| dc.contributor.author | Li, Wei | |
| dc.contributor.author | Wang, Honghui | |
| dc.contributor.author | Zhang, Yu | |
| dc.contributor.author | Fang, Xin | |
| dc.contributor.author | Tian, Xiaojie | |
| dc.contributor.author | Leng, Dingxin | |
| dc.contributor.author | Xie, Yingchun | |
| dc.contributor.author | Liu, Guijie | |
| dc.date.accessioned | 2026-08-20T11:17:24Z | |
| dc.date.available | 2026-08-20T11:17:24Z | |
| dc.date.copyright | 2025/12/16 | |
| dc.date.issued | 2026 | |
| dc.identifier.other | JLEED9.EYENG-6058.pdf | |
| dc.identifier.uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4311977 | |
| dc.description.abstract | AbstractAs a crucial component of wind power generation systems, wind turbines must operate
safely to prevent sudden failures. This requires effective identification of abnormal
operational states. In this study, we propose a novel approach for ... | |
| dc.publisher | American Society of Civil Engineers | |
| dc.title | Abnormal State Identification Method for Wind Turbines Based on Improved DBSCAN and Random Forests | |
| dc.type | Journal Article | |
| dc.journal.volume | 152 | |
| dc.journal.issue | 2 | |
| dc.journal.title | Journal of Energy Engineering | |
| dc.identifier.doi | 10.1061/JLEED9.EYENG-6058 | |
| dc.journal.fristpage | 04025110-1 | |
| dc.journal.lastpage | 04025110-15 | |
| dc.page | 15 | |
| dc.tree | Journal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 002 | |
| dc.contenttype | Fulltext | |
