Rotor Blade Pitch Imbalance Fault Detection for Variable-Speed Marine
Current Turbines via Generator Power Signal Analysis
Abstract
Marine hydrokinetic (MHK) turbines extract renewable energy from oceanic
environments. However, due to the harsh conditions that these turbines
operate in, system performance naturally degrades over time. Thus,
ensuring efficient condition-based maintenance is imperative towards
guaranteeing reliable operation and reduced costs for hydroelectric
power.
This paper proposes a novel framework aimed at identifying and
classifying the severity of rotor blade pitch imbalance faults
experienced by marine current turbines (MCTs). In the framework, a
Continuous Morlet Wavelet Transform (CMWT) is first utilized to acquire
the wavelet coefficients encompassed within the 1P frequency range of
the turbine’s rotor shaft. From these coefficients, several statistical
indices are tabulated into a six-dimensional feature space. Next,
Principle Component Analysis (PCA) is employed on the resulting feature
space for dimensionality reduction, followed by the application of a
K-Nearest Neighbor (KNN) machine learning algorithm for fault detection
and severity classification. The framework’s effectiveness is validated
using a high-fidelity MCT numerical simulation platform, where results
demonstrate that pitch imbalance faults can be accurately detected 100%
of the time and classified based upon severity more than 97% of the
time.