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Enhancing transmission line protection with adaptive ANN-based relay for high resistance fault diagnosis

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Abstract

In modern power systems, accurate and timely detection of faults is crucial for ensuring system stability and reliability. The presence of high resistance in fault path curtails current and causes conventional distance relays to malfunction. These methods often require two-end measurements for accurate assessment of fault resistance necessitates an expensive communication channel. This paper proposes an innovative approach to enhance transmission line protection through an adaptive artificial neural network (ANN)-based relay system. The relay system integrates three ANN units: the fault detection unit, fault classification unit, and fault location unit, each tailored to detect, classify, and locate faults, respectively. By utilizing single-end measurements and employing discrete Fourier transform for feature extraction, the proposed algorithm efficiently diagnoses various fault conditions, including high resistance faults. Additionally, the algorithm dynamically updates its characteristics based on the estimated fault resistance (using one cycle post-fault data and the status of each ANN unit) in real-time, ensuring adaptability to changing system conditions, especially when the fault resistance falls beyond the scope of the training data. Simulation results on a 400-kV, 50-Hz transmission system demonstrate the robustness and effectiveness of the proposed approach in accurately identifying fault events under varying fault parameters, while also accounting for arcing faults and transducer errors. The suitability of the proposed method for real-time operations has been validated using OPAL-RT digital simulator. The adaptability of the proposed method for higher order systems is verified by performing a test case on the modified WSCC 9-bus system. The results support the adaptability and effectiveness of the proposed relaying algorithm in securing the transmission line under various conditions, including high resistance faults.

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Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, Jharkhand, India

    Janardhan Rao Moparthi & Krishna Naick Bhukya

  2. Department of Electrical Engineering, National Institute of Technology, Raipur, 492010, Chhattisgarh, India

    Durga Prasad Chinta & Monalisa Biswal

Authors
  1. Janardhan Rao Moparthi
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  2. Krishna Naick Bhukya
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  3. Durga Prasad Chinta
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  4. Monalisa Biswal
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Contributions

JRM helped in conceptualization, formal analysis, investigation, and original draft, KNB helped in formal analysis, investigation, supervision, editing, and review, DPC helped in formal analysis, editing, and review, and MB contributed to review, supervision, and editing.

Corresponding author

Correspondence to Janardhan Rao Moparthi.

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Appendix: Test system data

Appendix: Test system data

1.1 Parameters of two-bus model

Source data (sources 1 and 2)

400 kV, 50 Hz, 3 GVA

Source impedance

Z1G = 0.656 + j7.502 Ω

Z0G = 1.167 + j11.256 Ω

Transmission line length

300 km

Transmission line impedance

Z1L = 0.028 + j0.325 Ω

Z0L = 0.275 + j1.027 Ω

Load data

200 MW, 0.8 pf lagging

1.2 Parameters of WSCC 9-bus model

Generator ratings

G1: 16.5 kV, 247.5 MVA, 60 Hz

G2: 18 kV, 192 MVA, 60 Hz

G3: 13.8 kV, 128 MVA,60 Hz

Transformer ratings

T1: 16.5/230 kV, 60 Hz, 100 MVA

T2: 18/230 kV, 60 Hz, 100 MVA

T3: 13.8/230 kV, 60 Hz, 100 MVA

Load data

Load A: 100 MW + j35 MVAR

Load B: 125 MW + j50 MVAR

Load C: 90 MW + j30 MVAR

Line (7–8) length

100 km

Line (7–8) parameters

Z1L = 0.0449 + j0.3805 Ω

Z0G = 0.1124 + j0.7611 Ω

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Moparthi, J.R., Bhukya, K.N., Chinta, D.P. et al. Enhancing transmission line protection with adaptive ANN-based relay for high resistance fault diagnosis. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02369-w

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