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This paper proposes adaptive voltage control strategies for a three-wire dynamic voltage restorer based on a self-adaptive fuzzy neural network for estimation of fundamental weight components and optimal extreme learning machine control for smooth voltage regulation. The SAFNN overcomes the sequential tuning process of classical ANFIS controllers by simultaneously optimizing the structure and parameters of unknown system dynamics. The construction of SAFNN does not require control base rules in the initial phase and this enhances the learning capability to track the approximated voltage error. The proposed SAFNN employs the linear transformation of the input signal to optimize the required fuzzy rules and speed up the online learning process to achieve the higher accuracy of the control results. The meta-algorithms whale optimization algorithm and Harris hawks optimization is employed for online dynamic behavior identification. The SAFNN-WOA optimizes the membership function to estimate direct and quadrature axis voltage for prediction performance in terms of fast-tracking predefined signals, fewer oscillations, and voltage compensation ability. The dc-link voltage is accurately estimated by employing OELM-HHO and provides improved power quality performance during voltage disturbances. The OELM has the inherent feature of randomly generating the initial weights which effectively evaluates the output weight during the online training procedure and enhances the learning rate. The statistical tools MSE, RMSE, ME, SD, and R during the training stage were evaluated as 168.9052, 12.9964, − 5.5524e−07, 12.9965 and during the testing stage the value are 168.9611, 12.9985, − 0.11566, 12.9986 and 0.78654. These statistical assessment values confirm the goodness-of-fit of the predictive model. The result shows that the proposed hybrid controls SAFNN-WOA and OELM-HHO-based DVR significantly achieves the prediction accuracy for both training and test dataset. Nevertheless, the proposed intelligent DVR model outperforms the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this article, a hybrid approach is implemented namely, neural network training (NNT) based machine learning (ML) estimator inspired by artificial neural network (ANN) and self-adaptive neuro-fuzzy inference system (ANFIS) to tackle the voltage aggravations in the power distribution network (DN). In this work, potential of swarm intelligence technique namely particle swam optimization (PSO) is analysed to obtain an optimum prediction model with certain modifications in training algorithm parameters. In practice, when the systems are continuously subjected to parametric changes or external disturbances, then ample time is dedicated to tune the system to regain its stable performance. To improve the dynamic performance of the system intelligence-based techniques are proposed to overcome the shortcomings of conventional controllers. So, gain tuning process based on the intelligence system is a desirable choice. The statistical tools are used to proclaim the effectiveness of the controllers. The obtained MSE, RMSE, ME, SD and R were evaluated as 0.0015959, 0.039949, −0.00089838, 0.039941 and 1 in the training phase and 0.0015372, 0.039207, −0.0005657, 0.039203 and 1 in the testing phase, respectively. The results revealed that the ANFIS-PSO network model could accomplish a better DC voltage regulation performance when it is compared to the conventional PI. The proposed intelligence strategies confirm that the predicted DVR model based on NNT-ML and ANFIS has faster convergence speed and reliable prediction rate. Moreover, the simulation results show that the dynamic response is improved with proposed PSO based NNT based ML and ANFIS (Takagi-Sugeno) that significantly compensates the voltage based PQ issues. The proposed DVR is actualized in MATLAB/SIMULINK platform. [ABSTRACT FROM AUTHOR]