Determination of Power System Topological Observability using Improved Hopfield Neural Network

Surender Reddy Salkuti, Jung Chan-Mook

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. 

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References


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