CN114844032A - Wind power plant wind power prediction method and system - Google Patents
Wind power plant wind power prediction method and system Download PDFInfo
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Abstract
The invention discloses a wind power plant wind power prediction method and a system, which relate to the technical field of wind power prediction and comprise the following steps: a data acquisition module: the system is used for acquiring the landform, the fan distribution, the fan power generation characteristic and the climate characteristic data of the power station; dividing a modeling module; a power generation prediction module; a data transmission module; a human-computer interaction module; a data statistics module; and an information reporting module. According to the method, multiple meteorological sources are screened, selected and combined to carry out collective power prediction on the meteorological sources more suitable for the station, and the prediction service center sends weather forecast provided by a numerical simulation technology and electric field prediction model parameters adjusted according to actual meteorological measurement data covering the whole country every day in addition to the weather forecast provided by the numerical simulation technology, so that the timeliness of prediction model adjustment is ensured, and the system prediction precision is further ensured, thereby saving resources and reducing economic loss.
Description
Technical Field
The invention relates to the technical field of wind power prediction, in particular to a wind power prediction method and a wind power prediction system for a wind power plant.
Background
The wind power prediction technology is used for predicting the power output by a wind power place in a future period of time so as to arrange a scheduling plan. This is because wind energy belongs to unstable energy with random fluctuation, and large-scale wind power is incorporated into the system, which inevitably brings new challenges to the stability of the system. The power generation dispatching mechanism needs to know the wind power output power for hours in the future. The method is divided according to the output prediction time scale of the wind power plant, and comprises long-term prediction, medium-term prediction, short-term prediction and ultra-short-term prediction. With the increasingly mature wind power generation technology, the capacity of a single wind power machine and the scale of a grid-connected wind power plant are continuously enlarged, and the proportion of wind power in the total power generation amount of a power system is increased year by year. The penetration power of a wind power plant is continuously increased, a series of problems brought to a power system are increasingly prominent, and the safety, stability, economy and reliability in operation of the power system are seriously affected. The wind power is predicted timely and accurately, and the safety, stability, economy and controllability of the power system can be obviously enhanced.
The currently used wind power prediction system has certain problems in the aspects of day-ahead/day-ahead ultra-short term prediction data accuracy, day-ahead/day-ahead ultra-short term prediction data reporting rate, meteorological data qualification rate, day-ahead correlation coefficient and the like, so that a wind power plant is examined by a power grid for a large amount of on-grid electricity every month in the aspect of wind power prediction, and huge economic loss is caused.
Disclosure of Invention
The invention aims to solve the problems that in the prior art, certain problems exist in the aspects of day-ahead/day-ahead ultra-short term prediction data accuracy, day-ahead/day-ahead ultra-short term prediction data reporting rate, meteorological data qualification rate, day-ahead correlation coefficient and the like, so that a large amount of online electricity is checked by a power grid every month in the wind power prediction aspect of a wind power plant, and huge economic loss is caused.
In order to achieve the purpose, the invention adopts the following technical scheme:
a wind power plant wind power prediction method comprises the following steps:
acquiring topography, landform, fan distribution, fan power generation characteristics and climate characteristic data of a power station;
dividing the wind field into 3 or more areas according to the acquired data, and independently establishing an electric field prediction model for each area;
the model carries out power generation prediction of the wind power plant according to the climate characteristics of the geographical position, the numerical weather forecast, the historical data condition of the wind power plant and the running state condition of the wind turbine;
the prediction service center sends a prediction result and sends electric field prediction model parameters adjusted according to actual meteorological measurement data covering the whole country;
the man-machine interaction interface displays information in real time, and analyzes and inputs the information;
counting the displayed information and the analyzed and input information;
and reporting the information of the statistical information data.
