Advances in Offshore Wind

Song, Dongran; Fan, Tianhui; Li, Qingan; Joo, Young Hoon

doi:10.3390/jmse12020359

Cited by 2 publications

(1 citation statement)

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“…China's coastal areas are rich in wind energy resources, which has created excellent conditions for the rapid development of the offshore wind power industry. As a key field of renewable energy expansion, offshore wind power is becoming an important direction of wind energy utilization [1]. In the past few years, China's offshore wind power industry has developed rapidly from the initial stage and made remarkable achievements.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification

Zhang,

Liu,

Yan

2024

Electronics

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The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind processes under different incoming currents and different weather are very different, and the spatio-temporal correlation law of offshore wind processes is highly complex, which leads to traditional power prediction not being able to accurately predict the short-term power of offshore wind farms. Therefore, aiming at the characteristics and complexity of offshore wind power, this paper proposes an innovative short-term power prediction method for offshore wind farms based on a Gaussian mixture model (GMM). This method considers the correlation between units according to the characteristics of the measured data of units, and it divides units with high correlation into a category. The Bayesian information criterion (BIC) and contour coefficient method (SC) were used to obtain the optimal number of groups. The average intra-group correlation coefficient (AICC) was used to evaluate the reliability of measurements for the same quantized feature to select the representative units for each classification. Practical examples show that the short-term power prediction accuracy of the model after unit classification is 2.12% and 1.1% higher than that without group processing, and the mean square error and average absolute error of the short-term power prediction accuracy are reduced, respectively, which provides a basis for the optimization of prediction accuracy and economic operation of offshore wind farms.

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Section: Introduction 1backgroundmentioning

confidence: 99%