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In current scenario islanding in smart grid a big challenge that results, in various uncertainties in the system parameters, also leads to degrade the power quality (PQ), and can also become threat to the maintenance workers. In this study a new passive islanding detection technique for grid-connected distributed generation (DG) units is proposed. The presented method employs an Unscented Kalman filter (UKF) to extract and filter the harmonic contents of the voltage signal measured at DG end. A three-phase voltage signal is measured at PCC (point of common coupling) and then it be taken as a test signal for analysis of islanding event detection. Here, at first a residual signal is produced using UKF to detect the changes occurring in the power system. Then, at second step, a total harmonic distortion (THD) is estimated by the UKF). The variation of THD classifies between islanding events and normal events. The IEEE 9-bus test system simulated in Matlab/Simulink is used as a test bed to assess the performance of the proposed approach. The proposed method is enormously analysed under various islanding and non-islanding scenarios. The results obtained demonstrated that the proposed method can successfully differentiate between the two events. Moreover, it also provides high reliability by eliminating the non-detection zone (NDZ) and stands robust against any mal-operation.

Power management in advanced grid systems requires the seamless integration of diverse renewable energy sources. This study investigates the optimization of a grid-connected system comprising a photovoltaic (PV) solar panel, energy storage system, fuel cell (FC), and diesel generator (DG) using the bioinspired metaheuristic technique called jellyfish optimization (JF). The objective is to maximize power generation from the PV system under normal and partial shading conditions. The performance of JF is compared against particle swarm optimization (PSO) using various parameters. As India heavily relies on solar PV, the results highlight JF’s exceptional effectiveness in extracting maximum power during partial shading scenarios. Inspired by the active and passive motions of jellyfish in the ocean, the JF algorithm is utilized. To further optimize the power output, the system is integrated with an efficient battery management system, PEM fuel cell stacking, and diesel generators. The system’s performance is analyzed using fast Fourier transform (FFT) to evaluate harmonic distortions, which consistently meet the limits specified in IEEE STD 1547-2018. Furthermore, unscented Kalman filter-based analysis is employed to assess total harmonic distortion (THD) and power rating for the grid system across various renewable energy scenarios. The contribution of the jellyfish optimization (JF) algorithm lies in its ability to efficiently and effectively maximize power generation from the PV system, regardless of normal or partial shading conditions. JF, a bioinspired metaheuristic optimization technique, successfully emulates the collective behavior of jellyfish in the ocean to identify optimal solutions. In this study, JF outperforms particle swarm optimization (PSO) in terms of power generation under partial shading conditions. Notably, JF exhibits remarkable capability in exploring the search space and discovering the global optimum, even when the system operates under challenging conditions. Overall, this study demonstrates the tremendous potential of JF in maximizing power generation in grid-connected systems with renewable energy sources while also highlighting the benefits of integrating additional components to further enhance the system performance.

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