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

Walter M. Villa-Acevedo, Jesús M. López-Lezama and Delia G. Colomé
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Walter M. Villa-Acevedo: Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, Calle 70 No 52-21, Medellín 050010, Colombia
Jesús M. López-Lezama: Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Antioquia, Calle 70 No 52-21, Medellín 050010, Colombia
Delia G. Colomé: Instituto de Energía Eléctrica, Facultad de Ingeniería, Universidad Nacional de San Juan, Avenida Libertador General San Martín 1109 (Oeste), San Juan 5400, Argentina

Energies, 2020, vol. 13, issue 4, 1-19

Abstract: 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.

Keywords: kernel extreme learning machine algorithm; machine learning techniques; near real time; voltage stability assessment; voltage stability index (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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