moises vidal

The paper describes an algorithm to detect and classify electrical events related to power quality. The events detection is based on monitoring the statistical characteristics of the energy of the error signal, which is defined as the difference between the monitored waveform and a sinusoidal wave generated with the same magnitude, frequency, and phase as the fundamental sinusoidal component. The novel feature is event recognition based on a neural network that uses the error signal as input. Multi-rate techniques are also employed to improve the system operation for on-line applications. Software tests were performed showing the good performance of the system.

Turbo equalization is a receiver scheme intended to combat the detrimental effects of intersymbol interference (ISI) in digital transmissions protected by error-control codes (ECC). Traditionally, these equalizers are implemented with the optimal MAP (maximum a posteriori probability), or Bayesian algorithm. Thus, considering the close relationship between the fuzzy and Bayesian equalizers, this paper introduces a turbo-fuzzy equalization (TFEQ) approach to cope with ISI and high-power impulse noise effects in broadband digital communication channels. Simulation results for power line communication (PLC) channels evidence the applicability of our scheme to overcome the aforementioned impairments when high-rate transmission is considered

This contribution presents performance analysis of clustered-OFDM system applied to power line communication (PLC) applications when bitloading algorithm is taken into account. The attained results verify that, in comparison with the standard OFDM system, the use of clustered-OFDM for downlink data transmission in a shared outdoor and low-voltage power line cables among several users, can provide improved performance. Computational results verify that clustered-OFDM system can increase the bit rate, reduce the computational complexity at the receiver side, and reduce the bit error ratio for each individual user.

This paper introduces a new waveform coding technique, based on wavelet transform, for power quality monitoring purposes. The proposed enhanced data compression method (EDCM) presents a complete adaptive signal processing approach to estimate the fundamental sinusoidal component and separate it from the transient ones in the monitored signal. When these nonstationary components are submitted to the compression technique, the sparse representation property of the wavelet transform provides an improvement in the compression ratio. Also, the degradation inserted by the lossy compression process is minimized. Simulation results confirm the effectiveness of the proposed method when compared to the standard solution, characterized by the compression of the whole monitored signal.