Abstract
The paper presents single and multiple Power Quality (PQ) disturbance classifiers which are based on Discrete Wavelet Transform (DWT) and Random Forest (RF). Features are extracted using the DWT which represents the original signal into coefficients at different levels, namely the approximate and detail coefficients. These coefficients contain the information which is used to discern the type of PQ disturbance. The models are developed using RF which classify 10 types of PQ disturbances. In the experiment, different parameters of DWT are examined regarding the effects to the performance of the classifiers. Results show that all of the classifiers have satisfactory classification performance. Also, it was observed that the DWT parameters affect the robustness to noise and overall accuracy of the classifiers.
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