  Reduced system dependability and higher maintenance costs may be the
consequence of poor electric power quality, which can disturb normal equipment
performance, speed up aging, and even cause outright failures. This study
implements and tests a prototype of an Online Sequential Extreme Learning
Machine (OS-ELM) classifier based on wavelets for detecting power quality
problems under transient conditions. In order to create the classifier, the
OSELM-network model and the discrete wavelet transform (DWT) method are
combined. First, discrete wavelet transform (DWT) multi-resolution analysis
(MRA) was used to extract characteristics of the distorted signal at various
resolutions. The OSELM then sorts the retrieved data by transient duration and
energy features to determine the kind of disturbance. The suggested approach
requires less memory space and processing time since it can minimize a large
quantity of the distorted signal's characteristics without changing the
signal's original quality. Several types of transient events were used to
demonstrate the classifier's ability to detect and categorize various types of
power disturbances, including sags, swells, momentary interruptions,
oscillatory transients, harmonics, notches, spikes, flickers, sag swell, sag
mi, sag harm, swell trans, sag spike, and swell spike.
