Considering Environmental Health and Energy Resources to Design Transformers

Abstract

Background: Environmental health has become a worldwide debating issue among researchers, scientists, and governments. Although fossil fuels have been the greatest energy resources for the human, their consumption leads to injecting greenhouse gases into the air, which can affect the existence of all living species dramatically. In fact, fossil fuels consumption pollutes the environment because of the injection of greenhouse gases which result in global warming. On the other hand, reductions in fossil fuel and drinking water resources have highlighted the importance of energy management and loss reduction in brand-new management strategies and manufacturing methods. Distribution transformers are one of the most used devices in power distribution networks. Hence, it seems to be logical to consider transformer losses as a definitive factor in design and construction procedures. Furthermore, such design procedures require powerful tools to solve complex non-linear equations and find the best solutions in the shortest time. Researchers and scientists have always had a great challenge regarding finding the best solutions for their analysis. In actuality, it is hard to solve complex non-linear problems by means of traditional calculation methods even if a researcher employs a powerful computer. For this reason, metaheuristic optimization algorithms have found great popularity among scientists. These algorithms seek the best solution through a solution pool at an appropriate pace. Therefore, a researcher can save more time and energy in solving an intricate problem. Objective: In this paper, the authors aim to modify the conventional Teaching-Learning Based Optimization (TLBO) algorithm to design an optimum distribution transformer by consideration of the transformer Total Owning Cost (TOC). The TOC consists of operational and initial manufacturing costs of a distribution transformer. The used raw materials affect the manufacturing cost and it would be decreased if the copper and iron volumes are reduced in the construction instruction. However, the operational cost that includes iron and copper losses must not be forgotten in design analysis. Indeed, loss consideration is of great significance to keep energy from frittering away and as a result, protects environmental resources. Materials and Methods: A novel approach based on an optimization technique for distribution transformer design problems is presented in this paper. The entire expenses of a transformer consist of transformer construction and operating costs. In other words, by reducing copper and iron volumes, the initial cost of a transformer will be decreased. Due to the electromagnetic and electrical losses in transformers, the initial cost of a transformer is not its entire design problem and the operating cost must be considered in the design algorithm. Appropriate limits on efficiency, voltage regulation, temperature rise, no-load current and winding fill factor are the constraints on the transformer design problems in the international standards. With respect to these constraints, the transformer designer can minimize the volume of the core and windings. In this paper, the Conditional Teaching-Learning Based Optimization (CTLBO) algorithm determines the appropriate transformer properties, while transformer construction cost and its operating cost are selected as an objective function for optimization method. In order to attain a suitable voltage regulation, transformer impedance is chosen as an optimization constraint. Results: The result of the paper demonstrates the proposed algorithm ability in reducing the Total Owning Cost (TOC) during transformer lifetime, which could be useful for energy distribution companies. In addition, the result analysis proves that the total losses of the transformer are reduced by the proposed approach in comparison to conventional design techniques. Then, more energy will be saved in the power grid when the proposed transformer is utilized in the power network. Conclusion: In this paper, a suitable method to design an optimum distribution transformer is proposed which enables manufacturers to construct their product based on the proposed method. In this way, it can be claimed that not only more money will be saved during the transformer operation, but also the energy consumption will be decreased drastically. Therefore, world resources will remain for future generations.