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Machine learning approach to differentiate excitation failure in synchronous generators from power swing

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Abstract

Loss of Excitation (LOE) is the most considerable fault in Synchronous generators since it affects both the generators and power network. The traditional protection method for LOE is based on impedance trajectory of the machine with negative offset mho relay. Meanwhile the traditional method experiences malfunctions and speed dip in LOE detection. This paper presents machine learning approach to detect LOE fault as well as classification logic to discriminate LOE fault from power swing conditions due to Line fault. This paper utilizes Hotelling’s-T2 statistical method to calculate Hotelling’s-T2 based Fault Indices (HT2-FI) for fault detection and Support Vector Machine (SVM) for classification. The time series data of electrical quantities such as Terminal voltage and Reactive Power of the generator are extracted from simulated Single Machine Infinite Bus test system and used as input data. These data involved in calculation of HT2-FI and in development of classification logic. The proposed method is simulated and verified for complete, partial LOE conditions and power swing conditions. Simulation outcomes depict the remarkable signs of the proposed method in LOE identification from power swing. Comparative assessment also reports that the method is capable of saving time in detecting LOE.

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Acknowledgments

The authors are thankful to the authorities of Thiagarajar College of Engineering, Madurai – 625015 to do this research work. This work was supported by Department of Science and Technology-Women Scientist Scheme-A fellowship scheme under Ref DST–WOS-A File No: SR/WOS-A/ET-9/2018 (2019-2022).

Funding

This work was supported by Department of Science and Technology-Women Scientist Scheme-A fellowship scheme under Ref DST–WOS-A File No: SR/WOS-A/ET-9/2018 (2019–2022).

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Authors and Affiliations

  1. EEE, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India

    Hemavathi Ramadoss & Geethanjali Muthiah

Authors
  1. Hemavathi Ramadoss
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  2. Geethanjali Muthiah
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Contributions

HR: Problem identification and methodology has been formulated. Entire simulation work was carried out. Analysis and interpretation of results was performed. Draft Manuscript preparation was done. GM: Review of the research work and verification of results has been done. Manuscript review and correction was performed. HR and GM: Final version of the manuscript has been reviewed for submission.

Corresponding author

Correspondence to Hemavathi Ramadoss.

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Appendix

Appendix

SMIB System Details.

Generator

Transformer

Transmission Line

Loads

S = 187 MVA,

V = 13.8 kV,

f = 50 Hz,

No. of Phases = 3, H = 3.7,

Rs = 0.00285,

Type of stator connection = star,

Pole pairs = 20

S = 250 MVA,

VH = 13.8 kV,

VL = 230 kV,

f = 50 Hz

Length = 100 km

Load1 = 13.8 kV,

Load2 = 230 kV

Excitation System Details.

Exciter Type

Parameters

IEEE Type1 system

Tr = 0.002 s, Ka = 300, Ta = 0.001 s, Ke = 1, Te = 0, Kf = 0.001, Tf = 0.1, Efmax = 11.5, Efmin = -11.5

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Ramadoss, H., Muthiah, G. Machine learning approach to differentiate excitation failure in synchronous generators from power swing. Electr Eng 105, 2041–2054 (2023). https://doi.org/10.1007/s00202-023-01784-9

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