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Department of Electronic & Electrical Engineering, Unit Catalogue 2007/08


EE50114 Artificial intelligence in power systems

Credits: 6
Level: Masters
Modular: no specific semester
Assessment: CW 30%, EX 70%
Requisites:
Before taking this unit you must take EE50104

Aims & Learning Objectives:
Aims: To provide the fundamental principles of various artificial intelligent techniques and insight of how to apply those techniques to solve practical problems.
Objectives: After completing this module, students should be able to: distinguish the differences between intelligent techniques and conventional techniques; be aware of the opportunities where intelligent techniques might be most beneficial; be able to construct simple intelligent systems to solve practical problems; be able to further enhance the performances of intelligent techniques.
Content:
Expert Systems (ES): major characteristics of expert systems; knowledge representation techniques; inference techniques; rule-based expert systems; applications in power systems. Fuzzy Logic (FL): fuzzy set theory; fuzzy inference; fuzzy logic system; fuzzy control; applications in power systems. Neural Networks (NS): artificial neurons and neural networks; learning process: Error-correction learning, Hebbian learning, Boltzmann learning, competitive learning, supervised/unsupervised learning; Perception and multilayer perception; self-organising Kohonen networks; Hopfield neural networks; practical implementation and applications. Hybrid systems: typical hybrid intelligent techniques, applications in power systems.