The University of Osaka · Engineering
Professor Fei Xiao's research lab specializes in data-driven intelligent systems for power systems and advanced materials, focusing on real-time security assessment, power quality disturbance detection, and machine learning applications in materials science. The lab develops probabilistic risk models, multi-objective optimization, and advanced signal processing techniques to enhance power system reliability and situational awareness. It also pioneers interpretable machine learning workflows for predicting material properties, particularly in high-entropy shape memory alloys, enabling accelerated materials discovery. The integration of smart meter data, wavelet transforms, and random matrix theory underscores the lab’s commitment to solving practical challenges in energy systems and materials engineering.
Figures are computed from collected data and may differ slightly.
Traditional online security assessment determines whether the system is secure or not, but how secure or insecure is not explicitly indicated. This paper develops probabilistic indices, risk, to assess real-time power system security level. Risk captures not only event likelihood, but also consequence. System security level associated with low voltage and overload can be optimally controlled, using the NSGA multiobjective optimization method. A security diagram is used to visualize operating con
Power quality (PQ) disturbance in power systems has been a concern for operators and customers. The purpose is to locate and forecast the presence of PQ disturbances to suppress or avoid their negative effects on power grid and appliances. This study, using a multi-hidden Markov model (MHMM), motivates data-driven tools to achieve situation awareness of PQ disturbance. We first design a modified adaptive-sorted neighborhood method that consists of blocking and merging phases to locate PQ disturb
Machine learning approaches (ML) based on data-driven models are conducive to accelerating the assessments of the martensitic transformation peak temperature (Tp) of TiZrHfNiCoCu high entropy shape memory alloys (HESMAs) over a huge composition space. In this work, an interpretable machine learning workflow was established through dataset construction, feature selection, modeling and validation, and model interpretation. We identified a set of key feature combinations closely related to Tp, by e
This study presents a new technique for power quality (PQ) disturbance detection. The technique focuses on voltage sags and interruptions that are related to various faults, i.e. transmission line, feeder, and transformer faults. A maximal overlap discrete wavelet transform‐based PQ detection algorithm is proposed to provide accurate points of disturbance initiation and recovery. The proposed PQ detection algorithm is robust even without a detection threshold and independent of the sampling freq
Illegal use of electricity has been a major concern in power system industries for a long time. Fraudulent large‐scale consumption of electricity may result in an unbalanced demand–supply gap. This study proposed a data‐driven electricity theft detector that is based on random matrix theory with the widespread use of smart meters and advanced metering infrastructure. The application of an augmented matrix as the data source is the key step of the proposed method, indicating the correlations betw
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