An indispensable element in addressing the current problem of non-ionizing electromagnetic pollution in the environment is a review of the levels of exposure to the electric and magnetic fields produced by the lines of electric power transmission and distribution systems. In order to establish the exposure levels, it is necessary to determine the model of the lines. Considering that a computational simulation is a helpful tool for power system analysis, this article presents a computational tool developed in Matlab App Designer for the model-in-sequence components of the parameters that make up a transmission line. This tool allows the user to work in a friendly and parameterizable environment according to the performed tests. In order to verify the tool’s performance, two case studies are implemented. The first one is for a transposed transmission line and the second one for a non-transposed transmission line. The results obtained are compared with commercial software, acquiring a maximum error of 0.16402 %.
<span lang="EN-US">There is an interest in the biological effects of exposure to low-frequency electromagnetic fields issued by transmission lines on animals and humans. The fields generated by the lines are relevant for the design and operation of power systems. The study of the electric and magnetic fields in the transmission networks implemented commercial simulators bases on the finite element method. These commercial simulators are characterized by accuracy and high hardware and software requirements. This work presents CEM-LT, a tool that accurately precisely the electric and magnetic field in the transmission lines, with simple and intuitive handling and low processing times, making it ideal for being implemented together with optimization methods. The electric and magnetic field in the servant area for two case studies is analyzed to evaluate the accuracy and processing times. The level of accuracy is characterized by comparing the results with COMSOL obtaining errors of less than 2.4%. The case study with the highest computational requirement achieved a processing time of 3,027 seconds.</span>
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