Applications of Big Data and AI in Electric Power Systems Engineering

Applications of Big Data and AI in Electric Power Systems Engineering

Tahir Cetin Akinci
Copyright: © 2020 |Pages: 21
DOI: 10.4018/978-1-5225-9687-5.ch009
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Abstract

The production, transmission, and distribution of energy can only be made stable and continuous by detailed analysis of the data. The energy demand needs to be met by a number of optimization algorithms during the distribution of the generated energy. The pricing of the energy supplied to the users and the change for investments according to the demand hours led to the formation of energy exchanges. This use costs varies for active or reactive powers. All of these supply-demand and pricing plans can only be achieved by collecting and analyzing data at each stage. In the study, an electrical power line with real parameters was modeled and fault scenarios were created, and faults were determined by artificial intelligence methods. In this study, both the power flow of electrical power systems and the methods of meeting the demands were investigated with big data, machine learning, and artificial neural network approaches.
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Introduction

In today's modern societies, electric energy is an inevitable concept. Electrical energy is a social and economic requirement for the development of society. For the last thirty years, a great deal of research has been undertaken to analyze and solve the problems of electrical power systems. Most of the research is on control theory, power electronics drivers and economic analysis. In recent years, the development of artificial intelligence and its methods has made this technology applicable in many areas. In this study, some methods have been proposed in order to provide supply balance by investigating the methods of using electric power systems with artificial intelligence techniques. Research on electrical power system can be examined in two groups as modeling and analysis. In this study, fault scenarios were created by using the energy transmission line model: these defects were then determined by artificial intelligence methods.

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