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A data-driven fault detection scheme for DC distribution networks based on the adaptive boosting technique

Author

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  • Li, Bo
  • Liao, Kai
  • Yang, Jianwei
  • He, Zhengyou

Abstract

Direct current (dc) distribution networks are rapidly growing, but they still face serious challenges of severe converter damage due to low inertia, short fault duration, and fast-growing dc fault currents. In this regard, a data-driven fault detection scheme for dc distribution networks is presented using machine learning techniques. To have a good fault detection capability of dc distribution networks, the fault characteristics of the faulted and healthy dc lines are analyzed with lots of fault data. After preprocessing, the fault feature vector is constructed to identify the faulted line using multi-dimensional fault features. Additionally, the adaptive boosting technique, which is a kind of machine learning, is used for the data-driven fault detection scheme. It is yielded with the abilities of fault identification and fault pole discrimination, tolerance to fault location, transition resistance, and noise interference. Further, a large amount of fault data is obtained by PSCAD/EMTDC to verify the proposed detection scheme. Test results demonstrate that the proposed fault detection scheme can achieve sensitive and accurate fault identification and fault pole selection within 2.5 ms. The proposed scheme is immune to noise interference and effectively adapts to changes in voltage level and topology with high accuracy.

Suggested Citation

  • Li, Bo & Liao, Kai & Yang, Jianwei & He, Zhengyou, 2024. "A data-driven fault detection scheme for DC distribution networks based on the adaptive boosting technique," Applied Energy, Elsevier, vol. 374(C).
    • Handle: RePEc:eee:appene:v:374:y:2024:i:c:s0306261924013321
    DOI: 10.1016/j.apenergy.2024.123949
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    References listed on IDEAS

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    1. Wang, Ting & Zhang, Chunyan & Hao, Zhiguo & Monti, Antonello & Ponci, Ferdinanda, 2023. "Data-driven fault detection and isolation in DC microgrids without prior fault data: A transfer learning approach," Applied Energy, Elsevier, vol. 336(C).
    2. Bayati, Navid & Balouji, Ebrahim & Baghaee, Hamid Reza & Hajizadeh, Amin & Soltani, Mohsen & Lin, Zhengyu & Savaghebi, Mehdi, 2022. "Locating high-impedance faults in DC microgrid clusters using support vector machines," Applied Energy, Elsevier, vol. 308(C).
    3. Dash, P.K. & Rekha Pattnaik, Smruti & N.V.D.V. Prasad, Eluri & Bisoi, Ranjeeta, 2023. "Detection and classification of DC and feeder faults in DC microgrid using new morphological operators with multi class AdaBoost algorithm," Applied Energy, Elsevier, vol. 340(C).
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