Decision, Optimization and Computacional Intelligence

Code:

M.EEC019

Acronym:

DOIC

Keywords
Classification	Keyword
OFICIAL	Other Technical Areas

Instance: 2021/2022 - 1S

Active?	Yes
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Master in Electrical and Computer Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.EEC	51	Syllabus	1	-	6	45,5	162

Teaching language

Portuguese

Objectives

To enable the students approach optimization and decision problems and apply computacional intelligence techniques to electric power systems

Learning outcomes and competences

As a result of learning, the students should manifest that followin competences:

Problem formulation in the framework of multiple criteria decision analysis. Application of decision aid methodologies. (CDIO 1.3, 2.3) Representation of uncertainties with fuzzy sets. Application of methods based on fuzzy reasoning. (CDIO 1.3, 2.1, 2.3, 4.3, 4.4) Application of methods based on non-linear optimization. Understanding of the fundamentals of meta-heuristics and application to solve problems. (CDIO 1.3, 2.1, 2.3, 4.3, 4.4) Understanding the concepts of neural computing and apply them to a diversity of problems. (CDIO 1.3, 2.1, 2.3, 4.3, 4.4) Development of autonomous work ability (CDIO 2.5) and of team work ability (CDIO 3.1, 3.2, 3.3)

Working method

Presencial

Program

General concepts related to multiple criteria analysis, risk and uncertainty. Decision aid methods. Fuzzy models for the study of DC and AC power flows. Non-linear programming. Gradient methods. Non-linear programming with constraints. Linear and non-linear DC model for the optimal power flow problem with constraints. Evolutionary algorithms, particle swarm algorithms and other meta-heuristics. Neural Networks.

Mandatory literature

Grainger, John J.; Power System Analysis. ISBN: 0-07-113338-0
Vladimiro Miranda; Computação Evolucionária Fenotípica, 2005
Vladimiro Miranda; DESPACHO ECONOMICO DE SISTEMAS DE PRODUÇÃO-TRANSPORTE - modelização e algoritmos , 1996
Clemen, Robert T.; Making hard decisions with decision tools. ISBN: 0-534-36597-3
Vladimiro Miranda; Algumas Notas sobre Programação Não Linear, 1986
Manuel Matos; Notas sobre Ajuda à Decisão Multicritério

Teaching methods and learning activities

General theoretical classes with transparency or power point support. Theory/practice classes presenting study cases, solving problems and assisting students in their work assignments.

Software

Matlab

keywords

Technological sciences > Technology > Energy technology > Electricity grid systems
Physical sciences > Mathematics > Applied mathematics > Operations research
Technological sciences > Engineering > Electrical engineering

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Trabalho escrito	50,00
Exame	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de relatório/dissertação/tese	56,00
Estudo autónomo	50,00
Frequência das aulas	56,00
Total:	162,00

Eligibility for exams

Delivery of two assignment works with a minimum of 8/20 each.

Calculation formula of final grade

CF=(1/2).E+(1/4).T1+(1/4).T2

Partials >= 8/20

Special assessment (TE, DA, ...)

Assignment reports must be delivered up to the same deadlines fixed for other students. In the special cases defined by NGA, an additional laboratory exam will be required.

Classification improvement

Assignments evaluation cannot by its nature be improved.

Observations

Link for theory classes (Vladimiro Miranda)
https://videoconf-colibri.zoom.us/j/85078052662?pwd=Yk1nOWhwZnRvZllWOUhUZzJkU3JKQT09