast;
Fig. 7 is that the pattern simulation relative humidity result of the embodiment of the present invention and the statistical indicator of measured value contrast.
Embodiment
Below in conjunction with embodiment and accompanying drawing, to the detailed description further of the present invention's do, but embodiments of the present invention are not limited thereto.
Embodiment:
Electrical network weather data is applied to the appraisal procedure of the weather forecast that becomes more meticulous, and comprises the following steps:
S1, experimental designs:
For understanding the WRF pattern predictive ability to electrical network microclimate along the line, and what impact the logarithm value forecast in existing assimilation system of each key element of observational data has, and the present embodiment devises 4 schemes, as shown in Figure 1:
Scheme 1: do ambient field with NCEP forecast fields data, does not do the assimilation of any observational data, utilizes numerical weather prediction model simulating grid meteorological along the line;
Scheme 2: do ambient field with NCEP forecast fields data, and assimilate meteorological sounding data, utilizes numerical weather prediction model simulating grid meteorological along the line, and the assimilation period is 96 hours;
Scheme 3: do ambient field with NCEP forecast fields data, and assimilate meteorological sounding data and electrical network weather observation data, utilize numerical weather prediction model simulating grid meteorological along the line, the assimilation period is 24 hours;
Scheme 4: do ambient field with NCEP forecast fields data, and assimilate meteorological sounding data and electrical network weather observation data, utilize numerical weather prediction model simulating grid meteorological along the line, the assimilation period is 96 hours.
Described numerical weather prediction model is WRF pattern, and the present embodiment adopts 3.5 editions WRF Three-dimensional Variational Data Assimilation Systems and pattern; As shown in Figure 2, being specifically set to of WFR pattern:
Adopt triple nested, its outermost layer grid lattice point number is 90 × 60, and HORIZONTAL PLAID distance is 27km, middle layer grid lattice point number is 72 × 72, and HORIZONTAL PLAID is apart from being 9km, and innermost layer grid lattice point number is 90 × 90, HORIZONTAL PLAID is apart from being 3km, and vertical direction is delamination 37 layers all, and top of model air pressure is 100hpa.In the present embodiment, Selection Center point is 30.5N, 103.7E.
When utilizing Three-dimensional Variational Data Assimilation technology to assimilate electrical network observational data, the Observations quality and the quantity that enter assimilation system directly affect assimilation result, the quality of observational data determines primarily of observation instrument and observation process, and quantity is determined by the quality control of assimilation process.
S2, become more meticulous weather forecast accuracy evaluation: the analogue value of electrical network being surveyed the experimental program of website measured value and above-mentioned design along the line carries out comparative evaluation, thus choose that deviation is minimum, forecast numerical simulation scheme the most accurately.
In described step S2, concrete quantum evaluation comprises:
S21, wind speed: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE, standardization mean deviation NMB and standardization average error NME;
S22, wind direction: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE;
S23, temperature: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE, standardization mean deviation NMB and standardization average error NME;
S24, relative humidity: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE, standardization mean deviation NMB and standardization average error NME;
S25, the result obtained according to S21 ~ S24, analysis and evaluation numerical simulation result and electrical network survey the extent of deviation of website measured value along the line, thus choose that deviation is minimum, forecast numerical simulation scheme the most accurately.
In the present embodiment, website wind speed is surveyed along the line according to electrical network, wind direction, the analogue value of temperature and the relative humidity measured value of 15 minutes and WRF pattern carries out comparative evaluation, electrical network surveys the distributing position of website along the line as shown in Figure 3, Fig. 4 is that the WRF pattern simulation wind speed result of the present embodiment and the statistical indicator of measured value contrast, the WRF pattern simulation wind direction result of Fig. 5 the present embodiment and the statistical indicator of measured value contrast, the WRF pattern simulation temperature results of Fig. 6 the present embodiment and the statistical indicator of measured value contrast, the WRF pattern simulation relative humidity result of Fig. 7 the present embodiment and the statistical indicator of measured value contrast.
