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The untimely detection of the fault can endanger lives and destroy equipment. Identifying the occurrence of HIF in a power system is a cumbersome task, as fault current falls within the normal current range. The paper analyses current signals from radial and mesh distribution networks and features extracted during HIF and non\u2010HIF conditions by using the local binary pattern (LBP), local neighbor gradient pattern (LNGP), local neighbor descriptive pattern (LNDP), and local gradient pattern (LGP). In the proposed algorithm, 1D signal analysis for HIF detection in the MV distribution system is performed for the first time for fault analysis. The Kruskal\u2013Wallis test was carried out to get the best feature sets from the extracted features. HIF and non\u2010HIF were classified by bidirectional long short\u2013term memory (Bi\u2010LSTM) for the selected feature sets. Among the four algorithms, LGP attains the best accuracy for both networks; hence, the paper recommends that LGP with Bi\u2010LSTM is more effective for detecting HIF occurrence.<\/jats:p>","DOI":"10.1155\/jece\/3310174","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T04:48:28Z","timestamp":1736398108000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["High\u2010Impedance Fault Detection in Electric Power Distribution Network Using Local Pattern Transformation Methods and Bidirectional Long Short\u2013Term Memory"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1687-3300","authenticated-orcid":false,"given":"Rini Varghese","family":"P","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1703-9767","authenticated-orcid":false,"given":"M. S. 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