Douglas Jussaume

2papers

2 Papers

52.0SYApr 17
Integrating AI and Simulation for Teaching Power System Dynamics: An Interactive Framework for Engineering Education

Osasumwen Cedric Ogiesoba-Eguakun, Phani Kumar Inkollu, Rupesh Sah et al.

Artificial Intelligence (AI), especially cloud platforms and large language models (LLMs), is changing how engineering is taught by making learning more interactive and flexible. However, in electrical engineering and energy systems, students often find power system dynamics difficult to understand because the concepts are abstract, math-heavy, and there are limited opportunities for hands-on practice. This paper presents an AI-based interactive learning framework that combines simulation with intelligent feedback to improve understanding and student engagement. The framework has three connected parts: an AI layer that provides explanations and guidance, a simulation layer that models system behavior, and a user layer that allows students to interact with the system in real time. These parts work together in a continuous loop where students explore how the system behaves, change parameters, and receive feedback based on the results. The paper also provides a step-by-step process to help educators design and apply AI-supported learning environments, including breaking down concepts, using simulations, and assessing performance. This method helps students learn through practice and better understand how ideas from class apply to real power systems. It also provides a practical way to improve electrical engineering education and helps students get ready to use AI tools carefully and responsibly in engineering.

33.1ETApr 20
Scattering-Matrix-Based Parametric Characterization of a Two-Port Bridged-T Network for Microstrip Filter Applications

Naser Khatti Dizabadi, Douglas Jussaume

The purpose of this study is to characterize a two-port Bridged-T network using transmission (T) and scattering (S) matrices. Using mathematical derivations, scattering parameters including S11, S12, S21, and S22 have been derived from the T and S matrices to permit a detailed investigation of the network's performance. As two of the most relevant parameters in the design of microstrip filters, both the magnitude and phase of S11 and S21 have been parametrically calculated after normalizing the frequency. Furthermore, when the inductors L1 and L2 are identical, all even coefficients of the numerator polynomial in the S11 transfer function are eliminated, leaving only the odd coefficients behind. Based on this feature, the bridged-T circuit is designed to operate as a high-pass filter. Therefore, the magnitude and phase of both S11 and S21 have been simulated for the designed filter with a corner frequency of 1 GHz. Simulation results performed by Keysight ADS show that S11 and S21 for the high-pass filter built upon the bridged-T network have sharp roll-off ratios of -30dB/GHz and -32dB/GHz respectively.