Many economies in the world have adopted energy-efficiency requirements or incentive programs mandating or promoting the use of energy-efficient transformers. On the other hand, increases in transformer efficiency are subject to increases in transformer weight and size, sometimes as much as 50% or more. The transformer manufacturing industry is therefore faced with the challenge to develop truly optimum designs. Transformer design optimization (TDO) is a mixedinteger nonlinear programming problem having a complex and discontinuous objective function and constraints, with the objective of detailed calculation of the characteristics of a transformer based on national and/or international standards and transformer user requirements, using available materials and manufacturing processes, to minimize manufacturing cost or total owning cost while maximizing operating performance. This paper gives a detailed comparative analysis of the application of five modern nature-inspired metaheuristic optimization algorithms for the solution of the TDO problem, demonstrated on three test cases, and proposes two algorithms, for which it has been verified that they possess guaranteed global convergence properties in spite of their inherent stochastic nature. A pragmatic benchmarking scheme is used for comparison of the algorithms. It is expected that the use of these two algorithms would have a significant contribution to the reduction of the design and manufacturing costs of transformers.

Abstract:Transformer design optimization methods presented in the literature rarely yield solutions directly applicable in production; the design engineer usually needs to convert the theoretical solution to a practical one. This problem is addressed in this paper, and a discrete transformer design optimization method is proposed that yields solutions with commercially available or productionally feasible dimensions, thus eliminating the need for further efforts of the design engineer to make the theoretical solution a feasible one. The cuckoo search, a nature-inspired metaheuristic algorithm, is used as the optimization algorithm in this study, and it is shown that the guaranteed global optimum solution is attained in a single run. Furthermore, a simple method is proposed to reduce the number of objective function and constraint calculations. The method is based on skipping calculations for design vectors recurring during the search process by use a caching technique. It is envisaged that the use of the proposed method will make a significant contribution to the streamlining of the quotation and design processes in the transformer industry as well as standardization of production materials.

Education is the key element that will enable Turkey to reach the contemporary civilization level. In this context, it is extremely important that educational institutions and related organizations formulate the right strategies to increase the quality of education in Turkey to the level in developed countries and even carry it forward. On the other hand, the decrease in student success rates in higher education and the increasing unemployment rates of university graduates in recent years are due to wrong or inadequate strategies. Furthermore, employers point out that the level of university graduates has steadily decreased since the beginning of this century. Discovery and extraction of information such as relations, patterns, deviations and trends existing among the data in databases form the basis of data mining. This extracted information provides the basic data required for operational decision support systems. By using data mining methods, it is possible to make predictions about the problems in education with high accuracy and deduce meaningful results. For this reason, educational data mining is widely used both in Turkey and abroad. In this study, a survey made among the students of Blended Education Program offered at Sakarya University Faculty of Computer and Information Sciences Computer Engineering department has been evaluated using data mining association analysis method in order to be able to determine the factors affecting satisfaction and success of students. Open source code data mining software have been used in the study.