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Bio
His research interests include industrial informatics, condition monitoring and diagnosis, high voltage engineering and electrical insulation, power systems, wireless sensor networks, and sensor signal processing. His research work is closely associated with the Australian electricity supply industry.
His current research is "Power System Asset Management" with the focus on (1) sensing and signal processing to improve the visibility of electricity network asset condition; and (2) data mining with uncertain reasoning for various applications of electricity networks with high penetration of renewables. Some of his recent works are as below.
Short-term photovoltaic generation forecasting based on Bayesian network with spatial-temporal correlation analysis . We developed an inference model built upon Bayesian network for a very short-term PV generation forecast. The model utilizes historic PV generation data and weather data and incorporates spatial similarity and temporal correlation to support PV output forecast.
Photovoltaic nowcasting incorporating sky images. We developed a deep learning-based model, which can fully utilize both sky images and historic PV output data. The model learns features from local spatio-temporal information embedded in sky images, global spatio-temporal correlations embedded in PV output datasets of a number of distributed PV systems and weather characteristics embedded in exogenous dataset. The obtained three types of hidden features are aggregated and applied to predict the PV output.
Forecasting of a single household electrical load for home energy management systems. We have developed a consumption scenario-based probabilistic load forecasting (PLF) algorithm to provide the load forecasting of an individual household.
Optimal power flow considering uncertainty set of wind power. We have proposed a risk-based contingency-constrained optimal power flow model, in which an adjustable uncertainty set of wind power is developed with network contingencies explicitly incorporated. The model is capable of securing the network against both wind power fluctuations and contingencies in a probabilistic manner with the optimal balance between operation cost and risk.
Data-driven power system asset remaining useful life estimation. Utilizing various condition monitoring data of power system asset, we applied a state-space model method to asset's remaining useful life estimation. To solve the nonlinear and non-Gaussian model, a particle filtering (Sequential Monte Carlo) approach is adopted. The posterior probability density function of the state variable obtained from the particle filtering is used to determine the asset remaining useful life.
His current research is "Power System Asset Management" with the focus on (1) sensing and signal processing to improve the visibility of electricity network asset condition; and (2) data mining with uncertain reasoning for various applications of electricity networks with high penetration of renewables. Some of his recent works are as below.
Short-term photovoltaic generation forecasting based on Bayesian network with spatial-temporal correlation analysis . We developed an inference model built upon Bayesian network for a very short-term PV generation forecast. The model utilizes historic PV generation data and weather data and incorporates spatial similarity and temporal correlation to support PV output forecast.
Photovoltaic nowcasting incorporating sky images. We developed a deep learning-based model, which can fully utilize both sky images and historic PV output data. The model learns features from local spatio-temporal information embedded in sky images, global spatio-temporal correlations embedded in PV output datasets of a number of distributed PV systems and weather characteristics embedded in exogenous dataset. The obtained three types of hidden features are aggregated and applied to predict the PV output.
Forecasting of a single household electrical load for home energy management systems. We have developed a consumption scenario-based probabilistic load forecasting (PLF) algorithm to provide the load forecasting of an individual household.
Optimal power flow considering uncertainty set of wind power. We have proposed a risk-based contingency-constrained optimal power flow model, in which an adjustable uncertainty set of wind power is developed with network contingencies explicitly incorporated. The model is capable of securing the network against both wind power fluctuations and contingencies in a probabilistic manner with the optimal balance between operation cost and risk.
Data-driven power system asset remaining useful life estimation. Utilizing various condition monitoring data of power system asset, we applied a state-space model method to asset's remaining useful life estimation. To solve the nonlinear and non-Gaussian model, a particle filtering (Sequential Monte Carlo) approach is adopted. The posterior probability density function of the state variable obtained from the particle filtering is used to determine the asset remaining useful life.
Research Interests
Papers共 116 篇Author StatisticsCo-AuthorSimilar Experts
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Measurementpp.114999, (2024)
IEEE Transactions on Dielectrics and Electrical Insulationno. 5 (2023): 1974-1982
IEEE Transactions on Dielectrics and Electrical Insulationno. 2 (2023): 869-876
IEEE Transactions on Plasma Scienceno. 10 (2022): 3732-3741
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