Voltage stabilization is an important task for the microgrid power control. The long transient response of voltage caused by the grid transition from the grid-connected mode to the islanded mode or the power variations would deteriorate the operation of the voltage protective relay. This would lead to the difficulty of integration for the renewable energy and the storage system. To solve this problem, the asymmetric membership function based wavelet petri fuzzy neural network (AMFWPFNN) controller is proposed for the voltage stabilization control of storage system in this paper to provide fast response speed and mitigate the transient impact. To investigate the performance of the proposed microgrid controller and examine the compliance with the settings in IEC Std. 60255, Cimei Island in Taiwan is studied. Through the hardware in the loop (HIL) mechanism built with OPAL-RT real-time simulator, the effectiveness of proposed controller can be verified.

With the penetration of renewable energy, the power quality (PQ) becomes the important issue for operation stability of power system. The efficient monitoring mechanism for different multiple PQ disturbances is the fundamental infrastructure for modernization of power system. However, the diverse PQ signals result in numerous analysis techniques and make the integration of PQ monitoring difficult. To resolve the above-mentioned problem, the Prony-based PQ analysis mechanism is proposed in this paper. The commonly seen PQ disturbances, such as the fundamental frequency deviation, harmonics, interharmonics, voltage fluctuations (flickers), and PQ events, can be easily analyzed and detected with Prony-based technique and integrated for the design of monitoring system. The performance of proposed analysis mechanism can be verified by the implementation in the Pingtung ac microgrid of Taiwan.

In a low-voltage electric distribution network, arc fault presents a high energy density electricity-discharging phenomenon between conductors, which is often caused by aging of electric facilities, loose contacts and terminals, or insulation failure due to internal and external destructions. A large amount of heat may be created during this discharging, which will further cause the risk of fire hazards to mitigate in the residential environment. Currently, many utility grid operators and electricity users are still devoted to seeking effective detection technology for arc fault protection. This paper proposes a hybrid approach that combines discrete wavelet transform (DWT), empirical mode decomposition (EMD), and dynamic time warping (DTW) methods for low-voltage AC series arc fault detection. In DWT, it uses time–frequency domain characteristics of the arc current signal to extract the occurrence of arc fault. In EMD, it decomposes the complex arc fault current signal into a finite intrinsic mode (IMF) signal; then, instantaneous amplitude of IMF signal is obtained by Hilbert–Huang transform (HHT) as a feature for arc fault identification. Firstly, the results of arc fault detections depend on the results from DWT and EMD. When both two methods detect different results, DTW method will be activated, using the similarity measurements between normal and arc fault current waveforms as an assistant measure to determine the occurrence of arc fault. The performance of the proposed approach is tested and validated using various electric appliances, and the results show that the proposed approach can effectively detect low-voltage AC series arc fault.

This study presents a voltage restoration control (VRC) based on battery energy storage system (BESS), which can be used for both supporting power source and voltage compensation. Voltage restoration is an important task for the power control of microgrid during utility disturbances. One of the disturbances is caused by short circuit on power line of the microgrid, which may lead to voltage sag and even blackout of the microgrid system. To tackle this problem, the recurrent wavelet petri fuzzy neural network (RWPFNN) controller is proposed in this study for the VRC of BESS to provide fast control response to mitigate the transient impact. Moreover, to examine the compliance with the requirements of low voltage ride through (LVRT) of the photovoltaic (PV) plant and investigate the performance of the proposed VRC, the microgrid built in Cimei Island in Penghu Archipelago, Taiwan, is investigated. Furthermore, the PV system, the wind turbine generator (WTG) system and the BESS are connected to the same point of common coupling (PCC) with separated step-up transformers in the microgrid. In addition, the diesel generators provide the main power sources and form the isolated microgrid system. Through the hardware in the loop (HIL) mechanism, which is built using OPAL-RT real-time simulator, with two floating-point digital signal processors (DSPs), the effectiveness of proposed intelligent controllers can be verified and demonstrated.

Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification.

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).