PQD's Detection and Classification Under Normal and Noisy Conditions Based on RADWT & SVM Based Technique

In recent days, there has been a significant increase in the amount of industrial and residential loads. Increasing the load might result in problems with the power quality on the distribution side. Because of difficulties about the quality, these capabilities of erratic power have been reduced. This may on occasion lead to potentially hazardous fire accidents, which in this case resulted in the loss of both lives and property. voltage sag, voltage swell, fluctuations, switching transients, flickers, and harmonics are the primary Power Quality Disturbances (PQDs). These PQDs need to be swiftly and precisely recognized by power quality analyzers despite the fact that they are sensitive to detection. In this particular instance, this is the key justification for rapidly and accurately identifying any problems with the power quality. The detection and classification of these PQ disturbances is now a difficult task for electrical engineers in the modern day. Because of this, a large number of researchers are focusing their attention on the issue. In this article, the formulation and simulation of power quality disturbances are discussed. MATLAB is used as the programming environment for the mathematical representation of PQDs that have been formulated. In order to analyze the PQD signals, the RADWT wavelet transform is used. In order to categorize the information obtained from the decomposed PQD signals, Support Vector Machine Learning Classifier is used.