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Electrical energy production based on wind power has become the most popularrenewable resources in the recent years because it gets reliable clean energywith minimum cost. The major challenge for wind turbines is the electrical andthe mechanical failures which can occur at any time causing prospectivebreakdowns and damages and therefore it leads to machine downtimes and toenergy production loss. To circumvent this problem, several tools andtechniques have been developed and used to enhance fault detection anddiagnosis to be found in the stator current signature for wind turbinesgenerators. Among these methods, parametric or super-resolution frequencyestimation methods, which provides typical spectrum estimation, can be usefulfor this purpose. Facing on the plurality of these algorithms, a comparativeperformance 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 thatESPRIT and R-MUSIC algorithms have high capability of correctly identifying thefrequencies of fault characteristic components, a performance ranking had beencarried out to demonstrate the efficiency of the studied methods in faultsdetecting.

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