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Multi‐rotor floating wind turbines are among the innovative technologies proposed in the last decade in the effort to reduce the cost of wind energy. These systems are able to offer advantages in terms of smaller blades deployed offshore, cheaper operations, fewer installations, and sharing of the floating platform. As the blade‐pitch actuation system is prone to failures, the assessment of the associated load scenarios is commonly required. Load assessment of blade‐pitch fault scenarios has only been performed for single‐rotor solutions. In this work, we address the effect of blade‐pitch system faults and emergency shutdown on the dynamics and loads of a two‐rotor floating wind turbine. The concept considered employs two NREL 5‐MW baseline wind turbines and the OO‐Star semi‐submersible platform. The blade‐pitch faults investigated are blade blockage and runaway, that is, the seizure at a given pitch angle and the uncontrolled actuation of one of the blades, respectively. Blade‐pitch faults lead to a significant increase in the structural loads of the system, especially for runaway fault conditions. Emergency shutdown significantly excites the platform pitch motion, the tower‐bottom bending moment, and tower torsional loads, while suppressing the faulty blade flapwise bending moment after a short peak. Shutdown delay between rotors increases significantly the maxima of the torsional loads acting on the tower. Comparison of blade loads with data from single‐rotor spar‐type study show great similarity, highlighting that the faulty blade loads are not affected by (1) the type of platform used and (2) the multi‐rotor deployment.

In this paper, the fault detection and isolate problem is investigated for the pitch system of floating wind turbine. In the addressed system model, the system noises and measurement noises are correlated, and the measurement is affected by the missing phenomena. A Kalman filter is designed to handle the correlated noises and estimate the pitch angle, and a residual of the measurement of the pitch system is constructed to detection the faults. Then the fault isolation algorithm is presented based on a multi-attention mechanism one-dimensional convolutional neural network, which is employed to accurately isolate the faults. The simulation results show that the proposed method can significantly improve the accuracy of fault detection and isolation, which the fault isolation accuracy of the simulation results reaches 99.15%.

Individual pitch control (IPC) is an effective and widely used strategy to mitigate blade loads in wind turbines. However, conventional IPC fails to cope with blade and actuator faults, and this situation may lead to an emergency shutdown and increased maintenance costs. In this paper, a fault‐tolerant individual pitch control (FTIPC) scheme is developed to accommodate these faults in floating offshore wind turbines (FOWTs), based on a Subspace Predictive Repetitive Control (SPRC) approach. To fulfill this goal, an online subspace identification paradigm is implemented to derive a linear approximation of the FOWT system dynamics. Then, a repetitive control law is formulated to attain load mitigation under operating conditions, both in healthy and faulty conditions. Since the excitation noise used for the online subspace identification may interfere with the nominal power generation of the wind turbine, a novel excitation technique is developed to restrict excitation at specific frequencies. Results show that significant load reductions are achieved by FTIPC, while effectively accommodating blade and actuator faults and while restricting the energy of the persistently exciting control action.

This paper presents the design of a fault detection and diagnosis system for a quadrotor unmanned aerial vehicle under partial or total actuator fault. In order to control the quadrotor, the dynamic system is divided in two subsystems driven by the translational and the rotational dynamics, where the rotational subsystem is based on a linear parameter-varying model. A robust linear parameter-varying observer applied to the rotational subsystem is considered to detect actuator faults, which can occur as total failures (loss of a propeller or a motor) or partial faults (degradation). Furthermore, fault diagnosis is done by analyzing the displacements of the roll and pitch angles. Numerical experiments are carried out in order to illustrate the effectiveness of the proposed methodology.

In this article, we propose a recent iterative learning algorithm for sensor data fusion to detect pitch actuator failures in wind turbines. The development of this proposed approach is based on iterative learning control and Lyapunov’s theories. Numerical experiments were carried out to support our main contribution. These experiments consist of using a well-known wind turbine hydraulic pitch actuator model with some common faults, such as high oil content in the air, hydraulic leaks, and pump wear.

It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.

In wind power generation, one aim of wind turbine control is to maintain it in a safe operational status while achieving cost-effective operation. The purpose of this paper is to investigate new techniques for wind turbine fault detection based on supervisory control and data acquisition (SCADA) system data in order to avoid unscheduled shutdowns. The proposed method starts with analyzing and determining the fault indicators corresponding to a failure mode. Three main system failures including generator failure, converter failure and pitch system failure are studied. First, the indicators data corresponding to each of the three key failures are extracted from the SCADA system, and the radar charts are generated. Secondly, the convolutional neural network with ResNet50 as the backbone network is selected, and the fault model is trained using the radar charts to detect the fault and calculate the detection evaluation indices. Thirdly, the support vector machine classifier is trained using the support vector machine method to achieve fault detection. In order to show the effectiveness of the proposed radar chart-based methods, support vector regression analysis is also employed to build the fault detection model. By analyzing and comparing the fault detection accuracy among these three methods, it is found that the fault detection accuracy by the models developed using the convolutional neural network is obviously higher than the other two methods applied given the same data condition. Therefore, the newly proposed method for wind turbine fault detection is proved to be more effective.

This paper presents the recent control technologies being researched for floating offshore wind energy system (FOWES). FOWES has been getting many attentions recently as an alternative energy system utilizing vast sustainable wind resource away from land with little restriction by human societies, artificial and natural obstacles. However, not only due to the harsh environmental conditions such as strong wind, wave, and current, but also due to the platform motions such as surge, sway, heave, pitch, roll, and yaw, there could occur many problems including less energy capture than expected, frequent emergency stops, turbine structural instability, and fatigues resulting in early failures, which stay the levelized cost of energy (LCOE) still high compared to conventional fixed offshore wind energy system. These risks could be lowered by operating the turbine close to the optimum point and harvesting wind energy efficiently even under strong wind conditions with the properly applied control technologies, while reducing the loads on structural components. Many researches have been actively going on not only by numerical approaches, but also by experimental tests. This study is wrapping the most recent researches on control technologies for promising floating offshore wind energy system according to different substructure designs such as a spar type, semi-submergible type, tension-leg platform (TLP) type, and barge type, and discusses about its challenges as well.

The monitoring of wind turbine (WT) systems allows operators to maximize their performance, consequently minimizing untimely shutdowns and related hazard situations while maximizing their efficiency. Indeed, the rational monitoring of WT ensures the identification of the main sources of risks at a proper time, such as internal or external failures, hence leading to an increase in their prevention by limiting the faults’ occurrence regarding the different components of wind turbines, achieving production objectives. In this context, the present paper develops a practical monitoring approach using a numerical fault-detection process for the pitch system based on a benchmark wind turbine (WT) model with the main aim of improving safety and security performance. Therefore, the proposed fault-diagnosis procedure deals with eventual faults occurring in the actuators and sensors of the pitch system. In this proposed approach, a simple, logical process is used to generate the correct residuals as fault information based on the redundancy in the actuators and sensors of the pitch sub-systems. The obtained results demonstrate the effectiveness of this proposed process for ensuring the tasks of the fault diagnosis and condition monitoring of the WT systems, and it can be a promising approach for avoiding major damage in such systems, leading to their operational stability and improved reliability and availability.

Driven by the development of machine learning (ML) and deep learning techniques, prognostics and health management (PHM) has become a key aspect of reliability engineering research. With the recent rise in popularity of quantum computing algorithms and public availability of first-generation quantum hardware, it is of interest to assess their potential for efficiently handling large quantities of operational data for PHM purposes. This paper addresses the application of quantum kernel classification models for fault detection in wind turbine systems (WTSs). The analyzed data correspond to low-frequency SCADA sensor measurements and recorded SCADA alarm logs, focused on the early detection of pitch fault failures. This work aims to explore potential advantages of quantum kernel methods, such as quantum support vector machines (Q-SVMs), over traditional ML approaches and compare principal component analysis (PCA) and autoencoders (AE) as feature reduction tools. Results show that the proposed quantum approach is comparable to conventional ML models in terms of performance and can outperform traditional models (random forest, k-nearest neighbors) for the selected reduced dimensionality of 19 features for both PCA and AE. The overall highest mean accuracies obtained are 0.945 for Gaussian SVM and 0.925 for Q-SVM models.