Search

Due to the lack of historical output data of new wind farms, there are difficulties in the scheduling and planning of power grid and wind power output scenario generation. The randomness and uncertainty of meteorological factors lead to the results of traditional scenario generation methods not having the ability to accurately reflect their uncertainty. This article proposes a RAC-GAN-based scenario generation method for a new wind farm output. First, the Pearson coefficient is adopted in this method to screen the meteorological factors and obtain the ones that have larger impact on wind power output; Second, based on the obtained meteorological factors, the Grey Relation Analysis (GRA) is used to analyze the meteorological correlation between multiple wind farms with sufficient output data and new wind farms (target power stations), so that the wind farm with high meteorological correlation is selected as the source power station. Then, the K-means method is adopted to cluster the meteorological data of the source power station, thus generating the target power station scenario in which the cluster information serves as the label of the robust auxiliary classifier generative adversarial network (RAC-GAN) model and the output data of the source power station is considered as the basis. Finally, the actual wind farm output and meteorological data of a region in northeast China are employed for arithmetic analysis to verify the effectiveness of the proposed method. It is proved that the proposed method can effectively reflect the characteristics of wind power output and solve the problem of insufficient historical data of new wind farm output. [ABSTRACT FROM AUTHOR]

Concept factorization (CF), unlike nonnegative matrix factorization (NMF), can handle data with negative values by approximating the original data with two low-dimensional nonnegative matrices and itself. Nevertheless, existing CF-based methods continue to suffer from the two issues specified as follows: (1) Their effectiveness is reduced by the high degree of factorization freedom and the two-stage mismatch between factorization and category acquisition, and (2) their robustness drops significantly when dealing with complex noise. In response to the aforementioned issues, we propose a robust spectral-embedded bilateral orthogonal concept factorization (RSOCF) model for clustering. It constrains the factor matrices as orthogonal matrices to decrease the freedom and obtain samples' categories directly after factorization, which can significantly improve clustering effectiveness. Moreover, correntropy is introduced into RSOCF to improve its robustness to complex noise. To optimize the non-convex RSOCF model, a half-quadratic-based algorithm is devised. Numerous experiments demonstrate that RSOCF surpasses other state-of-the-art methods in terms of clustering effectiveness and robustness. • A novel robust concept factorization-based clustering model is proposed. • A fast algorithm is proposed to optimize the non-convex model. • Numerous experiments validate its clustering effectiveness and robustness. [ABSTRACT FROM AUTHOR]