{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T21:02:55Z","timestamp":1767560575036,"version":"3.48.0"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"36","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Timely and accurate fault detection and localization are essential for reliable operation of distribution networks. This paper presents a hybrid edge\u2013cloud framework that integrates convolutional neural networks (CNNs) with edge computing to achieve real-time performance. The proposed method distributes computational tasks such that edge devices handle data acquisition, preprocessing, and CNN-based inference, while cloud servers manage model retraining and historical data storage. The CNN architecture comprises three convolutional layers with ReLU activation, max-pooling, and two fully connected layers optimized for lightweight inference. A 33 kV distribution network model was used to generate fault scenarios, including single line-to-ground, double line-to-ground, line-to-line, three-phase, and three-phase-to-ground faults under varying resistances and loads. Experimental results show that the proposed framework achieves 100% fault-type classification accuracy, an average fault localization error of 0.18 km (vs. 1.25 km for impedance-based methods), and a 50% latency reduction compared to cloud-only implementations. These results confirm that the framework enhances both responsiveness and resilience, offering a scalable solution for modern distribution network fault management.<\/jats:p>","DOI":"10.31449\/inf.v49i36.10032","type":"journal-article","created":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T20:59:13Z","timestamp":1767560353000},"source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Edge\u2013Cloud CNN Framework for Real-Time Fault Detection and Localization in Distribution Networks"],"prefix":"10.31449","volume":"49","author":[{"given":"Yunchu","family":"Qin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fugui","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingzhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2025,12,20]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10032\/6086","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10032\/6086","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T20:59:13Z","timestamp":1767560353000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/10032"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,20]]},"references-count":0,"journal-issue":{"issue":"36","published-online":{"date-parts":[[2026,1,4]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v49i36.10032","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12,20]]}}}