In this study, a three-phase four-wire distribution static compensator (DSTATCOM) is proposed to improve power quality, including the compensation of the three-phase unbalanced grid currents, the total harmonic distortion (THD) reduction of the grid current, and the power factor (PF) correction. Moreover, when different types of loads vary in the power system, the instantaneous power follows into or out of the DC-link capacitor in the DSTATCOM and results in poor transient responses of the grid current and DC-link voltage and performance deterioration. Hence, the DC-link voltage control plays a significant part in the DSTATCOM under load variation. For the purpose of mending the transient responses of the grid currents and DC-link voltage control and the performance of the DSTATCOM, the conventional proportional-integral (PI) controller is substituted with a novel online trained wavelet Takagi-Sugeno-Kang fuzzy neural network (WTSKFNN) controller in this study. Furthermore, the network structure and the online learning method of the proposed WTSKFNN controller are described in detail. Finally, the experimental results are given to certify the feasibility and effectiveness of the DSTATCOM using the proposed WTSKFNN controller for the power quality enhancement and the DC-link control improvement under load variation.

A three-phase four-leg inverter-based shunt active power filter (APF) is proposed to compensate three-phase unbalanced currents under unbalanced load conditions in grid-connected operation in this study. Since a DC-link capacitor is required on the DC side of the APF to release or absorb the instantaneous apparent power, the regulation control of the DC-link voltage of the APF is important especially under load variation. In order to improve the regulation control of the DC-link voltage of the shunt APF under variation of three-phase unbalanced load and to compensate the three-phase unbalanced currents effectively, a novel Petri probabilistic fuzzy neural network (PPFNN) controller is proposed to replace the traditional proportional-integral (PI) controller in this study. Furthermore, the network structure and online learning algorithms of the proposed PPFNN are represented in detail. Finally, the effectiveness of the three-phase four-leg inverter-based shunt APF with the proposed PPFNN controller for the regulation of the DC-link voltage and compensation of the three-phase unbalanced current has been demonstrated by some experimental results.

A unified power quality conditioner (UPQC) is developed for the mitigation of grid-voltage and -current distortions under steady-state and dynamic conditions. The proposed configuration integrates series and shunt inverters through a common DC-link capacitor. The capacitor functions as an energy buffer by accommodating the power exchanged between the two inverters. Following an abrupt grid-voltage disturbance or load transition, the DC-link capacitor must instantaneously deliver or absorb power to maintain the power balance between the inverters while sustaining the required compensation. The resulting transient energy exchange can produce pronounced DC-link voltage excursions, with adverse consequences for system stability and compensation accuracy. Rapid and accurate regulation of the DC-link voltage is therefore essential for maintaining the dynamic compensation performance of the UPQC. To improve this regulation, a compensatory wavelet fuzzy neural network incorporating asymmetric membership functions (CWFNN-AMFs) is introduced in place of the conventional proportional–integral (PI) controller. The network architecture and its online learning algorithm are derived in detail. Finally, experimental results under steady-state and dynamic conditions demonstrate that the CWFNN-AMF-controlled UPQC improves both power quality compensation and DC-link voltage regulation, thereby verifying the feasibility and effectiveness of the proposed control framework.