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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Modeling And Simulation Of Microgrid, Ahmad Alzahrani, Mehdi Ferdowsi, Pourya Shamsi, Cihan H. Dagli
Modeling And Simulation Of Microgrid, Ahmad Alzahrani, Mehdi Ferdowsi, Pourya Shamsi, Cihan H. Dagli
Electrical and Computer Engineering Faculty Research & Creative Works
Complex computer systems and electric power grids share many properties of how they behave and how they are structured. A microgrid is a smaller electric grid that contains several homes, energy storage units, and distributed generators. The main idea behind microgrids is the ability to work even if the main grid is not supplying power. That is, the energy storage unit and distributed generation will supply power in that case, and if there is excess in power production from renewable energy sources, it will go to the energy storage unit. Therefore, the electric grid becomes decentralized in terms of control …
Chaotic Behavior In High-Gain Interleaved Dc-Dc Converters, Ahmad Alzahrani, Pourya Shamsi, Mehdi Ferdowsi, Cihan H. Dagli
Chaotic Behavior In High-Gain Interleaved Dc-Dc Converters, Ahmad Alzahrani, Pourya Shamsi, Mehdi Ferdowsi, Cihan H. Dagli
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, chaotic behavior in high gain dc-dc converters with current mode control is explored. The dc-dc converters exhibit some chaotic behavior because they contain switches. Moreover, in power electronics (circuits with more passive elements), the dynamics become rich in nonlinearity and become difficult to capture with linear analytical models. Therefore, studying modeling approaches and analysis methods is required. Most of the high-gain dc-dc boost converters cannot be controlled with only voltage mode control due to the presence of right half plane zero that narrows down the stability region. Therefore, the need of current mode control is necessary to …
Solar Irradiance Forecasting Using Deep Neural Networks, Ahmad Alzahrani, Pourya Shamsi, Cihan H. Dagli, Mehdi Ferdowsi
Solar Irradiance Forecasting Using Deep Neural Networks, Ahmad Alzahrani, Pourya Shamsi, Cihan H. Dagli, Mehdi Ferdowsi
Electrical and Computer Engineering Faculty Research & Creative Works
Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying …