Year: 2025
Type: article
Abstract: The identification of critical fault information plays a crucial role in ensuring the reliability and stability of power systems. However, existing fault-identification technologies heavily rely on high-dimensional sensor data, which often contain redundant and noisy information. Moreover, conventional data preprocessing approaches typically employ fixed time windows, neglecting variations in fault characteristics under different system states. This limitation may lead to incomplete feature selection and ineffective dimensionality reduction, ultimately affecting the accuracy of fault classification. To address these challenges, this study proposes a method of critical fault information identification that integrates a scalable time window with Principal Component Analysis (PCA). The proposed method dynamically adjusts the time window size based on real-time system conditions, ensuring more flexible data capture under diverse fault scenarios. Simultaneously, PCA is employed to reduce dimensionality, extract representative features, and remove redundant noise, thereby enhancing the quality of the extracted fault information. Furthermore, this approach lays a solid foundation for the subsequent application of deep learning-based fault-diagnosis techniques. By improving feature extraction and reducing computational complexity, the proposed method effectively alleviates the workload of operation and maintenance personnel while enhancing fault classification accuracy. Our experimental results demonstrate that the proposed method significantly improves the precision and robustness of fault identification in power systems.
Source: Computation
Authors: Pan Zhang, Lei Guo, Zhicheng Huang, Zhoupeng Rao, Ying Zhang, Zhi Sun, Rui Xu, Deng Li
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4410130240