Preferably, the model predicts the power generation of the wind power plant according to the climate characteristics of the geographic position, the numerical weather forecast, the historical data condition of the wind power plant and the running state condition of the wind turbine generator, and the prediction comprises the following steps:
forecasting the day of the wind power plant;
forecasting, predicting and forecasting a wind power plant in real time;
and predicting and forecasting the power change curve, wherein the time resolution is 15 min.
Preferably, the daily forecast: submitting the next day 0 to a power grid dispatching organization according to regulations before the specified time every day: and wind power active power of 96 nodes is obtained in 15 minutes to 24 minutes every 15 minutes.
And (3) real-time forecasting: and according to the power grid dispatching requirement, reporting wind power prediction data from 15 minutes to 4 hours in the future in a rolling way every 15 minutes, and executing the wind power prediction data in a rolling way every 15 min.
Preferably, the human-computer interaction interface displays information in real time, and analyzes and inputs the information;
the real-time display information comprises the following steps: and displaying real-time wind resource monitoring information, real-time power, real-time unit state and predicted power content.
The statistical analysis comprises: and (4) carrying out statistical analysis on the contents of wind resource monitoring information, actually measured power, predicted power information, prediction accuracy and the like.
The analyzing and entering comprises the following steps: and predicting the starting capacity, stopping the maintenance plan, and analyzing and recording the contents of the network-regulated new energy power generation plan.
Preferably, the displayed information and the information analyzed, analyzed and input are counted;
the statistics comprises the following steps:
the historical power data statistics comprise data integrity statistics, frequency distribution statistics and change rate statistics;
the historical meteorological data statistics comprise data integrity statistics, wind speed frequency distribution statistics and wind direction frequency distribution statistics;
the wind power plant operation parameter statistics comprises statistics of parameters of generated energy, effective power generation time, maximum output and generation time thereof, synchronization rate, utilization hours and average load rate;
error statistics: the error statistics can be carried out on the prediction result in any time interval, and the error indexes comprise root mean square error and average absolute error rate.
Preferably, information reporting is carried out on the statistical information data;
and according to the technical requirements of power grid dispatching, short-term power prediction, ultra-short-term power prediction, real-time monitoring of the anemometer tower, fan overhaul capacity, installed capacity of a wind power plant, commissioning capacity and maximum output information in a standard format are reported.
Preferably, the short-term power prediction result is calculated in a ratio of 0: reporting power curves of 96 time nodes every 15 minutes in a form of every 15 minutes before 11 hours in the morning, reporting the power curves 1 time every 15 minutes in ultra-short-term power prediction, and reporting the real-time monitoring data of the anemometer tower 1 time every 5 minutes; and the wind power plant fan overhaul capacity, installed capacity, commissioning capacity and maximum output information are used as the headers of short-term power prediction reporting data and are reported in a file format.
A wind farm wind power prediction system comprising:
a data acquisition module: the system is used for acquiring the landform, the fan distribution, the fan power generation characteristic and the climate characteristic data of the power station;
a division modeling module: the system comprises a wind field acquisition module, a power generation module and a power generation module, wherein the power generation module is used for generating power for the wind field according to acquired data;
a power generation prediction module: the model is used for predicting the power generation of the wind power plant according to the climate characteristics of the geographic position, the numerical weather forecast, the historical data condition of the wind power plant and the running state condition of the wind turbine;
a data sending module: the electric field prediction model parameter adjusting device is used for transmitting a prediction result by the prediction service center and transmitting an electric field prediction model parameter adjusted according to actual meteorological measurement data covering the whole country;
a human-computer interaction module: the system is used for displaying information in real time on a human-computer interaction interface, and analyzing, analyzing and inputting the information;
a data statistics module: the statistical analysis and input device is used for counting the displayed information and the analyzed and input information;
an information reporting module: and the system is used for reporting the information of the statistical information data.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, multiple meteorological sources are screened, selected and combined to carry out collective power prediction on the meteorological sources more suitable for the station, and the prediction service center sends weather forecast provided by a numerical simulation technology and electric field prediction model parameters adjusted according to actual meteorological measurement data covering the whole country every day in addition to the weather forecast provided by the numerical simulation technology, so that the timeliness of prediction model adjustment is ensured, and the system prediction precision is further ensured, thereby saving resources and reducing economic loss.
