Power Grid Health Assessment Using Machine Learning Algorithms

被引：1

作者：

Ioanes, Andrei ^{[1
]}

Tirnovan, Radu ^{[1
]}

机构：

[1] Tech Univ Cluj Napoca, Cluj Napoca, Romania

来源：

2019 11TH INTERNATIONAL SYMPOSIUM ON ADVANCED TOPICS IN ELECTRICAL ENGINEERING (ATEE) | 2019年

关键词：

Power Grid; Smart Grid; Machine Learning; Long Short-Term Memory Algorithm;

D O I：

10.1109/atee.2019.8724920

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：

Development and successful implementation of Artificial Intelligence concepts with a focus on Neural Networks in different technical environments raise the question of their applicability in the transition from classic power grids to smart grids. In this paper power grid status is evaluated based on deviation between measured system parameters and design values while also considering historical values.

引用

页数：4

共 9 条

[1] Gers F.A., 2000, Recurrent nets that time and count
[2] GERS FA, 2000, LEARNING FORGET CONT
[3] Hochreiter S., 1977, NEURAL COMPUTATION
[4] System health monitoring and prognostics - a review of current paradigms and practices
Kothamasu, R
Huang, SH
VerDuin, WH
[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2006, 28 (09) : 1012 - 1024
[5] Lipton ZC, 2015, ARXIV150600019V4CSLG
[6] Schuster Mike, 1997, IEEE T
[7] Sutskever I., 2014, LEARNING EXECUTE
[8] Sutskever I., 2014, NeurIPS, V2, P3104
[9] Venkatasubramanian Venkat, REV PROCESS FAULT 1

← 1 →