Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review

This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

/pdf/application-of-big-data-and-machine-learning-in-smart-grid-1taystle2u.pdf

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

The feasibility of monitoring the health of wind turbine (WT) gearboxes based on the lubricant pressure data in the supervisory control and data acquisition system is investigated in this paper. A deep neural network (DNN)-based framework is developed to monitor conditions of WT gearboxes and identify their impending failures. Six data-mining algorithms, the k- nearest neighbors, least absolute shrinkage and selection operator, ridge regression (Ridge), support vector machines, shallow neural network, as well as DNN, are applied to model the lubricant pressure. A comparative analysis of developed data-driven models is conducted and the DNN model is the most accurate. To prevent the overfitting of the DNN model, a dropout algorithm is applied into the DNN training process. Computational results show that the prediction error will shift before the occurrences of gearbox failures. An exponentially weighted moving average control chart is deployed to derive criteria for detecting the shifts. The effectiveness of the proposed monitoring approach is demonstrated by examining real cases from wind farms in China and benchmarked against the gearbox monitoring based on the oil temperature data.

Wind Turbine Gearbox Failure Identification With Deep Neural Networks

A review of stochastic resonance in rotating machine fault detection

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE).

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Wind turbines have been developed fast in the recent years and at the same time have brought some problems. It is important to maintain the healthy condition of the running turbine because the consequences after faults are miserable for both the company and owner. There is a constant need to reduce the costs of operating and maintaining the turbines. Therefore, it is very important to detect the faults/failures early so as to minimize downtime and maximize productivity. This paper reviewed the structure of wind turbines and analyzed the different components of wind turbines in order to detect the faults that may happen. Meanwhile, this paper mainly reviewed fault diagnosis methods of wind turbines in the last three years. The main purpose of this paper is to supply some information on structure healthy condition monitoring (SHCM) and fault diagnosis in wind turbines for related researchers.

X.N. Lu

Papers

The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review