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The rational layout of tidal stream turbines (TSTs) is beneficial for making full use of tidal stream energy. It is essential to consider the wake radius and velocity distribution for determining the spacing between the TSTs. The wake is primarily affected by the turbulence intensity. The attenuation of turbulence results in the non-linear expansion of the wake. Additionally, the high turbulence in the near wake region inhibits the velocity deficit, which is more evident under high ambient turbulence intensity. Therefore, a radius and minimum velocity Jensen (RMV-Jensen) model is proposed to predict the wake radius and the wake velocity distribution downstream of a TST. The RMV-Jensen model consists of a radius block (R-Block) and a minimum velocity block (MV-Block). The R-Block is a piecewise exponential function based on the turbulence attenuation in the wake region, accurately describing the change of the wake expansion coefficient. The MV-Block is a wake minimum velocity model, and the inhibitory effect of turbulence on velocity deficit is considered for the first time. The RMV-Jensen model is applied to predict the wake distribution in the Zhoushan sea area. The prediction accuracy of the RMV-Jensen model is improved by 10%–20% compared to that of the classical Jensen model, according to the experimental results. [ABSTRACT FROM AUTHOR]

Federated learning (FL) facilitates the collaborative optimization of fault diagnosis models across multiple clients. However, the performance of the global model in the federated center is contingent upon the effectiveness of the local models. Low-quality local models participating in the federation can result in negative transfer within the FL framework. Traditional regularization-based FL methods can partially mitigate the performance disparity between local models. Nevertheless, they do not adequately address the inconsistency in model optimization directions caused by variations in fault information distribution under different working conditions, thereby diminishing the applicability of the global model. This paper proposes a federated adversarial fault diagnosis method driven by fault information discrepancy (FedAdv_ID) to address the challenge of constructing an optimal global model under multiple working conditions. A consistency evaluation metric is introduced to quantify the discrepancy between local and global average fault information, guiding the federated adversarial training mechanism between clients and the federated center to minimize feature discrepancy across clients. In addition, an optimal aggregation strategy is developed based on the information discrepancies among different clients, which adaptively learns the aggregation weights and model parameters needed to reduce global feature discrepancy, ultimately yielding an optimal global model. Experiments conducted on benchmark and real-world motor-bearing datasets demonstrate that FedAdv_ID achieves a fault diagnosis accuracy of 93.09% under various motor operating conditions, outperforming model regularization-based FL methods by 17.89%. [ABSTRACT FROM AUTHOR]

Challenges exist in the power generation efficiency and safety of marine current turbines (MCTs), as the MCT blades are often attached by foreign objects when operating underwater. It is essential for the stable operation of an MCT to recognize attachments timely and accurately. However, underwater imaging suffers from blurry edges due to light attenuation and scattering. It is challenging for accurate recognition through underwater images since blurry edges result in unclear edge features. To alleviate this problem, LAW-IFF Net is proposed in this paper, which mainly contains two parts. Firstly, this paper proposes to transform the local averages of feature maps into weight matrices, namely the locally average weighting (LAW) mechanism. It is intended to alleviate the edge gradient reduction caused by blurry edges. Secondly, the proposed improved feature fusion (IFF) mechanism aims to overcome the deviation caused by the feature fusion of different attention branches based on spatial attention. At the same time, the lightweight networks are combined with the proposed method to improve the computation speed and ensure the timeliness of recognition. Experimental results on the MCT dataset show the superiority of the proposed method in terms of accuracy and speed of attachment recognition in images with blurry edges. The experimental results on publicly available datasets show the applicability of the proposed method to other underwater images. [ABSTRACT FROM AUTHOR]