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Recently power quality (PQ) has become one of the major issues for residential and industries sectors in south India. To improve power quality, detecting the particular type of disturbance is the foremost thing .So monitoring is needed to detect the power quality disturbance that occurs in a short duration of time. In this paper a power quality analyzer setup is designed using a Arduino microcontroller to capture the real time power quality disturbances that occur in a single phase power system, especially it is used to collect sample of signals across single phase industrial loads from different small scale industries. The samples are stored in the computer, then features are extracted and given as input to classifiers .Support vector machine (SVM) is identified as the most suitable classifier and performance istested using generated samples like normal, sag, swell, interruption, harmonics and transient signals, Finally the PQ analyzer is implemented for real time signals taken from small scale industries. It is observed that this arrangement is of low cost and useful to small scale industries in rural areas .The performance is compared with other feed forward neural network (FFNN) classifier. This proposed method would enhance the detection of PQ distortions in commercial and domestic applications, also provides a faster operation with less cost.

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