Active distributed generations (ADGs) are more prevalent near consumer premises. However, the ADG penetration contribute a lot of dynamic changes in power distribution networks which cause different protection and control issues. Islanding is one of the crucial problems related to such ADGs; on the other hand, islanding detection is also a challenging aspect. Therefore, an extensive review of islanding real-time depiction and islanding detection strategies (IDS) is provided in this work. Initially, the focus is on islanding detection concept depiction, islanding detection standardization, benchmark test systems for IDS validation, and software/tools and an analysis of their pros and cons. Then, the detailed classification of IDSs is presented with an emphasis on remote and local methods. Passive, active, and hybrid can be used further to categorize local IDSs. Moreover, the statistical comparative analysis of the IDSs based on the non-detection-zone (NDZ), cost-effectiveness, and false operation are mentioned. The research gap and loopholes in the existing work based on limitations in the existing work are presented. Finally, the paper is concluded with detailed recommendations.

In modern distribution networks, integration of distributed generations (DGs) at consumer premises is common. However, islanding detection in such a grid‐tied distributed generation (GTDG) network is the topmost challenge to ensure power quality and reliable operation. This paper presents a modified passive islanding detection scheme (MPIDS) for the GTDG network, using a One‐D recursive Median filter (ODRMF) algorithm. Firstly, at the point of common coupling (PCC), 3‐phase voltage is acquired. Next, the ODRMF is employed to extract 3rd harmonic and residuals from the acquired voltage signal. Afterward, two novel islanding detection indices developed are: (1) First change detection (FCD), which is calculated from the residuals, and (2) delta selected harmonic distortion (∆SHD), which is computed from 3rd harmonic content of the voltage signal. Finally, variations in the two indices are compared with the threshold to identify islanding. To validate the efficacy of the presented MPIDS, rigorous simulations in MATLAB/ Simulink are conducted on the UL‐1741 and IEEE 13‐bus GTDGs systems under different conditions. The results illustrate that the presented strategy is highly reliable in all assessed conditions and can detect the islanding as well as non‐islanding incidents effectively under balanced and unbalanced load/generation conditions with a very low non‐detection zone.

The microgrids operate in tie-up (TU) mode with the main grid normally, and operate in isolation (IN) mode without the main grid during faults. In a dynamic operational regime, protecting the microgrids is highly challenging. This article proposes a new microgrid protection scheme based on a state observer (SO) aided by a recurrent neural network (RNN). Initially, the particle filter (PF) serves as a SO to estimate the measured current/voltage signals from the corresponding bus. Then, a natural log of the difference between the estimated and measured current signal is taken to estimate the per-phase particle filter deviation (PFD). If the PFD of any single phase exceeds the preset threshold limit, the proposed scheme successfully detects and classifies the faults. Finally, the RNN is implemented on the SO-estimated voltage and current signals to retrieve the non-fundamental harmonic features, which are then utilized to compute RNN-based state observation energy (SOE). The directional attributes of the RNN-based SOE are employed for the localization of faults in a microgrid. The scheme is tested using Matlab® Simulink 2022b on an International Electrotechnical Commission (IEC) microgrid test bed. The results indicate the efficacy of the proposed method in the TU and IN operation regimes on radial, loop, and meshed networks. Furthermore, the scheme can detect both high-impedance (HI) and low-impedance (LI) faults with 99.6% of accuracy.

Microgrids (MGs) offers grid-connected (GC) and islanded (ID) operational modes. However, these dynamic modes of operation pose different microgrid protection challenges. This paper presents a new protection method for MGs based on a discrete one-dimensional recursive Median filter (1-DRMF). In the first step, the 1-DRMF is applied on a measured current signal on every single phase individually for targeted feature extraction. Then, the median filter deviation (MFD) and the selected harmonic distortion (SHD) are computed from the current signals of all phases independently. In the second step, the upsurges in the MFD and the SHD of all phases are cross-checked with the pre-established threshold value of 0.3 to identify and categorize fault incidents. Finally, the directional properties of three-phase (3-p) reactive energy are employed in order to pinpoint the faulty line section (LS). Many simulations were executed on MATLAB/Simulink to validate the sustainable performance of the established method. Results prove that the scheme can detect, classify, and locate the solid and high impedance faults (HIF) in the GC as well as the ID modes under radial and meshed scenarios.

Recently, the concept of microgrids has emerged in the world due to the integration of distributed energy resources (DERs) at the distribution end. The design of a reliable protection strategy is one of the top-most challenges associated with microgrids. This is because of the transition of microgrids between grid-tied and autonomous modes of operation. This paper presents a state-of-the-art microgrid protection scheme based on the Kalman filter (KF). The proposed scheme uses the one-end current signal of a distribution line for the detection and classification of faults. Firstly, the KF is applied to each phase of a three-phase current signal individually to generate residuals and total harmonic distortion (THDs). Next, the variations in the residuals and THDs of each phase are compared with pre-specified threshold values to detect the faulty events in the microgrid. As each phase is processed through KF individually, therefore, the proposed scheme is inherently phase segregated. Afterward, the KF is applied to extract the third harmonic component from the three-phase current and voltage signals. Then, the KF-based reactive power (KFBRP) is obtained from the extracted third harmonic components. Finally, the directional properties of the threephase KFBRP are used to locate the faulty section in the microgrids. Extensive simulations in MATLAB/ Simulink software are performed for the grid-tied as well as the autonomous modes of operation under radial and meshed topologies. The results show that the proposed scheme is highly robust in all testing scenarios without any false tripping and blinding issues.