The amalgamation of SVR and ANFIS models with synchronized phasor measurements for on-line voltage stability assessment

• Main Authors: Amroune, M. (Author), Bouktir, T. (Author), Musirin, I. (Author), Othman, M.M (Author)
• Format: Article
• Language: English
• Published: MDPI AG 2017
• Subjects: Adaptive neuro fuzzy inference systems (ANFIS); Adaptive neuro-fuzzy inference system; Ant lion optimizer; Electric load flow; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Mean square error; Metals; Optimizers; Phase measurement; Phasor measurement unit; Phasor Measurement Unit (PMUs); Phasor measurement units; Soft computing; Statistical performance; Support vector regression; Support vector regression (SVR); Synchronized phasor measurements; System stability; Voltage control; Voltage stability; Voltage stability margins
• Online Access: View Fulltext in Publisher; View in Scopus

 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. © 2016 by the authors. Licensee MDPI, Basel, Switzerland.