An effective data-driven machine learning hybrid approach for fault detection and classification in a standalone low-voltage DC microgrid

Deb, Anindita; Jain, Arvind Kumar

doi:10.1007/s00202-024-02334-7

Electr Eng

2024

DOI: 10.1007/s00202-024-02334-7

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An effective data-driven machine learning hybrid approach for fault detection and classification in a standalone low-voltage DC microgrid

Anindita Deb,

Arvind Kumar Jain

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Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty

Barkhi,

Poorhossein,

Hosseini

2024

Sci Rep

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Microgrids (MGs) can enhance the consumers’ reliability. Nevertheless, besides significant outcomes, some challenges arise. Regarding the intermittent nature of Renewable Energy Resources (RESs), MGs are not operated radially. Accordingly, the reliable protection of MGs considering uncertainty in RESs is crucial for planners and operators. This paper uses data analysis to extract knowledge from locally available measurements using RMS values of symmetrical components. The learning-based characteristic of the suggested technique with a low computational burden exempts the need for an available communication infrastructure in the MG. The Support Vector Machine (SVM) technique is applied to train the Intelligent Electronic Devices to have a reliable MG protection scheme. The proposed method, which performs fault detection and classification together, just requires local information and functions effectively to discriminate faulty from normal conditions considering different uncertainty of resistance faults. Digital simulations on an MV test network were conducted to construct an appropriate database to consider some aspects of uncertainty in the network. The various faults considering their uncertainty, the different modes of operation, the uncertainty of RESs generation, and the load levels are combined to produce myriad scenarios. The simulation results confirm the effectiveness of the proposed adaptive protection approach in accurately distinguishing different system modes and consistently protecting the MG, achieving an accuracy rate of 99.75%. Furthermore, it offers the MG an optimal protection scheme that is not limited by selectivity constraints across diverse conditions.

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Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty

Barkhi,

Poorhossein,

Hosseini

2024

Sci Rep

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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An effective data-driven machine learning hybrid approach for fault detection and classification in a standalone low-voltage DC microgrid

Cited by 1 publication

References 45 publications

Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty

Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty

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