A Survey of Data Mining Techniques for Smart Grid Systems

Mrs. Gresha Bhatia; Dr. Mrs. Suneeta Sane

Electricity generated through steam and hydro – turbines have been serving us since ages. It has reached its limits. Outages, blackouts are becoming inevitable. So, there is a need to develop a system that is able to identify the links right from the source of electricity generation to the consumers that are vulnerable to failures, automatically switch over to other network load if need be , be prepared prior to such a situation and hence take corrective measures to restore the electricity transparently. All this can be done by applying machine learning and data mining algorithms to the existing infrastructure. This leads to development of a system that is smart, intelligent and works transparently towards its goal i.e. preventing power failures. The aim of this paper is to focus on the existing electricity generation infrastructure, the factors that affect the current system and the need for Smart Grid. The various methods that have been concentrated on are that of machine learning and data mining techniques that can be mapped to these smart grid environments. For each of these techniques namely - data mining, ranking, visualization and testing, we have highlighted the key points of every technique stating their advantages, features of each. In this paper we have also tried to analyze the evaluation framework that would aid in monitoring grid activities depending on the type of data, anticipate and respond to system disturbances proactively based on the method applied to minimize the impact of power failure on consumers and thereby improvise the overall performance of the system.

Select Volume / Issues:

• Volume 3 Issue 6, Nov. 2012

Year:

2012

Type of Publication:

Article

Keywords:

Data mining; Electrical infrastructure; evaluation framework; Smart grid

Journal:

IJECCE

Volume:

3

Number:

6

Pages:

1325-1329

Month:

November