Outline

Overview

The unit aims to cover advanced topics in power electronics and it applications. In particular, the power electronics interface design and implementation for microgrid, smart grids and modern power systems which have received tremendous attention in recent years. Many countries including Australia are developing different power electronics technologies such as integrating renewable energy sources into the grid, managing charging and discharging of high power energy storage system, controlling the reactive power of power electronics interfaces for grid stability, and adding communication capability to power electronics interfaces for smart meter implementation. The unit assumes prior fundamental knowledge of power electronics systems and applications, including the ability to analyse basic power converters for all four conversions (ac-ac, ac-dc, dc-ac, and ac-dc), and design and implement various applications, such as motor drive and battery charger, with the consideration of electrical characteristics of semiconductors and passive elements. This unit will cover advanced technologies on power electronics interfaces for smart grids and microgrid implementation, which include dynamic voltage restorer, active power filter, reactive power compensation, energy storage management, hybrid energy sources optimisation, multilevel inverter and control, D-STATCOM, etc. To analyse these advanced power conversion systems, some analytical techniques will be introduced. This includes resonant converters, soft-switching technique, ac equivalent circuit modeling, converter control and input/output filter design.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	ELEC3204
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Sinan Li, sinan.li@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Paper-based Final Exam This is a RESTRICTED OPEN book exam - specified materials permitted, Calculator - non-programmable; One A4 sheet of handwritten and/or typed notes double-sided allowed.	42%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Practical skill	Lab #1 Lab activity and report quality	6%	Week 04 Due date: 28 Aug 2026 at 23:59 Closing date: 04 Sep 2026	two weeks (10 working days)	AI allowed
Outcomes assessed:
Written work	Project proposal Submit via Turnitin Evaluated by writing quality and value.	5%	Week 04 Due date: 28 Aug 2026 at 23:59 Closing date: 04 Sep 2026	3 weeks	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Quiz via Canvas Canvas Quiz (open book)	5%	Week 07 Due date: 17 Sep 2026 at 23:59 Closing date: 24 Sep 2026	55 minutes during lecture hours	AI prohibited
Outcomes assessed:
Practical skill	Assignment #1 Analysis, design, and simulation	3%	Week 07 Due date: 18 Sep 2026 at 23:59 Closing date: 25 Sep 2026	2 weeks (or 10 working days)	AI allowed
Outcomes assessed:
Practical skill	Assignment #2 Calculation, design, analysis, and simulation	3%	Week 09 Due date: 09 Oct 2026 at 23:59 Closing date: 16 Oct 2026	2 weeks (or 10 working days)	AI allowed
Outcomes assessed:
Practical skill	Lab #2 Performance and report quality	6%	Week 11 Due date: 23 Oct 2026 at 23:59 Closing date: 30 Oct 2026	2 weeks (or 10 working days)	AI allowed
Outcomes assessed:
Q&A following presentation, submission or placement	Project Q&A Q&A following presentation	8%	Week 12 Due date: 29 Oct 2026 at 23:59 Closing date: 05 Nov 2026	6 minutes	AI prohibited
Outcomes assessed:
Practical skill	Lab #3 Performance and report quality	6%	Week 12 Due date: 30 Oct 2026 at 23:59 Closing date: 06 Nov 2026	2 weeks (or 10 working days)	AI allowed
Outcomes assessed:
Presentation	Project presentation Submit on Canvas	7%	Week 12 Due date: 29 Oct 2026 at 23:59 Closing date: 05 Nov 2026	15-minute presentation	AI allowed
Outcomes assessed:
Written work	Final Project Report Evaluated by the technical value and writing quality.	9%	Week 13 Due date: 06 Nov 2026 at 23:59 Closing date: 13 Nov 2026	from Week 2 to Week 13	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Writen work: This assignment will require you to integrate information from lectures and practicals to create a concise written argument.
Pre-lab work: These work will test your understanding of material covered in that week's practical class. These work need to be completed before your scheduled practical class. A student who has not finished pre-lab work on Canvas are still allow to do the scheduled practical class but will lose half of the full lab marks (i.e., 3% of the overall unit marks) rather than given an improvised private catch-up session.
Written exam: The final exam will cover all material in the unit from both lectures, practical classes and tutorial classes. The exam might have a mixture of multiple choice questions and short answer questions,essay questions, numerical calculation questions. The final format and scope will be covered in the lecture.
In-person practical, skills, or performance task or test: This quiz will be an exam-like test. Student must complete the quiz in-person and in-class. The quiz will be based on Canvas, consisting of single choice questions and numerical calculation questions.
Q&A following presentation, submission or placement: This is the Q&A session after the project presentation has been submitted. Each student must answer the panel’s questions independently, clearly, accurately and professionally. The Project Q&A is an individual assessment, and each student is assessed on their own responses.
Presentation: This is a 15-minute project presentation.
Practical skill: This is where students will need to actually build, test, analysis, evaluate and record what Lecture has covered, linking theory to practice.

