TY - JOUR
T1 - Disturbances identification in renewable energy system using multi-kernel decomposition fusion method
AU - Xu, Guangda
AU - Wang, Bo
AU - Wang, Zhaohua
AU - Zhang, Jian
AU - Li, Guowu
N1 - Publisher Copyright:
© 2026
PY - 2026/6/16
Y1 - 2026/6/16
N2 - The cumulative installed capacity of renewable energy sources (RES) such as wind energy has expanded rapidly due to the growing demand for clean energy. However, investments in equipment such as inverters and converters have exacerbated the degradation of power quality, leading to voltage fluctuations and harmonic distortion. To address complex disturbance identification, this study proposes a novel power quality disturbance (PQD) identification method based on a multi-kernel decomposition fusion (MKDF) framework. First, dynamic variational mode decomposition is employed for waveform decomposition, ensuring minimal signal decomposition energy loss. Then, an improved twin support vector machine is developed to integrate global and local characteristics. By combining decomposition and identification techniques, the MKDF framework is designed to effectively address complex power quality disturbances coupled with mechanical failure-induced specific frequency modulations in electrical signals, and thus accurately account for mechanical failure effects that are distinct from standard IEEE-1159 PQD features. Additionally, experimental evaluations, including a scenario with outer race bearing fault interference, demonstrate that the proposed method outperforms several recently developed approaches. It maintains 98.88% overall accuracy and 98.71% F1-score under mechanical failure interference, while delivering an end-to-end execution time of only 94 ms for real-time on-site monitoring.
AB - The cumulative installed capacity of renewable energy sources (RES) such as wind energy has expanded rapidly due to the growing demand for clean energy. However, investments in equipment such as inverters and converters have exacerbated the degradation of power quality, leading to voltage fluctuations and harmonic distortion. To address complex disturbance identification, this study proposes a novel power quality disturbance (PQD) identification method based on a multi-kernel decomposition fusion (MKDF) framework. First, dynamic variational mode decomposition is employed for waveform decomposition, ensuring minimal signal decomposition energy loss. Then, an improved twin support vector machine is developed to integrate global and local characteristics. By combining decomposition and identification techniques, the MKDF framework is designed to effectively address complex power quality disturbances coupled with mechanical failure-induced specific frequency modulations in electrical signals, and thus accurately account for mechanical failure effects that are distinct from standard IEEE-1159 PQD features. Additionally, experimental evaluations, including a scenario with outer race bearing fault interference, demonstrate that the proposed method outperforms several recently developed approaches. It maintains 98.88% overall accuracy and 98.71% F1-score under mechanical failure interference, while delivering an end-to-end execution time of only 94 ms for real-time on-site monitoring.
KW - Multi-kernel decomposition fusion
KW - Power quality disturbance
KW - Renewable energy system
KW - Support vector machine
KW - Variational mode decomposition
UR - https://www.scopus.com/pages/publications/105037027529
U2 - 10.1016/j.measurement.2026.121653
DO - 10.1016/j.measurement.2026.121653
M3 - Article
AN - SCOPUS:105037027529
SN - 0263-2241
VL - 278
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
M1 - 121653
ER -