Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing

Abstract

This paper addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained are projected into the baseline PCA model. When both sets of data-the baseline and the data from the current wind turbine-are compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrate that the proposed strategy provides and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines.

On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis

“…, σ × L. 7. Perform a sensor-based group scaling and find the PCA model P ∈ M (σ×L)×(σ×L) (R) (also called loading matrix) as detailed in [5,Section 3.1]. Consider the reduced matrix P := P(:, 1 : ) ∈ M (σ×L)× (R) related to the highest eigenvalues, formed by the first columns of matrix P. 8.…”

“…The fault detection strategy is based on the work by Pozo and Vidal [5], where multiway principal component analysis (MPCA) is first applied and then the so-called Welch-Satterthwaite method [19] to test for the equality of means. One of the key issues of the strategy presented in [5] is the way the data is collected and arranged in a matrix. To create the baseline pattern or PCA model, we measure the σ = 6 sensors from a healthy wind turbine and all the collected data is organized in a matrix X ∈ M n×(N×L) as follows:…”

“…, L in the matrix represents the number of sensors. For the sake of completeness, we summarize in the following itemized list the fault detection strategy presented in [5], which is also illustrated in Figure 1: • Sensor group-scaling is applied to matrix X in Equation (7) so the mean of each column is 0 and the standard deviation of each sensor submatrix X (s) , s = 1, . .…”

“…These are standalone systems that require installation of sensors and hardware. In this paper, the fault detection strategy presented in [5] is used. It uses on-line SCADA (supervisory control and data acquisition) data already available at an industrial wind turbine to provide advance warning of failures.…”

Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

“…The current need for data gathering and analysis is highlighted in [8] proposing "new conceptual expert systems that automate data processing and provide analysis to enhance decision-making". Efforts have been made with such goal by implementing SCADA-data-driven methods [9] that also look into optimizing the number of sensors while reducing the detection time [10,11].…”