Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario
Alejandro Verduzco (Universidad Autónoma de Nuevo León)
Paula Páramo Balsa (University of Seville)
Francisco M. Gonzalez-Longatt (University of South-Eastern Norway)
Manuel Andrade (Universidad Autónoma de Nuevo León)
Martha Nohemi Acosta Montalvo (University of South-Eastern Norway)
José L. Torres (TU Delft - Intelligent Electrical Power Grids)
P. Palensky (TU Delft - Intelligent Electrical Power Grids)
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Abstract
This research paper presents a method that uses measurements of voltages angles, as provided by phasor measurement units (PMU), to accurately detect the sudden disconnection of a generation unit from a power grid. Results in this research paper have demonstrated, in a practical fashion, that a multi-layer perceptron (MLP) neural network (NN) can be appropriately trained to detect and identify the sudden disconnection of a generation unit in a multi-synchronous generation unit power system. Synthetic time-series bus voltage angles considering low inertia scenarios in the IEEE 39 bus system were used to train the MLP NN. The training process is speeded up by using four GPUs hardware. The simulations results have confirmed the successful detection and identification of the generator outage.