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.

Dissolved gas analysis (DGA) is one of the most important methods to analyze fault in power transformers. In general, DGA is applied in monitoring systems based upon an autoregressive model; the current value of a time series is regressed on past values of the same series, as well as present and past values of some exogenous variables. The main difficulty is to decide the order of the autoregressive model; this means determining the number of past values to be used. This study proposes a wavelet-like transform to optimize the order of the variables in a nonlinear autoregressive neural network to predict the in oil dissolved gas concentration (DGC) from sensor data. Daubechies wavelets of different lengths are used to create representations with different time delays of ten DGC, which are then subjected to a procedure based on principal components analysis (PCA) and Pearson’s correlation to find out the order of an autoregressive model. The representations with optimal time delays for each DGC are applied as input in a multi-layer perceptron (MLP) network with backpropagation algorithm to predict the gas at the present and future times. This approach produces better results than choosing the same time delay for all inputs, as usual. The forecasts reached an average mean absolute percentage error (MAPE) of 5.763%, 1.525%, 1.831%, 2.869%, and 5.069% for C₂H₂, C₂H₆, C₂H₄, CH₄, and H₂, respectively.

The main purpose of this paper is to study and evaluate the use of induction voltage regulators in distribution networks with several sources of distributed generation. Because this type of generation is not steady and is dependent on weather conditions, such as wind and solar irradiation, the voltage at several points of a network may exhibit significant variation. Therefore, conventional voltage regulators will be subject to a larger number of tap changes, which shorten their useful lifetime. In this context, an induction voltage regulator, which has not been explored in the literature, is an alternative that can overcome this problem, since it can maintain a constant voltage level in the output without its components wearing out.

This paper presents the development of distance sensors to measure air-gap irregularities in synchronous generators. Among various causes, air-gap anomalies may arise due to shaft eccentricity, which will be dealt with in this work. A prototype machine was specially manufactured allowing the deformation of rotor and stator yokes, as well as the radial displacement of its shaft position. These features permit assessment of the performance of the sensors under study. The developed sensors are of the capacitive and inductive types, placed in the wedges of the stator slot openings, and symmetrically distributed along the air-gap surface.

The prediction of partial discharges in hydrogenerators depends on data collected by sensors and prediction models based on artificial intelligence. However, forecasting models are trained with a set of historical data that is not automatically updated due to the high cost to collect sensors’ data and insufficient real-time data analysis. This article proposes a method to update the forecasting model, aiming to improve its accuracy. The method is based on a distributed data platform with the lambda architecture, which combines real-time and batch processing techniques. The results show that the proposed system enables real-time updates to be made to the forecasting model, allowing partial discharge forecasts to be improved with each update with increasing accuracy.