Data Mining and Machine Learning Technique in Contingency Analysis of Power System with UPFC

Autor:	Ravi V. Angadi, Suresh Babu Daram, P. S Venkataramu, V Joshi Manohar
Rok vydání:	2022
Předmět:	Energy Engineering and Power Technology Electrical and Electronic Engineering
Zdroj:	Distributed Generation & Alternative Energy Journal.
ISSN:	2156-6550 2156-3306
Popis:	This paper explains how to predict the severity of the system by connecting with and without a unified power flow controller under different load and n-1 conditions by calculating the LVSI and by the application of data mining and machine learning techniques. A large amount of data will occur during the process of contingency analysis and it is necessary that how to get this to the system value to assess the severity of the system. With the help of data mining and machine learning techniques, analysis of data generated from the simulations for different load conditions are carried out and is used to estimate the severity of the line. In this, the IEEE 30 Bus System is used by calculating the line voltage stability index and the simulation work is carried out by using MATLAB and WEKA software.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::14fe8ccbb0c6191dc2811d48f5b25ad2 https://doi.org/10.13052/dgaej2156-3306.3751 Zobrazit plný text záznamu