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This research work has designed an algorithm to identify islanding events using the current signals in a distribution grid interfaced with renewable energy (RE) sources and remote areas. A medianbased islanding recognition factor (MIRF) is designed by processing the current signal using Stockwell transform (ST). A current rate of change of islanding recognition factor (CRCIRF) is computed by differentiating the root mean square (RMS) current concerning time. The MIRF and CRCIRF are multiplied element by element to calculate the current-based islanding recognition factor (IRFC) used to recognize islanding events and non-islanding events. Simple decision rules are used to discriminate Islanding events from the faulty and the operational events by comparing peak magnitude of IRFC with pre-set threshold values. This IDM effectively recognizes islanding events in the presence of noise with 10 dB signal-to-noise (SNR) level. The performance of IDM is established on a practical distribution feeder. Developed work is executed in MATLAB/Simulink.

Power quality issues are handled very well by filter technologies. In recent years, the advancement of hybrid active power filters (HAPF) has been enhanced due to ease of control and flexibility as compared to other filter technologies. These filters are a beneficial asset for a power producer that requires a smooth filtered output of power. However, the design of these filters is a daunting task to perform. Often, metaheuristic algorithms are employed for dealing with this nonlinear optimization problem. In this work, a new hybrid metaheuristic algorithm (Marine Predator Algorithm and Sine Cosine Algorithm) has been proposed for selecting the best parameters for HAPF. The comparison of different algorithms for obtaining the HAPF parameters is also performed to show case efficacy of the proposed hybrid algorithm. It can be concluded that the proposed algorithm produces robust results and can be a potential tool for estimating the HAPF parameters. The confirmation of the performance of the proposed algorithm is conducted with the results of fitness statistical results, boxplots, and different numerical analyses.

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