ABSTRACT

Monopile offshore wind turbines (OWT) are the most commonly used type of OWTs for offshore wind farms. Modal parameters are crucial for damage detection and safety evaluation of the monopile OWTs. However, modal parameter identification of the monopile OWTs is challenging due to the complex marine environment, noise disturbance, non-stationary and nonlinear response. In this study, operational modal analysis (OMA) of the monopile OWT was conducted based on the in-situ monitoring data at an offshore wind farm. Various time and frequency domain signal processing methods combined with the OMA were utilized to investigate the response characteristics and time-variant feature of the monopile OWT. The results indicate that the modal parameters of the OWT have been successfully identified. The false modes caused by the noise or external excitation can be eliminated by the modal assurance criterion (MAC).

INTRODUCTION

According to the long-term government plan, China's offshore wind power capacity will reach about 1.3×104 million kilowatts in 2035 (Liu et al., 2021); However, according to the statistics of the National Energy Administration, by the end of June 2021, the cumulative installed capacity of offshore wind power in China was only about 11.134 million kW, indicating a broad prospect for offshore wind power development. Meanwhile, the development of offshore wind turbines (OWT) is technically difficult and costly. To reduce the operation and maintenance costs of OWTs, it is necessary to timely and accurately monitor and evaluate its operation conditions.

So far, most of the offshore wind farms are equipped with the safety monitoring system including strain, temperature, and acceleration sensors. However, the amount of sensors on each OWT is not sufficient due to cost and installation difficulty consideration. On the other hand, the large amount of monitoring data with noise and error makes it difficult for the administrators to correlate it with the OWT performance Therefore, the dynamic characteristics, response features and time-variant mechanisms of the OWT are not clearly investigated yet and thus the function of the safety monitoring system is undermined.

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