The shunt active power filter (SAPF) is an effective means for the modification of power quality. However, the compensation performance of SAPF would be deteriorated when the unbalanced nonlinear loads are present in the power system. To enhance the compensation performance of SAPF, the adaptive frequency-based reference compensation current control strategy is proposed in this paper. The proposed solution procedure can be divided into three stages including adaptive frequency detection, phase synchronization, and adaptive compensation. With the tracking of power system frequency, the phase synchronization for the SAPF compensation can be effectively modified under the power variation of unbalanced nonlinear loads. Based on the correct synchronization phase angle, the reference compensation current of SAPF can be accurately obtained with the adaptive linear neural network (ALNN) in the stage of adaptive compensation. In addition, the direct current (DC)-link voltage of SAPF can also be effectively regulated to maintain the compensation performance. To verify the effectiveness of the proposed adaptive frequency-based reference compensation current control strategy, the comprehensive case studies implemented with the hardware-in-the-loop (HIL) mechanism are performed to examine the compliance with the specification limits of IEEE Standard 519-2014. The experimental results reveal that the performance of proposed SAPF control strategy is superior to that of the traditional instantaneous reactive power compensation control technique (p-q method) and sliding discrete Fourier transform (DFT).

To perform the fault protection for the microgrid in grid-connected mode, the wavelet energy fuzzy neural network-based technique (WEFNNBT) is proposed in this paper. Through the accurate activation of protective relay, the microgrid can be effectively isolated from the utility power system to prevent serious voltage fluctuation when the power quality of power system is disturbed. The proposed WEFNNBT can be divided into three stages—feature extraction (FE), feature condensation (FC), and disturbance identification (DI). In the FE stage, the feature of power signal at the point of common coupling (PCC) between microgrid and utility power system would be extracted with discrete wavelet transform (DWT). Then, the wavelet energy and variation of singular power signal can be obtained according to Parseval Theorem. To determine the dominant wavelet energy and enhance the robustness to the noise, the feature information is integrated in the FC stage. The feature information then would be processed in the DI stage to perform the fault identification and activate the protective relay if necessary. From the experimental results, it is realized that the proposed WEFNNBT can effectively perform the fault protection of microgrid.