Time domain differential protection scheme applied to power transformers
•The main innovations and contributions of the proposed scheme are as follows:•The accuracy of combination of ITD and EBT protection scheme for a power transformer is good.•Time-domain extracted statistical features with distinctive behavior make them useful for fast protection purpose.•There is no...
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Veröffentlicht in: | International journal of electrical power & energy systems 2023-12, Vol.154, p.109465, Article 109465 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •The main innovations and contributions of the proposed scheme are as follows:•The accuracy of combination of ITD and EBT protection scheme for a power transformer is good.•Time-domain extracted statistical features with distinctive behavior make them useful for fast protection purpose.•There is no need to tune parameters due to adaptive nature of ITD.•A wide variety of scenarios has been investigated.
This paper presents a new differential protection scheme for power transformers based on Intrinsic Time-Scale Decomposition (ITD) and Ensemble of Bagged Tree (EBT) as a classifier. The ITD decomposes the input nonstationary signal into Proper Rotation Components (PRCs) and a monotonic trend signal. In the proposed scheme, statistical time-domain features extracted from the ITD output are fed to the EBT classifier, which classifies different events, including internal fault, internal fault combined with inrush current, different inrush currents (energizing transformer, sympathetic, and recovery inrush current), and external fault. The combination of ITD and EBT tools requires only a limited sample of post-fault data to protect the transformer. The proposed scheme is tested under various operating conditions to confirm its accuracy, and the effect of current transformer (CT) saturation on the scheme's performance is evaluated. The scheme is immune to noisy conditions and does not require transformer parameters. The power systems were simulated using PSCAD/EMTDC and MATLAB software. |
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ISSN: | 0142-0615 |
DOI: | 10.1016/j.ijepes.2023.109465 |