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The MSc (Power Engineering) programme is designed for Electrical Engineering graduates who are practicing engineers, R&D managers, power system designers or industry planners who seek an in-depth understanding of power electronics and drives technology, issues of power quality, power system modeling, planning, operation and control. The programme aims to equip students to adapt to the challenging demands of modern power industries.
Have a good relevant bachelor's degree
Relevant working experience is an advantage
For applicants whose native language is not English, TOEFL/IELTS score is to be submitted with the application for admission:
TOEFL Score (Test dates must be within 2 years or less from the date of application):
≥ 563 (paper-based)
≥ 223 (computer-based)
≥ 85 (internet-based)
IELTS Score (Test date must be within 2 years or less from the date of application):
≥ 6.0
Applicants without TOEFL/IELTS would still be eligible to apply, but they may be subjected to an interview/test if deemed necessary by the School.
Programme Structure
There are two options of study, one with coursework only, and the other with coursework and dissertation. Each course is of 3 AUs and consists of 39 hours of lectures. Candidates who undertake a project of 6 AUs must submit a dissertation on it. Students are encouraged to choose the dissertation option only if they have a strong interest in pursuing further research studies.
Option 1 | Option 2 |
| 10 courses (30 AUs in total) | 8 courses + dissertation project (30 AUs in total) |
| 4 specialized electives (≥ 12 AUs) | 4 specialized electives (≥ 12 AUs) |
| 6 general electives (≤ 18 AUs) | 4 general electives (≤ 12 AUs) |
| Dissertation (6 AUs) |
Note: The programme structure will be subject to change without prior notice.
Duration
Both full-time and part-time programmes are offered (unless stated). Part-time candidates are expected to obtain permission from their employer before admission to the programme. Most classes are conducted in the evenings, while examinations are conducted during office hours.
Type of Coursework Programme | Minimum Candidature | Maximum Candidature |
| Master of Science (Full-Time) | 1 year | 3 year |
| Master of Science (Part-Time) | 2 year | 4 year |
Programme Calendar
| Semester 1 | August to December |
| Semester 2 | January to May |
| Week 1 to 14 | Lecture (Inclusive of 1-week recess) |
| Week 15 to 17 | Examinations |
| Other | Vacation |
Graduate courses offered by Master of Science (MSc) Power Engineering:
Specialized Elective Courses (Students are required to take a min of 4 out of all the 6 specialized elective courses
| Course Code | Course Title | Course Content | AUs |
| EE6501 | Power Electronic Converters | Introduction. AC-to-DC Converters. DC-to-DC Converters. DC-to AC Converters. | 3 |
| EE6503 | Modern Electrical Drives | Components of drives. Types of loads. Modelling of mechanical systems. Selection of drive components. Control theory and closed-loop control. Transient processes. | 3 |
| EE6508 | Power Quality | Concept of Power Quality. Voltage Fluctuations and Variations. Transient Over-voltages. Harmonic Distortions. | 3 |
| EE6509 | Renewable Energy Systems In Smart Grids | Introduction to Power Systems with Distributed Generation. Distributed Generation. Energy Storage. Smart Grids. | 3 |
| EE6510 | Power System Operation & Planning | Forecasting and Scheduling. Network Application Functions. Probability and Reliability. Generation and Transmission Planning. | 3 |
| EE6511 | Power System Modelling & Control | Steady-state Power System Networks. Network Components. Stability Analysis. Power System Control. | 3 |
General Elective Courses
| Course Code | Course Title | Course Content | AUs |
| EE6008 | Collaborative Research & Development Project | Project Charter, Design and prototype development, Project implementation, Testing and instrumentation, Project report, Oral presentation, Demonstration | 3 |
| EE6010 | Project Management & Technopreneurship | Project Initiation and Planning. Project Scheduling and Implementation. Project Monitoring, Control and Evaluation. Innovation and Entrepreneurship. | 3 |
