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The detection of disturbances in electrical power systems must be as fast as possible to avoid serious consequences. The occurrence of sags, particularly in Wind Electrical Synchronous Generators, may cause failures or even the destruction of the generator. This paper presents a comparison between a FPGA and a DSP system for Voltage Sags detection. The use of Field Programmable Gate Arrays (FPGAs) and Digital Signal Processor (DSP) to monitor the quality of Electrical Power Systems allows the fast detection of sags and other related electric power disturbances. The developed system used a version of the Wavelet transform, the Redundant Discrete Wavelet Transform (RDWT), for the detection of the sags. For a fast Sag detection, the current and voltage wavelet coefficient energies were used. The results obtained show that both implementations, with FPGA and with DSP are efficient alternatives for the fast SAGs detection and can also be used to other power quality related disturbances monitoring.

Introduction: Premature Ventricular Contraction (PVC) is among the most common types of ventricular cardiac arrhythmia. However, it only poses danger if the person suffers from a heart disease, such as heart failure. Hence, this is an important factor to consider in heart disease people. This paper presents an ECG real-time analysis system for PVC detection. Methods: This system is based on threshold adaptive methods and Redundant Discrete Wavelet Transform (RDWT), with a real-time approach. This analysis is based on wavelet coefficients energy for PVC detection. It is presented also a study to find the most indicated wavelet mother for ECG analysis application among the following wavelet families: Daubechies, Coiflets and Symlets. The system detection performance was validated on the MIT-BIH Arrhythmia Database. Results: The best results were verified with db2 wavelet mother: the Sensitivity Se = 99.18%, Positive Predictive Value P+ = 99.15% and Specificity Sp = 99.94%, on 80.872 annotated beats, and 61.2 s processing speed for a half-hour record. Conclusion: The proposed system exhibits reliable PVC detection, with real-time approach, and a simple algorithmic structure that can be implemented in many platforms.

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