Abstract
This paper presents the methodology to detect and identify the type of fault that occurs in the shunt compensated static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes (NB) classifiers. To study this, the network model is designed using Matlab/Simulink. Different types of faults, such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and the three-phase (LLLG) fault, are applied at disparate zones of the system, with and without STATCOM, considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using Daubechies (db) mother wavelet of db4 to extract the features, such as the standard deviation (SD) and energy values. Then, the extracted features are used to train the classifiers, such as Multi-Layer Perceptron Neural Network (MLP), Bayes and the Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results obtained reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), percentage relative absolute error (% RAE) and percentage root relative square error (% RRSE) than both MLP and the Bayes classifier.
Highlights
Restructuring and deregulation of a power system with increases in energy demand, environmental hurdles, economic factors and right of way, forces the utilities to use the transmission lines to their thermal limits
The simulation is carried out for the power system model depicted in Figure 1, and various plausible faults such as Line to Ground (LG), Line to Line (LL), LLG
It is seen from the results, the magnitude of current signal increases for the system with static compensator (STATCOM) device, and the same is presented in the form of a waveform; for the case of the LG fault in the system with and without
Summary
Restructuring and deregulation of a power system with increases in energy demand, environmental hurdles, economic factors and right of way, forces the utilities to use the transmission lines to their thermal limits. The connection of renewable energies into the grid causes an unbalance in the system voltage All of these problems can be resolved economically by enhancing the thermal stability of the line through the placement of a flexible AC transmission systems (FACTS) device into the system [1]. In order to ensure the secure and safe operation of the power system network, it is essential to implement an effective protection scheme within the shortest time span to avoid the cascading failure of the system This is achieved through an advanced fault classification technique that supports an effective, reliable, fast and secured way of relaying operation in the protective system [4]. The remainder of the paper is organized as follows: Section 2 deals with the system model studied, and Section 3 portrays the proposed method of fault classification with detailed explanation about the extraction of features using DWT analysis. The conclusion and future work is made in the last part of the paper
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