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Articles 1 - 3 of 3
Full-Text Articles in Engineering
Recent Advances Of Wind-Solar Hybrid Renewable Energy Systems For Power Generation: A Review, Pranoy Roy, Jiangbiao He, Tiefu Zhao, Yash Veer Singh
Recent Advances Of Wind-Solar Hybrid Renewable Energy Systems For Power Generation: A Review, Pranoy Roy, Jiangbiao He, Tiefu Zhao, Yash Veer Singh
Electrical and Computer Engineering Faculty Publications
A hybrid renewable energy source (HRES) consists of two or more renewable energy sources, such as wind turbines and photovoltaic systems, utilized together to provide increased system efficiency and improved stability in energy supply to a certain degree. The objective of this study is to present a comprehensive review of wind-solar HRES from the perspectives of power architectures, mathematical modeling, power electronic converter topologies, and design optimization algorithms. Since the uncertainty of HRES can be reduced further by including an energy storage system, this paper presents several hybrid energy storage system coupling technologies, highlighting their major advantages and disadvantages. Various …
Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao
Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao
Electrical and Computer Engineering Faculty Publications
Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …
Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao
Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao
Electrical and Computer Engineering Faculty Publications
Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …