Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of weak pulses being masked by noise in early failure signals of rolling element bearings, a novel fault diagnosis method is proposed based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved TFR demodulation method. Initially, the decomposition of vibration signals using CEEMDAN is carried out to obtain several intrinsic mode functions (IMFs). Subsequently, a novel KC indicator that combines kurtosis and the correlation function is designed to select the effective components for signal reconstruction. Finally, an innovative approach based on the continuous wavelet transform (CWT) for multi-scale demodulation analysis in the domain of time–frequency representation (TFR) is also introduced to extract the envelope spectrum. Further fault diagnosis can be achieved by the identification of the fault characteristic frequency (FCF). This study focuses on the theoretical exploration of bearing faults diagnosis algorithms, employing modeling and simulation techniques. The effectiveness and feasibility of the proposed method are validated through the analysis of simulated signals and experimental signals provided by the Center for Intelligent Maintenance Systems (IMS) of the University of Cincinnati and the Case Western Reserve University (CWRU) Bearing Data Center. The method demonstrates the capability to identify various types of bearing faults, including outer race and inner race faults, with a high degree of computational efficiency. Comparative analysis indicates a significant enhancement in fault diagnostic performance when compared to existing methods. This research contributes to the advancement of effective bearing fault diagnosis methodologies for wind turbines, thereby ensuring their reliable operation.

With the increasing complexity of offshore wind turbine structures and the rapid expansion of wind power projects, efficient, reliable, and robust fault diagnosis and condition monitoring methods have become crucial for effective operation and maintenance management. Wind turbine condition monitoring plays a pivotal role in improving operational efficiency. However, most existing fault diagnosis techniques based on vibration signals are designed for rotating mechanical equipment operating at constant speeds. In contrast, offshore wind turbines experience continuously varying speeds, especially during start-up, shutdown, and under fluctuating wind conditions, leading to rotor speed variations that complicate monitoring. This paper presents a comprehensive analysis of the vibration and fault characteristics of key components in the main drivetrain of offshore wind turbines, with a particular focus on monitoring non-stationary (variable speed) operations. Unlike conventional approaches, this work specifically addresses the challenges posed by the dynamic operating conditions of offshore wind turbines, providing insights into multi-component vibration signal feature extraction and fault diagnosis under variable-speed scenarios. The comparative analysis offered in this paper highlights the limitations of current methods and outlines key directions for future research, emphasizing practical solutions for fault diagnosis and condition monitoring in offshore wind turbine operations under variable-speed conditions. This study not only fills a gap in the current literature but also provides valuable guidance for enhancing the reliability and efficiency of offshore wind turbine maintenance.

The wake development of a tidal turbine should be fully considered in the array arrangement. There are many studies on wake characteristics, mainly focusing on a conventional horizontal-axis turbine, while a ducted turbine has attracted little attention. This paper investigates the wake characteristic of a ducted turbine using flume experiments and large eddy simulations. An analytical wake model of the ducted turbine is proposed and verified by the wake profile under different inflow velocities and the downstream turbine performance under different tandem arrangements. The results show that a ducted turbine wake still maintains a high self-similarity, and the wake profile is approximately the double-Gaussian curve. Compared with a conventional tidal turbine, a ducted turbine has a faster wake recovery speed, but a larger radial influence range. Therefore, ducted turbine arrays should be configured with wider radial distances and shorter axial distances.