CHAPTER 43Genetic Algorithm Applied on

Network Reconfiguration:

Implementation of A

Multi-Objective Algorithm As An

Undergraduate Interdisciplinary

Project

Wandry Rodrigues Faria*, Marcelo Escobar de Oliveira␄ and Hugo Xavier

Rocha␞

426 Chapter 43. Genetic Algorithm Applied...

*Engenharia Elétrica, Instituto Federal de Goiás, Itumbiara, Brazil

E-mail: wandryrodrigues@hotmail.com

␄Engenharia Elétrica, Instituto Federal de Goiás, Itumbiara, Brazil

E-mail: marcelo.oliveira@ifg.edu.br

␞Engenharia Elétrica, Instituto Federal de Goiás, Itumbiara, Brazil

E-mail: hugo.rocha@ifg.edu.br

Abstract: The electric power distribution systems are, mostly, constructed as

weakly meshed networks. However, they operate as radial systems, which

means that the power flows in only one way: from the substations to the con-

sumers and there is only one electrical circuit connecting those two points.

Even though the system operates as radial, there are physical links connecting

points that belong the same or a different feeder or substation. Those links

are mechanical switches that can be used to isolate an area in the event of an

electrical failure or maintenance or to supply electrical power to an area using

another source. Besides the reliability incensement, by using the switches

correctly the technical power losses may decrease and the consumer’s voltage

magnitude might be elevated. The manipulation of the electrical distribution

system aiming an optimal operation point is called network reconfiguration

and is a well spread concept. Due to the several possible reconfigurations, since

427

there are many switches in the system, the algorithms based on exact methods

demands too much time and computational effort, on the other hand, by using

metaheuristics the efforts are reduced and the solutions are optimized. This

paper presents the development of a metaheuristic algorithm created as the

result of an undergraduate interdisciplinary project combining two courses

from the Electrical Engineering major of the Instituto Federal de Goiás (IFG),

Campus of Itumbiara: Introduction to the Electrical Power Systems and Topics

on Artificial Intelligence. The presented tool uses a multi-objective Genetic

Algorithm that uses an NSGA-II routine to evaluate voltage magnitude on

the loads and the system’s total power loss. The tool was tested using IEEE’s

test systems and presented satisfying results, however, the major gain is the

strengthening of the multidisciplinary activities applied to the Engineering

teaching. Interdisciplinarity has been the object of study to many research

groups around the world. Thus, this paper salients the relevance of projects

involving two or more courses as a mean to encourage the students to evolve a

critical view of problems and the search for solutions using multiple course’s

subjects.

Keywords: Backward-forward Power Flow, Distribution Systems, Genetic Algo-

rithm, Multi-objective Optimization, Network Reconfiguration.

428 Chapter 43. Genetic Algorithm Applied...

43.1 BackgroundOver the last few years, the scientific community has been pressed to solve

huge society’s issues. The electrical Engineering branch, for example, has the

challenge to increase the amount of available electrical power in a sustainable

and environmentally friendly way. Most of the challenges cannot be solved by

a single research field, therefore it is fundamental to use interdisciplinarity1.

However, applying various points of view and approaches to a single problem

may lead to a discrepancy on the issue’s cause and obstruct the converging to

a solution1. It is proposed in1 the introduction to interdisciplinary research

as a tool to prepare the students to deal with real problems involving multiple

fields of study. Another worldwide concern, presented in2, 3, is the arduous-

ness found by electrical Engineering students to apply theoretical concepts

to researches and projects design. Both2 and3 present a multidisciplinary ap-

proach combined to prototypes design as a solution to the lack of contact with

the practical application of subjects. Since the focus of the IFG-Itumbiara’s

Electrical Engineering major is Electrical Power Systems and given the great

number of courses containing programming languages, the presented project,

which was developed as an assignment to two disciplines, aims to ease the

teaching of both Electrical Power Systems – as well as its analysis – and Program-

ming Languages – as well as its applications to solving electrical Engineering

43.2. Purpose 429

problems.

43.2 PurposeIn order to assure the optimal operation point of a distribution system, or

yet on the event of a system failure and a partial system isolation is needed,

the reconfiguration network study is applied. On this evaluation, a solution

with the fewer unsupplied consumers and minimum power loss, respecting

the system’s limitations, is sought. The distribution network electrical circuit’s

is changed by maneuvering mechanical switches placed on the system4. How-

ever, there are several switches in the distribution system which makes the

evaluation of every possible configuration a very complex problem. Due to

the infinity of possible reconfigurations, manual technics to evaluate the cir-

cuits cannot be applied. A common approach to solving the problem is the

employment of computational tools.

