Big Data osana sähkönjakeluautomaatiota ja sähköverkon hallintaa

All fields of industry are constantly seeking ways to improve their efficiency. This is now especially true for power systems as they are facing one of the biggest challenges yet – how to cope with constantly increasing demands for electricity distribution with ageing power grid. Utilization of the big data mining in power systems presents one possible way to improve cost-efficiency and achieve higher level of reliability even with the ageing infrastructure. The target of this thesis is to research and develop ways to get additional information out of the currently mostly ignored disturbance recordings and history of process data. The complexity of big data mining poses a great challenge for system developers. Power systems are among the best systems to get-started with big data mining solutions as they consist mainly of structured and semi-structured databases with vast amounts of information. The different naming conventions used in different systems along with great variety of different protocols hinders the easy comparison of information obtained from separate systems. 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 naming convention is chosen so that it supports the needs of current and future needs as well. The creation of a reliably structured central database is one of the key elements of practical data mining solution. A system concept called Smart System Analyser developed for big data mining in power systems is presented next. It consists of relational SQL historian database and a novel calculation engine built around currently existing proven products. System components are described in detail and their operation explained. The practical parts of this thesis is about the testing of this novel system first in simulated environment and then with actual power distribution company data. Even the early stages of the pilot testing show the potential for future development and benefit from power system data mining. An application is made for protection operation time calculation using the presented novel system. It is ran with data obtained from disturbance recordings and the results are visualized in a web interfac