Yusuf Sönmez

Application of gravitational search algorithm for optimal reactive power dispatch problem

In this paper, Gravitational Search Algorithm (GSA) is applied to solve the optimal reactive power dispatch (ORPD) problem. The ORPD problem is formulated as a nonlinear constrained single-objective optimization problem where the real power loss and the bus voltage deviations are to be minimized separately. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system consisting 6 generator and compared other algorithms reported those before in literature. Results show that GSA is more efficient than others for solution of single-objective ORPD problem.

A data fusion framework with novel hybrid algorithm for multi-agent Decision Support System for Forest Fire

In this study Forest Fire Decision Support System (FOFDESS) which is a multi-agent Decision Support System for Forest Fire has been presented. Depending on the existing meteorological state and environmental observations, FOFDESS does the fire danger rating by predicting the forest fire and it can also approximate fire spread speed and quickly detect a started fire. Some data fusion algorithms such as Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), Fuzzy Switching (FS) and image processing have been used for these operations in FOFDESS. These algorithms have been brought together by a designed data fusion framework and a novel hybrid algorithm called NABNEF (Naive Bayes Aided Neural-Fuzzy Algorithm) has been improved for fire danger rating in FOFDESS. In this state, FOFDESS is an integrated system which includes the dimensions of prediction, detection and management. As a result of the experiments, it was found out that FOFDESS helped determining the most accurate strategy for fire fighting by producing effective results.

An educational tool for power electronics circuits

In this study, an educational tool has been prepared for a shorter term and more economic education of power electronics circuits. In parallel with the improvements of semiconductor technology, the development of power electronics circuits has magnified the importance of either teorical or practical education of power electronics course. The education of power electronic circuits in laboratory is an agelong, costly piece of work. In this study, to overcome the mentioned negativities, a tool has been prepared for the education of power electronic circuits. The tool, which has been prepared on C++ Builder environment has a flexible structure and a graphical interface. It has enabled the analysis of working principles of the circuits and traceability of the system response by the help of graphics, under different conditions created by changing the values of circuit elements.

Symbiotic organisms search optimization algorithm for economic/ emission dispatch problem in power systems

This paper presents symbiotic organisms search (SOS) algorithm to solve economic emission load dispatch (EELD) problem for thermal generators in power systems. The basic objective of the EELD is to minimize both minimum operating costs and emission levels, while satisfying the load demand and all equality–inequality constraints. In other research direction, this multi-objective problem is converted into single-objective function by using price penalty factor approach in order to solve it with SOS. The proposed algorithm has been implemented on various test cases, with different constraints and various cost curve nature. In order to see the effectiveness of the proposed algorithm, its results are compared to those reported in the recent literature. The results of the algorithms indicate that SOS gives good results in both systems and very competitive with the state of the art for the solution of the EELD problems.

Optimal power flow incorporating FACTS devices using Gravitational Search Algorithm

This paper aims to solve the optimal power problem (OPF) incorporating flexible AC transmission systems (FACTS) devices using Gravitational Search Algorithm (GSA) that minimizes the fuel cost function in the problem. In the optimization problem, Thyristor controlled series compensation (TCSC) and thyristor controlled phase shifter (TCPS) FACTS devices are considered to find their optimum location in transmission lines. In order to evaluate the effectiveness of proposed algorithm, it has been tested on modified IEEE 30 bus system and compared with particle swarm optimization (PSO) and hybrid tabu search and simulated annealing (TS/SA) approach which are used in solving the same problem and reported before in the literature. Results show that GSA produces better results than others and has fast computing time for solving OPF problem with FACTS.

Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects

Abstract In this study, symbiotic organisms search (SOS) algorithm is proposed to solve the dynamic economic dispatch with valve-point effects problem, which is one of the most important problems of the modern power system. Some practical constraints like valve-point effects, ramp rate limits and prohibited operating zones have been considered as solutions. Proposed algorithm was tested on five different test cases in 5 units, 10 units and 13 units systems. The obtained results have been compared with other well-known metaheuristic methods reported before. Results show that proposed algorithm has a good convergence and produces better results than other methods.

Application of Symbiotic Organisms Search Algorithm to solve various economic load dispatch problems

This paper proposes the application of Symbiotic Organisms Search (SOS) Algorithm to solve the various Economic Load Dispatch (ELD) problems. Both classical ELD problem which has smooth fuel cost function and nonconvex ELD problem which has nonconvex and discontinuous fuel cost function due to considering of some practical constraints like valve point effects, ramp rate limits and prohibited generating zones have been solved in the study. Three different test cases have been used to show the efficiency and reliability of the proposed algorithm. 38-unit test system has been used for classical ELD and 3-unit and 15-unit test systems have been used for nonconvex ELD problem. Results have been compared to various heuristic methods reported before in the literature and they show that proposed algorithm converges to the global optimum in early iterations and can produce superior results than others in the solution of ELD problems which have both smooth and nonconvex and discontinuous fuel cost function.

Optimal scheduling of short-term hydrothermal generation using symbiotic organisms search algorithm

In this study, the Symbiotic Organisms Search (SOS) algorithm is proposed to solve the short-term hydrothermal generation scheduling (STHGS) problem. This problem aims to optimize the power generation strategy produced by hydroelectric and thermal plants by minimizing the total fuel cost function while satisfying some operational constraints. In order to evaluate the effectiveness of the SOS, it has been tested on a system having a hydro plant with four-cascaded reservoir and a thermal plant. Results have been compared other metaheuristic methods. Results obtained from the experiment show that the proposed algorithm produces better results than the other methods and shows a good convergence.

A comperative study on novel machine learning algorithms for estimation of energy performance of residential buildings

This study aims to improve the energy performance of residential buildings. heating load (HL) and cooling load (CL) are considered as a measure of heating ventilation and air conditioning (HVAC) system in this process. In order to achive an effective estimation, hybrid machine learning algorithms including, artificial bee colony-based k-nearest neighbor (abc-knn), genetic algorithm-based knn (ga-knn), adaptive artificial neural network with genetic algorithm (ga-ann) and adaptive ann with artificial bee colony (abc-ann) are used. Results are compared classical knn and ann methods. Thence, relations between input and target parameters are defined and performance of well-known classical knn and ann is improved substantialy.

Asenkron Motorlarda Rotor Oluğunun Farklı Optimizasyon Yöntemleri ile Optimizasyonunun Gerçekleştirilmesi ve Performansa Etkilerinin İncelenmesi

Bu calismada asenkron motorlarda optimal rotor oluk olculerinin, Sequential Nonlineer Programming (SNP), Genetik Algoritma (GA) ve Sequential Mixed Integer NonLinear Programming (SMINP) yontemleri ile bulunmasi gerceklestirilmis ve karsilastirmali olarak motor performansina olan etkisi incelenmistir. Gerceklestirilen optimizasyon calismasindaki amac maksimum motor veriminin saglandigi oluk geometrisini elde etmektir. Simulasyon calismalari, Ansys Maxwell paket programi kullanilarak gerceklestirilmistir. Gerceklestirilen optimizasyon calismasi sonrasinda elde edilen rotor oluk geometrisine gore Ansys Maxwell 2D programinda motor modeli olusturulmus ve gerekli analizler gerceklestirilmistir. Elde edilen sonuclara gore, en verimli motor geometrisi GA algoritmasi kullanildiginda elde edilmektedir.