Ensemble machine learning approach to identify excitation failure in synchronous generators

Hemavathi, R.; Geethanjali, M.

doi:10.1016/j.engfailanal.2023.107506

Cited by 1 publication

(1 citation statement)

References 41 publications

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“…Another approach to the adaptive generator out-of-step protection, this incorporating the phasor measurement units (PMU), was suggested in [14]. Furthermore, detecting the loss of excitation condition of synchronous generators has been tackled by applying ML methods as well, e.g., [15][16][17]. Detection of islanding by means of the ML was proposed by Meera et al in [18].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Classifier for Supporting Generator’s Impedance-Based Relay Protection Functions

Sarajcev,

Lovric

2024

Energies

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Transient stability of the electric power system still heavily rests on a timely and correct operation of the relay protection of individual power generators. Power swings and generator pole slips, following network short-circuit events, can initiate false relay activations, with negative repercussions for the overall system stability. This paper will examine the generator’s underimpedance (21G) and out-of-step (78) protection functions and will propose a machine learning based classifier for supporting and reinforcing their decision-making logic. The classifier, based on a support vector machine, will aid in blocking the underimpedance protection during stable generator swings. It will also enable faster tripping of the out-of-step protection for unstable generator swings. Both protection functions will feature polygonal protection characteristics. Their implementation will be based on European practice and IEC standards. Classifier will be trained and tested on the data derived from simulations of the IEEE New England 10-generator benchmark power system.

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