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Reduced system dependability and higher maintenance costs may be theconsequence of poor electric power quality, which can disturb normal equipmentperformance, speed up aging, and even cause outright failures. This studyimplements and tests a prototype of an Online Sequential Extreme LearningMachine (OS-ELM) classifier based on wavelets for detecting power qualityproblems under transient conditions. In order to create the classifier, theOSELM-network model and the discrete wavelet transform (DWT) method arecombined. First, discrete wavelet transform (DWT) multi-resolution analysis(MRA) was used to extract characteristics of the distorted signal at variousresolutions. The OSELM then sorts the retrieved data by transient duration andenergy features to determine the kind of disturbance. The suggested approachrequires less memory space and processing time since it can minimize a largequantity of the distorted signal's characteristics without changing thesignal's original quality. Several types of transient events were used todemonstrate the classifier's ability to detect and categorize various types ofpower disturbances, including sags, swells, momentary interruptions,oscillatory transients, harmonics, notches, spikes, flickers, sag swell, sagmi, sag harm, swell trans, sag spike, and swell spike.

Power Quality Event Recognition and Classification Using an Online Sequential Extreme Learning Machine Network based on Wavelets