| EE6102 | Cyber Security & Blockchain Technology | Cyber Security Threat Landscape, Industry 4.0 and Cyber Security, Cyber Security Education, Awareness and Compliance, Cyber Security Planning, Policies and Compliance, Cyber Security Risk Assessments and Biometric-based Security approaches, Public key Infrastructure (PKI), Web Security and role of firewalls and Intrusion Detection, Online Payment, and Cryptocurrencies. Basics of Blockchain technology, Types of blockchain Technology, Blockchain Technology Applications for Industry 4.0, use cases and real-world case studies | 3 |
| EE6203 | Computer Control Systems | Discrete-time system modelling and analysis. Cascade compensation. State-space design methods. Optimal control. Design and implementation of digital controllers. | 3 |
| EE6221 | Robotics & Intelligent Sensors | Overview of robotics. Motion planning and control. Mobile robots . Controller hardware/software systems. Sensor systems and integration. | 3 |
| EE6225 | Multivariable Control Systems Analysis & Design | Basic control algorithms. Model Predictive Control. Multivariable control. Plant parameter estimation. Case studies in process control. | 3 |
| EE6285 | Computational Intelligence | Introduction to Fuzzy Logic, Introduction to Fuzzy Sets, Introduction to Fuzzy Inference Systems, Fuzzy Logic Applications, Introduction to Genetic Algorithm, Fundamental Concepts of Artificial Neural Networks and Neural Network Architectures, Neural Network Applications | 3 |
| EE6301 | Smart Biosensors & Systems For Healthcare | Introduction to biosensors and healthcare; Optical biosensors-fundamentals; Optical biosensors-applications; Biomedical imaging with optical technologies; Introduction to electrical biosensors- fundamentals; Introduction to electrical biosensors- fabrications; Applications of electrical biosensor; Introduction to bio-intelligent systems; Artificial intelligence in medical sensing and imaging | 3 |
| EE6303 | Electromagnetic Compatibility Design | EMC Regulatory Requirements. Non-Ideal Behaviors of Passive Components. Conducted EMI and Filter Design. Electromagnetic Shielding. Basic Grounding Concept. Crosstalk. Printed Circuit Board Layout and Radiated EMI. Electrostatic Discharge. Radio Frequency Interference. Emission and Susceptibility Measurement Methods. | 3 |
| EE6341 | Advanced Analog Circuits | Low Noise Circuits, Wide-Bandwidth Amplifiers, Power Amplifiers, Active Filters, DC-DC Converters | 3 |
| EE6405 | Natural Language Processing | Traditional: Bag-of-words, Preprocessing, Term weighting scheme, Feature extraction,. Topic modeling , ML classifiers and clustering methods, Evaluation Metrics, Word Embeddings Deep Neural Networks: Graph convolutional network, Seq2Seq, Attention mechanism, Transformers and self-attention, Pretrained Language Models, Fine-tuning (hyper-parameter tuning), Applications (chatbot, machine translation, sentiment analysis, summarisation, classification, generation, auto-complete) | 3 |
| EE6406 | Analytic and Ensemble Learning | This course aims to equip students with a foundational understanding of machine learning from both analytic and ensemble perspectives. The curriculum is designed to offer an overview of the key technologies underpinning modern machine learning and deep learning. Initially, the course will recap the basic components of machine learning. It will then delve into fundamental learning techniques, including linear methods for regression, linear methods for classification, and polynomial plus kernel methods—all grounded in linear algebra. Subsequently, students will explore applied ensemble methods based on bagging and boosting. The course will cover classical ensemble methods such as Random Forest, AdaBoost, and standard Gradient Boosting, as well as more advanced boosting techniques, including XGBoost and LightGBM, which find practical applications in the industry. In addition, the course will also introduce reinforcement learning and its connection with ensemble learning. To highlight the practical relevance, the course will include examples from industrial contexts. | 3 |
| EE6407 | Genetic Algorithms & Machine Learning | Review of Combinatorics and Probability. Introduction of Genetic Algorithms. Differential Evolution. Particle Swarm Optimization. Advanced Techniques. Principles of Machine Learning. Paradigms of Machine Learning. Kernel Methods. | 3 |
| EE6483 | Artificial Intelligence & Data Mining | Structures and Strategies for State Space Representation & Search. Heuristic Search. Data Mining Concepts and Algorithms. Classification and Prediction methods. Unsupervised Learning and Clustering Analysis. | 3 |
