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

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.

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

“…Mimicking ethological, biological, or physics phenomena is the main means of meta-heuristic algorithms in solving optimization problems [21]. The algorithms applied to SVR include GA [15], [22], FA [4], gray wolf optimization (GWO) [23], PSO [10], ALO [11], DA [12], SSA [13], whale optimization algorithm (WOA) [24], [25], elephant herding algorithm (EHO) [26], simulated annealing (SA) [27], etc.…”

“…The particle swarm optimization (PSO) was employed to elect parameters of SVR for the prediction of total organic carbon content [10]. The ant lion optimizer (ALO) was adapted to seek for the SVR's optimal parameters for on-line voltage stability assessment [11]. The dragonfly algorithm (DA) was drawn into SVR to obtain the optimal parameters for the prediction and application of porosity [12].…”

Artificial Intelligence Techniques in Smart Grid: A Survey

“…Thus, a voltage stability assessment (VSA) model, which can evaluate the voltage stability of the system in a timely fashion, would be a prevention. Numerous AI-based models are proposed in VSA, such as ANN [115], SVM [116], decision trees [117], and FL [118]. Ashraf et al [115] used an ANN model to estimate the loading margin of power systems and testified to the effectiveness on Institute of Electrical and Electronics Engineers 14 bus and 118 bus test systems.…”

“…To improve the accuracy of line trip fault prediction, Wang et al [131] proposed a stacked sparse autoencoder-based network with SVM and PCA to demonstrate its application to real-world data. [106] 2017 TSA ELM, TF Tan et al [108] 2017 TSA CNN, SAEs Liu et al [109] 2017 TSA Ensemble, NN, ELM Ashraf et al [115] 2017 VSA ANN Amroune et al [118] 2017 VSA SVR, FL Baltas et al [99] 2018 TSA Decision tree, SVM, ANN Mosavi et al [105] 2018 TSA ANN Yu et al [107] 2018 TSA RNN, LSTM Amroune et al [119] 2018 VSA SVR Mohammadi et al [116] 2018 VSA SVM Hu et al [104] 2019 TSA SVM Wang et al [14] 2019 FSA ELM Kamari et al [114] 2019 OSA PSO Amroune et al [122] 2019 VSA Survey Wang et al [110] 2020 TSA DBN Shi et al [111] 2020 TSA CNN Shi et al [111] 2020 OSA CNN Xiao et al [113] 2020 OSA MRFR Yang et al [120] 2020 VSA Spectrum estimation method Meng et al [117] 2020 VSA Decision tree Liu et al [121] 2021 VSA Random Forest…”

An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm

“…However, poor choices of penalty factor, kernel type and others can markedly reduce the SVR's capability. Hence, many heuristic algorithms are combined with SVR to realize SVR parameter tuning, including the Henry gas solubility optimization (HGSO) [4], salp swarm algorithm (SSA) [5], dragonfly algorithm (DA) [6], ant lion optimizer (ALO) [7], particle swarm optimization (PSO) [8], firefly algorithm (FA) [9] and others . With the addition of these heuristic algorithms, the prediction accuracy and generalization ability have been greatly improved.…”