In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can eff...In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.展开更多
极端冰灾天气下线路覆冰闪络跳闸是激发大电网脆弱性、导致大面积停电的重要因素。覆冰闪络跳闸等现象的风险评估是建立冰灾防御体系的基础。基于电网层面,进行了冰灾天气下线路覆冰闪络跳闸的风险状态识别与风险建模。根据覆冰闪络跳...极端冰灾天气下线路覆冰闪络跳闸是激发大电网脆弱性、导致大面积停电的重要因素。覆冰闪络跳闸等现象的风险评估是建立冰灾防御体系的基础。基于电网层面,进行了冰灾天气下线路覆冰闪络跳闸的风险状态识别与风险建模。根据覆冰闪络跳闸特性分析,界定了输电网绝缘系统的脆弱点,即覆冰期绝缘系统冰凌桥接、融冰期冰凌断流等临界点。进一步建立了覆冰闪络状态的划分原则与风险等级,并对预测冰况进行了模糊模式识别和风险评级,为运行人员提供了动态风险信息。针对数据的小样本、多输入等特点,采用统计学习理论结构风险最小化方法,构建了最小二乘支持向量机(least squares support vector machine,LSSVM)冰闪跳闸风险评估模型,依据贝叶斯(Bayesian)证据推理优化模型参数。通过与误差反向传播人工神经网络(artificial neural networkwith error back propagation,BP-ANN)算法对比,验证了该模型的有效性。最后通过脆弱性指标分析了网架结构破坏的严重性与电网绝缘系统的脆弱性。展开更多
基金Project supported by the National Natural Science Foundation of China （Grant Nos 40575036 and 40325015）.Acknowledgement The authors thank Drs Zhang Pei-Qun and Bao Ming very much for their valuable comments on the present paper.
文摘In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.
文摘极端冰灾天气下线路覆冰闪络跳闸是激发大电网脆弱性、导致大面积停电的重要因素。覆冰闪络跳闸等现象的风险评估是建立冰灾防御体系的基础。基于电网层面,进行了冰灾天气下线路覆冰闪络跳闸的风险状态识别与风险建模。根据覆冰闪络跳闸特性分析,界定了输电网绝缘系统的脆弱点,即覆冰期绝缘系统冰凌桥接、融冰期冰凌断流等临界点。进一步建立了覆冰闪络状态的划分原则与风险等级,并对预测冰况进行了模糊模式识别和风险评级,为运行人员提供了动态风险信息。针对数据的小样本、多输入等特点,采用统计学习理论结构风险最小化方法,构建了最小二乘支持向量机(least squares support vector machine,LSSVM)冰闪跳闸风险评估模型,依据贝叶斯(Bayesian)证据推理优化模型参数。通过与误差反向传播人工神经网络(artificial neural networkwith error back propagation,BP-ANN)算法对比,验证了该模型的有效性。最后通过脆弱性指标分析了网架结构破坏的严重性与电网绝缘系统的脆弱性。
