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- Title
Forecasting Voltage Collapse when Large-Scale Wind Turbines Penetrated to Power Systems Using Optimally Pruned Extreme Learning Machines (OPELM) - Case Study: Electric Power System South Sulawesi-Indonesia.
- Authors
GUNADIN, Indar Chaerah; SISWANTO, Agus; SAFRIZAL, Safrizal; SYUKRIYADIN, Syukriyadin; ROSYADI, Marwan; MUSLIMIN, Zaenab; GASSING; RASYID, Ramly
- Abstract
The problem of voltage collapse is a major issue in the operation of the current power system, especially when the penetration of wind turbines into the system continues to increase. The intermittency of the wind turbine has an impact on the stability of the system voltage. Fast Voltage Stability Index (FVSI) is used as a parameter for the condition of the system with the phenomenon of voltage collapse. This study aims to observe and predict the value of the Line stability index using Optimally Pruned Extreme Learning Machine (OP-ELM). The test case in this study is the South Sulawesi-Indonesia Electric Power System, with a total wind turbine penetration of 142 MW. From the simulation, it can be seen that OPELM can do forecasting very well with an error rate of 0.0886%.
- Subjects
INDONESIA; ELECTRIC power systems; MACHINE learning; WIND turbines; LOAD forecasting (Electric power systems); VOLTAGE; FORECASTING
- Publication
Przegląd Elektrotechniczny, 2022, Vol 98, Issue 5, p80
- ISSN
0033-2097
- Publication type
Article
- DOI
10.15199/48.2022.05.15