Data-driven FDI for wind farms using W-SVM

Abstract

The adoption of clean, renewable energy has brought to the forefront an increase in the studies and research around their reliable and efficient implementation. The increasing demand of wind-turbine generated power has led to the construction of larger turbines which require higher reliability guarantees in order to operate with reduced down-times and moderate repair costs. The use of advanced techniques for fault detection and isolation (FDI), and the subsequent fault tolerant control implementation in wind turbines is one of the proposed solutions to reduce losses in efficiency and ensure their continued operation. Although the implementation of FDI strategies in wind turbines have been developed greatly in the last decade, little work has been done at the wind farm level; this approach can solve the problems of detecting certain faults that have proven to be difficult to detect at the wind turbine level (e.g. those caused by mechanical wear on the internal structure of the wind turbine). This article presents the results of designing a data-driven FDI strategy for a wind turbine farm system via Weighted Support Vector Machines (W-SVM), achieving a fast and reliable way to detect faults with reduced missed detections, low number of false positive and fast enough detection rates.