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Machine Learning Paradigms pp 269–313Cite as

Intelligent Data Analysis in Electric Power Engineering Applications

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Part of the Intelligent Systems Reference Library book series (ISRL,volume 149 )

Abstract

This chapter presents various intelligent approaches for modelling, generalization and knowledge extraction from data, which are applied in different electric power engineering domains of the real world. Specifically, the chapter presents: (1) the application of ANNs, inductive ML, genetic programming and wavelet NNs, in the problem of ground resistance estimation, an important problem for the design of grounding systems in constructions, (2) the application of ANNs, genetic programming and nature inspired techniques such as gravitational search algorithm in the problem of estimating the value of critical flashover voltage of insulators, a well-known difficult topic of electric power systems, (3) the application of specific intelligent techniques (ANNs, fuzzy logic, etc.) in load forecasting problems and in optimization tasks in transmission lines. The presentation refers to previously conducted research related to the application domains and briefly analyzes each domain of application, the data corresponding to the problem under consideration, while are also included a brief presentation of each intelligent technique and presentation and discussion of the results obtained. Intelligent approaches are proved to be handy tools for the specific applications as they succeed to generalize the operation and behavior of specific parts of electric power systems, they manage to induce new, useful knowledge (mathematical relations, rules and rule based systems, etc.) and thus they effectively assist the proper design and operation of complex real world electric power systems.

Keywords

  • Ground Resistance
  • Load Forecasting
  • Grounding Systems
  • Inductive Machine Learning (IML)
  • Flashover

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  1. High Voltage Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Politechniou Street, Zografou Campus, 15780, Athens, Greece

    V. P. Androvitsaneas

  2. Management & Decision Engineering Lab, Department of Financial and Management Engineering, University of the Aegean, 41 Kountouriotou Street, 82100, Chios, Greece

    K. Boulas & G. D. Dounias

Authors
  1. V. P. Androvitsaneas
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  2. K. Boulas
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  3. G. D. Dounias
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Corresponding authors

Correspondence to V. P. Androvitsaneas or G. D. Dounias .

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Editors and Affiliations

  1. University of Piraeus , Piraeus, Greece

    Prof. Dr. George A. Tsihrintzis

  2. University of Piraeus , Piraeus, Greece

    Dr. Dionisios N. Sotiropoulos

  3. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, New South Wales, Australia

    Prof. Lakhmi C. Jain

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Androvitsaneas, V.P., Boulas, K., Dounias, G.D. (2019). Intelligent Data Analysis in Electric Power Engineering Applications. In: Tsihrintzis, G., Sotiropoulos, D., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 149 . Springer, Cham. https://doi.org/10.1007/978-3-319-94030-4_11

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