2027 Unit information is now available. View 2027 unit information
Planned Year of Introduction: 2028
Introduction
This unit equips students with the skills to analyse, design, and manage advanced electrical systems, with a focus on smart grids, industrial automation, and AI-driven decision-making. Covering the full lifecycle of electrical systems, from installation and maintenance to optimisation and automation, students will develop expertise in diagnostic techniques, reliability analysis, and system performance enhancement.
Students will explore the integration of Artificial Intelligence (AI) in electrical engineering, applying data-driven methods to optimise electrical systems while considering ethical and regulatory implications. The unit emphasises practical application through real-world system analysis, system design, and AI-driven optimisation projects, preparing students to implement industry-standard monitoring, control, and automation solutions.
By engaging with technical reporting and consulting-oriented communication, students will refine their ability to present complex engineering solutions to industry stakeholders.
Summary
| Unit name | Applications in Electrical Engineering |
| Unit code | ENG427 |
| Credit points | 12.5 |
| College/School | Sciences and Engineering School of Engineering |
| Discipline | Engineering |
| Coordinator | Doctor Benjamin Millar |
| Delivered By | University of Tasmania |
| Level | Honours |
Sustainable Development Goals
The Unit Coordinator has identified that this unit aligns with the following UN Sustainable Development Goals. We welcome your thoughts and feedback on the alignment of the unit with these goals.
Learning Outcomes
- Analyse and optimise real-world electrical systems, applying advanced diagnostic and troubleshooting techniques to enhance system reliability and performance
- Design and implement smart grid or industrial automation systems, selecting appropriate components and applying relevant standards to ensure operational efficiency and compliance
- Utilise AI technologies to improve decision-making and efficiency in smart grids or industrial automation scenarios
- Communicate complex system designs and operational strategies effectively through detailed professional reports aimed at a consulting audience
Fee Information
| Field of Education | Commencing Student Contribution 1,3 | Grandfathered Student Contribution 1,3 | Approved Pathway Course Student Contribution 2,3 | Domestic Full Fee 4 |
|---|---|---|---|---|
| not applicable |
1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.
If you have any questions in relation to the fees, please contact UniConnect or more information is available on StudyAssist.
Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.
Requisites
Prerequisites
ENG323 Control Systems|ENG324 Data Acquisition and CommunicationsTeaching
| Teaching Pattern | 1 x 2 hour lecture per week 1 x 2 hour Tutorial per week 2 hour workshop/labs per fortnight |
|---|---|
| Assessment | System Analysis Project (30%)|AI Assignment (35%)|Design Project (35%) |
| Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
| Required |
Required readings will be listed in the unit outline prior to the start of classes. |
|---|
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.