Abstract
In this paper, flashover characteristics of polluted cup-pin insulators have investigated by means of laboratory test and a mathematical model. Data from experimental works combined with the model results from a new mathematical modelling are used to derive algorithm for Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) for determining the critical Flashover characteristics (current and voltage). Series of laboratory testing and measurement are carried for 1:1, 1:5, 1/10 and 1:15 ratios of top to bottom surface salt deposit density on cup and pin polluted insulators (T/B). The new model was derived based on dimensional analysis approach of the parameters which commonly effect the phenomenon of pollution flashover of insulators. This model was developed by establishment the relationship between flashover current and voltage, arc constant, arc length and layer pollution conductivity of insulator. The constant of arc A and n is determined using genetic algorithm. Comparative studies have evidently shown that the proposed AI-based technique gives the satisfactory results compared to the analytical model and test data with the coefficient of determination R-Square value more than 96%.
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Salem, A.A., Abd-Rahman, R., Al-Gailani, S.A., Kamarudin, M.S., Othman, N.A., Jamail, N.A.M. (2020). Artificial Intelligence Techniques for Predicting the Flashover Voltage on Polluted Cup-Pin Insulators. In: Saeed, F., Mohammed, F., Gazem, N. (eds) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-33582-3_35
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DOI: https://doi.org/10.1007/978-3-030-33582-3_35
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