ESTIMATION OF SYNCHRONOUS MOTOR EXCITATION CURRENT USING MULTIPLE LINEAR REGRESSION MODEL OPTIMIZED BY SYMBIOTIC ORGANISMS SEARCH ALGORITHM

Yıl 2018, Cilt: 4 Sayı: 2, 210 - 218, 19.12.2018

Emre Çelik

https://doi.org/10.22531/muglajsci.466308

Cited By: 1

Öz

In this paper, an effective and simpler means of estimating the excitation current of a synchronous motor (SM) is presented for power factor correction task. First, a multiple linear regression model with four predictor variables such as motor load current, actual power factor, power factor error and excitation current change is formed to estimate the SM excitation current. Then, recently introduced symbiotic organisms search (SOS) algorithm is employed in the hope of searching better values of regression coefficients in that model using the data collected from the prepared experimental setup. The supremacy of SOS over some recently published algorithms such as genetic algorithm, artificial bee colony and gravitational search algorithm is widelyattested through comparative computer simulations for the similar compensation system. The results exhibited in this article show that the proposed SOS algorithm outperforms the other reported popular algorithms from the aspects of simplicity, robustness and accuracy. In view of this, the suggested tuning of regression coefficients of the multiple linear regression model yields a better estimating performance of SM excitation current than the earlier studies.

Anahtar Kelimeler

Synchronous motor, power factor correction, multiple linear regression model, symbiotic organisms search algorithm, optimization, optimization

SİMBİYOTİK ORGANİZMALAR ARAMA ALGORİTMASI İLE OPTİMİZE EDİLMİŞ ÇOKLU DOĞRUSAL REGRESYON MODELİ KULLANILARAK SENKRON MOTOR UYARTIM AKIMININ TAHMİNİ

Öz

Bu
belgede güç faktörü düzeltme işlemi için senkron motor (SM) uyartım akımının
tahminine yönelik etkili ve basit bir yol sunulmuştur. Bu işlem için ilk olarak
motor yük akımı, gerçek güç faktörü, güç faktörü hatası ve uyartım akımının
değişimi karar değişkenleri olarak ele alınarak çoklu doğrusal regresyon modeli
oluşturulmuştur. Ardından hazırlanan deneysel düzenekten toplanan veriler
kullanılarak bu modeldeki regresyon katsayılarının iyileştirilmesi amacıyla yeni
ortaya konulan simbiyotik organizmalar arama algoritmasından faydalanılmıştır. Bu
algoritmanın benzer kompanzasyon işlemi için genetik algoritma, yapay arı
kolonisi ve yerçekimi algoritması gibi yakın zamanda yayınlanan algoritmalara
olan üstünlüğü karşılaştırmalı bilgisayar simülasyonları ile gösterilmiştir. Bu
makalede sergilenen sonuçlar, sunulan tekniğin bahsi geçen literatürdeki
algoritmalara göre basitlik, gürbüzlük ve doğruluk açılarından daha iyi
performans verdiğini göstermiştir. Bu bağlamda çoklu doğrusal regresyon model
katsayıların önerilen şekilde ayarı önceki çalışmalardan daha iyi SM uyartım
akımı tahmin performansı sağlamıştır.

Anahtar Kelimeler

Senkron motor, güç faktörü düzeltme, çoklu doğrusal regresyon modeli, simbiyotik organizmalar arama algoritması, optimizasyon

Kaynakça

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