AIAA Guidance, Navigation, and Control Conference

Wind turbines are increasingly growing larger, becoming more complex, and being located in more remote locations, especially offshore. Interest in advanced controllers for normal operation has expanded in recent years, but fault detection and fault tolerant control for wind turbines is a less well-developed area of interest. In this benchmark challenge, we have reworked a previous challenge paper to present a more sophisticated wind turbine model - a modern 5 MW turbine implemented in the FAST software - and updated fault scenarios. These updates enhance the realism of the challenge and will therefore lead to solutions that are significantly more useful to the wind industry. This paper presents the challenge model and the requirements for challenge participants. In addition, it provides additional information about the faults selected for the challenge and their basis in field data.

Wind turbine fault detection and fault tolerant control

Experimental investigation of ablation and pyrolysis processes of carbon-phenolic ablators in atmospheric entry plasmas

Accurate and reliable state of charge (SoC) estimation becomes increasingly important to provide a stable and efficient environment for Lithium-ion batteries (LiBs) powered devices. Most data-driven SoC models are built for a fixed ambient temperature, which neglect the high sensitivity of LiBs to temperature and may cause severe prediction errors. Nevertheless, a systematic evaluation of the impact of temperature on SoC estimation and ways for a prompt adjustment of the estimation model to new temperatures using limited data has been hardly discussed. To solve these challenges, a novel SoC estimation method is proposed by exploiting temporal dynamics of measurements and transferring consistent estimation ability among different temperatures. First, temporal dynamics, which is presented by correlations between the past fluctuation and the future motion, are extracted using canonical variate analysis. Next, two models, including a reference SoC estimation model and an estimation ability monitoring model, are developed with temporal dynamics. The monitoring model provides a path to quantitatively evaluate the influences of temperature on SoC estimation ability. After that, once the inability of the reference SoC estimation model is detected, consistent temporal dynamics between temperatures are selected for transfer learning. Finally, the efficacy of the proposed method is verified through a benchmark. Our proposed method not only reduces prediction errors at fixed temperatures (e.g., reduced by 24.35 $\%$ at –20  $^{\circ }$ C, 49.82 $\%$ at 25  $^{\circ }$ C) but also improves prediction accuracies at new temperatures.

Transfer Learning-Based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures

Multiscale inverted residual convolutional neural network for intelligent diagnosis of bearings under variable load condition

Nowadays, wind energy has become one of the most concerning renewable energy sources due to its low damage to nature and low cost. Wind turbines are usually deployed in remote areas and harsh environments, so they (especially gearboxes) are more likely to suffer faults. Therefore, it is necessary to vigorously develop wind turbine gearbox fault detection method to provide more accurate fault alarms. This paper proposes a fault detection method based on dilated convolutional neural network(DCNN). Compared to the traditional convolutional neural network, the dilated convolution can maintain a larger receiving field to speed up the monitoring speed, and is more suitable for real-time fault monitoring. Finally, an experimental wind turbine gearbox is used to verify the effectiveness of the method.

Wind Turbine Gearbox Fault Detection based on Dilated Convolutional Neural Networks

Wind turbine gearbox condition monitoring is an essential part of the modern wind energy industry Condition monitoring system can detect gearbox failures in time to avoid further damage Data-driven condition monitoring methods have been widely used to ensure operation safety and reduce the downtime of wind turbines Fault detection of wind turbine gearbox is usually based on vibration signals However, faulty samples are not easy to collect in practice Hence, it can be regarded as an imbalance data classification problem In this paper, a generative adversarial networks (GAN) based method is proposed for the detection of gearbox faults In the proposed method, the vibration signals are first pre-processed using short-term Fourier transformations (STFT) Then a GAN model is trained with healthy samples A low score is obtained while inputting healthy samples into the neural networks of trained GAN If the samples are collected in faulty scenarios, the trained GAN model will give a high score Therefore, the faulty samples can be discriminated from healthy samples The effectiveness of the proposed method is demonstrated with wind turbine gearbox condition monitoring vibration datasets by comparisons with other methods

A GAN Based Fault Detection of Wind Turbines Gearbox

Cement Stabilized Materials with Use of RoadCem Additive

Over the past decades, Floating Offshore Wind Turbine (FOWT) has gained increasing attention in wind engineering due to the rapidly growing energy demands. However, difficulties in turbine maintenance will increase due to the harsh operational conditions. Fault diagnosis techniques play a crucial role to enhance the reliability of FOWTs and reduce the cost of offshore wind energy. In this paper, a novel data-driven fault diagnosis method using regularized dynamic canonical correlation analysis (RDCCA) and Fisher discriminant analysis (FDA) is proposed for FOWTs. Specifically, to overcome the collinearity problem that exists in measured process data, dynamic canonical correlation analysis with a regularization scheme, is developed to exploit the relationship between input and output signals. Then, the residual signals are generated from the established RDCCA model for fault detection. To further classify the fault type, an FDA model is trained from the residual signals of different training faulty data sets. Simulations on a FOWT baseline model based on the widely used National Renewable Energy Laboratory FAST simulator are carried out to demonstrate the feasibility and efficacy of the proposed fault detection and classification method. Results have shown many salient features of the proposed method with potential applications in FOWTs.

Ping Wu

Papers

Wind Turbine Gearbox Fault Detection based on Dilated Convolutional Neural Networks

A GAN Based Fault Detection of Wind Turbines Gearbox

Cement Stabilized Materials with Use of RoadCem Additive

Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

Floating offshore wind turbine fault diagnosis using stacked denoising autoencoder with temporal information