PS4002 Optimization Techniques to Power System Engineering Syllabus:
PS4002 Optimization Techniques to Power System Engineering Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
Discriminate the capabilities of bio-inspired system and conventional methods in solving optimization problems
Examine the importance of exploration and exploitation swarm intelligent system to attain near global optimal solution
Distinguish the functioning of various swarm intelligent systems
Employ various bio-inspired algorithms for Power systems engineering applications
UNIT I FUNDAMENTALS OF SOFT COMPUTING TECHNIQUES
Definition-Classification of optimization problems – Unconstrained and Constrained optimization Optimality conditions – Introduction to intelligent systems – Soft computing techniques – Conventional Computing versus Swarm Computing – Classification of meta-heuristic techniques – Single solution based and population based algorithms – Exploitation and exploration in population based algorithms – Properties of Swarm intelligent Systems – Application domain – Discrete and continuous problems – Single objective and multi-objective problems.
UNIT II GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION
Genetic algorithms – Genetic Algorithm versus Conventional Optimization Techniques – Genetic representations and selection mechanisms; Genetic operators – different types of crossover and mutation operators – Bird flocking and Fish Schooling – anatomy of a particle – equations based on velocity and positions – PSO topologies – control parameters – GA and PSO algorithms for solving ELD problem.
UNIT III ANT COLONY OPTIMIZATION and ARTIFICIAL BEE COLONY ALGORITHMS
Biological ant colony system – Artificial ants and assumptions – Stigmergic communications – Pheromone updating – local-global – Pheromone evaporation – ant colony system- ACO Models – Touring ant colony system -max min ant system – Concept of elistic Ants – Task partitioning in honey bees – Balancing foragers and receivers – Artificial bee colony (ABC) algorithms – binary ABC algorithms – ACO and ABC algorithms for solving Economic Dispatch of thermal units.
UNIT IV SHUFFLED FROG-LEAPING ALGORITHM and BAT OPTIMIZATION ALGORITHM
Bat Algorithm – Echolocation of bats – Behaviour of microbats – Acoustics of Echolocation – Movement of Virtual Bats – Loudness and Pulse Emission – Shuffled frog algorithm – virtual population of frogs – comparison of memes and genes – memeplex formation – memeplexupdation – BA and SFLA algorithms for solving ELD and optimal placement and sizing of the DG problem.
UNIT V MULTI OBJECTIVE OPTIMIZATION
Multi-Objective Optimization Introduction – Concept of Pareto optimality – Non-dominant sorting Technique – Pareto fronts-best compromise solution – min-max method-NSGA-II algorithm and applications to power systems.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Students able to
CO1 understand the capabilities of bio-inspired system and conventional methods in solving optimization problems
CO2 implement the genetic algorithm and particle swarm optimization technique to solve the ED problems
CO3 understand and implement the ant colony algorithm and artificial bee colony algorithms to PS problems
CO4 implement the shuffled frog-leaping algorithm and bat optimization algorithm for solving ELD and optimal placement and sizing of the DG problem
CO5 understand and implement the multi-objective optimization techniques to implement in power system problems
REFERENCES:
1. Xin-She Yang, “Recent Advances in Swarm Intelligence and Evolutionary Computation”, Springer International Publishing, Switzerland, 2015.
2. Kalyanmoy Deb, “Multi-Objective Optimization using Evolutionary Algorithms”, John Wiley & Sons, 2001.
3. James Kennedy and Russel E Eberheart, “Swarm Intelligence”, The Morgan Kaufmann Series in Evolutionary Computation, 2001.
4. Eric Bonabeau, Marco Dorigo and Guy Theraulaz, “Swarm Intelligence-From natural to Artificial Systems”, Oxford university Press, 1999.
5. David Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Pearson Education, 2007.
6. Konstantinos E. Parsopoulos and Michael N. Vrahatis, “Particle Swarm Optimization and Intelligence: Advances and Applications”, Information science reference, IGI Global, 2010.
7. N P Padhy, “Artificial Intelligence and Intelligent Systems”, Oxford University Press, 2005.
8. D.P. Kothari, J.S. Dhillon, “Power System Optimization”, PHI, 2nd edition, 30 December 2010.