Intelligent PV Power System with Unbalanced Current Compensation Using CFNN-AMF

A novel method is proposed to compensate the three-phase unbalanced currents of power grid under three-phase unbalanced load for a two-stage photovoltaic (PV) power system without the augmentation of active power filter. The PV power system is composed of an interleaved dc/dc converter and a three-level neutral-point clamped inverter. Moreover, the PV power system possesses the smart inverter function, in which the output active and reactive powers of the PV inverter are predetermined by a power factor according to grid codes of the utilities. In the proposed method, dq0-axis compensation currents are obtained through low-pass filters to compensate the three-phase unbalanced currents of power grid. Furthermore, in order to improve the control performance of the dc bus voltage of the PV power system under unbalanced load variation condition, an online-trained compensatory neural fuzzy network with an asymmetric membership function (CFNN-AMF) is proposed to replace the traditional proportional-integral controller for the dc bus voltage control. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. In addition, the dimensions of the Gaussian membership functions are directly extended to AMFs. Additionally, the proposed controllers of the PV power system are implemented by two control platforms using floating-point digital signal processor. Finally, excellent compensation performance for the three-phase currents of power grid under three-phase unbalanced load can be achieved from the experimental results.