Anomaly detection of wind turbines based on stationarity analysis of SCADA data

Phong B. Dao, Tomasz Barszcz and Wieslaw J. Staszewski

Renewable Energy, 2024, vol. 232, issue C

Abstract: This study presents a stationarity-based method, based on sliding window principle, for wind turbine monitoring and anomaly detection. Initially, the window is formed with a reference data set collected from a wind turbine under healthy condition. During the monitoring process, the data window is regularly updated with the latest samples recorded by the SCADA system. The Augmented Dickey-Fuller (ADF) test is exploited to compute the stationary statistical characteristics of the data within the window at each updating step. Any abrupt changes in the ADF t-statistics indicate the fault occurrence. The method can simultaneously monitor various parameters without using normal behaviour models. Its monitoring and fault detection mechanism relies on the consecutive accumulation of variations in stationarity of SCADA signals in the data window over time. Through the validation using two SCADA data sets, the method has proven its capability in monitoring wind turbines and detecting faults at the early stage.

Keywords: Wind turbine; Condition monitoring; Anomaly detection; Stationarity analysis; Sliding window; Augmented dickey-fuller test (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:232:y:2024:i:c:s0960148124011443

DOI: 10.1016/j.renene.2024.121076

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