Convolutional Neural Networks (CNNs) for Power System Big Data Analysis

被引：0

作者：

Plathottam, Siby Jose ^{[1
]}

Salehfar, Hossein ^{[1
]}

Ranganathan, Prakash ^{[1
]}

机构：

[1] Univ North Dakota, Elect Engn, Grand Forks, ND 58202 USA

来源：

2017 NORTH AMERICAN POWER SYMPOSIUM (NAPS) | 2017年

关键词：

Deep Learning; Machine Learning; Convolutional NN; Feed Forward NN; wind power generation; Artificial intelligence;

D O I：

暂无

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：

The concept of automated power system data analysis using Deep Neural Networks (as part of the routine tasks normally performed by Independent System Operators) is explored and developed in this paper. Specifically, we propose to use the widely-used Deep neural network architecture known as Convolutional Neural Networks (CNNs). To this end, a 2-D representation of power system data is developed and proposed. To show the relevance of the proposed concept, a multi-class multi-label classification problem is presented as an application example. Midcontinent ISO (MISO) data sets on wind power and load is used for this purpose. TensorFlow, an open source machine learning platform is used to construct the CNN and train the network. The results are discussed and compared with those from standard Feed Forward Networks for the same data.

引用

页数：6