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

Mohammed Amroune, Ismail Musirin, Tarek Bouktir and Muhammad Murtadha Othman
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Mohammed Amroune: Department of Electrical Engineering, University of Ferhat Abbas Setif 1, Setif 19000, Algeria
Ismail Musirin: Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam 40450, Malaysia
Tarek Bouktir: Department of Electrical Engineering, University of Ferhat Abbas Setif 1, Setif 19000, Algeria
Muhammad Murtadha Othman: Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam 40450, Malaysia

Energies, 2017, vol. 10, issue 11, 1-18

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.

Keywords: voltage stability; phasor measurement unit; support vector regression; adaptive neuro-fuzzy inference system; ant lion optimizer (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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