Handling superfluous and insignificant features in high-dimension data sets incidents led to a long-term demand for system anomaly detection. Ignoring such elements with spectral instruction not speeds up the analysis process but again facilitates classifiers to make accurate selections during attack perception stage, when wrestling with huge-scale and heterogeneous data. In this paper, for dimensionality reduction of data, we use Correlation-based Feature Selection (CFS) and Naïve Bayes (NB) classifier techniques. The proposed Intrusion Detection System (IDS) classifies attacks using a Multilayer Perceptron (MLP) and Instance-Based Learning algorithm (IBK). The accuracy of the introduced IDS is 99.87% and 99.82% with only 5 and 3 features out of 78 features for IBK. Other metrics such as precision, Recall, F-measure, and Receiver Operating Curve (ROC) also confirm the principal performance of IBK compared to MLP.
Leakage current is a very important parameter in the performance evaluation and monitoring of outdoor insulators. This paper presents experimental results of leakage current under different contaminated conditions of silicone rubber insulators. HTV silicone rubber sheets were utilized for tests and pollution was coated artificially in laboratory. The effect of temperature, humidity and pollution severity on leakage current of silicone rubber insulators were investigated. It was observed that leakage current magnitude increases with an increase in relative humidity, temperature, NSDD, and level of pollution severity. A discrete wavelet transforms (DWT) technique based on multi-resolution analysis (MRA) was used to study the time-frequency characteristics of leakage current. DWT-MRA technique can be used to detect frequency bands corresponding to different pollution severity levels and location of dry bands. These results can be used to predict dry band arcing activity on the surface of outdoor polymeric insulators.
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