| EE6497 | Pattern Recognition & Deep Learning | Introduction, probability review, Bayesian Inference, Mixture Models and EM Algorithm, Markov Models and Hidden Markov Models, Sampling, Markov chain Monte Carlo (MCMC), Neural Networks, Deep Learning (CNN, RNN), Training Deep Networks, Deep Network Architectures, Applications, Generative Models, Self-supervised Learning. | 3 |
| EE6506 | Power Semiconductor Based Converter In Renewable Energy Systems | Module 1: Overview of power electronic circuits and semiconductor devices, Module 2: Power diodes and thyristors as switching devices, Module 3: Power transistors as switching devices 2, Module 4: Protection of devices from overheating di/dt, dv/dt, Module 5: Passive components and magnetics, Module 6: Renewable energy systems | 3 |
| EE6507 | Switched Mode Power Supplies | Introduction. Basic Switched Mode Power Supplies. Advanced Switched Mode Topologies. Soft switching Converters. Synchronous Rectifier and Multi-element Converters. Control, Modelling and Design. | 3 |
| EE6512 | High Voltage Engineering & System Protection | Computational Methods for Electric Field. Insulation Engineering. System faults. Protection of Plants and Lines. System Aspects of Protection. | 3 |
| EE6534 | Modern Distribution System With Renewable Resources | Operation of distribution systems. Power quality. Solar power systems. Wind power systems. | 3 |
| EE6604 | Advanced Topics In Semiconductor Devices | Bipolar transistor operating principles. Bipolar device modeling. State-of-the-art bipolar structures. MOS device operation. MOSFET modeling. MOS device scaling effects. Semiconductor memories. Semiconductor heterojunctions and devices. New devices and future trends. | 3 |
| EE7204 | Linear Systems | Input/Output System Models. State Space Representation. Norms of Signals and Systems. Decomposition of Linear Time-Invariant Systems. Linear Feedback Design. Convex Optimization for Linear System Analysis and Design. | 3 |
| EE7205 | Research Methods | Research Preparation and Planning. Research Sources and Review. Quantitative Methods for Data Analysis. Experimental research methods. Academic Writing & Presentation | 3 |
| EE7207 | Neural Networks and Deep Learning | The key topics to be covered in the context of deep neural networks and deep learning will encompass convolutional neural networks (CNN), modern recurrent neural networks (RNN), the attention mechanism and the transformer, self-supervised learning, graph neural networks, all of which represent the cutting-edge methods in the realm of deep learning. In addition, some typical applications and advanced topics of deep learning will be introduced. | 3 |
Note: the above curriculum is subject to change.
Tuition Fees
Our five MSc programmes (Communications Engineering, Computer Control & Automation, Electronics, Power Engineering and Signal Processing and Machine Learning) are self-financed programmes.
Students of these programmes are not eligible for Service Obligation/ MOE Subsidies.
The tuition fees per course (3 AUs) and per dissertation (6 AUs) for admission from August 2025 onwards are shown in the table as follows:
| From August 2025 Intake Onwards | |
|---|---|
| Per Course (3 AUs): SGD$5,615.68* | Per Dissertation (6 AUs): SGD$11,231.36* |
| Minimum Total Tuition Fees (30 AUs): SGD$56,156.80* | |
SC/SPR Incentive From the academic year starting in 2024, Singaporeans and permanent residents who enrol in self-financed master’s by coursework programmes at NTU will enjoy a $5,000 subsidy, with those in need of financial aid will receive up to $10,000. For academic year starting in 2025, the $5,000 subsidy for Singaporeans and permanent residents will continue to apply. New students enrolled in AY25 in need of financial aid will receive up to $15,000. NTU Alumni Incentive | |
*Inclusive of Goods and Service Tax (GST)
A Goods and Services Tax (GST) of 9% is levied on the import of goods, as well as nearly all supplies of goods and services in Singapore starting 1 Jan 2024.
All fees listed above are in Singapore dollars (S$) and subject to annual revision by the school. The tuition fee is exclusive of living expenses and miscellaneous student fees.
The deposit fee of S$5,000 is payable upon acceptance of the offer and is non-refundable. It will be deducted from the full tuition fee.
Awards in MSc Programme
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