# Application of EOS-ELM with binary Jaya-based feature selection to real-time transient stability assessment using PMU data

arXiv: https://arxiv.org/abs/1809.02859 · HTML: https://mlsanity.com/paper/1809.02859/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2018.

## Built on by (most cited first, 6 of 6)

- [A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems](https://mlsanity.com/paper/2112.03265/) (2021)
- [PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning](https://mlsanity.com/paper/2308.03953/) (2023)
- [Hybrid Self-Attention NEAT: A novel evolutionary approach to improve the NEAT algorithm](https://mlsanity.com/paper/2112.03670/) (2021)
- [A Random Forest and Current Fault Texture Feature-Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR](https://mlsanity.com/paper/2211.03789/) (2022)
- [The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature](https://mlsanity.com/paper/2304.11880/) (2023)
- [Research on short-term load forecasting model based on VMD and IPSO-ELM](https://mlsanity.com/paper/2410.05300/) (2024)

## Abstract (arXiv)

Recent studies show that pattern-recognition-based transient stability assessment (PRTSA) is a promising approach for predicting the transient stability status of power systems. However, many of the current well-known PRTSA methods suffer from excessive training time and complex tuning of parameters, resulting in inefficiency for real-time implementation and lacking the online model updating ability. In this paper, a novel PRTSA approach based on an ensemble of OS-extreme learning machine (EOSELM) with binary Jaya (BinJaya)-based feature selection is proposed with the use of phasor measurement units (PMUs) data. After briefly describing the principles of OS-ELM, an EOS-ELM-based PRTSA model is built to predict the post-fault transient stability status of power systems in real time by integrating OS-ELM and an online boosting algorithm, respectively, as a weak classifier and an ensemble learning algorithm. Furthermore, a BinJaya-based feature selection approach is put forward for selecting an optimal feature subset from the entire feature space constituted by a group of system-level classification features extracted from PMU data. The application results on the IEEE 39-bus system and a real provincial system show that the proposal has superior computation speed and prediction accuracy than other state-of-the-art sequential learning algorithms. In addition, without sacrificing the classification performance, the dimension of the input space has been reduced to about one-third of its initial value.