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Computational Intelligence and Power Systems

Code: PDEEC0034     Acronym: CIPS

Keywords
Classification Keyword
OFICIAL Electrical and Computer Engineering

Instance: 2019/2020 - 1S

Active? Yes
Responsible unit: Department of Electrical and Computer Engineering
Course/CS Responsible: Doctoral Program 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
PDEEC 2 Syllabus since 2015/16 1 - 7,5 70 202,5

Teaching Staff - Responsibilities

Teacher Responsibility
Vladimiro Henrique Barrosa Pinto de Miranda

Teaching - Hours

Recitations: 3,00
Type Teacher Classes Hour
Recitations Totals 1 3,00
Vladimiro Henrique Barrosa Pinto de Miranda 3,00

Teaching language

English

Objectives

This course aims at making students familiar with a number of tools pertaining to the domain of computatinal intelligence, which will be useful in dealing with power and energy system models in their research activity, in outher courses and in their profesional life.

The students having taken this course shall be able to develop models under the compytational intelligence paradigm, to programm algorithms and to discuss their results in terms of accuracy, effort and credibility.

Learning outcomes and competences

As a result of the course, the student will be better prepared to use Computational Intelligence methods.

Working method

Presencial

Program

The course syllabus includes: 
- Evolutionary Computing: history, basic concepts; self adaptive models; convergence; theoretical foundations. Variants: genetic algorithms, evolutionary programming/evolution strategies. 
- Particle Swarm Optimization: mouvement equation, convergence; convergence control, constriction. 
- Evolutionary Particle Swarms: self-adaptive recombination.
- Cross-entropy optimization.
- Application of evolutionary swarm and cross-entropy algorithms to power system problems and discussion of the results. Application in reliability: population-based methods. 
- Concept of mappers. Criticism of Minimum Square Error (MSE) as a training cost function. Introduction to Information Theoretic Learning (ITL). Training mappers under an Entropy-related MEE cost criterium. Correntropy and MCC criterium. Application of mappers in power system problems.
- Autoencoders and deep networks. Data compression and feature reduction. Interpretation in the information flow context. Unsupervised training using Infornation Theoretic concepts. 
- Clustering using ITL concepts. Renyi Entropy and Cauchy-Schwarz divergence. Kulback-Leibler divergence. Mean Shift algorithms and Information Theoretic Mean Shift.  
- Polynomial networks: introduction to GDHM (Group Data Handling Method). Construction/training algorithm. Application of GDHM in power system problems.

Mandatory literature

Vladimiro Miranda; REDESIGNING MODELS WITH INFORMATION THEORETIC LEARNING CONCEPTS: A SHORT OVERVIEW, 2018 (Text supplied by the lecturer)

Teaching methods and learning activities

Lectures and assignments.

keywords

Physical sciences > Mathematics > Algorithms
Technological sciences > Engineering > Electrical engineering
Technological sciences > Engineering > Systems engineering

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation Weight (%)
Apresentação/discussão de um trabalho científico 20,00
Exame 50,00
Trabalho prático ou de projeto 30,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Apresentação/discussão de um trabalho científico 10,00
Elaboração de projeto 40,00
Frequência das aulas 50,00
Total: 100,00

Eligibility for exams

In order to have his/her attendance of the course recognized, the student must complete all assignments.

Calculation formula of final grade

In order to obtain a passing mark, the student must complete all the assignments with a positive evaluation and have a minimum of 8/20 in the final exam.
To compute the final mark, the exam will entre with a weight of 50% and the set of assignments with a weight of 50%.

Special assessment (TE, DA, ...)

No exceptions allowed to the general evaluation method.

Classification improvement

In order to improve the evaluation results, the student may be submitted to a second exam and may request a period to improve one of the assignments.
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