As the onshore wind farm technology matures, offshore wind energy has attracted increasing attention. Zhejiang has coastal areas with massive potential for wind resources because of its geographical location. Therefore, understanding the wind resources in these areas can lay a foundation for future development and utilization. On this basis, this study used the measured wind field data of a wind farm along the coast of Zhejiang from March 2014 to February 2015 and from March 2016 to February 2018 to investigate and compare the characteristics of wind energy resources, including average wind speed, Weibull shape and scale factors, wind direction variation, and wind energy density. Then, the capacity coefficient of a wind turbine predicted using the wind farm data was compared with the actual capacity coefficients of two wind turbines in the wind farm in 2019. Results revealed the following observations: The overall variations in the evaluation indicators followed clear patterns over the 3 years. For example, the main wind direction in the same season was the same, and the variations in the monthly average wind speed, the monthly wind power density, and the theoretical capacity factors were highly similar. The time-series data indicated that the difference in the indicators between summer and autumn was significantly larger than that between other seasons, with the maximum difference in monthly average wind speed of 1.46 times and the maximum difference in monthly wind power density of 1.5 times. The comparison results of the capacity coefficient showed that the theoretical and actual capacity coefficients were extremely close when the monthly average wind speed was less than 6 m/s, with the average difference being less than 9%. When the monthly average wind speed was greater than 6 m/s, the proximity between the theoretical and actual capacity coefficients was reduced, with an average difference of more than 9% and a maximum value of 28%. In general, the overall characteristics of wind resources in coastal areas of Zhejiang exhibited similar trends but fluctuated considerably in some months. Wind energy forecasts had significant discrepancies from the actual operation indicators of the wind farm when the wind speed was high.

Based on wind field data measured during the landfall of Typhoon Jangmi in Wenzhou in 2008, this study analyzes wind field characteristics, including wind speed, wind direction, probability density, turbulence intensity, gust factor, peak factor, power spectrum, turbulence integral scale, coherence, and the autocorrelation coefficient of Typhoon Jangmi. Results showed that the wind field characteristics for the east and west measuring points were basically the same and followed an approximately similar pattern. The probability density of fluctuating wind tends to obey a Gaussian distribution. The turbulence intensity gradually decreases with increasing 10 min averaged wind speed, but the reduction rate gradually drops. The turbulence intensity is affected by the change in a time interval because turbulence intensity decreases as the time interval increases. With an increase in the 10 min average wind speed and time interval, the gust factor decreases. The peak factor decreases, though insignificantly, with increasing mean wind speed, and the distribution of peak factors is greatly scattered. The variation in the peak factor with time is in good agreement with the Durst curve. The gust factor increases as the turbulence intensity increases and is in line with the empirical curves of Ishizaki, Choi, and Cao. The power spectra of the fluctuating wind speed of Typhoon Jangmi in all directions agree well with Von Karman’s empirical spectrum. The turbulence integral scale increases slightly with increasing average wind speed, and the distribution is relatively scattered. The coherence of the fluctuating wind speed components matches the exponential function proposed by Davenport, and the autocorrelation coefficient decreases as $\mathsf{\tau }$ increases.

Two WJ‐3 anemometers placed at the same height on the top of an architectural engineering building in Wenzhou University are used to determine the wind speed of Typhoon Morakot during its landing in real time. This study aims to explore Typhoon Morakot’s wind field characteristics, including mean wind speed, probability density distribution of fluctuating wind speed, power spectral density, correlation analysis, and coherence, on the basis of data measured by the two anemometers. Results show that the probability density distribution of the fluctuating wind speed of the typhoon follows the Gaussian distribution, and the measured cross‐power spectrum of fluctuating wind speed is in good agreement with the modified Karman spectrum. The autocorrelation decreases with the increase in time interval (τ). The longitudinal autocorrelation coefficient decays rapidly with the increase in τ, and the lateral autocorrelation coefficient decays at an unchanged rate. The exponential attenuation coefficients of the longitudinal and transverse fluctuating wind speeds increase with the increase in the mean wind speed, and their mean values are 10.86 and 15.33, respectively. The change trends of the coherence coefficients of the two wind speed components with the mean wind speed are the same. The measured coherence coefficients of the two wind speed components are in good agreement with the exponential function.

A proper wind speed probability distribution model and an appropriate parameter estimation method can greatly improve the accuracy of prediction by indicators of wind energy. Therefore, in this paper, four groups of wind speed data were collected over a year from different altitudes in 2 typical coastal areas of Zhejiang Province, and then fitted by five wind speed probability distribution models, including Weibull, Rayleigh, Gamma, logarithmic normal and Inverse Gaussian distributions. After verification by multiple goodness-of-fit indicators, it was found that Weibull distribution was the optimal model for fitting the wind speed in the studied regions. The maximum likelihood estimation (MLE) method, power density (PD) method, moment method (MM), empirical method of Justus (EMJ), alternative maximum likelihood method (AMLM) and least-squares method (GM) were then used to calculate Weibull parameters at the four different places and for fitting accuracy analysis. The results showed that MLE, PD and MM methods achieved highly similar results in each of the goodness-of-fit indicators. Moreover, the Weibull distribution obtained by these three methods accorded better with the actual wind speed distribution, compared with that obtained by the other 3 methods.

The southeast coastal region of China is frequently affected by typhoons. The observation station was chosen to be located on the roof of Wenzhou University’s architectural engineering building to collect real-time wind speed data during the landfalling of Typhoon Morakot to investigate the properties of the near-ground wind field of typhoons. The turbulence characteristics of the near-ground wind and its variation with time intervals are analyzed on the basis of real-time measured data. The results show that the turbulence intensity only changes with the mean wind speed under relatively low wind speeds. The gust factors exhibit a scattered distribution under low wind speeds and tend to cluster together when the wind speed exceeds 8 m/s. With increasing time intervals, the turbulence intensity and the gust factor gradually decrease. The relationship between turbulence intensity and gust factor is obtained by the measured data and then compared with the empirical formulas. The peak factor remains constant while the mean wind speed changes, but diminish as the time intervals rise. The turbulence integral scale of typhoons slightly increases with the increasing mean wind speed, and its value falls between 70 and 150.