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Energies 2017, 10(11), 1693; doi:10.3390/en10111693

The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment

1
Department of Electrical Engineering, University of Ferhat Abbas Setif 1, Setif 19000, Algeria
2
Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
*
Author to whom correspondence should be addressed.
Received: 3 September 2017 / Revised: 17 October 2017 / Accepted: 18 October 2017 / Published: 25 October 2017
(This article belongs to the Section Electrical Power and Energy System)
View Full-Text   |   Download PDF [2294 KB, uploaded 25 October 2017]   |  

Abstract

This paper presents the application of support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS) models that are amalgamated with synchronized phasor measurements for on-line voltage stability assessment. As the performance of SVR model extremely depends on the good selection of its parameters, the recently developed ant lion optimizer (ALO) is adapted to seek for the SVR’s optimal parameters. In particular, the input vector of ALO-SVR and ANFIS soft computing models is provided in the form of voltage magnitudes provided by the phasor measurement units (PMUs). In order to investigate the effectiveness of ALO-SVR and ANFIS models towards performing the on-line voltage stability assessment, in-depth analyses on the results have been carried out on the IEEE 30-bus and IEEE 118-bus test systems considering different topologies and operating conditions. Two statistical performance criteria of root mean square error (RMSE) and correlation coefficient (R) were considered as metrics to further assess both of the modeling performances in contrast with the power flow equations. The results have demonstrated that the ALO-SVR model is able to predict the voltage stability margin with greater accuracy compared to the ANFIS model. View Full-Text
Keywords: voltage stability; phasor measurement unit; support vector regression; adaptive neuro-fuzzy inference system; ant lion optimizer voltage stability; phasor measurement unit; support vector regression; adaptive neuro-fuzzy inference system; ant lion optimizer
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Amroune, M.; Musirin, I.; Bouktir, T.; Othman, M.M. The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment. Energies 2017, 10, 1693.

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