Detailed information for each assessment can be found on Canvas.’ (e.g. assessment rubrics) and submission instructions are published on Canvas

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date: Deduction of 5% of the maximum mark for each calendar day after the due date. After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Unit introduction, power quality and power factor correction (Part A)	Lecture (2 hr)
Week 01	Self study of advanced power electronics	Self-directed learning (3 hr)
Week 02	Power factor correction (Part B)	Lecture (2 hr)
	Lab 1: Power factor Correction	Practical (3 hr)
	Self learning and R&D in power electronics and sustainable energy system to fulfil the requirements of term project	Self-directed learning (5 hr)
Week 03	Soft Switching and Resonant Converters	Lecture (2 hr)
	Tut1: Active power factor correction and research project support	Tutorial (3 hr)
	Self-learning and R&D in power electronics and sustainable energy system to fulfil the requirements of term project	Self-directed learning (5 hr)
	Self-learning and R&D in power electronics and sustainable energy system to fulfil the requirements of term project	Self-directed learning (4 hr)
Week 04	Mathmatical modelling by Averaging	Lecture (2 hr)
	Tut 2 -Soft switching and research project support	Tutorial (3 hr)
	Self-learning and R&D in power electronics and sustainable energy system to fulfil the requirements of term project	Self-directed learning (5 hr)
Week 05	Linearization for dynamic modeling	Lecture (2 hr)
	Tut 3 – Modelling and research project support	Tutorial (3 hr)
	Self-learning and R&D in power electronics and sustainable energy system to fulfil the requirements of term project	Self-directed learning (5 hr)
Week 06	Control system 1: Controller synthesis	Lecture (2 hr)
	Tut 4 – Control, assignment, simulation, and research project support	Tutorial (3 hr)
	Self-learning and prepare for the quiz on Week7	Self-directed learning (5 hr)
Week 07	Control system 2: robustness & Quiz 1	Lecture (2 hr)
	Tut 5 - DAB, research project support	Tutorial (3 hr)
	Self-learning and R&D in power electronics and sustainable energy system to fulfil the requirements of term project	Self-directed learning (5 hr)
Week 08	Dual active bridge and bidirectional power conversion	Lecture (2 hr)
	Tut 6: Assignment, simulation, and research project support	Tutorial (3 hr)
	Self-learning and R&D in power electronics and sustainable energy system to fulfil the requirements of term project	Self-directed learning (5 hr)
Week 09	Introduction to PV power systems	Lecture (2 hr)
	Lab 2: Zero voltage switching	Practical (3 hr)
	Preparing for project report and presentation	Self-directed learning (5 hr)
Week 10	PV system simulation and PV side converters	Lecture (2 hr)
	Lab 3: Dynamic modelling and control	Practical (3 hr)
	Preparing for project report and presentation	Self-directed learning (5 hr)
Week 11	Grid-side Converter and Maximum power point tracking	Lecture (2 hr)
	Make-up lab: Lab session for special consideration	Practical (3 hr)
	Prepare for final exam	Self-directed learning (5 hr)
Week 12	Project presentation	Lecture (2 hr)
	Tut 7: Project presentation	Tutorial (3 hr)
	Prepare for final exam	Self-directed learning (5 hr)
Week 13	Unit summary and discussion of final exam	Lecture (2 hr)
	Tut 8: Final exam support and Q&A	Tutorial (3 hr)
	Prepare for final exam	Self-directed learning (5 hr)
Week 14 (STUVAC)	Prepare for final exam	Self-directed learning (6 hr)