Drawings
Fig. 1 is a schematic overall flow diagram of a wind power plant wind power prediction method and system provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, a wind power plant wind power prediction method includes the following steps:
s1: acquiring topography, landform, fan distribution, fan power generation characteristics and climate characteristic data of a power station;
s2: dividing the wind field into 3 or more areas according to the acquired data, and independently establishing an electric field prediction model for each area;
s3: the model carries out wind power plant power generation prediction according to the climate characteristics, numerical weather forecast, wind power plant historical data conditions and the running state conditions of the wind turbine generator at the geographic position, and the prediction comprises the following steps:
wind power plant daily forecast, daily forecast: submitting the next day 0 to a power grid dispatching organization according to regulations before the specified time every day: wind power active power of 96 nodes is calculated every 15 minutes when the wind power is divided into 24 minutes; the power prediction accuracy rate of the wind power plant 0-24h day before is more than or equal to 80%, and when the power prediction accuracy rate is less than 80%, the power prediction accuracy rate is assessed according to the following formula:
day-ahead accuracy daily assessment electric quantity (80% -accuracy) x P N X 1 (hr);
in the formula: p Mi Is the actual power at time i, P Pi Is a day-ahead power predicted value at the moment i, Cap is the available capacity of the wind power plant, n is the number of samples, P N Rated capacity for the wind farm;
forecasting, forecasting and forecasting in real time of the wind power plant, and forecasting in real time: according to the power grid dispatching requirement, the wind power prediction data is reported to be executed in a rolling mode every 15min, wherein the wind power prediction data is executed in a rolling mode every 15min in the future 15-4 hours;
the correlation coefficient between the prediction and the actual power of the wind power plant 0-24h day before is more than or equal to 0.68, and less than 0.68 is counted as one-time unqualified, and each time, the grid-connected electricity quantity is checked according to 0.1% of the current month of the wind power plant. The method for calculating the wind power prediction correlation coefficient comprises the following steps:
coefficient of correlation
In the formula: n is the number of samples, P Mi Measured power at time i, P Pi For the predicted power at time i,
is the average of the measured power for all samples,
predicting an average of the power for all samples;
predicting and forecasting a power change curve, wherein the time resolution is 15 min;
the accuracy of the 4 th hour forecasting of the ultra-short term power of the wind power plant is more than or equal to 85 percent and is assessed according to the following formula when the accuracy is less than 85 percent:
ultra-short-term accuracy daily assessment electric quantity (85% -accuracy) x P N X 1 (hr);
in the formula: p Mi Is the actual power at time i, P Pi Predicting the predicted value of the 4 th hour (i moment) for the ultra-short-term power, wherein Cap is the available capacity of the wind power plant, n is the number of samples in the power generation period, and P is N Rated capacity for the wind farm;
s4: the prediction service center sends a prediction result and sends electric field prediction model parameters adjusted according to actual meteorological measurement data covering the whole country;
s5: the human-computer interaction interface displays information in real time, analyzes and inputs the information, and the real-time display information comprises the following steps: and displaying real-time wind resource monitoring information, real-time power, real-time unit state and predicted power content.
The statistical analysis comprises: and (4) carrying out statistical analysis on the contents of wind resource monitoring information, actually measured power, predicted power information, prediction accuracy and the like.
