Big data mining and analytics in substation automation and network management

Data gathered from distribution networks

This thesis begins with the study of current naming conventions used in the power systems. Two standards, the COMTRADE and the IEC 61850, that define the organizing of data are looked into. This information is used to create a novel naming convention for future use within big data mining applications.

The main objective of this thesis is to study and test different ways to use the data gathered from distribution network more effectively. The scope of this thesis is limited to examining ways to benefit more from process data and disturbance recordings.

The goal is to develop, test and evaluate the system capable of gathering, storing, automatically analyzing the network data and visualizing the findings in an easy to read form in an actual electricity distribution network.

In addition to this a novel naming convention was developed for use in future databases which utilize these automatic analyzing functions.

The structure

The chapter 2 addresses the state of today’s electricity distribution environment. Current power system is reviewed and the expectations for it are listed. Its basic architecture and conventions of controlling it through substation automation systems is presented.

This chapter is intended to give a baseline and motivation for which this thesis is all about. Some thoughts are given into why uninterrupted electricity distribution is so important in today’s world and how the future looks from the smart grid perspective.

When the need is established the chapter 3 moves closer into solution by giving a thorough introduction into one of the biggest phenomena affecting the automation systems – the big data mining.  In this chapter big data is given a basic definition and some ways how to benefit from it.

At the end of the Chapter 3 a scenario of how a future electricity distribution company might harness the big data to its benefit is presented.

The chapter 4 describes the disturbance recording and standard defining its form as well as a novel naming convention developed specifically for automatic analyzing algorithms which bases heavily on the already widely adopted IEC 61850 standard.

In the chapter 5 the smart system analyzer concept is presented as a possible solution for big data mining in power system applications. The system components are introduced and their functions explained. At the end of this chapter a simple proof of concept testing is done in a simulated environment.

The 6th and last major chapter of this thesis covers a practical pilot testing of the system in an actual electricity distribution system. Few analyzing cases are presented with the solutions for solving them.

Finally a possible way to visualize the results is presented.