Fuzzy Logic Control in Energy Systems
COURSE SYLLABUS (PDF Version) COURSE RESOURCES
ELKL7132 - Fuzzy Logic Control in Energy Systems 3+0+0 ECTS: ?
| Year / Semester | : | Fall | |
| :Course Level | : | Graduate | |
| Compulsory / Elective | : | Elective | |
| Department | : | Electrical and Electronics Engineering | |
| Prerequisite | : | None | |
| Education system | : | Face to face | |
| Course Duration | : | 14 weeks – 3 hours per week | |
| Faculty Member | : | Prof. Dr. İsmail H. ALTAŞ | |
| Alternative Faculty Member | : | None | |
| Language of Instruction | : | English | |
| Internship | :: | None |
Objectives of the Course
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The students are subject to learn basic concepts in fuzzy logic control and fuzzy decision making processes applied to power systems, renewable energy systems such as solar and wind, energy management systems and micro grids. The students will also be able to develop and design fuzzy subsystems, partition the universes, developing fuzzy decision makers and controllers and simulating those models in Matlab/Simulink.
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Contents of the Course
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The course covers the basic concepts of fuzzy set theory. Partitioning the universes of power and energy systems. Problem definitions in power and energy systems using fuzzy terms. Fuzzy logic based solution approaches to load-frequency and excitation control of power systems. Fuzzy logic control for damping oscillations in power systems. Application of fuzzy logic control to wind and solar PV systems such as in utility integration and MPPT operation. Fuzzy logic in micro grids and energy management systems.
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Learning Outcomes
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Upon successful completion of the course, the students will be able to :
LO - 1: Have sufficient knowledge on fuzzy logic control and decision making LO - 2: Have sufficient information on fuzzy logic control of load-frequency systems LO - 3: Have sufficient knowledge on fuzzy logic control of excitation systems LO - 4: Develop fuzzy logic controller and decision maker to damp oscillations in power systems LO - 5: Model fuzzy logic controllers and decision makers for wind energy control and operation LO - 6: Model fuzzy logic controllers and decision makers for PV solar energy control and operation LO - 7: Using fuzzy logic in micro LO - 8: Using fuzzy logic in energy management systems |
Teaching Plan
Week 1 Basics of Fuzzy Sets, Fuzzy operations, Fuzzy Partition, Fuzzy sets for Representing Uncertain data
Week 2 Fuzzy sets for Representing Uncertain data
Week 3 Uncertain data in PV solar, wind and wave energy systems
Week 4 Uncertain data in Power Systems
Week 5 Fuzzy relations and fuzzy inference systems
Week 6 Fuzzy implication and fuzzy decision-making
Week 7 Decision making in energy systems
Week 8 Intelligent decision making in energy systems
Week 9 Midterm exam
Week 10 FLC of Single Machine Connected to Infinite Bus
Week 11 FLC of Exciter Systems
Week 12 FLC in Wind Energy Systems
Week 13 FLC in PV Energy Systems
Week 14 FLC for oscillation in power systems
Week 15 FLC for micro grids and energy management
Week 16 Final exam
Text Book / Course Material
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Evaluation Methods
| Method | Week | Date | Duration (Hour) | Contribution (%) |
| Midterm | 9 | 2 | 30 | |
| Presentation | 14 - 15 | 2 | 10 | |
| Project | 15 | 2 | 10 | |
| End of term exam | 16 | 2 | 50 |
Student Work Load and its Distribution
| Type of work | Duration (hours pw) | Number of weeks |
| Lectures (face to face teaching) | 2 | 14 |
| extracurricular work | 2 | 10 |
| Preparation for the Midterm Exam | 2 | 7 |
| Midterm | 2 | 1 |
| Homework | 1 | 10 |
| Project | 1 | 10 |
| End of term exam | 1 | 5 |
| Other 1 | 2 | 1 |
