Determination of Power System Topological Observability using Improved Hopfield Neural Network

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Published: Sep 9, 2016
DOI: https://doi.org/10.37936/ecti-eec.2017151.171285
Keywords:
Topological Observability Hopfield Neural Networks Power System Configuration Power System State Estimation

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S. Surender Reddy
Department of Railroad and Electrical Engineering, Woosong University
Chan-Mook Jung
Department of Railroad and Civil Engineering, Woosong University

Abstract

This paper formulates the power system Topological Observability (TO) problem as an integer programming problem, and develops a new methodology based on Improved Hopfield Neural Network (IHNN) for the determination of TO in power system networks. These complex power systems require accurate and efficient controls that makes the control centers to work efficiently. These control centers are equipped with Supervisory Control and Data Acquisition (SCADA) systems allowing to acquire information about the power system, and its transmission to control centers in real time. The computations in real time environment are reaching a limit, as far as conventional computer based algorithms are concerned. Hence, it is required to find out newer methods for these applications, which can be implemented on hardware to outperform their software counterpart. Therefore, this paper solves the TO problem using IHNN. This algorithm is based on neural networks and can easily be implemented on dedicated hardware. 

Article Details

How to Cite
Reddy, S. S., & Jung, C.-M. (2016). Determination of Power System Topological Observability using Improved Hopfield Neural Network. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 15(1), 32–40. https://doi.org/10.37936/ecti-eec.2017151.171285
Issue
Vol. 15 No. 1 (2017): Regular Issue
Section
Electrical Power Systems

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