In advance accurate prediction of wind turbine blade icing fault is of fundamental importance. Deep learning is the mainstream prediction technique while existing research about the prediction of wind turbine blade icing fault has primarily derived a single deep learning model. This study introduces the group method of data handling (GMDH) technique and proposes a GMDH-based selective deep ensemble (GSDE) model. First, the model combines the convolution neural network (CNN) with recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) to construct CNN–RNN, CNN–LSTM, and CNN–GRU, respectively. Together with CNN, four cost-sensitive deep neural networks based on focal loss are formed and used as the basic prediction models. Second, a series of training sets are constructed by the Chi-square test. Four different basic prediction models are trained on each training set, and the prediction results of all base predictors are obtained. Third, the GMDH technique is applied to the cost-sensitive selective deep ensemble for final prediction results. Experiments are conducted to deeply verify the prediction performance of the GSDE model on two wind turbine datasets collected by the supervisory control and data acquisition (SCADA) system. Results show that the proposed model outperforms five existing ensemble models and five single deep learning models. 

Prediction of wind turbine blade icing fault based on selective deep ensemble model

Support of artificial intelligence, renewable energy and sustainability is currently increasing through the main policies of developed countries, e.g., the White Paper of the European Union. Wind energy is one of the most important renewable sources, growing in both onshore and offshore types. This paper studies the most remarkable artificial intelligence techniques employed in wind turbines monitoring systems. The principal techniques are analysed individually and together: Artificial Neural Networks; Fuzzy Logic; Genetic Algorithms; Particle Swarm Optimization; Decision Making Techniques; and Statistical Methods. The main applications for wind turbines maintenance management are also analysed, e.g., economic, farm location, non-destructive testing, environmental conditions, schedules, operator decisions, power production, remaining useful life, etc. Finally, the paper discusses the main findings of the literature in the conclusions.

A Comprehensive Review of Artificial Intelligence and Wind Energy

An essential attribute of software quality is software reliability. To achieve higher reliability, the testing phase with detected and corrected flaws is incorporated in the software development. The fault correction process (FCP) includes the fault detection process (FDP) to develop the software reliability growth model (SRGM). This is difficult to integrate because due to several reasons, including the effects of staffing levels and the interdependence of faults. It limits the applicability of the analytical model. Because of the adoption of data-driven methodologies such as Artificial Intelligence (AI) technology, no precise FCP and FDP assumptions are necessary. In this article, we proposed a hybrid long short-term memory (LSTM) with BrainStorm Optimization and Late Acceptance Hill Climbing (BSO-LAHC) algorithm of a stepwise prediction model for software fault detection and correction. The fault detection and correction procedure has great influence by considering the testing effort. While compared to the existing methods, the proposed hybrid with the BSO-LAHC algorithm demonstrated superior results by using Firefox and bug tracking system Bugzilla datasets. The proposed model’s effectiveness is confirmed via empirical study. Based on the Bugzilla and firefox datasets, the proposed mean square error performance is 1.92 and 21.44 respectively. Additionally, the proposed method is less expensive and takes less time to execute. In Bugzilla version 5.0.4, releases 2 and 3 had a determination coefficient of 99.2% and 98.9%, respectively. The FCP is 27% more effective than previous approaches, and the FDP is 32% more effective. 

Enhancing Software Reliability and Fault Detection Using Hybrid Brainstorm Optimization-Based LSTM Model

Experimental study on ice adhesion forces of the copper plates with specific contact angle and roughness

Blade icing seriously affects wind turbines’ aerodynamic performance and output power. Timely and accurately predicting blade icing status is crucial to improving the economy and safety of wind farms. However, existing blade icing prediction methods cannot effectively solve the problems of unbalanced icing/non-icing data and low prediction accuracy. In order to solve the above problems, this paper proposes a wind turbine blade icing prediction method based on the focal loss function and CNN-Attention-GRU. First, the recursive feature elimination method combined with the physical mechanism of icing is used to extract features highly correlated with blade icing, and a new feature subset is formed through a sliding window algorithm. Then, the focal loss function is utilized to assign more weight to the ice samples with a lower proportion, addressing the significant class imbalance between the ice and non-ice categories. Finally, based on the CNN-Attention-GRU algorithm, a blade icing prediction model is established using continuous 24-h historical data as the input and the icing status of the next 24 h as the output. The model is compared with advanced neural network models. The results show that the proposed method improves the prediction accuracy and F1 score by an average of 6.41% and 4.27%, respectively, demonstrating the accuracy and effectiveness of the proposed method. 

Wind Turbine Blade Icing Prediction Using Focal Loss Function and CNN-Attention-GRU Algorithm

Icing Prediction of Fan Blade based on a Hybrid Model

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Icing Prediction of Fan Blade based on a Hybrid Model