The analyzing and entering comprises the following steps: predicting the starting capacity, stopping the maintenance plan, and analyzing and inputting the contents of the network-regulated new energy power generation plan;
s6: counting the displayed information and the analyzed and input information;
the statistics comprises the following steps:
the historical power data statistics comprise data integrity statistics, frequency distribution statistics and change rate statistics;
the historical meteorological data statistics comprise data integrity statistics, wind speed frequency distribution statistics and wind direction frequency distribution statistics;
the wind power plant operation parameter statistics comprises statistics of parameters of generated energy, effective power generation time, maximum output and generation time thereof, synchronization rate, utilization hours and average load rate;
error statistics: error statistics can be carried out on the prediction result in any time interval, and error indexes comprise root mean square error and average absolute error rate;
s7: reporting the information of the statistical information data, and reporting short-term power prediction, ultra-short-term power prediction, real-time monitoring of a wind measuring tower, fan overhaul capacity, installed capacity of a wind power plant, commissioning capacity and maximum output information in a standard format according to the technical requirements of power grid dispatching; short-term power prediction results are reported in the next day 0: reporting power curves of 96 time nodes every 15 minutes in a form of every 15 minutes before 11 hours in the morning, reporting the power curves 1 time every 15 minutes in ultra-short-term power prediction, and reporting the real-time monitoring data of the anemometer tower 1 time every 5 minutes; and the wind power plant fan overhaul capacity, installed capacity, commissioning capacity and maximum output information are used as the headers of short-term power prediction reporting data and are reported in a file format.
A wind farm wind power prediction system comprising:
a data acquisition module: the system is used for acquiring the landform, the fan distribution, the fan power generation characteristic and the climate characteristic data of the power station;
a division modeling module: the system comprises a wind field acquisition module, a power generation module and a power generation module, wherein the power generation module is used for generating power for the wind field according to acquired data;
a power generation prediction module: the method is used for predicting the power generation of the wind power plant by the model according to the climate characteristics, the numerical weather forecast, the historical data condition of the wind power plant and the running state condition of the wind turbine generator at the geographical position, and the prediction comprises the following steps:
wind power plant daily forecast, daily forecast: submitting the next day 0 to a power grid dispatching organization according to regulations before the specified time every day: wind power active power of 96 nodes is calculated every 15 minutes when the wind power is divided into 24 minutes; the power prediction accuracy rate of the wind power plant 0-24h day before is more than or equal to 80%, and when the power prediction accuracy rate is less than 80%, the power prediction accuracy rate is assessed according to the following formula:
day-ahead accuracy daily assessment electric quantity (80% -accuracy) x P N X 1 (hr);
in the formula: p Mi Is the actual power at time i, P Pi Is a day-ahead power predicted value at the moment i, Cap is the available capacity of the wind power plant, n is the number of samples, P N Rated capacity for the wind farm;
forecasting, forecasting and forecasting in real time of the wind power plant, and forecasting in real time: according to the power grid dispatching requirement, the wind power prediction data is reported to be executed in a rolling mode every 15min, wherein the wind power prediction data is executed in a rolling mode every 15min in the future 15-4 hours;
the correlation coefficient between the prediction and the actual power of the wind power plant 0-24h day before is more than or equal to 0.68, and less than 0.68 is counted as one-time unqualified, and each time, the grid-connected electricity quantity is checked according to 0.1% of the current month of the wind power plant. The method for calculating the wind power prediction correlation coefficient comprises the following steps:
in the formula: n is the number of samples, P Mi Measured power at time i, P Pi For the predicted power at time i,
is the average of the measured power for all samples,
predicting an average of the power for all samples;
predicting and forecasting a power change curve, wherein the time resolution is 15 min;
the accuracy of the 4 th hour forecasting of the ultra-short term power of the wind power plant is more than or equal to 85 percent and is assessed according to the following formula when the accuracy is less than 85 percent:
ultra-short-term accuracy daily assessment electric quantity (85% -accuracy) x P N X 1 (hr);
in the formula: p Mi Is the actual power at time i, P Pi Predicting the predicted value of the 4 th hour (i moment) for the ultra-short-term power, wherein Cap is the available capacity of the wind power plant, n is the number of samples in the power generation period, and P is N Rated capacity for the wind farm;
a data sending module: the electric field prediction model parameter adjusting device is used for transmitting a prediction result by the prediction service center and transmitting an electric field prediction model parameter adjusted according to actual meteorological measurement data covering the whole country;
a human-computer interaction module: the system is used for displaying information in real time on a human-computer interaction interface, and analyzing, analyzing and inputting the information;
a data statistics module: the statistical analysis and input device is used for counting the displayed information and the analyzed and input information;
an information reporting module: and the system is used for reporting the information of the statistical information data.
