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A self-constructing intelligence-based learning law is proposed for the prediction of power quality mitigation through a dynamic voltage restorer (DVR). To tackle voltage-based power quality concerns in the distribution network, a fuzzy neural-based hybridization control approach using an evolving Takagi–Sugeno–Kang (e-TSK) and radial basis function (RBFN) is developed. The estimated value of fundamental weight components is achieved by the eTSK predictive model. The evaluated weight values are used to update the parameters for the generation of the reference load voltage components. The RBFN is used to achieve the desired task of dc and ac link voltage regulation. The self-tuned control algorithm is developed based on an intelligence technique that avoids the trapping of local optima, and early convergence, and reduces the voltage oscillation to obtain an optimal solution. A meta-heuristic optimization approach of grey wolf optimization (GWO) and grey wolf-cuckoo search (GWOCS) is selected to optimize the parameters of eTKS and RBFN to find the best-fitted predictive model under the dynamic state. The hybridization of the model with GWO and GWOCS is implemented for improving the training and generalization capability. The performance of VSC-based DVR is validated using the d-SPACE Micro lab box under the various power quality scenario.

Rising technological advancement has resulted in increased demand for power. As a result of this tendency, conventional fuel stocks have been severely exhausted. Energy suppliers are concerned about the sustainability of their grid's electricity dependability as fuel sources dwindle. Also, to reduce carbon footprints, governments throughout the world are exerting pressure on the energy sector to convert to cleaner energy sources. In order to lower the system's dependence on traditional fuel reserves during peak demand hours and progress towards introducing greener energy into the network with no discernible generation intermittency, an efficient scheduling model has been developed in this article to regulate end-user load as well as solar generation. The scheduling model is built with a long-short term memory (LSTM) network-based solar energy prediction approach integrated with a load and solar energy scheduling model based on Teaching-Learning Optimization (TLBO). In addition to effective scheduling, a secure trade platform based on blockchain technology is being developed for utility and prosumer interaction in a cyber-secured environment. The suggested technique is intended for three-phased distribution networks, and its effectiveness is demonstrated using a 906-bus low voltage European distribution network. The proposed methodology is developed using MATLAB and Remix IDE interface. The effectiveness of the considered optimization in the proposed scheduling model is tested against another population-based optimization based identical scheduling model.

This article investigates the mitigation of voltage-based power quality disturbances through dynamic voltage restorer (DVR). The proposed momentum adaption-based fractional least mean square (LMS) algorithm (ma-FLMS) with an additional feature of adaption term is implemented in updating the weight rule for estimating the reference load voltage in the control of DVR. The ma-FLMS has the property of updating the weight terms by employing the previously computed gradient in the current updated weight rule. This improves the accuracy of the estimated signal and convergence rate. The fractional-order proportional–integral–derivative (FOPID) is implemented to offer the more promising results for the generation of a reference control signal on the dc- and ac-link voltages. The meta-algorithm approach is employed to estimate the FOPID gains to reduce the computational effort. The autonomous group particle swarm optimizer (AGPSO) is used to demonstrate the results with high accuracy and convergence rate. Owing to the superior performance and flexibility in tuning, the proposed AGPSO-FOPID provides better settling time values, maximum overshoot, and undershoot of 0.14 s, 2.5%, and 5.6%. The proposed ma-FLSM with FOPID is tested under a variety of voltage disturbances and shows less steady-state error. The effectiveness is tested through simulation and experimental performance under different operating scenarios.