Abstract
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.
Highlights
The power pollution in power systems, which results from rectifiers, arc furnaces, nonlinear loads and switching power supplies, has been gained wide attention in recent years [1,2]
The Petri net (PN), a Petri probabilistic fuzzy neural network (PPFNN), which integrates the advantages of PN and PFNN, is first proposed in this study to improve the transient and steady-state responses of the DC-link voltage of the shunt active power filter (APF) under unbalanced load change and to compensate the three-phase unbalanced currents effectively
L =1 where wl is the connective weight between the rule layer and the output layer; y(N) equals the control current ies shown in Figure 2 for the regulation control of the DC-link voltage in the shunt APF
Summary
The power pollution in power systems, which results from rectifiers, arc furnaces, nonlinear loads and switching power supplies, has been gained wide attention in recent years [1,2]. In [14], a hybrid APF, which is composed of a series APF and a shunt-connected passive filter, was adopted to improve the power quality. Owing to the above advantages of the PFNN and the PN, a Petri probabilistic fuzzy neural network (PPFNN), which integrates the advantages of PN and PFNN, is first proposed in this study to improve the transient and steady-state responses of the DC-link voltage of the shunt APF under unbalanced load change and to compensate the three-phase unbalanced currents effectively. A three-phase four-leg inverter-based shunt APF is proposed to compensate the three-phase unbalanced currents under three-phase unbalanced load in grid-connected operation. In order to improve the control performance of the DC-link voltage of the shunt APF under unbalanced load variation condition, an online trained PPFNN is proposed as a regulation.
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