Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

Abstract

Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

“…The PHM cycle is given in Figure 5. The articulation of PHM components can be explained as follows [4,18]:…”

“…When a failure is detected, the diagnosis makes it possible to isolate and identify the damaged component; the prognosis then aims to project the state of the system into the future. Then the PHM cycle includes 7 layers [4,[15][16][17][18]; (1) data acquisition, (2) data processing, (3) status detection, (4) diagnosis, (5) prognosis, (6) decision, and (7) human-machine interface. More details on this topic can be found in [1][2][3][4].…”

“…The choice of a method for HI extraction depends mainly on the type of data collected, and the considered application [1][2][3][4][5][18][19][20][21][22][23][24]. As highlighted in Figure 9, the HI can be found using signal-based and model-based methods.…”

“…Degradation models work with a single HI. However, we can use principal component analysis or other fusion techniques to generate a fused HI that incorporates information from more than one HI [2,4,18,19,[26][27][28].…”

“…In this regard, Saxena et al (2009, 2010) [2,18] proposed a set of metrics for evaluating key elements of RUL predictions, including "prognostic horizon (PH)", "timeliness", "precision" and "accuracy", etc. These metrics are detailed in [1,2,4].…”