Data-driven Feature Description of Heat Wave Effect on Distribution System

被引:2
|
作者
Zhang, Yang [1 ]
Mazza, Andrea [1 ]
Bompard, Ettore [1 ]
Roggero, Emiliano [2 ]
Galofaro, Giuliana
机构
[1] Politecn Torino, Dept Energy, Turin, Italy
[2] Ireti SpA, Grp IREN, Turin, Italy
来源
关键词
Data analytics; distribution system; heat wave; resilience; kernel density estimation; Gaussian mixture model; CLUSTERING METHOD; RESILIENCE;
D O I
10.1109/ptc.2019.8810712
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
During the last years, the effects of the climate change have become more and more evident. In particular, urban regions, where is more common the use of underground cables, are experiencing the strong effect of extremely high temperature conditions and low humidity. This phenomenon, known in literature as "heat wave", should be properly evaluated for highlighting its effect on the system operation and planning, as well as for properly scheduling appropriate maintenance interventions. This paper presents a three-step procedure aiming to characterize the heat wave phenomenon in terms of "most significant features" and, on this basis, recognizing the days as "critical" and "non-critical". The weather conditions of the city of Turin (Italy) and the faults that have affected the local network in the last 10 years have been considered. This approach will be useful for system operators for integrating the weather information in distribution system operation and planning procedures.
引用
收藏
页数:6
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