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A Comprehensive Method for Planning and Selection of Distribution Transformers Ratings and Their Asset Management

Hajiaghapour Moghimi, Mehran | 2016

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 48916 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Vakilian, Mehdi
  7. Abstract:
  8. A significant part of the power system energy losses occurs in the distribution transformers. The high loss share of these transformers in yearly electric energy losses of the country’s power network contributes to about 8% of the 20% total national electric energy losses. On the other hand due to the high rate of yearly increase in electric energy usage in the country, optimal distribution transformer planning and sizing pays an important role in reduction of these losses in the coming years. To address these concerns, the distribution companies should optimize the utilization of electric energy sources which results in an increase in their profits. This thesis introduces a new detailed method for optimal and smart selection of distribution transformers power rating and efficiency class. This will subsequently increase the distribution companies’ profits through reduction in the respected energy losses. The past methods which are still used in the country, didn’t simulate the replacement of a distribution transformer with another one (of different rating). This thesis models different scenarios, realizing a yearly load growth in the region, besides possibility of stablishing a new substation to develop a comprehensive algorithm for distribution transformer planning and its replacement during its life. Final target of this work is the development of a code for optimized planning of a new distribution substation. The proposed algorithm is developed for Tehran’s Large Electricity Distribution Company and demonstrated promising results
  9. Keywords:
  10. Distribution Transformer ; Demand Response ; Electricity Distribution Network ; Optimum Selection ; S Shape Load Growth

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