Application of Random Forest and Hidden Markov Models for Automatic and Fast Classification of Power Quality Signals
Swarnabala Upadhyaya

Swarnabala Upadhyaya, B. Tech, Electrical Engineering, Orissa school of Mining Engineering (Government College of Engineering, Keonjhar), Odisha.

Manuscript received on January 12, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 2856-2862 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1382029420/2020©BEIESP | DOI: 10.35940/ijitee.D1382.029420
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Abstract: In this paper, wavelet transform, namely the maximal overlap discrete Wavelet Transform (MODWT) and the second generation Wavelet Transform (SGWT) have been implemented. These wavelet transforms are applied to get selected features of the signals. Features are used as inputs to two types of classifiers namely, Hidden Markov Model (HMM) classifiers and the Random Forest (RF) classifier in the both absence and presence of Noise to evaluate the efficiency. The classification accuracy (CA) calculated using these classifiers clearly shows that the RF classifiers is a better classifier then the HMM classifier as it possess higher recognition rate at all levels of noise along with the pure PQ signals. Another important property of RF classifier is the proper classification of large number of class of both slow and the fast disturbances. 
Keywords: Hidden Markov Model , Maximal Overlap Discrete Wavelet Transform, Power Quality Disturbance, Random Forest, Second Generation Wavelet Transform.
Scope of the Article: Classification