# Early Warning Condition Transient Stability on South Sulawesi System using Extreme Learning Machine
> Ashad B.A.

URL kanonis: https://discover.unhas.ac.id/publications/early-warning-condition-transient-stability-on-south-sulawesi-system-using-extre
Jurnal / Konferensi: Proceedings 2nd East Indonesia Conference on Computer and Information Technology Internet of Things for Industry Eiconcit 2018
Tahun terbit: 2018
DOI: https://doi.org/10.1109/EIConCIT.2018.8878568
Citations: 2

## Authors
- Ashad B.A.

## Abstract
The electrical systems, the addition of loads can result in fewer stability limits, if there is interference, it can cause black out. In this study analyzing early warning, by observing the limits of stability in the event of a disturbance before black out in the South Sulawesi electricity system. This study observed an early warning system consisting of 44 buses and 15 generators using a Voltage stability margin (VSM) in the event of a disruption. From the training data about each disruption from various buses that occur then learning to use Extreme Learning (ELM) engines is used to detect early warnings during transient conditions. From the ELM simulation results can work quickly 0.0001 and 0.0024 and the error value is low so that it can be known before a blackout occurs.

## Keywords
- Blackout
- Warning system
- Transient (computer programming)
- Extreme learning machine
- Margin (machine learning)
- Stability (learning theory)
- Computer science
- Early warning system
- Electric power system
- Event (particle physics)
- Electricity
- Control theory (sociology)
- Artificial intelligence
- Engineering
- Machine learning
- Telecommunications
- Power (physics)
- Artificial neural network
- Electrical engineering
- Control (management)
- Physics
- Quantum mechanics
- Operating system

---
Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
Saat mengutip, gunakan DOI bila tersedia atau URL kanonis di atas.