Effects of Heatwaves on the Failure of Power Distribution Grids: a Fault Prediction System Based on Machine Learning

被引：1

作者：

Atrigna, Mauro ^{[1
]}

Buonanno, Amedeo ^{[1
]}

Carli, Raffaele ^{[2
]}

Cavone, Graziana ^{[2
]}

Scarabaggio, Paolo ^{[2
]}

Valenti, Maria ^{[1
]}

Graditi, Giorgio ^{[3
]}

Dotoli, Mariagrazia ^{[2
]}

机构：

[1] Ctr Portici Neaples, Smart Grid & Energy Networks Lab TERIN STSN SGRE, Dept Energy Technol & Renewable Energy Sources, Naples, Italy

[2] Polytech Bari, Dept Elect & Informat Engn, Bari, Italy

[3] Ctr Casaccia Rome, Dept Energy Technol & Renewable Energy Sources TE, Rome, Italy

来源：

2021 21ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2021 5TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE) | 2021年

关键词：

Power system reliability; Power system failures; machine learning;

D O I：

10.1109/EEEIC/ICPSEurope51590.2021.9584751

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Nowadays, a power system failure can drastically affect the reliability and normal operation of power distribution grids. The preparation for these failure events is currently approached with post-event analysis to identify the area of the system that requires the most resources in order to prevent future failures. Nevertheless, the forecasting of such events can be useful to anticipate the failure and possibly avoid it. In this work, we employ several machine learning approaches to analyze historical failure data and predict power grid outages based on operational and meteorological data. The approach is tested with real failure data of a power distribution network in the South of Italy, demonstrating advantageous results also to determine areas requiring particular attention.

引用

页数：5

共 25 条

[1] [Anonymous], ARPA SNPA SISTEMA NA
[2] [Anonymous], E DISTRIBUZIONE SPA
[3] Power System Resilience: Current Practices, Challenges, and Future Directions
Bhusal, Narayan
Abdelmalak, Michael
Kamruzzaman, Md
Benidris, Mohammed
[J]. IEEE ACCESS, 2020, 8 (08): : 18064 - 18086
[4] Bishop Christopher M, 2006, PATTERN RECOGN, V128, P1, DOI [10.1117/1.2819119, DOI 10.1117/1]
[5] Bragatto T, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2019 IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE), DOI 10.1109/EEEIC.2019.8783963
[6] Random forests
Breiman, L
[J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
[7] Calcara L., 2019, 2019 AEIT INT ANN C, P1
[8] XGBoost: A Scalable Tree Boosting System
Chen, Tianqi
Guestrin, Carlos
[J]. KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, : 785 - 794
[9] Analyzing the Impact of Failures in the Electric Power Distribution Grid
Chiaradonna, Silvano
Di Giandomenico, Felicita
Masetti, Giulio
[J]. 2016 SEVENTH LATIN-AMERICAN SYMPOSIUM ON DEPENDABLE COMPUTING (LADC), 2016, : 99 - 108
[10] Faivre O., 2016, FORECAST FAULTS HEAT

← 1 2 3 →