Comparison of Intelligent Methods for Thermal Assessment of Power Cables under Geometrical Parameter Variations

In this paper, the thermal field of underground power cable is solved using two intelligent techniques which are newly introduced for thermal assessment of power cables. A backpropagation neural network (BPNN) and an adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the cable temperature under geometrical parameter variations. The effect of cable spacing and cable burial depth on cable ampacity is investigated. The two models are trained using an input-output pattern generated using finite element (FE) method, which is extensively used in this field. Furthermore, a portion of the FEgenerated output results, which have not been provided to the models – as input data in the training phase, were utilized to compare the cable temperature of the three methods (FE, ANN and ANFIS). The results of the two intelligent methods show high agreement with the finite element solution, which confirms that introducing intelligent techniques provides a reliable and simple alternative approach for the thermal field evaluation by avoiding the numerous computational complexities of the numerical methods.