(1) wind speed
As shown in Figure 4, the value of the coefficient R of scheme 1-3, index of coincidence IOA, root-mean-square error RMSE, standardization mean deviation NMB and standardization average error NME is almost equal, illustrates that the impact of the wind speed simulation of scheme 2 and scheme 3 on Control experiment is not obvious.The related coefficient of scheme 4 has brought up to 0.51, index of coincidence reaches 0.7 nearly, root-mean-square error RMSE is also down to 1.32m/s, explanation pattern is when adding electrical network microclimate observational data and assimilating all the period of time, the variation tendency of the analogue value and observed reading is more identical, and degree is better than about 50% of other three kinds of schemes.
(2) wind direction
As shown in Figure 5, because wind direction is considered according to 360 degree of clockwise direction, so standardization mean deviation NMB and standardization average error NME statistical parameter are not considered in the assessment of wind direction, so be set to Null at NMB and NME in table 6.Similar to wind speed, the analog result of scheme 1-3 is closely similar, changes less, but overall coefficient R and index of coincidence IOA all higher, more identical with the variation tendency of observed reading.Compare three kinds of schemes, scheme 4 pairs of analog results improve to some extent, and are better than other three kinds of schemes equally.
(3) temperature
As shown in Figure 6, the related coefficient of 4 kinds of schemes has all exceeded 0.7, index of coincidence all reaches more than 0.8, root-mean-square error RMSE is also only at about 3 DEG C, standardization mean deviation NMB and standardization average error NME is lower, and 4 kinds of scheme statistical parameters are all more similar, illustrate the simulate effect of temperature better, accurately can catch variation tendency and the temperature value of temperature.Assimilation scheme is not obvious on the impact of temperature, can consider closing temperature assimilation in this area forecast and simulation process.
(4) relative humidity
As shown in Figure 7,4 kinds of scheme simulations to relative humidity all show good variation tendency and the capturing ability to peak value.Scheme 2 and the impact of scheme 3 on Control experiment little, wherein scheme 2 is equal with the statistical parameter of scheme 1, scheme 3 changes to some extent, but amplitude is less, it is larger that scheme 4 changes amplitude, related coefficient and index of coincidence reach 0.7 and 0.82 respectively, and standardization mean deviation NMB also reaches lower value-0.01, illustrate that the assimilation of observational data all the period of time is more obvious on the impact of relative humidity.
The above results shows: scheme 4 is better than scheme 1,2,3, and Choice 4 is numerical simulation scheme.And the above results shows every evaluation index equally has consistance, consider the practicality of evaluation index, standardization mean deviation NMB demonstrates forecast and the extent of deviation of observation, has stronger directive significance.
As mentioned above, the present invention can be realized preferably.
Claims (5)
1. electrical network weather data is applied to the appraisal procedure of the weather forecast that becomes more meticulous, and it is characterized in that, comprises the following steps:
S1, experimental designs: comprise following 4 schemes:
Scheme 1: do ambient field with NCEP forecast fields data, does not do the assimilation of any observational data, utilizes numerical weather prediction model simulating grid meteorological along the line;
Scheme 2: do ambient field with NCEP forecast fields data, and assimilate meteorological sounding data, utilizes numerical weather prediction model simulating grid meteorological along the line, and the assimilation period is 96 hours;
Scheme 3: do ambient field with NCEP forecast fields data, and assimilate meteorological sounding data and electrical network weather observation data, utilize numerical weather prediction model simulating grid meteorological along the line, the assimilation period is 24 hours;
Scheme 4: do ambient field with NCEP forecast fields data, and assimilate meteorological sounding data and electrical network weather observation data, utilize numerical weather prediction model simulating grid meteorological along the line, the assimilation period is 96 hours;
S2, become more meticulous weather forecast accuracy evaluation: the analogue value of electrical network being surveyed the experimental program of website measured value and above-mentioned design along the line carries out comparative evaluation, thus choose that deviation is minimum, forecast numerical simulation scheme the most accurately.
