Uğur Güvenç

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.

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.

Fuzzy diffusion filter with extended neighborhood

Anisotropic diffusion filters, which are motivated from heat diffusion between mediums, have become a widely used technique in the field of image processing. In the initial proposals of anisotropic diffusion filters, 4-neighborhood values with diffusivity functions are computed independently for each spatial location because of numerical approximation. However, anisotropic diffusion filters could not be used in real-time image and video processing applications because they need diffusivity parameters, which must be specified by users in every sampling period. In this study, a fuzzy adaptive diffusion filter using extended neighborhood without diffusivity functions has been developed. The fuzzy adaptive diffusion filter does not require any parameter chosen by user and therefore they could be employed in real-time applications. In the fuzzy adaptive diffusion filter, a similarity transformation by means of relation matrix and fuzzy logic is carried out. Accordingly, the similarity image, output of transformation, is directly used as a heat diffusion coefficient in the diffusion filter. Results show that the fuzzy adaptive diffusion filter is very efficient for removing noise in image while preserving edges.

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.

A novel edge detector based on light refraction law

In this paper, light refraction law based a novel edge detection algorithm was described. In the proposed method, center pixel deemed a light source. Neighbor pixels are deemed different environments refracted the light. The minimum value of ratio of these different environments refraction indices qualifies the edge knowledge of center pixels. Edges are determined rigorously in the image through this designed method. As compared with classical method, there isnpsilat very complex computing in this method.

Exploring the effect of distribution methods on meta-heuristic searching process

In this study, the effect of distributions of solution candidates on the problem space in the meta-heuristic search process and the performance of algorithms has been investigated. For this purpose, solution candidates have been created with random and gauss (normal) distributions. Search performance is measured separately for both types of distribution of algorithms. The performances of the algorithms have been tested on the most popular and widely used benchmark problems. Experimental studies have been conducted on the most recent meta-heuristic search algorithms. It has been seen that the search performance of algorithms varies considerably depending on the method of distribution. In fact, better results were obtained than the distribution methods used in the original versions of the algorithms. Algorithms have revealed their abilities in terms of neighborhoods searching, getting rid of local minimum traps and speeding up searches.