Research on quality assessment method based on statistical analysis of full life cycle of power grid equipment
- DOI
- 10.2991/978-94-6463-326-9_52How to use a DOI?
- Keywords
- power grid equipment; full life cycle; big data statistical analysis; quality assessment method
- Abstract
The quality of power grid equipment is related to the safe and stable operation of the power grid, and to a large extent determines the future operation and maintenance, maintenance, the cost. How to carry out equipment selection and supplier selection, and do a good job in equipment access, is an important issue facing the power grid companies. Combined with the whole life cycle theory, considering the influence of different equipment operation life, defects, and early decommissioning due to quality problems, build a relatively complete equipment defect rate, failure rate and equipment life evaluation model, and combining the three factors to form the overall evaluation of equipment quality, to provide a more scientific decision basis for equipment selection procurement and equipment operational strategy optimization.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Ying Wang AU - Fangshun Xiao AU - Jiawei Lin AU - Xuemei Zhu AU - Meihua Zou PY - 2023 DA - 2023/12/30 TI - Research on quality assessment method based on statistical analysis of full life cycle of power grid equipment BT - Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023) PB - Atlantis Press SP - 504 EP - 510 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-326-9_52 DO - 10.2991/978-94-6463-326-9_52 ID - Wang2023 ER -