Systems of Artificial Inteligence in Power System

Објавено: October 12, 2018
  1.    Course Title Systems of Artificial Inteligence in Power System
  2.    Code 3ФЕИТ04З026
  3.    Study program EEUM
  4.    Organizer of the study program (unit, institute, department) Faculty of Electrical Engineering and Information Technologies
  5.    Degree (first, second, third cycle) First cycle
  6.    Academic year/semester IV/7   7.    Number of ECTS credits 6.00
  8.    Lecturer Dr Atanas Iliev
  9.    Course Prerequisites  

10.    Course Goals (acquired competencies):  Acquisition of expert knowledge about the engineering aspects of expert systems, fuzzy-logic systems, neural networks and genetic algorithms, and the possibilities for their application in power engineering and engineering management

11.    Course Syllabus: Historical development and ongoing challenges to artificial intelligence systems. Characteristics of expert systems. Presenting expert knowledge with rules. Boolean and multi-valued logic. Engineering aspects of fuzzy-logical systems. The theory of the fuzzy set. Fuzzy numbers and operations with them. Fuzzy logic controllers and their application in the electric power system. Neural networks. Types of activation function and Learning methods. Application of neural networks for the load forecast, inflow in HPP, wind speed, solar radiation. Fuzzy neural network. ANFIS models Introduction to genetic algorithms – selection, recombination of mutations, population algorithms. Application of the genetic algorithms in the power engineering. evolutionary computation Evolutionary algorithms and computation.
12.    Learning methods:  Lectures supported by presentations, interactive lectures, auditory exercises with solving practical examples, individual homeworks.
13.    Total number of course hours 3 + 1 + 1 + 0
14.    Distribution of course hours 180
15.    Forms of teaching 15.1. Lectures-theoretical teaching 45
15.2. Exercises (laboratory, practice classes), seminars, teamwork 30
16.    Other course activities 16.1. Projects, seminar papers 30
16.2. Individual tasks 30
16.3. Homework and self-learning 45
17.    Grading 17.1. Exams 25
17.2. Seminar work/project (presentation: written and oral) 20
17.3. Activity and participation 5
17.4. Final exam 50
18.    Grading criteria (points) up to 50 points     5 (five) (F)
from 51 to 60 points     6 (six) (E)
from 61 to 70 points     7 (seven) (D)
from 71 to 80 points     8 (eight) (C)
from 81 to 90 points     9 (nine) (B)
from 91 to 100 points   10 (ten) (A)
19.    Conditions for acquiring teacher’s signature and for taking final exam Regular attendance on lectures and completion of the homeworks, laboratory exercises and project assignments
20.    Language Macedonian and English
21.    Method of monitoring of teaching quality Internal evaluations and surveys
22.    Literature
22.1. Required Literature
No. Author Title Publisher Year
1 А. Kaufmann

Fuzzy Mathematical Models in Engineering and Management Science

 Elsevier Science Publishing 1998
2 T. Dillon, D. Niebur Network Applications in Power Systems CRL Publishing 2001
3 Atanas Iliev Systems for Artificial inteligence in Power Engineering FEET – Skopje 2010
22.2. Additional Literature
No. Author Title Publisher Year
1 S. Milenkovic  Artificial Neural Networks  Zaduzbina Andrejevic  1997
2  Mohamaed El-Hawary  Electric Power Applications of Fuzzy Systems  IEEE Press  1998