大气压的差值对风速空间相关性预测的影响分析

doi:10.12096/j.2096-4528.pgt.19126

摘要/Abstract

摘要：

风速空间相关性预报是风电功率预测的有效方法。为提高风速空间相关性预报的效果，文中分析了动力气象学中大气运动的基本方程，可知气压梯度力、地球引力以及摩擦力是大气运动的基本动力。在短期、超短期风电功率预测的时间尺度里，地球引力、摩擦力通常可以看作已知量或不变量，因此气压梯度力是引起风的首要动力。定量分析了我国东南沿海发展空间相关性预报的价值，认为我国季风区的空间相关性明显超过欧美，且东南沿海的近海风能明显高于陆地，特别适合空间相关性预测，采用大气压的空间相关性预测是我国季风区风电功率预测的可靠基础方法之一。

关键词: 风电功率预测, 风速, 气压, 压差, 空间相关性

Abstract:

Spatial correlation prediction of wind speed is an effective method for wind power prediction. In order to improve the effect of spatial correlation prediction of wind speed, the basic equations of atmospheric motion in dynamic meteorology were analyzed, and pressure gradient force, Earth gravity force and the friction force are the basic forces of atmospheric motion. In the time scale of short-term and super-short-term wind power prediction, gravity and friction of the earth can usually be regarded as known quantities or invariants, so pressure gradient force is the primary force causing wind. The value of development spatial correlation and prediction was quantitative analyzed on the southeastern coast of China. It shows that the spatial correlation of monsoon region in China is obviously higher than that in Europe and America, and the southeast coast of offshore wind power are significantly higher than the land, especially suitable for spatial correlation prediction. The spatial correlation of atmospheric pressure prediction is one of the reliable basics methods for prediction of monsoon wind power in China.

Key words: wind power prediction, wind speed, atmospheric pressure, pressure difference, spatial correlation

中图分类号:

TK89

杨正瓴, 王如雪, 乔健, 张玺, 杨钊, 张军. 大气压的差值对风速空间相关性预测的影响分析[J]. 发电技术, 2020, 41(6): 617-624.

Zhengling YANG, Ruxue WAGN, Jian QIAO, Xi ZHANG, Zhao YANG, Jun ZHANG. Analysis of the Influence of Atmospheric Pressure Difference on Spatial Correlation Prediction of Wind Speed[J]. Power Generation Technology, 2020, 41(6): 617-624.

图/表 8

图1 ECMWF在欧洲地区提前60 h和72 h风速预报误差

Fig.1 The wind speed prediction errors in Europe for ECMWF ahead of 60 and 72 hours

图2 ECMWF对欧洲地区风速预报的均方根误差与提前时间

Fig.2 The Root mean square error of Europe wind speed forecast by ECMWF and the ahead time

表1 澳仔风速预测的误差统计

Tab. 1 Predictive error statistics of Aozi wind speed

影响因子	预测误差/(m/s)
影响因子	平均误差	均方根误差	平均绝对误差
风速	0.219 4	1.499 9	1.205 6
压差+温差+风向+风速	0.151 8	1.493 8	1.175 7
气温+风速	-0.089 6	1.452 8	1.107 1
气压+风速	0.132 9	1.441 9	1.124 3
压差+风速	0.074 8	1.399 5	1.120 4
气压+气温+风速	-0.005 2	1.392 0	1.155 3
压差+温差+风速	-0.165 4	1.342 9	1.061 3

图3 近30年逐月平均的全球位势高度分布

Fig.3 The global monthly average geopotential height distribution of recent 30 years

图4 我国某地风速时间序列及其小波周期图

Fig.4 Time series and wavelet periodogram of wind speed in China

图5 荷兰某地风速时间序列及其小波周期图

Fig.5 Time series and wavelet periodogram of wind speed in the Netherlands

表2 2组风速时间序列各个时期Hurst指数统计

Tab. 2 The statistics of Hurst exponents of two wind speed time series during each period

区域	冬季风1	春天过渡	夏季风	秋天过渡	冬季风2	全年平均
中国	0.869 2	0.868 0	0.836 7	0.846 3	0.872 3	0.858 9
荷兰	0.946 3	0.947 8	0.949 3	0.949 1	0.953 4	0.945 9

表3 2组风速时间序列各个时期信息熵统计

Tab. 3 The statistics of information entropies of two wind speed time series during each period

区域	冬季风1	春天过渡	夏季风	秋天过渡	冬季风2	全年平均
中国	3.270 7	3.326 2	2.815 0	3.367 4	3.329 9	3.195 0
荷兰	3.222 3	2.930 3	2.729 7	2.569 1	3.148 5	2.888 4

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