Attendance and class requirements

Tutorials: 8 sessions of 3-hour tutorials that will develop skills relating to practical engineering, computer simulation, problem-solving, theoretical analysis, and final exam preparation.
Laboratories: 3 sessions of 3-hour laboratories that will look at modern power electronic systems relating to control engineering.
Independent research project (own time): Individual work on designing, simulation, and implementation of advanced power conversion systems.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Weidong Xiao, Power Electronics Step-by-Step: Design, Modeling, Simulation, and Control, McGraw-Hill Education, 2021.
High-quality journal papers.
Industry white papers and application notes.
Weidong Xiao, Photovoltaic Power System: Modeling, Design, and Control (1). Wiley, 2017. 1119280346.
Robert W. Erickson, Dragan Maksimović, Fundamentals of Power Electronics, Springer, 2020.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Design and practice power factor correction and soft switching in power electronics
LO2. Dynamic modeling and control of power converters
LO3. Develop advanced simulation skill to prove concept of power conversion and control
LO4. Develop a deeper understanding of practical control engineering for power electronics
LO5. Analyze and design bidirectional power conversion
LO6. Design power electronics and control engineering for solar PV power generation
LO7. Gain hand-on experience dealing with power electronics.
LO8. Professional approach to engineering project proposal, presentation, and report

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

- More time is spent on tutorial sessions to support students' learning. - Optimised course structure to streamline the learning experience. - Making sure fair evaluation of students' performance for Quiz.

Additional information

Use of Artificial Intelligence in Assessments

The use of artificial intelligence (AI), including generative AI tools such as ChatGPT, Microsoft Copilot, Gemini, Claude, translation tools, paraphrasing tools, referencing tools, coding assistants, and simulation-support tools, must follow the University of Sydney’s academic integrity requirements and the specific “Use of AI” status stated for each assessment task. Where AI use is permitted or limited, students may use AI only in ways that support their own learning, analysis, research, design, coding, simulation, writing, or revision, and they remain fully responsible for the accuracy, originality, integrity, and scholarly quality of the submitted work. Every submission must include an AI Use Statement. If AI was used, the statement must acknowledge the tool name, version, publisher, URL where possible, and clearly explain the specific contribution of AI, for example whether it assisted with brainstorming, topic selection, literature searching, source summarisation, coding, simulation implementation, debugging, data analysis, writing, editing, translation, referencing, or presentation. The statement must also clearly explain the student’s own contribution, including the intellectual decisions, technical work, verification, interpretation, critical analysis, calculations, simulations, writing, and final judgement completed by the student. AI-generated content must not be submitted as if it were the student’s own independent work, and all AI-assisted outputs must be checked against reliable sources, course materials, and the student’s own reasoning. Students must not use AI in assessments where AI is prohibited, unless explicit permission has been given in the unit outline or assessment instructions. Students must not input University teaching materials, another student’s work, confidential information, personal or health information, or third-party intellectual property into AI tools unless they have explicit permission to do so. If no AI was used, the submission must still include an AI Non-Use Statement explaining why AI was not used, for example because the student chose to complete the task independently, because AI use was prohibited, because AI was not appropriate for the task, or because the student wanted the submitted work to reflect only their own reasoning, calculations, coding, simulation, and writing process.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

ELEC5207: Advanced Power Conversion Technologies

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links