Control of Grid-Connected Converters using Deep Learning
| dc.contributor.author | Ghidewon-Abay, Sengal | en |
| dc.contributor.committeechair | Mehrizi-Sani, Ali | en |
| dc.contributor.committeemember | Syed, Mazheruddin | en |
| dc.contributor.committeemember | Jin, Ming | en |
| dc.contributor.committeemember | Kekatos, Vasileios | en |
| dc.contributor.department | Electrical Engineering | en |
| dc.date.accessioned | 2023-01-13T09:00:21Z | en |
| dc.date.available | 2023-01-13T09:00:21Z | en |
| dc.date.issued | 2023-01-12 | en |
| dc.description.abstract | With the rise of inverter-based resources (IBRs) within the power system, the control of grid-connected converters (GCC) has become pertinent due to the fact they interface IBRs to the grid. The conventional method of control for grid-connected converters (GCCs) such as the voltage-sourced converter (VSC) is through a decoupled control loop in the synchronous reference frame. However, this model-based control method is sensitive to parameter changes causing deterioration in controller performance. Data-driven approaches such as machine learning can be utilized to design controllers that are capable of operating GCCs in various system conditions. This work reviews different machine learning applications in power systems as well as the conventional method of controlling a VSC. It explores a deep learning-based control method for a three-phase grid-connected VSC, specifically utilizing a long short-term memory (LSTM) network for robust control. Simulations of a conventional controlled VSC are conducted using Simulink to collect data for training the LSTM-based controller. The LSTM model is built and trained using the Keras and TensorFlow libraries in Python and tested in Simulink. The performance of the LSTM-based controller is evaluated under different case studies and compared to the conventional method of control. Simulation results demonstrate the effectiveness of this approach by outperforming the conventional controller and maintaining stability under different system parameter changes. | en |
| dc.description.abstractgeneral | The desire to minimize the use of fossil fuels and reduce carbon footprints has increased the usage of renewable energies also known as inverter-based resources (IBRs) within the power grid. These resources add a level of complexity to operating the grid due to the fluctuating nature of IBRs and are connected to the power grid through grid-connected converters (GCCs). The control method conventionally used for GCCs is derived by accounting for the system parameters, creating a mathematical model under constant parameters. However, the parameters of the system are susceptible to changes under different operating and environmental conditions. This results in poor performance from the controller under various operating conditions due to its inability to be adaptive to the system. Data-driven approaches such as machine learning are becoming increasingly popular for their ability to capture the dynamics of a system with limited knowledge. The different applications of machine learning within power systems include fault diagnosis, energy management, and cyber security. This work explores the use of utilizing deep learning techniques for a robust approach of controlling GCCs. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:36252 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/113155 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Long short-term memory | en |
| dc.subject | grid-connected converter | en |
| dc.subject | deep learning | en |
| dc.subject | transient response | en |
| dc.subject | neural networks | en |
| dc.title | Control of Grid-Connected Converters using Deep Learning | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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