Real-Time Power Quality Event Monitoring System Using Digital Signal Processor for Smart Metering Applications

Due to the enormous increase of domestic and industrial loads in the smart grid infrastructure, the power quality issues are very frequent. It is essential to monitor the quality of power being supplied to customers. To identify the quality of the power effectively at various locations, a simple solution is needed that limits the usage of computing resources and can also be deployed in remote location. This paper proposes a low-computational, automatic, real-time PQ monitoring system based on the Hilbert transform (HT), fuzzy logic and threshold based classifiers. The major contribution of the proposed method is based on sample-to-sample process that can detect the events timely, unlike a ten-cycle window-based methods. Six power quality disturbances are synthetically generated using mathematical model as per the IEEE 1159–1195 standard. The methodology utilizes HT for the extraction of the instantaneous amplitude from the filtered signal. Thereby, the essential features are extracted and fed to classifier to improve the recognition capability. The robustness of the proposed algorithm is verified in a MATLAB environment with different signal-to-noise ratios. An experimental prototype has also been developed using TMS320F28379D Launchpad to validate the proposed PQ monitoring algorithm using both synthetic and real-time PQ signals. The real-time implementation demonstrates that the proposed PQ sensing hardware and PQ disturbance analysis software are effective, fast, and accurate.