A new embedded power quality event classification system based on the wavelet transform

In this paper, a real time embedded intelligent recognition system is proposed for diagnosing the power quality problems. The intelligent recognition system has a structure capable of classifying and detecting the power quality problems in real time. Hardware applications of the wavelet transform and decision tree classifier are realized inside the recognition system using with field programmable gate array (FPGA) device. These methods operating as embedded in the FPGA environment provide real-time diagnosis of power quality problems. A new approach of this recognition system is its capability to simultaneously detect a power quality event in the power systems and power quality disturbances occurring on each phase following the event. In this paper, 2 different recognition systems that online and offline are presented. The online recognition system operates in the fields with signal processing and classification structures embedded. In the offline system, the distinctive features of the event signals obtained from the online system are used and these signals are classified in the computer environment by means of the least square support vector machines. A prototype model of the power system is created in the laboratory environment in order to test the FPGA-based online intelligent recognition system, determine accuracy rates and evaluate its performance. Power quality event types are created in wide parameter ranges on this model. Obtained results from both recognition systems indicated the hardware and software designs of our embedded systems are quite effective, fast, and have high success performance.