Abstract
This manuscript presents a method based on S-transform (ST) and extreme learning machine (ELM) to identify the causes of voltage sag. Exact recognition of voltage sag causes (VSCs) can help decrease the problems initiated due to voltage dip in electric power system. ST is a well-known time–frequency analysis technique. Initially, the extracted voltage signals are pre-processed through ST and several statistical features are extracted, which are later applied as inputs to ELM classifier for voltage sag cause detection. Here, three significant causes are simulated for voltage sag in MATLAB/Simulink, like (i) single-phase and three-phase fault, (ii) starting of induction motor and (iii) energization of transformer. The performance of the proposed technique is compared with other existing voltage sag cause classification techniques. The classification results indicate the ability of the proposed technique for detection and classification of VSCs more accurately.
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Patnaik, B., Panigrahi, R.R., Mishra, M., Jena, R.K., Swain, M.k. (2020). Detection and Classification of Voltage Sag Causes Based on S-Transform and Extreme Learning Machine. In: Sharma, R., Mishra, M., Nayak, J., Naik, B., Pelusi, D. (eds) Innovation in Electrical Power Engineering, Communication, and Computing Technology. Lecture Notes in Electrical Engineering, vol 630. Springer, Singapore. https://doi.org/10.1007/978-981-15-2305-2_22
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DOI: https://doi.org/10.1007/978-981-15-2305-2_22
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