Power System Topology Identification using Neural Networks, Part II - Node Processing

M. Delimar, Z. Hebel, and I. Pavi? (Croatia)


Topology, anomalies, neural networks, power system,state estimation


Power system state estimation is a basic function in power system control and analysis. The topological structure of the power system changes during power system operation and must be checked before state estimation to avoid gross error during state estimation. This paper presents a model for identifying anomalies in power system topology based on neural networks. The procedure is divided into two parts: nodes and branch processing. This paper focuses on node processing. Several types of neural networks were tried out on different power system network configurations. The results obtained using multilayer percepton with backpropagation learning algorithm are presented and analyzed.

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