Dataset Information

Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems.

ABSTRACT: As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model's training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

SUBMITTER: Yousaf MZ

PROVIDER: S-EPMC11297239 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems.

Yousaf Muhammad Zain MZ Singh Arvind R AR Khalid Saqib S Bajaj Mohit M Kumar B Hemanth BH Zaitsev Ievgen I

Scientific reports 20240802 1

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Me ...[more]

PMID: 39095527

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Dataset Information

Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems.

Publications

Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems.

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