Anomaly detection in current and voltage data is critical to ensuring the reliability and safety of electrical systems. This research presents a comprehensive framework that combines feature extraction and machine learning to identify irregularities in time series measurements. The study evaluates five anomaly detectio...
Krisdahim Kogoya, Yu-Hao Li, Hui Liu et al.· 2026 6th Power System and Gr...· 0 citations
Small current grounding faults pose persistent challenges in power systems due to their low-magnitude signatures and the complexity of timely, accurate detection. While deep learning methods have recently gained attention, traditional time-series and statistical techniques remain foundational in industrial applications...
Eric Obeng Akese Danso, Rathnayaka Pathiranage Ravindi Kawushini, Tchepseu Pateng Uriche Cabrel et al.· 2026 6th Power System and Gr...· 0 citations
Fault detection in power systems is critical for ensuring reliability, safety, and operational efficiency. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have revolutionized fault detection by leveraging voltage and current data visualization. This review paper synthesizes find...
Javed Hossain, Taharimul Islam, Hui Liu et al.· 2026 6th Power System and Gr...· 0 citations
This paper presents a comprehensive evaluation of machine learning algorithms for voltage timeseries prediction in power systems. We compare six models: Linear Regression, Random Forest, XGBoost, Support Vector Regression (SVR), k-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks using multiple metric...
Md Nur Alam, Hui Liu, Javed Hossain et al.· 2026 6th Power System and Gr...· 0 citations
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