ESPRIT Method Enhancement for Real-time Wind Turbine Fault Recognition

Abstract

Early fault diagnosis plays a very important role in the modern energy production systems. The wind turbine machine requires a regular maintenance to guarantee an acceptable lifetime and to minimize production loss. In order to implement a fast, proactive condition monitoring, ESPRIT-TLS method seems the correct choice due to its robustness in improving the frequency and amplitude detection. Nevertheless, it has a very complex computation to implement in real time. To avoid this problem, a Fast-ESPRIT algorithm that combined the IIR band-pass filtering technique, the decimation technique and the original ESPRIT-TLS method were employed to enhance extracting accurately frequencies and their magnitudes from the wind stator current. The proposed algorithm has been evaluated by computer simulations with many fault scenarios. Study results demonstrate the performance of Fast-ESPRIT allowing fast and high resolution harmonics identification with minimum computation time and less memory cost.

Fault Detection of Wind Turbine Induction Generators through Current Signals and Various Signal Processing Techniques

“…Other models used for HOS fault monitoring and detection include estimation of signal parameter via rotational invariance technique (ESPRIT) and PRONY. ESPRIT belongs to the subspace parametric spectrum estimation methods expressed by Equations (52) and (53) [79], whereas according to reference [80], the PRONY method is used to model the sampled data of a signal using a linear system of complex exponential functions. As described in reference [81], the extensive use of the power converter when the DFIG works below the synchronous speed causes the current to have a high content of interharmonics that can cause resonance, in addition to damage to capacitors, insulation, control elements, and protection.…”

Intelligent Fault Detection Device for Wind Turbine in Real Time Condition Monitoring

“…For these reasons, there is an increase need to implement a robust efficient remote maintenance strategy to ensure uninterrupted power in the modern wind systems [3]. This on line surveillance allow an early detection of mechanical and electrical faults preventing major component failures, facilitating a proactive response, anticipating the final shutdown of wind generators, minimizing downtime and maximizing productivity by analysis of measured physical signals continuously collected from different types of sensors [4], [5], [6]. This is why reliability of wind turbines becomes an important topic in scientific research and in industry.…”

“…Most of the recent researches have been oriented toward electrical monitoring, as it would be the most practical technique and less costly. Another powerful tool used for diagnosis of an induction motor or generator utilizing the result of the spectral analysis of the stator current to indicate an existing or incipient failure is current stator analysis (CSA) [1], [4], [5], [6], [7]. Furthermore, with recent digital signal processor (DSP) and with wireless communication technology developments, it is possible to detect electric machines faults prior to possible catastrophic failure in real-time based on the stator line current allowing precise and low-cost [7].…”