University College Dublin (IRELAND)
About this paper:
Appears in: EDULEARN19 Proceedings
Publication year: 2019
Pages: 97-106
ISBN: 978-84-09-12031-4
ISSN: 2340-1117
doi: 10.21125/edulearn.2019.0031
Conference name: 11th International Conference on Education and New Learning Technologies
Dates: 1-3 July, 2019
Location: Palma, Spain
This paper discusses the implementation of Jupyter notebook-based laboratories for the module “Electrical Energy Systems” taught by the authors in the academic year 2018/2019 at University College Dublin.

Jupyter is an open-source project that started in 2014 based on IPython, and has quickly become popular in scientific and academic communities. Jupyter notebooks are interactive webpages, that can be easily and efficiently designed for testing live code, embedding narrative text and visualizing results. The term “computational narrative” has been coined for these virtual notebooks and well summarizes the features of Jupyter notebooks. Jupyter notebooks make it simple to implement bits of code and design engineering problem and exercises.

The second author has taught the module “Electrical Energy Systems” for three years now. In the first two years, the students were asked to do standard electrical machine-based labs. These labs posed several logistic problems due to the high number of students (about 160) and the low number of available machines (5 transformers and 5 rotating machines). More importantly, machine-based labs were not particularly suited to second-year students, due to the intrinsic risks associated with high-voltage devices and the need for the students to have properly assimilated the concepts of “modeling” and “measuring”. These are indeed subtle engineering concepts that only more mature students, e.g., third or fourth year ones, can appreciate and put into practice in the labs

In the academic year 2017/18, the authors have set up Jupyter-based labs for a small (about 15 students) fourth-year module, namely “Power Systems Dynamics and Control”, which is offered exclusively to Electrical Engineering students. The success of this experiment, duly documented in a paper presented at EDULEARN 2018, motivated the implementation of the labs for this larger and heterogeneous (students are from mechanical, electrical and electronic engineering programs) module “Electrical Energy Systems.”

This paper shows that compared to machine-based ones, Jupyter-based labs can improve the students performance and learning experience. This is done by increasing the variety and number of exercises that they have to solve and gently introducing them to the concept of “modeling” physical devices through equations. Each lab is designed so that the students have to solve “inverse problems”, i.e., finding through a trial-and-error technique the parameters of a given machine or simple power system to satisfy a given design requirements. Through plots the students can visualize important variables associated to each electrical device or system being studied.

The final paper will provide the following contributions.
• A description of the key features of Jupyter notebooks that make them adequate for second-year labs on electrical energy systems.
• A complete example of a laboratory activity that illustrates modeling issues and an inverse engineering problem.
• The experience and comments of the teaching assistant that has set up and run the laboratories of the module “Electrical Energy Systems”.
• The feedback of the undergraduate students that attended the module “Electrical Energy Systems”.
Electrical energy systems, electrical machines, modeling, inverse problems, Jupyter project, Python, computer-based laboratory.