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A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm

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Abstract This paper compares the performance of three population-based algorithms including particle swarm optimization (PSO), evolutionary programming (EP), and genetic algorithm (GA) to solve the multi-objective optimal power flow (OPF) problem. The unattractive characteristics of the cost-based OPF including loss, voltage profile, and emission justifies the necessity of multi-objective OPF study. This
study presents the programming results of the nine essential single-objective and multi-objective functions of OPF problem. The considered objective functions include cost, active power loss, voltage stability index, and emission. The multiobjective optimizations include cost and active power loss,
cost and voltage stability index, active power loss and voltage stability index, cost and emission, and finally cost, active power loss, and voltage stability index. To solve the multiobjective OPF problem, Pareto optimal method is used to form the Pareto optimal set. A fuzzy decision-based mechanism
is applied to select the best comprised solution. In this work, to decrease the running time of load flow calculation, a new approach including combined Newton–Raphson and Fast-Decouple is conducted. The proposed methods are tested on IEEE 30-bus test system and the best method for each objective is determined based on the total cost and the convergence values of the considered objectives. The programming results indicate that based on the inter-related nature of the objective functions, a control system cannot be recommended based on individual optimizations and the secondary criteria should also be considered.

Keywords Evolutionary programming · Genetic algorithm · Multi-objective optimal power flow · Particle
swarm · Pareto optimal method

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Informasi Detail

Judul Seri

Electronic Engineering (2015) Volume 97:1–12

No. Panggil

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Penerbit

: Springer-Verlag Berlin Heidelberg., 2015

Deskripsi Fisik

Electronic Engineering (2015) Volume 97:1–12

Bahasa

English

ISBN/ISSN

1432-0487

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TEKNIK ELEKTRO

Info Detail Spesifik

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Pernyataan Tanggungjawab

ETY

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