Identifying Early Stator Fault Severity in DFIGs based on Adaptive Feature Mode Decomposition and Multiscale Complex Component Current Trajectories

• Published in: IEEE transactions on instrumentation and measurement Vol. 73; p. 1
• Main Authors: Zhao, Shouwang, Chen, Yu, Liang, Feng, Zhang, Sichao, Shahbaz, Nadeem, Wang, Shuang, Zhao, Yong, Deng, Wei, Cheng, Yonghong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive systems; Circuit faults; Complex signal trajectory; Decomposition; Doubly fed induction generators; Entropy; Fault detection; Fault diagnosis; Feature extraction; Frequency analysis; Generators; Harmonics; Induction generators; Interturn short circuit; Mode decomposition; Multiscale analysis; Pattern analysis; Quantitative assessment; Resonant frequencies; Short circuits; Signature analysis; Stators; Symmetrized dot pattern; Trajectory; Trajectory analysis; Wind power generation; Zero sequence current
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2024.3443347

Early fault detection is critical for ensuring reliable operation in wind power generation systems employing Doubly Fed Induction Generators (DFIGs). Although the widespread use of motor current signature analysis (MCSA) for noninvasive fault detection, early-stage faults in DFIGs often present with weak characteristic fault information, posing challenges for detection amidst noise and interference signals. This paper proposes a novel method to assess the severity of early stator interturn short circuits (ITSCs) in DFIGs by combining adaptive feature mode decomposition (AFMD) with multiscale analysis of complex component current trajectories. AFMD is applied to perform quadratic time-frequency decomposition of current signals, resulting in a series of modal components with diverse frequency contents. By selecting the low-frequency current modal components with the fundamental frequency and low-order harmonics, high-frequency interferences and noise are effectively filtered out. The extracted low-frequency current modal components undergo multiscale Park vector trajectory analysis, thereby enhancing the expression of weak fault characteristics associated with early ITSCs. Further analysis involves the extraction of negative sequence current (NSC) and zero-sequence current (ZSC) components from the low-frequency modal components. The residual ZSC signal is constructed by filter out the fundamental frequency and low-order harmonic components from the ZSC component of the low-frequency modal. Analysis of the symmetrized dot pattern of the residual ZSC signal captures the evolution process of early-stage ITSCs with a low number of turns. Additionally, the negative sequence current of the low-frequency modal components is qualitatively and quantitatively evaluated for early low-turn insulation short circuits of varying severity levels. Experimental validation on a 100 kW DFIG test platform demonstrates the efficacy of the proposed method in enhancing the detection and diagnosis of early-stage ITSC faults.