Power System Voltage Stability Estimation Using Neural Networks and Phasor Measurement Units

Faezeh Gholami; Mohamadreza Ahmadi

doi:10.15866/iremos.v6i1.2319

Power System Voltage Stability Estimation Using Neural Networks and Phasor Measurement Units

Faezeh Gholami^(1*), Mohamadreza Ahmadi⁽²⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/iremos.v6i1.2319

Abstract

In this paper, a Multi-Layered Perceptron (MLP) Neural Network (NN) is proposed to nullify the effects of the measured data errors associated with Phasor Measurement Units (PMUs). With this MLP NN, power system voltage stability is estimated by calculating Voltage Collapse Indices (VCIs). Several VCIs are compared with two evaluation criteria to determine those appropriate indices that should be trained using NN as the target output. In other words, the data obtained from the PMU are exploited as the MLP NN inputs, and the exact informative VCIs are considered as its output. The capability of the proposed method is demonstrated by applying it to the New England 39-bus test system. The obtained results confirm the validity of the proposed technique.
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Keywords

Neural Network; Voltage Stability; Phasor Measurement Unit

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