Determination of Power System Topological Observability using Improved Hopfield Neural Network

Surender Reddy Salkuti, Jung Chan-Mook


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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M. Shahraeini, M. H. Javidi, "A Survey on Topological Observability of Power Systems", IEEE Power Engineering and Automation Conference, Sept. 2011, pp. 373 - 376.

J.W. Wang, V. H. Quintana, “A Decoupled Orthogonal Row Processing Algorithm for Power System State Estimation,” IEEE Trans. Power App. and syst., vol. 103, pp. 2337–2344, Aug. 1984.

A.Monticelli, C.A.F.Murati, and F.F.Wu, “A Hybrid State Estimator: Solving Normal Equations by Orthogonal Transformations,” IEEE Trans. Power App. and syst., vol. 105, pp. 3460–3468, Dec. 1985.

F. C. Aschmoneit, N. M. Peterson, and E. C. Adrian, “State Estimation with Equality Constraints,” Proc. of 10th PICA,Toronto, Canada, May 1977, pp. 427–430.

S. Vazquez-Rodriguez, A.Faina, B.Neira-Duenas, “An Evolutionary Technique with Fast Convergence for Power System Topological Observability Analysis”, IEEE Congress on Evolutionary Computation, 2006, pp. 3086-3090.

A.S.Costa, S. Seleme, and R. Salgado, “Equality Constraints in Power System State Estimation via Orthogonal Row-Processing Techniques,” Proc. IFAC Electrical Energy Syst., Brazil, 1985, pp. 43–49.

A.Gjelsvik, S.Aam, and L.Holten, “Hachtel’s Augmented

Matrix method—A rapid method for Improving Numerical Stability in Power System Static State Estimation,” IEEE Trans. Power App. and syst., vol. 104, pp. 2987–2993, Nov. 1985.

W.E.Liu, F.F.Wu, L.Holten, A.Gjelsvik, and S.Aam, “Computational issues in the Hachtel’s Augmented Matrix method for Power System State Estimation,” Proc. of Power System Computation, Lisbon, Portugal, 1987.

F.Alvarado, W.Tinney, “State Estimation Using Augmented Blocked Matrices,” IEEE Trans. Power Syst., vol. 5, pp. 911–921, Aug. 1990.

G. Madhusudhana Rao, H. Jain, " Power System Controlling Using Artificial Intelligence", Int. Journal on Recent Trends in Engineering & Technology, vol. 4, no. 4, pp. 63, Nov. 2010.

S. Vazquez-Rodriguez, R.J.Duro, "A genetic based technique for the determination of power system topological observability", Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Sept. 2003, pp. 48-52.

S. Urano, and H. Mori, "A Method for Determining Pseudo-Measurement State Values for Topology Observability of State Estimation in Power Systems", Electrical Engineering in Japan, vol. 179, no. 2, 2012.

R.R.Nucera, M.L.Gilles, “A Blocked Sparse Matrix Formulation for the solution of Equality-Constrained State Estimation,” IEEE Trans. Power Syst., vol. 6, pp. 214–224, Feb. 1991.

H.Mori and S.Tsuzuki, “Power System Topological Observability Analysis Using a Neural Network Model”, Proc. of Second Symposium on Expert Systems Application to Power Systems”, pp. 385-391, Seattle, Washington, U.S.A., July 1989.

D.Tank, J.Hopfield, “Simple 'Neural' Optimization Networks: An A/D Converter, Signal Decision Circuit, and a Linear Programming Circuit”, IEEE Trans. Circuits and syst., vol. 33, no. 5, pp. 533-541, May 1986.

F.C.Aschmoneit, N.M.Peterson, and E.C.Adrian, “An Orthogonal Row Processing Algorithm for Power System Sequential State Estimation,” IEEE Trans. Power App. and syst., vol. 100, pp. 3791–3800, Aug. 1981.


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