In general, computational routines use exact methods to solve a problem.

However, due to the huge number of possibilities when it comes to network

reconfiguration, these methods are proven inefficient – they demand too much

time and computational effort. The second method of problem solving used by

computational routines is named metaheuristic. This method seeks optimized

not-exact solutions which reduce time and computational effort4, 5.

430 Chapter 43. Genetic Algorithm Applied...

The software used by the concessionaires of electric energy and presented

in the academic environment to solving the distribution system reconfigu-

ration problem, in most cases, use metaheuristics methods4-7. Even though

metaheuristic computational routines and electrical power systems are com-

bined to solve problems in distribution systems, in college the evolutionary

algorithms – which are an example of metaheuristic routine – are not studied

on the Electrical Power System course, but on Topics on Artificial Intelligence.

The approach of the subjects as two isolated problems inhibits the student’s

comprehension as to the application of evolutionary algorithms in real prob-

lems and the practical analysis of the distribution systems. The presented

computational tool aims to demonstrate the evolutionary algorithm’s applica-

bility to solving real electrical Engineering problems promoting, at the same

time, behavior analysis of reality-based distribution systems.

43.3 MethodAn essential tool for distribution systems interpretation and analysis is the

power flow. The topic is covered by the power systems field courses and one of

the most used methods in distribution systems is the backward-forward sweep.

This well spread computational routine is described in4, 8 and was developed

as an assignment to Introduction to Electrical Power Systems.

43.3. Method 431

The requirement to Topics on Artificial Intelligence was the development of

an evolutionary algorithm. Considering that the power flow’s output data allow

a quality evaluation of the system, once interpreted it may be used as a fitness

function to an evolutionary algorithm. The power flow routine developed

to Introduction to Electrical Power Systems was then attached to the genetic

algorithm. It is shown in figure 43.1 some of the subjects and courses used in

the development of the metaheuristic algorithm.

Figure 43.1 – Undergraduate Courses and Subjects Related to the Computa-tional Tool.

The algorithm’s fitness function considers the voltage magnitude in each

node and the system’s total active power loss. The tool relies on an evolution-

ary multi-objective routine named NSGA-II, described in9, 10, to evaluate the

432 Chapter 43. Genetic Algorithm Applied...

solutions.

43.4 ResultsThe computational tool’s functionality was tested with IEEE’s test systems.

The results were compared to the ones obtained by other authors and were

satisfactory.

Seeing that the network reconfiguration problem solving through evolu-

tionary algorithms is a well-spread concept, to highlight the gains brought to

the student and professors involved in the development of the project is far

more important than presenting the optimized systems. In the first place, the

student could verify the application of many electrical Engineering courses

to the development of a tool which can be used in real action planning for

distribution systems.

Considering that all the subjects approached by the course can be found

on the program, and particularly to show the appropriateness of evolutionary

algorithms associated to electrical Engineering problems solving, the source

code may be applied to the Topics on Artificial Intelligence classes.

The comprehension of electrical power systems’ response to a certain load

factor – approached in Introduction to Electrical Power Systems – may be

enriched by practical examples using the power flow routine included in the

43.5. Conclusions 433

presented program. The simulation results can also be used in Transmission

and Distribution Systems as well as in Electrical Power Systems Analysis classes

to demonstrate in a practical way the gains of an optimized configuration to a

distribution network.

43.5 ConclusionsThe paper presents a computational tool developed as an assignment to the

electrical Engineering courses Topics on Artificial Intelligence and Introduction

to Electrical Power Systems. Notwithstanding, the concepts used to create the

routine are not limited to those two disciplines.

The project’s outcome is the result of the student’s critical sense growth

which was magnified by the multidisciplinarity approach. The student’s in-

crease of interest when assignments combine theoretical concepts, acquired on

the courses, and projects, especially with practical applications, is notorious.

Thus, the computational routine can be used to improve even more the

students’ comprehension and interest in correlated disciplines, in addition to

it, the tool may be used as a foundation to further interdisciplinary projects.

434 Chapter 43. Genetic Algorithm Applied...

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