Earticle

초록

영어

A new data-mining approach based on power curve profiles is put forward to monitor the power generation performance of wind turbines in this paper. Through assessing the wind-speed power datasets, the weakened power generation performance of turbines could be identified effectively by this approach. Shapes of power curve profiles over consecutive time intervals are constructed by fitting power curve models into wind-speed power datasets. In this research, we designed the Auto-adapt Optimal Interclass Variance algorithm, optimal constraint in each wind-speed power sub-dataset is explored for governing the data-driven method based on distance-based outlier detection and variance analysis model. The AOIV algorithm achieves the self-optimization of the threshold parameter and reaches a high degree of robustness to variations in wind-power generation performance monitoring. The blind industrial researches are conducted to validate the effectiveness of this approach, also indicates the decrease of error rates while detecting weakened power generation performance and the improvement of turbines’ power output.

목차

Abstract
 1. Introduction
 2. General Power Curve Properties
  2.1. Wind Energy and Power Curve
  2.2. Betz Theory and Power Curve
 3. Data-Mining Based on Power Curve
  3.1. Data Preprocessing
  3.2. Data-Mining with AOIV Algorithm
 4. Industrial Studies
  4.1. Data Preprocessing
  4.2. Contrastive Analysis between Algorithms
  4.3. Performance Monitoring of Turbines
 5. Conclusion
 References

키워드

저자

• Jianlou Lou [ School of Information Engineering, Northeast DianLi University, Jilin 132012, China ]
• Heng Lu [ School of Information Engineering, Northeast DianLi University, Jilin 132012, China ]
• Jia Xu [ Long Yuan(Beijing) Wind Power Engineering Technology CO.,LTD. Beijing 100034, China ]
• Zhaoyang Qu [ School of Information Engineering, Northeast DianLi University, Jilin 132012, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605