A cost‐effectiveness assessment model using grey correlation analysis for power transformer selection based on life cycle cost. Issue 1 (2014)

• Record Type: Journal Article
• Authors: Yiyi Zhang; Ruijin Liao; Lijun Yang; Xiaopin Deng; Huanchao Cheng; Cheng Lv
• Is Part Of: Kybernetes. Volume 43:Issue 1(2014)
• Page Start: 5
• Page End: 23
• Publication Date: 2014
• Subjects: Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.505
• DOI: 10.1108/K-07-2013-0160
• Languages: English
• ISSNs: 0368-492X

Abstract

<abstract> <title> <x xml:space="preserve"> Abstract </x> </title> <p> <bold>Purpose</bold> – Statistics show that selecting the best investment program based on both cost and effectiveness can avoid financial losses. However, investment evaluation of a power transformer is full of uncertainty as it is hard to obtain accurate and useful cost‐effectiveness results. Therefore, the purpose of the paper is to establish an investment evaluation model. <bold>Design/methodology/approach</bold> – The cost‐effectiveness evaluation model in this study used grey correlation analysis for the power transformer selection based on life cycle cost (LCC). Indices of cost and effectiveness factors were chosen to form a three‐level index system including quantitative and qualitative factors. Evidential reasoning was applied to quantify the qualitative indexes. Grey correlation analysis was applied to select the best investment program. <bold>Findings</bold> – The results from this study show that the proposed approach is effective and offers a new approach to evaluating transformer investment. <bold>Practical implications</bold> – The model was applied to an investing decision‐making problem of the transformer in a new substation in Wuhan, China. <bold>Originality/value</bold> – It is very important to select the best transformer program in the candidate investment programs because the investment program decides almost 70 percent of the LCC of the power transformer.</p> <ack> <title> <x xml:space="preserve"> Acknowledgements </x> </title> <p>The authors acknowledge Professor Stanislaw Grzybowski of Mississippi State University for his useful advices. The authors also acknowledge the editor and the referees' accurate and valuable comments on the manuscript. This project is financially supported by the Fundamental Research Funds for the Central Universities (CDJZR12110018) and the National Natural Science Foundation of China (51277187).</p> </ack> </abstract>