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Optimum Power Flow (OPF) is a nonlinear optimizationproblem that allows the optimum determination of steady state variables in thepower system. The OPF problem aims to minimize the cost of production, active/reactivepower losses and fuel emissions in the power system by keeping the parameterssuch as generator/bus voltage, busbar shunt current reactor/capacitor value,generator active/reactive power, transformer tap changer and line capacitywithin safe limits. In this paper, a new algorithm, Harris Hawk Optimization(HHO) algorithm, is used to minimize active power losses for OPF. Thisalgorithm was applied to a section of 13 bar Eastern Anatolia power system of Turkey. Then, the results of the testswere compared with the Vector evaluated Particle Swarm Optimization (VPSO) and Fractional Order Darwinian Particle Swarm Optimizationwith Constraint Threshold (FODPSO-CT) and the effectiveness of HHO was discussed., Optimum Yük Akış (OYA)problemi, güç sistemindeki sabit durum değişkenlerinin optimum şekilde belirlenmesini sağlayan doğrusalolmayan bir optimizasyon problemidir. OYA problemi jeneratör/baragerilimi, bara şönt akımı, reaktör/kapasitör değeri, jeneratöraktif/reaktif güç, trafo kademe değiştirici ve hat kapasitesi gibi verilerigüvenli sınırlar içinde tutarak güç sisteminde üretim maliyeti, aktif/reaktifgüç kayıpları, yakıt emisyon miktarı gibi çıktıları minimize etmeyi amaçlar. Buçalışmada OYA için aktif güç kayıplarının minimizasyonunda yeni bir algoritmaolan Harris Şahini Optimizasyon (HŞO)algoritması kullanılmıştır. Bu algoritma 13 baralık Türkiye Doğu Anadolu güçsisteminin bir kesitine uygulanmıştır. Sonrasında elde edilen test sonuçları literatürde bulunanVektörel Parçacık Sürü Optimizasyonu (VPSO) ve Eşik Değer kısıtlamalı Kesir DereceliDarwinian Parçacık Sürü Optimizasyonu (ED-KDDPSO) algoritmaları ilekarşılaştırılarak, HŞO’nun etkinliği tartışılmıştır.

In this paper, an approach to design a fractional-order integral operator s(lambda) where -1 < lambda

This paper presents a multi-objective stochastic optimization method for tuning of the controller parameters of Refrigeration Systems based on Vapour Compression. Stochastic Multi Parameter Divergence Optimization (SMDO) algorithm is modified for minimization of the Multi Objective function for optimization process. System control performance is improved by tuning of the PI controller parameters according to discrete time model of the refrigeration system with multi objective function by adding conditional integral structure that is preferred to reduce the steady state error of the system. Simulations are compared with existing results via many graphical and numerical solutions.

In this study, an experimental test platform of a low-cost coaxial rotors has been developed for implementation of Model Reference Adaptive Controller (MRAC) with Fractional Order Adjustment Rules by using MATLAB/Simulink. This paper provides a design method of MRAC with fractional order adjustment rule (FOAR-MRAC) that is implemented on a hardware based on ARM microcontroller. Setup of the test platform is presented and its performance is evaluated with real-time experimental measurements. This experimental study is useful to show the utilization of fractional order control systems on engineering applications by using low-cost hardware.