All Outputs (3)

A Low-Complexity Artificial Neural Network-Based Optimal Droop Gain Design Strategy for DC Microgrids Onboard the More Electric Aircraft (2023)
Journal Article
Hussaini, H., Yang, T., Bai, G., Urrutia-Ortiz, M., & Bozhko, S. (2023). A Low-Complexity Artificial Neural Network-Based Optimal Droop Gain Design Strategy for DC Microgrids Onboard the More Electric Aircraft. IEEE Transactions on Transportation Electrification, https://doi.org/10.1109/TTE.2023.3333270

This article proposes a new droop control design method based on a “reversed data training” of artificial neural network (ANN). Conventionally, after data collection, the ANN is used for forward mapping the control variables (inputs) and system respo... Read More about A Low-Complexity Artificial Neural Network-Based Optimal Droop Gain Design Strategy for DC Microgrids Onboard the More Electric Aircraft.

Artificial Intelligence-Based Hierarchical Control Design for Current Sharing and Voltage Restoration in DC Microgrid of the More Electric Aircraft (2023)
Journal Article
Hussaini, H., Yang, T., Bai, G., Urrutia-Ortiz, M., & Bozhko, S. (2024). Artificial Intelligence-Based Hierarchical Control Design for Current Sharing and Voltage Restoration in DC Microgrid of the More Electric Aircraft. IEEE Transactions on Transportation Electrification, 10(1), 566-582. https://doi.org/10.1109/tte.2023.3289773

In the conventional droop control method employed in the primary control layer, there is an inherent tradeoff between current-sharing accuracy and voltage regulation. Consequently, to achieve both accurate current sharing and maintain the bus voltage... Read More about Artificial Intelligence-Based Hierarchical Control Design for Current Sharing and Voltage Restoration in DC Microgrid of the More Electric Aircraft.

Optimal Droop Control Design Using Artificial Intelligent Techniques for Electric Power Systems of More-Electric Aircraft (2023)
Journal Article
Hussaini, H., Yang, T., Gao, Y., Wang, C., Urrutia, M., & Bozhko, S. (2024). Optimal Droop Control Design Using Artificial Intelligent Techniques for Electric Power Systems of More-Electric Aircraft. IEEE Transactions on Transportation Electrification, 10(1), 2192-2206. https://doi.org/10.1109/tte.2023.3271763

The design of the droop coefficient is one of the challenges for the droop control of converters, as it plays a key role in enhancing the performance of the droop control method. This article proposes an artificial neural network (ANN) based techniqu... Read More about Optimal Droop Control Design Using Artificial Intelligent Techniques for Electric Power Systems of More-Electric Aircraft.