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ABSTRACT Power quality issues encompass a spectrum of disturbances that affect the stability and reliability of the power supply. This study explores voltage‐related anomalies, including sags, surges, flickers, unbalances, harmonics, interruptions, and swells, examining their origins, characteristics, and repercussions. These issues arise from a myriad of sources, such as load fluctuations, equipment malfunctions, and grid dynamics. The ramifications of these disturbances extend to equipment malfunctions, reduced operational efficiency, and financial losses. Dynamic voltage restorer (DVRs) are series custom power devices for mitigating these voltage‐related anomalies and use real‐time monitoring and control to quickly inject exact voltages into the grid during disruptions, restoring voltage levels to acceptable levels. This study demonstrates the capacity of DVRs to successfully alleviate power quality concerns, hence improving equipment dependability and system stability by analyzing case studies and simulation findings. The control system used to supply switching signals to the voltage source converter (VSC) of the DVR is based on adaptive Bateman polynomial (ABMP). Traditional DVR control techniques often rely on fixed or linear control strategies, which may not adequately handle varying load conditions and disturbances. ABMP offers an adaptive approach where the control parameters can adjust dynamically based on real‐time system conditions. This adaptive capability enhances the accuracy of voltage restoration, ensuring that the DVR responds optimally to varying loads and disturbances. During voltage sag and swell conditions at the grid side, the VSC injects the compensating voltage in series with the feeder with a constant switching frequency. A battery energy‐supported system (BESS) based DVR is considered for the proposed system. The supply is connected to the critical and sensitive loads. The proposed control scheme is validated through extensive simulation and experimental results. [ABSTRACT FROM AUTHOR]

In this article, an adaptive normalized Sign Regression Least Mean Mixed Norm (ε-NSRLMMN) algorithm-based control technique is developed for the Shunt Active Power Filter (SAPF) to compensate for the grid current harmonics and reactive power under balanced and unbalanced loading conditions. The adaptive ε-NSRLMMN control algorithm improves power quality and makes the source current sinusoidal and power factor (PF) close to unity. It is used for load compensation in a three-phase distribution network. The fundamental active component is extracted from the distorted load current using an adaptive technique. This control algorithm has the advantages of improved convergence, better stability, lower harmonic distortion, less complexity and less steady-state error than the Sign regressor least mean mixed norm (SRLMMN), Least mean square (LMS) and least mean fourth (LMF). Adaptive ε-NSRLMMN is used for learning the fundamental active weight components of the load current. The parameters of the proposed algorithm are trained in real-time. A traditional PI controller has been used to regulate the dc-link voltage of the Voltage Source Converter (VSC). The performance of the proposed algorithm has been justified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

This article proposes a Hermite function‐based artificial neural network (HeANN) controller for shunt compensation in photovoltaic (PV) interfaced grid system. Various power quality (PQ) issues such as load change, poor power factor, reactive power demand and current harmonics are experienced by 1∅ distribution system. The proposed HeANN algorithm is utilized to improve the PQ issues of the distribution system. Shunt active power filter (SAPF) is operated in current control mode and injects the compensating current at the point of common coupling (PCC). The inverter is connected with the green energy resource, that is, PV at its direct current terminals and contributes its power to fulfil the load active power demand. The parameters of HeANN are trained in real time. A prototype hardware of SAPF is developed using sensor circuits and space 1104. The simulation results are validated by experimental results. The comparative analysis of the results establishes the superiority of the proposed control technique to other control techniques such as synchronous reference frame, second order generalized integrator and back propagation neural network in terms of convergence, computational burden and total harmonic distortion of grid current. [ABSTRACT FROM AUTHOR]