Novel strategy for fault e-diagnosis of wind energy conversion systems using wavelet analysis based on Rt-Lab and Arduino

Ahmed Cheriet, Abdeldjebar Hazzab, Abdelkader Bekri, Hicham Gouabi, Mohamed Habbab, Miloud Rezkallah, Ambrish Chandra, Hussein Ibrahim


The diagnosis of wind energy conversion systems (WECS) turns out to be necessary because of their relatively high cost of operation and maintenance. Wind turbines are hard-to-access structures, and they are often located in remote areas. Therefore, a remote diagnosis (e-diagnosis) is required. This paper proposes an alternative approach for the e-diagnosis of a WECS based on the discrete wavelet transform (DWT) and frequency analysis of the aero generator stator currents. To validate this approach, real-time hardware in the loop (HIL) is used to simulate in real-time the mathematical model of the induction generator on the OPAL-RT OP5600 platform to generate the stator currents and the rotor speed. The DWT is applied to the current signal, to generate the DWT signal, which has a huge number of points that are not supported for direct transmission by the Arduino Mega RobotDyn because of its limited sample time. The absolute values of the DWT peak points (MDWT) are sent as point’s packages form to the diagnosis station via the ESP8266 integrated Wi-Fi board of the Arduino Mega RobotDyn to monitor the SCIG states and determine the number of broken bars.


aero generators; e-Diagnosis; hardware in the loop; wavelet transforms; wind turbine

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