| Title | Key Feature Mining Method for Power-Cut Window Based on Grey Relational Analysis |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Zhang, Fengbin, Liu, Xingwei, Wei, Zechen, Zhang, Jiali, Yang, Nan, Song, Xuri |
| Conference Name | 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) |
| Date Published | dec |
| Keywords | Big Data, data mining, grey relational analysis, Human Behavior, Information management, key features, Key Management, maintenance engineering, maintenance plan, Manuals, Metrics, Planning, power grids, power system reliability, power-cut window, pubcrawl, Resiliency, Scalability |
| Abstract | In the process of compiling the power-cut window period of the power grid equipment maintenance plan, problems such as omission of constraints are prone to occur due to excessive reliance on manual experience. In response to these problems, this paper proposes a method for mining key features of the power-cut window based on grey relational analysis. Through mining and analysis of the historical operation data of the power grid, the operation data of new energy, and the historical power-cut information of equipment, the indicators that play a key role in the arrangement of the outage window period of the equipment maintenance plan are found. Then use the key indicator information to formulate the window period. By mining the relationship between power grid operation data and equipment power outages, this paper can give full play to the big data advantages of the power grid, improve the accuracy and efficiency of the power-cut window period. |
| DOI | 10.1109/IMCEC55388.2022.10019909 |
| Citation Key | zhang_key_2022 |
Key Feature Mining Method for Power-Cut Window Based on Grey Relational Analysis