Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods

TL;DR

This paper compares high-resolution frequency estimation methods for fault detection in wind turbine generators, highlighting ESPRIT and R-MUSIC as the most effective algorithms based on simulation results.

Contribution

It provides a performance comparison of parametric frequency estimation algorithms for fault detection in wind turbines, identifying the most robust methods.

Findings

ESPRIT and R-MUSIC algorithms effectively identify fault frequencies

High accuracy in fault detection demonstrated through MATLAB simulations

Performance ranking of algorithms based on robustness and efficiency

Abstract

Electrical energy production based on wind power has become the most popular renewable resources in the recent years because it gets reliable clean energy with minimum cost. The major challenge for wind turbines is the electrical and the mechanical failures which can occur at any time causing prospective breakdowns and damages and therefore it leads to machine downtimes and to energy production loss. To circumvent this problem, several tools and techniques have been developed and used to enhance fault detection and diagnosis to be found in the stator current signature for wind turbines generators. Among these methods, parametric or super-resolution frequency estimation methods, which provides typical spectrum estimation, can be useful for this purpose. Facing on the plurality of these algorithms, a comparative performance analysis is made to evaluate robustness based on different metrics: accuracy, dispersion, computation cost, perturbations and faults severity. Finally, simulation results in MATLAB with most occurring faults indicate that ESPRIT and R-MUSIC algorithms have high capability of correctly identifying the frequencies of fault characteristic components, a performance ranking had been carried out to demonstrate the efficiency of the studied methods in faults detecting.