An intelligent computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of a wind farm wind power prediction method as described.
A computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, implement the steps of a wind farm wind power prediction method as described.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. A wind power plant wind power prediction method is characterized by comprising the following steps:
acquiring topography, landform, fan distribution, fan power generation characteristics and climate characteristic data of a power station;
dividing the wind field into 3 or more areas according to the acquired data, and independently establishing an electric field prediction model for each area;
the model carries out power generation prediction of the wind power plant according to the climate characteristics of the geographical position, the numerical weather forecast, the historical data condition of the wind power plant and the running state condition of the wind turbine;
the prediction service center sends a prediction result and sends electric field prediction model parameters adjusted according to actual meteorological measurement data covering the whole country;
the man-machine interaction interface displays information in real time, and analyzes and inputs the information;
counting the displayed information and the analyzed and input information;
and reporting the information of the statistical information data.
2. The wind power plant wind power prediction method according to claim 1, characterized in that the model predicts the wind power plant power generation according to the climate characteristics of the geographical location, the numerical weather forecast, the wind power plant historical data condition and the operating state condition of the wind turbine generator, and the prediction comprises:
forecasting the day of the wind power plant;
forecasting, predicting and forecasting a wind power plant in real time;
and predicting and forecasting the power change curve, wherein the time resolution is 15 min.
3. A wind farm wind power prediction method according to claim 2, characterized by daily forecast: submitting the next day 0 to a power grid dispatching organization according to regulations before the specified time every day: and wind power active power of 96 nodes is obtained in 15 minutes to 24 minutes every 15 minutes.
And (3) real-time forecasting: and according to the power grid dispatching requirement, the wind power prediction data of the future 15 minutes to 4 hours are reported in a rolling way every 15 minutes, and the wind power prediction data are executed in a rolling way every 15 min.
4. The wind power plant wind power prediction method according to claim 1, characterized in that a human-computer interaction interface displays information in real time, and analyzes, analyzes and inputs the information;
the real-time display information comprises the following steps: and displaying real-time wind resource monitoring information, real-time power, real-time unit state and predicted power content.
The statistical analysis comprises: and (4) carrying out statistical analysis on the contents of wind resource monitoring information, actually measured power, predicted power information, prediction accuracy and the like.
The analyzing and entering comprises the following steps: and predicting the starting capacity, stopping the maintenance plan, and analyzing and recording the contents of the network-regulated new energy power generation plan.
5. A wind power plant wind power prediction method according to claim 1, characterized in that the displayed information and the analyzed and entered information are counted;
the statistics comprises the following steps:
the historical power data statistics comprise data integrity statistics, frequency distribution statistics and change rate statistics;
the historical meteorological data statistics comprise data integrity statistics, wind speed frequency distribution statistics and wind direction frequency distribution statistics;
the wind power plant operation parameter statistics comprises statistics of parameters of generated energy, effective power generation time, maximum output and generation time thereof, synchronization rate, utilization hours and average load rate;
error statistics: the error statistics can be carried out on the prediction result in any time interval, and the error indexes comprise root mean square error and average absolute error rate.
6. The wind power plant wind power prediction method according to claim 1, characterized by reporting information of statistical information data;
and according to the technical requirements of power grid dispatching, short-term power prediction, ultra-short-term power prediction, real-time monitoring of the anemometer tower, fan overhaul capacity, installed capacity of a wind power plant, commissioning capacity and maximum output information in a standard format are reported.