2. electrical network weather data according to claim 1 is applied to the appraisal procedure of the weather forecast that becomes more meticulous, it is characterized in that, described NCEP forecast fields data refers to the FNL whole world analysis of data that Environmental forecasting centre/American National Center for Atmospheric Research provides, and downloads and obtain from the data file store of American National Center for Atmospheric Research management;
Described electrical network weather observation data is the electrical network weather data that electrical network surveys the power transmission and transformation equipment state monitoring system collection of website along the line.
3. electrical network weather data according to claim 1 is applied to the appraisal procedure of the weather forecast that becomes more meticulous, and it is characterized in that, described numerical weather prediction model is WRF pattern.
4. electrical network weather data according to claim 3 is applied to the appraisal procedure of the weather forecast that becomes more meticulous, it is characterized in that, specifically being set to of WFR pattern: adopt triple nested, its outermost layer grid lattice point number is 90 × 60, and HORIZONTAL PLAID distance is 27km, middle layer grid lattice point number is 72 × 72, HORIZONTAL PLAID is apart from being 9km, and innermost layer grid lattice point number is 90 × 90, and HORIZONTAL PLAID distance is 3km, vertical direction is delamination 37 layers all, and top of model air pressure is 100hpa.
5. electrical network weather data according to claim 1 is applied to the appraisal procedure of the weather forecast that becomes more meticulous, and it is characterized in that, in described step S2, concrete quantum evaluation comprises:
S21, wind speed: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE, standardization mean deviation NMB and standardization average error NME;
S22, wind direction: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE;
S23, temperature: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE, standardization mean deviation NMB and standardization average error NME;
S24, relative humidity: calculate the coefficient R between the analogue value that electrical network surveys the experimental program of website measured value and above-mentioned design along the line, index of coincidence IOA, root-mean-square error RMSE, standardization mean deviation NMB and standardization average error NME;
S25, the result obtained according to S21 ~ S24, analysis and evaluation numerical simulation result and electrical network survey the extent of deviation of website measured value along the line, thus choose that deviation is minimum, forecast numerical simulation scheme the most accurately.
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| CN108280793A (en) * | 2018-02-12 | 2018-07-13 | 北京应用气象研究所 | Meteorological element Changes On Typhoon impact evaluation technology |
| CN108491602A (en) * | 2018-03-13 | 2018-09-04 | 广州大学 | A kind of general mood waits the fining analysis method of field data |
| CN109272230A (en) * | 2018-09-19 | 2019-01-25 | 中国气象局气象探测中心 | A kind of Data Quality Assessment Methodology and system of surface-based observing station air pressure element |
| CN109325633A (en) * | 2018-10-23 | 2019-02-12 | 中国电力科学研究院有限公司 | A kind of weather forecast set member choosing method and system |
| CN110110448A (en) * | 2019-05-10 | 2019-08-09 | 珠海深圳清华大学研究院创新中心 | A kind of weather simulation method based on WRF, system and readable storage medium storing program for executing |
| CN111239857A (en) * | 2020-02-18 | 2020-06-05 | 潘新民 | Strong wind forecasting method for special terrain |
| CN111581764A (en) * | 2019-02-18 | 2020-08-25 | 中国科学院深圳先进技术研究院 | Model precision evaluation method |
| CN112418512A (en) * | 2020-11-19 | 2021-02-26 | 中国环境科学研究院 | Based on PM2.5Air quality prediction method |
| CN115453661A (en) * | 2022-11-14 | 2022-12-09 | 中科星图维天信(北京)科技有限公司 | Weather forecasting method, weather forecasting device, weather forecasting equipment and storage medium |
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| CN110110448A (en) * | 2019-05-10 | 2019-08-09 | 珠海深圳清华大学研究院创新中心 | A kind of weather simulation method based on WRF, system and readable storage medium storing program for executing |
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| CN112418512A (en) * | 2020-11-19 | 2021-02-26 | 中国环境科学研究院 | Based on PM2.5Air quality prediction method |
| CN115453661A (en) * | 2022-11-14 | 2022-12-09 | 中科星图维天信(北京)科技有限公司 | Weather forecasting method, weather forecasting device, weather forecasting equipment and storage medium |
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