Abstract:One of the major problems in transmission lines is the occurrence of failures that affect the quality of the electric power supplied, as the exact localization of the fault must be known for correction. In order to streamline the work of maintenance teams and standardize services, this paper proposes a method of locating faults in power transmission lines by analyzing the voltage oscillographic signals extracted at the line monitoring terminals. The developed method relates time series models obtained specifically for each failure pattern. The parameters of the autoregressive integrated moving average (ARIMA) model are estimated in order to adjust the voltage curves and calculate the distance from the initial fault localization to the terminals. Simulations of the failures are performed through the ATPDraw R (5.5) software and the analyses were completed using the RStudio R (1.0.143) software. The results obtained with respect to the failures, which did not involve earth return, were satisfactory when compared with widely used techniques in the literature, particularly when the fault distance became larger in relation to the beginning of the transmission line.

This article presents an alternative way of evaluating the efficiency of the electric distribution companies in Brazil. This assessment is currently performed and designed by the National Electric Energy Agency (ANEEL), a Brazilian regulatory agency, to regulate energy prices. This involves calculating the X-factor, which represents the efficiency evolution in the price-cap regulation model. The proposed model aims to use a network Data Envelopment Analysis (DEA) model with the network dimension as an intermediate variable and to use Kohonen Self-Organizing Maps (SOM) to correct the difficulties presented by environmental variables. In order to find which environmental variables influence the efficiency, factor analysis was used to reduce the dimensionality of the model. The analysis still uses multiple regression with the previous efficiency as the dependent variable and the four factors extracted from factor analysis as independent variables. The SOM generated four clusters based on the environment and the efficiency for each distributor in each group. This allows for a better evaluation of the correction in the X-factor, since it can be conducted inside each cluster with a maintained margin for comparison. It is expected that the use of this model will reduce the margin of questioning by distributors about the evaluation.

With the expansion of connectivity and information exchange, monitoring Internet traffic becomes a priority in network management to identify anomalies and resource use. This paper presents a study of data traffic forecasting on a computer network, by using known approaching methods for Time Series analysis. The objective of this work is to monitoring the connection of users to network -based applications, including resource availability and network stability of a Brazilian educational institute. To estimate the traffic at a given time, the adjustments made with Exponential Smoothing, AR and ARIMA models were compa red in order to detect possible future abnormal behavior of network usage. The results indicate that the chosen models, mainly the ARIMA, can be used to predict both input and output traffic of a network, also allowing the generation of alerts in real time. It is possible to predict how Internet traffic will be in the next few moments in order to detect possible anomaly on the network in a short period of time when they differ considerably from the forecast made for that specific period. Efficient network monitoring favors the quality of applications and services available to users, helping the network manager to make decisions for maintenance and constant improvement.

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An electrical power system is subject to constant adversities due to its complexity, sensitivity and physical dimensions. Special emphasis is given to transmission lines (TL) that are the most vulnerable elements of an electrical system. Although most of the occurrences of distortions in the voltage signals from atmospheric discharges and overload are not detrimental to the energy supply, it is important to have control of these currents, since this allows the classification of the fault type and its geometric location on the transmission line. This study aims to compare different fault situations in a transmission line and to verify changes in time series models (TS). This study was carried out through computational tests performed with MatLAB®and RStudio® software. A total of 272faults were simulated in different situations. The obtained results were compared with the Traveling Wave Theory (TWT), another quite widespread fault localization technique. The above study revealed the applicability of time series in oscillographic data of fault situations in transmission lines with errors lower than 1.25%.