7. A wind farm wind power prediction method according to claim 6, characterized in that the short term power prediction results are calculated on the next day 0: reporting power curves of 96 time nodes every 15 minutes in a form of every 15 minutes before 11 hours in the morning, reporting the power curves 1 time every 15 minutes in ultra-short-term power prediction, and reporting the real-time monitoring data of the anemometer tower 1 time every 5 minutes; and the wind power plant fan overhaul capacity, installed capacity, commissioning capacity and maximum output information are used as the headers of short-term power prediction reporting data and are reported in a file format.
8. A wind farm wind power prediction system, comprising:
a data acquisition module: the system is used for acquiring the landform, the fan distribution, the fan power generation characteristic and the climate characteristic data of the power station;
a division modeling module: the system comprises a wind field acquisition module, a power generation module and a power generation module, wherein the power generation module is used for generating power for the wind field according to acquired data;
a power generation prediction module: the model is used for predicting the power generation of the wind power plant according to the climate characteristics of the geographic position, the numerical weather forecast, the historical data condition of the wind power plant and the running state condition of the wind turbine;
a data sending module: the electric field prediction model parameter adjusting device is used for transmitting a prediction result by the prediction service center and transmitting an electric field prediction model parameter adjusted according to actual meteorological measurement data covering the whole country;
a human-computer interaction module: the system is used for displaying information in real time on a human-computer interaction interface, and analyzing, analyzing and inputting the information;
a data statistics module: the statistical analysis and input device is used for counting the displayed information and the analyzed and input information;
an information reporting module: and the system is used for reporting the information of the statistical information data.
9. An intelligent computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of a wind farm wind power prediction method according to any of claims 1 to 7.
10. A computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, carry out the steps of the wind farm wind power prediction method according to any of claims 1 to 7.
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| CN202210321195.4A CN114844032A (en) | 2022-03-28 | 2022-03-28 | Wind power plant wind power prediction method and system |
| LU503687A LU503687B1 (en) | 2022-03-28 | 2023-03-18 | A wind power prediction method and system for wind farm |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116187559A (en) * | 2023-02-21 | 2023-05-30 | 华润电力技术研究院有限公司 | Centralized wind power ultra-short-term power prediction method, system and cloud platform |
| CN116317169A (en) * | 2023-05-17 | 2023-06-23 | 三峡智控科技有限公司 | Remote intelligent comparison platform and method for wind power prediction system |
| CN116544919A (en) * | 2023-05-08 | 2023-08-04 | 中国电建集团重庆工程有限公司 | Wind power plant power generation amount prediction method, device, equipment and medium |
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2022
- 2022-03-28 CN CN202210321195.4A patent/CN114844032A/en active Pending
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116187559A (en) * | 2023-02-21 | 2023-05-30 | 华润电力技术研究院有限公司 | Centralized wind power ultra-short-term power prediction method, system and cloud platform |
| CN116187559B (en) * | 2023-02-21 | 2024-03-15 | 华润电力技术研究院有限公司 | Centralized wind power ultra-short-term power prediction method, system and cloud platform |
| CN116544919A (en) * | 2023-05-08 | 2023-08-04 | 中国电建集团重庆工程有限公司 | Wind power plant power generation amount prediction method, device, equipment and medium |
| CN116544919B (en) * | 2023-05-08 | 2024-07-05 | 中国电建集团重庆工程有限公司 | Wind power plant power generation amount prediction method, device, equipment and medium |
| CN116317169A (en) * | 2023-05-17 | 2023-06-23 | 三峡智控科技有限公司 | Remote intelligent comparison platform and method for wind power prediction system |
| CN116317169B (en) * | 2023-05-17 | 2023-08-04 | 三峡智控科技有限公司 | Remote intelligent comparison platform and method for wind power prediction system |
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