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Transformers are one of the most important part in a power system and, especially in key-facilities, they should be closely and continuously monitored. In this context, methods based on the dissolved gas ratios allow to associate values of gas concentrations with the occurrence of some faults, such as partial discharges and thermal faults. So, an accurate prediction of oil-dissolved gas concentrations is a valuable tool to monitor the transformer condition and to develop a fault diagnosis system. This study proposes a nonlinear autoregressive neural network model coupled with the discrete wavelet transform for predicting transformer oil-dissolved gas concentrations. The data fitting and accurate prediction ability of the proposed model is evaluated in a real world example, showing better results in relation to current prediction models and common time series techniques.

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A new optical high voltage sensor using a Pockels microsingle crystal in a longitudinal modulation arrangement is presented. A prototype of the sensor is assembled and shows an advantage of directly measuring voltage levels up to 50 kV without any potential divider in a wide frequency bandwidth of direct current to 116 MHz. In conventional Pockels sensors, acoustic resonance driven by piezoelectric effect introduces oscillatory components to signals obtained from measurements of lightning impulse voltages. In the new sensor, the measured signal is free from the oscillatory components. An accuracy of 1.9% in good agreement with predicted values is obtained from the lightning impulse voltage measurement.