A digital twin-assisted algorithm for diagnosis of permanent magnet synchronous generator interturn short circuit fault and converter open circuit fault in wind power systems using Pearson correlation coefficient - Sun, Bin; Zhu, Ying; Wei, Zhinong (Engineering applications of artificial intelligence, 2025)
Interturn short-circuit faults (ISCFs) in permanent magnet synchronous generators (PMSGs) and open-circuit faults (OCFs) in the machine-side converters represent two critical reliability challenges in wind power systems. Conventional fault diagnosis approaches typically rely on dedicated models for each fault type for each fault type, leading to excessive system complexity and suboptimal computational efficiency. To overcome these limitations, this paper proposes a novel unified digital twin-assisted framework capable of simultaneous diagnosis of both PMSG ISCFs and converter OCFs within a single integrated architecture. The high-fidelity digital twin model based on one-dimensional convolutional neural networks is established to generate real-time reference value of current space vector (SV) for online fault detection, while Pearson correlation coefficient analysis enables accurate differentiation between ISCF and OCF. For ISCFs, the fault severity assessment is performed based on the deviation between reference and measured current SV, with the faulty phase identified using phase current root mean square (RMS) values. In the case of converter OCFs, the proposed method introduces a dual-stage identification process: single and dual insulated-gate bipolar transistor (IGBT) open faults are differentiated through severity estimation analysis, and the faulty IGBT is identified by evaluating the effective current interval ratio (ECIR) and normalized current average (NCA). The experimental results validate the effectiveness of the proposed method, and comparative analysis further demonstrates its superior performance in terms of parameter dependency and diagnostic efficacy.