Next Article in Journal
Paleoenvironment and Organic Matter Accumulation of the Upper Ordovician-Lower Silurian, in Upper Yangtze Region, South China: Constraints from Multiple Geochemical Proxies
Previous Article in Journal
A Low EMI DC-DC Buck Converter with a Triangular Spread-Spectrum Mechanism
Open AccessArticle

Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

1
Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, Calle 70 No 52-21, Medellín 050010, Colombia
2
Instituto de Energía Eléctrica, Facultad de Ingeniería, Universidad Nacional de San Juan, Avenida Libertador General San Martín 1109 (Oeste), San Juan 5400, Argentina
*
Author to whom correspondence should be addressed.
Energies 2020, 13(4), 857; https://doi.org/10.3390/en13040857 (registering DOI)
Received: 23 January 2020 / Revised: 7 February 2020 / Accepted: 12 February 2020 / Published: 15 February 2020
This paper presents a novel approach for Voltage Stability Margin (VSM) estimation that combines a Kernel Extreme Learning Machine (KELM) with a Mean-Variance Mapping Optimization (MVMO) algorithm. Since the performance of a KELM depends on a proper parameter selection, the MVMO is used to optimize such task. In the proposed MVMO-KELM model the inputs and output are the magnitudes of voltage phasors and the VSM index, respectively. A Monte Carlo simulation was implemented to build a data base for the training and validation of the model. The data base considers different operative scenarios for three type of customers (residential commercial and industrial) as well as N-1 contingencies. The proposed MVMO-KELM model was validated with the IEEE 39 bus power system comparing its performance with a support vector machine (SVM) and an Artificial Neural Network (ANN) approach. Results evidenced a better performance of the proposed MVMO-KELM model when compared to such techniques. Furthermore, the higher robustness of the MVMO-KELM was also evidenced when considering noise in the input data. View Full-Text
Keywords: kernel extreme learning machine algorithm; machine learning techniques; near real time; voltage stability assessment; voltage stability index kernel extreme learning machine algorithm; machine learning techniques; near real time; voltage stability assessment; voltage stability index
Show Figures

Graphical abstract

MDPI and ACS Style

Villa-Acevedo, W.M.; López-Lezama, J.M.; Colomé, D.G. Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach. Energies 2020, 13, 857.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop