Data Fusion Based on an Iterative Learning Algorithm for Fault Detection in Wind Turbine Pitch Control Systems

Abstract

In this article, we propose a recent iterative learning algorithm for sensor data fusion to detect pitch actuator failures in wind turbines. The development of this proposed approach is based on iterative learning control and Lyapunov’s theories. Numerical experiments were carried out to support our main contribution. These experiments consist of using a well-known wind turbine hydraulic pitch actuator model with some common faults, such as high oil content in the air, hydraulic leaks, and pump wear.

Relaxed Observer-Based H∞-Control for Markov Jump Fuzzy Systems with Incomplete Transition Probabilities and Sensor Failures

“…Remark 2. The proof of Theorem 2 has provided a relaxation technique to formulate the double-PLMI (32) in terms of tractable conditions (29) and (30). The double-PLMI is in a different form compared to [38,45] (i.e., 0 > Γ g (η k , ηk )), which includes a constant matrix Ω 2 in the third term of (32).…”

“…In vast real-world control systems, sensor operations are usually under the negative effects of electromagnetic or heating interference due to hazardous operating environments, whose impacts can lead to inaccurate measurements, operation failures, or even disastrous situations [26,27]. Moreover, contingent failures frequently happen for all sensors in any real-time control system [28,29], especially for systems with a large number of control loops. For aviation systems where safety is the highest priority, such as flight control and navigation systems, an observer-based control scheme must ensure flight performances or active safety control processes despite the low or high impacts of sensor failures.…”