Renewable energy sources like wind energy are copiously available without any limitation. Wind turbines are used to tap the potential of wind energy, which is available in millions of MW. Reliability of wind turbine is critical to extract this maximum amount of energy from the wind. We reviewed different techniques, methodologies and algorithms developed to monitor the performance of wind turbine as well as for an early fault detection to keep away the wind turbines from catastrophic conditions due to sudden breakdowns. To keep the wind turbine in operation, implementation of condition monitoring system (CMS) and fault detection system (FDS) is paramount and for this purpose ample knowledge of these two types of systems is mandatory. So, an attempt has been made in this direction to review maximum approaches related to CMS and FDS in this piece of writing.

Condition monitoring and fault detection of wind turbines and related algorithms: A review

Condition monitoring of wind turbines: Techniques and methods

An overview of time-based and condition-based maintenance in industrial application

This paper describes a set of anomaly-detection techniques and their applicability to wind turbine fault identification. It explains how the anomaly-detection techniques have been adopted to analyse supervisory control and data acquisition data acquired from a wind farm, automating and simplifying the operators' analysis task by interpreting the volume of data available. The techniques are brought together into one system to collate their output and provide a single decision support environment for an operator. The framework used is a novel multi-agent system architecture that offers the opportunity to corroborate the output of the various interpretation techniques in order to improve the accuracy of fault detection. The results presented demonstrate that the interpretation techniques can provide performance assessment and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines.

Online wind turbine fault detection through automated SCADA data analysis

http://www.relialab.org/Upload/files/On%20the%20concept%20of%20e-maintenance%20Review%20and%20current%20research.pdf

On the concept of e-maintenance: Review and current research

https://www.iit.comillas.edu/docs/IIT-06-055A-Preview.pdf

Javier del Pico

Papers

SIMAP: intelligent system for predictive maintenance application to the health condition monitoring of a windturbine gearbox