Article ID Journal Published Year Pages File Type
400337 International Journal of Electrical Power & Energy Systems 2016 11 Pages PDF
Abstract

•A single stage cost optimum transmission line design procedure is proposed.•Development of correlations for cost influencing parameters of the line.•Validation of correlations using Adjusted R2 and Durbin Watson statistics.•Development of mathematical model for cost assessment and optimization.•Genetic algorithm optimization yields design variables accounting for minimum cost.

In the era of ever increasing power demand and deregulation of the power industry globally, it becomes extremely essential for transmission utilities to strengthen their infrastructure for accommodating the changes posed by deregulation and transfer reliable power to consumers. Construction of transmission lines involves heavy investment and hence, a careful analysis needs to be carried out at the planning stage in order to take investment decisions. There is a need for assessing the cost of these lines based on scientific principles as compared to those adopted conventionally based on availability of standard designs and line designer’s experience. In the present study, an attempt has been made to develop a model for cost assessment and optimization of overhead power transmission lines using genetic algorithms. The results obtained from genetic algorithm optimization technique have been compared with that of interior point method based classical optimization technique. Correlations based on regression analysis were developed for cost influencing parameters of the transmission line in terms of the line design variables and are used in constructing the mathematical model for cost optimization. The developed model is capable of optimizing the cost while simultaneously selecting the optimum design variables influencing the cost of the line. The proposed methodology can be used by transmission line designers and developers to determine techno-economic viability for investment decision before undertaking detailed investigations.

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Physical Sciences and Engineering Computer Science Artificial Intelligence
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