Year: 2025
Type: article
Abstract: In addressing the potential anomalies in wind turbine monitoring data, it is essential to note that a single data source cannot independently ascertain the validity of data. This paper proposes a correlation coupling judgment algorithm designed to evaluate the validity of wind turbine monitoring data. By quantitatively analyzing the degree of correlation between various sensor data using the Pearson correlation coefficient, this study reveals that data characteristics significantly influence the correlation coefficient. The analysis also examines the effects of filtering, signal phase differences, and interference signals on correlation. The results indicate that effective data preprocessing, can dramatically enhance correlation, while phase shifts and noise interference significantly degrade it. Identifying and mitigating these interfering signals is thus established as a crucial prerequisite for defining reliable correlation criteria. Therefore, this study demonstrates that effective data preprocessing is a necessary step for any correlation-based validity assessment framework.
Source: Applied Sciences
DOI: 10.3390/app151910320
Authors: Guanwu Chen, Naichao Chen, Xuan Niu, Danmei Hu
Institution: Shanghai University of Electric Power
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G3322099464
OpenAlex ID: W4414426160