Wind energy is of great importance for future energy development. In order to fully exploit wind energy, wind farms are often located at high latitudes, a practice that is accompanied by a high risk of icing. Traditional blade icing detection methods are usually based on manual inspection or external sensors/tools, but these techniques are limited by human expertise and additional costs. Model-based methods are highly dependent on prior domain knowledge and prone to misinterpretation. Data-driven approaches can offer promising solutions but require a massive amount of labeled training data, which are not generally available. In addition, the data collected for icing detection tend to be imbalanced because, most of the time, wind turbines operate under normal conditions. To address these challenges, this article presents a novel deep class-imbalanced semisupervised (DCISS) model for estimating blade icing conditions. DCISS integrates class-imbalanced and semisupervised learning (SSL) using a prototypical network that can rebalance features and measure the similarities between labeled and unlabeled samples. In addition, a channel calibration attention module is proposed to improve the ability to extract features from raw data. The proposed model has been evaluated using the blade icing datasets of three wind turbines. Compared to the classical anomaly detection and state-of-the-art SSL algorithms, DCISS shows significant advantages in terms of accuracy. Compared to five different class-imbalanced loss functions, the proposed DCISS is competitive. The generalization and practicability of the proposed model are further verified in the use case of online estimation.

Wind farms are usually located in high-latitude areas, which bring a high risk of icing. Traditional methods of anti-blade-icing are limited by extra costs and potential damages to the original mechanical structure. Model-based methods are heavily dependent on mathematical models of the blade icing, which are prone to produce erroneous estimation. As data-driven models are better able to achieve competitive performances for the blade icing estimation, this article proposes a temporal attention-based convolutional neural network (TACNN). This novel data-driven model introduces a temporal attention module into a convolutional neural network, with the goal of determining the importance of sensors and timesteps and automatically identifying discriminative features from raw sensor data. Benchmark experiments on ten public datasets of multivariate time-series classification show competitive performance against the state-of-the-art methods. Compared with ten baseline networks and three widely used attention mechanisms, the TACNN shows significant advantages applying to three real-world datasets. These datasets are logged by the supervisory control and data acquisition system and contain operational and environmental measurements such as power and temperature. The ablation study and sensitivity study demonstrate the effectiveness of the key components of the TACNN. The practicability of the TACNN is further verified through online estimation testing.

Wind energy is a fast-growing renewable energy but faces blade icing. Data-driven methods provide talented solutions for blade icing detection, but a considerable amount of Internet of Things data needs to be collected to a central server, which may lead to the leakage of sensitive business data. To address this limitation, this article proposes BLADE , a Blockchain-empowered imbalanced federated learning (FL) model for blade icing detection. With the help of the Blockchain, the conventional FL is improved without worrying about the failure of the single centralized server and boosts the privacy preserving. A validation mechanism is introduced into the Blockchain to enhance the defense against poisoning attacks. In addition, a novel imbalanced learning algorithm is integrated into BLADE to solve the class imbalance problem in the sensor data. BLADE is evaluated on ten wind turbines from two wind farms. The experimental results verify the effectiveness, superiority, and feasibility of the proposed BLADE.

Sea state information is significant for the guide of maritime activities of autonomous vessels. The sea state estimation (SSE) model, powered by artificial intelligence (AI), has shown great effectiveness but is susceptible to malicious data attacks. These attacks can lead to significant declines in the system’s performance and result in incorrect predictions about the sea state. This study introduces SecureSSE, a strategy for protecting SSE models in autonomous marine vessels from adversarial attacks. This approach incorporates three main components: 1) the multiscale feature extraction learning (MFEL) module; 2) the feature convolution aggregation learning (FCAL) module; and 3) the perturbation examples training (PET) module. The PET module is specifically crafted to create perturbation examples that are in line with unaltered data, leveraging the capabilities of both the MFEL and FCAL modules to efficiently extract and integrate detailed features from ship motion data. Our proposed SecureSSE approach is shown to significantly improve the resilience of deep learning models against potential attacks. Through experimental testing, we have validated the effectiveness of this method in enhancing SSE. Additional ablation studies highlight the critical role of each module within the SecureSSE framework. To our knowledge, this is the first study to address adversarial attacks in this context and to propose a comprehensive defense mechanism for SSE systems in autonomous marine vessels.

Wind farms are typically located at high latitudes, resulting in a high risk of blade icing. Data-driven approaches offer promising solutions for blade icing detection, but they rely on a considerable amount of data. Data exchange between multiple wind farms would improve the performance of detection models, due to the spatio-temporal dependencies capable of reflecting different meteorological conditions. The traditional centralized approach for icing detection faces many challenges, including the requirement of high storage and computational capacity of the server, vulnerability to cyberattacks, and operators’ reluctance of sharing data for commercial reasons. To address these challenges, this article proposes a heterogeneous federated learning (FL) model for wind turbine blade icing detection. The structures of the server and client models in the presented method are different, in contrast to the traditional FL of sharing the same structure. In addition, this article addresses the class imbalance problem in the training data. Last, this article conducts comprehensive experiments to evaluate the proposed method using real-world data from 20 turbines in two wind farms, and compares it with two state-of-the-art FL models and five well-known class imbalance methods. The experimental results verify the effectiveness and superiority of the proposed method.

Blade icing detection becomes increasingly significant as it can avoid revenue loss and power degradation. Conventional methods are usually limited by additional costs, and model-driven methods heavily depend on prior domain knowledge. Data-driven methods, especially deep learning approaches without needing the time-consuming handcraft feature engineering, offer a promising solution for blade icing detection. However, the monitoring signals normally have complex and diverse features as wind turbine operates in complex environments, thus effective model is needed for data analyzing. Additionally, the distribution of monitoring data is imbalanced, which causes the abnormal data mining inadequate. In this work, a multilevel convolutional recurrent neural network (MCRNN), is proposed for blade icing detection. Specifically, discrete wavelet decomposition is leveraged to obtain multilevel features both from the time domain and the frequency domain. A parallel structure combining an LSTM branch and a CNN branch is established in each level for feature extraction. To alleviate the severe data imbalance, two mechanisms, including data resampling algorithm and class-rebalanced loss function, are investigated. Furthermore, a multi-step accumulation strategy is proposed to enhance the accuracy of real-time detection. Extensive studies demonstrate that the proposed MCRNN can achieve up 38.8% and 42.9% higher F1-score over the best baseline on the balanced data sets processed by data resampling algorithm and 23.9% and 30.6% higher on imbalanced data sets with MCRNN optimized by the class-rebalanced loss function. The real-time detection verifies the applicability of the proposed method and indicates that the proposed multi-step accumulation strategy can improve the accuracy of icing detection.