Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination

Author:

Paik Chunhyun1,Chung Yongjoo2ORCID,Kim Young Jin3ORCID

Affiliation:

1. Division of Industrial Convergence Systems Engineering, Dong-Eui University, Busan 47340, Republic of Korea

2. Department of E-Business, Busan University of Foreign Studies, Busan 46234, Republic of Korea

3. Department of Systems Management and Engineering & Industrial Systems Innovation Research Institute, Pukyong National University, Busan 48513, Republic of Korea

Abstract

The estimation of power curve is the central task for efficient operation and prediction of wind power generation. It is often the case, however, that the actual data exhibit a great deal of variations in power output with respect to wind speed, and thus the power curve estimation necessitates the detection and proper treatment of outliers. This study proposes a novel procedure for outlier detection and elimination for estimating power curves of wind farms by employing clustering algorithms of vector quantization and density-based spatial clustering of applications with noise. Testing different parametric models of power output curve, the proposed methodology is demonstrated for obtaining power curves of individual wind turbines in a Korean wind farm. It is asserted that the outlier elimination procedure for power curve modeling outlined in this study can be highly efficient at the presence of noises.

Funder

Basic Science Research Program

Publisher

MDPI AG

Subject

Artificial Intelligence,Applied Mathematics,Industrial and Manufacturing Engineering,Human-Computer Interaction,Information Systems,Control and Systems Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Wind and Solar Pattern Analysis for Enhanced Grid Planning: A Literature Review;2023 International Conference on Electrical, Computer and Energy Technologies (ICECET);2023-11-16

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