Enhancing the Protection scheme for FACTS-Based Transmission Lines using a Data Mining Model
Journal Title: UNKNOWN - Year 2015, Vol 4, Issue 4
Abstract
This review paper is based on the use of the thyristor control series compensator (TCSC) for the fault zone identification and protection of FACTS based transmission lines by using data mining model with the use of ensemble decision trees. In this work we are comparing the existing data mining technologies which are available for the fault zone in the transmission line. The kd tree algorithm is used to detect the fault zone and the random forest algorithm is applied to that zone for enhancing the prior results of the random forest which is only applied to data.The random forest algorithm gives larger accuracy and reliability as compared to the other existing technologies.
Technical Analysis of Indian Financial Market with the Help of Technical Indicators
This study includes the description of indicators which can be used for technical analysis of Indian market Nifty stocks. The indicators which have been used in this study are Moving Averages, Moving Averages cross rules...
Studies on Diethanolaminedithiocarbamate as Metal Complex, Complexing Agent and Stabilizer in Copper Methanesulphonate Bath
"Abstract We report a study on the use of diethanolaminedithiocarbamate (DEADTC) as complexing agent during electroless deposition of copper. For this, we study the nature and structure of the coordination complex betw...
Performance of Turbo Encoder and Turbo Decoder Using MIMO Channel
LTE (Long Term Evolution) is the upcoming standards towards 4G, which is designed to increase the capacity and throughput performance when compared to UMTS and WiMax. Turbo codes are a high performance forward error corr...
A Survey on Bayesian Visual Reranking
Visual re ranking is a method introduced mainly to refine text-based image search results. It utilizes visual information of an image to find the “true” ranking list from the noisy one done by the search based on texts....
Optimal Artificial Neural Network Modeling of Sedimentation yield and Runoff in high flow season of Indus River at Besham Qila for Terbela Dam
Optimal Artificial Neural Network Modeling of Sedimentation yield and Runoff in high flow season of Indus River at Besham Qila for Terbela Dam