Otomatik gerilim regülatör sistemi için karşıt tabanlı atom arama optimizasyon algoritması

Yıl 2020, Cilt: 35 Sayı: 3, 1141 - 1158, 07.04.2020

Serdar Ekinci , Ayşen Demirören Hatice Lale Zeynelgil Baran Hekimoğlu

https://doi.org/10.17341/gazimmfd.598576

Cited By: 11

Öz

Bu makale, arama uzayı
keşfini geliştirmek için karşıt tabanlı öğrenmeyi (OBL) kullanan atom arama
optimizasyon (ASO) algoritmasının değiştirilmiş bir versiyonunu sunmaktadır.
OBL, sezgisel-üstü algoritmaların performansını artırmak için yaygın olarak
kullanılan bir makine öğrenme stratejisidir. Yeni bir tasarım metodu olarak
sunulan karşıt tabanlı ASO (OBASO) algoritması, otomatik gerilim regülatörü
(AVR) sistemindeki oransal-integral-türevsel artı ikinci dereceden türevsel
(PIDD²) kontrolör parametrelerinin optimum değerlerinin bulunmasında
ilk kez önerilmiştir. Tasarım probleminde, zaman ağırlıklı karesel hatanın
integrali (ITSE) ve aşımı birlikte içeren yeni bir amaç fonksiyonu, önerilen
OBASO algoritması ile optimize edilerek PIDD² kontrolör
parametrelerinin en iyi değerleri bulundu. Önerilen OBASO ayarlı PIDD²
(OBASO-PIDD²) kontrolörün performansı, klasik ASO ayarlı PIDD²
(ASO-PIDD²) kontrolörün yanı sıra modern sezgisel-üstü
algoritmalarla ayarlanan PID, kesir dereceli PID (FOPID) ve PIDD²
kontrolörleriyle karşılaştırılmıştır. Önerilen yaklaşımın kararlılığını
değerlendirmek için karşılaştırmalı geçici hal ve frekans cevabı analizleri
gerçekleştirilmiştir. Ayrıca, AVR parametrelerindeki muhtemel değişimler göz
önüne alınarak önerilen yaklaşımın gürbüzlüğü test edilmiştir. Kapsamlı
simülasyon sonuçları ve mevcut diğer kontrolörler ile yapılan karşılaştırmalar,
yeni bir amaç fonksiyonuna sahip önerilen OBASO-PIDD² kontrolörün
üstün bir kontrol performansına sahip olduğunu ve model belirsizliklerine göre
sistem gürbüzlüğünü oldukça artırabildiğini göstermektedir.

Anahtar Kelimeler

Otomatik gerilim regülatörü, karşıt tabanlı öğrenme, atom arama optimizasyon algoritması, parametre ayarı

Kaynakça

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