LAUSR

The conventional semisupervised extreme learning machine (SS-ELM) algorithm can provide a solution to the lack of labeled samples in wind turbine blade icing fault detection, but its performance is limited by the irrationality of the spherical nearest neighbor graph (SNNG) calculation strategy. To solve this problem, a novel ellipsoidal semisupervised extreme learning machine (ESS-ELM) algorithm is proposed in this article and applied to wind turbine blade icing fault detection. In this study, we creatively propose a novel ellipsoidal nearest neighbor graph (ENNG) calculation strategy that considers the distribution information of the labeled samples to construct the ESS-ELM algorithm. Different from the conventional SNNG, the ENNG can adaptively assign corresponding calculation weights to each feature in the data space according to the Fisher criterion, which allows it to better match the smoothness assumption of the manifold regularization learning framework. In addition, the two adjustable parameters of the enhancement factor and the degradation factor are ingeniously introduced into the ENNG, which further ensure the applicability and security of the proposed ESS-ELM. The superiority of the ESS-ELM algorithm is verified by extensive benchmark industrial fault datasets and the real-world icing dataset of two wind turbines. It is demonstrated that the proposed ESS-ELM algorithm achieves better performance than the ELM, its existing variants, and some state-of-the-art wind turbine blade icing detection methods.