Power system transient stability margin estimation using neural networks

Abstract This paper proposes a methodology for estimating a normalized power system transient stability margin (ΔVn) using multi-layered perceptron (MLP) neural network with a fast training approach. The nonlinear mapping relation between the ΔVn and operating conditions of the power system is established using the MLP neural network. The potential energy boundary surface (PEBS) method along with a time-domain simulation technique is used to obtain the training set of the neural network. Results on the New England 10-machine 39-bus system demonstrate that the proposed method provides a fast and accurate tool to evaluate online power system transient stability with acceptable accuracy. In addition, based on the examination of generators rotor angles after faults, a method is presented to select the power system operating conditions that most effect the Δ V n for each fault.

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