{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T08:40:29Z","timestamp":1776847229210,"version":"3.51.2"},"reference-count":24,"publisher":"World Scientific Pub Co Pte Ltd","issue":"07","funder":[{"name":"State Grid Hunan Economic Research Institute 2025 Overhead Transmission Line Lightning Hazard Risk Assessment Technical Service Project","award":["B116A2250002"],"award-info":[{"award-number":["B116A2250002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2026,6,15]]},"abstract":"<jats:p>This study presents a data-driven framework that integrates machine-learning-based diagnosis with gradient-based optimization for lightning protection of transmission corridors. Six standardized features \u2014 ground resistance, insulator-string length, tower height, protection angle, ground inclination, and ground-flash density \u2014 are normalized to construct tower state vectors for model training and inference. The diagnostic model outputs risk scores and cause attributions, identifying ground-flash density as the dominant hazard (27.9%), followed by excessive ground inclination, increased tower height, and shortened insulator strings that elevate wrap-around strike risk. Using these diagnostic results, a gradient-based optimizer minimizes state deviations within engineering constraints to generate actionable protection measures, including arrester configuration and parameter adjustments. In a 220-kV case study covering 39 towers, 30 satisfied the standard and 9 were recommended for arrester installation; for the high-risk Tower #034, the trip-out rate decreased to 1.632 times\/(100 km\u22c5a). The proposed approach improves objectivity and efficiency relative to manual practice. Future work will incorporate cost weighting to enhance practical deployment.<\/jats:p>","DOI":"10.1142\/s0218001426510031","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T03:03:46Z","timestamp":1770174226000},"source":"Crossref","is-referenced-by-count":0,"title":["Synergistic Method for Machine-Learning-Based Diagnosis and Gradient-Based Optimization of Lightning Damage Mechanisms"],"prefix":"10.1142","volume":"40","author":[{"given":"Lei","family":"Chuanli","sequence":"first","affiliation":[{"name":"Economic and Technological Research Institute, State Grid Hunan Electric Power Company Limited, Changsha Hunan 410004, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Junyi","sequence":"additional","affiliation":[{"name":"Economic and Technological Research Institute, State Grid Hunan Electric Power Company Limited, Changsha Hunan 410004, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Zhe","sequence":"additional","affiliation":[{"name":"Economic and Technological Research Institute, State Grid Hunan Electric Power Company Limited, Changsha Hunan 410004, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Qingshan","sequence":"additional","affiliation":[{"name":"State Grid Hunan Provincial Electric Power Company Limited, Changsha Hunan 410004, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liu","family":"Yubin","sequence":"additional","affiliation":[{"name":"Economic and Technological Research Institute, State Grid Hunan Electric Power Company Limited, Changsha Hunan 410004, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhou","family":"Haiou","sequence":"additional","affiliation":[{"name":"Economic and Technological Research Institute, State Grid Hunan Electric Power Company Limited, Changsha Hunan 410004, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"S0218001426510031BIB001","first-page":"47","author":"Astorga J. M.","year":"2013","journal-title":"Rev. Fac. Ing. Univ. 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