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The increase in penetration levels of distributed generation (DG) into the grid has raised concern about undetected islanding operations. Islanding is a phenomenon in which the grid-tied inverter of a distributed generation system, and some of the local loads are disconnected from the grid. If this condition is not detected and the generation (e.g. from a photovoltaic energy source) remains operative, the isolated DG system will stay energised by the inverter. The phase mismatch between the grid and the inverter voltage makes this scenario undesirable since it could be hazardous for the maintenance operator and could harm the inverter and loads in the event of an unsynchronised reconnection of the grid. Consequently, the article presented a novel hybrid active anti-islanding approach for fast and reliably detecting unintended islanding. For the modelling and experimental setup, a multiphase grid-tied photovoltaic distributed generating system was employed, and it was regarded as a viable application. Initially, the study introduces a fault-tolerant control (FTC) technique of data-driven predictive control to limit the impact of grid faults on inverters. Furthermore, the article suggested the Sandia frequency and voltage shift (SFVS) approach for inverter-based distributed generation to identify an islanding state. The approach employs a positive feedback gain to minimise NDZ and THD; moreover, the system does not affect the power quality. To minimise system damage, this scenario necessitates the use of effective islanding detection algorithms. This study suggests using empirical mode decomposition (EMD) to enhance power quality to extract detailed coefficients, which are subsequently processed to detect common transient fluctuations during islanding. In the grid-tied inverter, random forest (RF) is also utilised to categorise the condition as islanding or non-islanding. This exhibits an acceptable trade-off between output power quality and islanding detection effectiveness. The quality of the data and the selection of hyperparameters impact the performance of ML models. Following that, hyperparameter adjustment of the random forest model is done using the memetic firefly algorithm (MFA), which has a considerable impact on overall classification performance. The hyperparameters of the suggested RF model have been successfully calculated to attain the maximum accuracy and the least loss. Results demonstrate that the suggested method produces less THD (2.44) than the conventional detection, leading to faster islanding detection, a better non-detection zone of 0.3639 Hz, and a shorter detection time of 0.89 ms, respectively. [ABSTRACT FROM AUTHOR]

This study considers the shipboard microgrid (MG) system's frequency oscillations problem against the unpredictability of renewable resources and load uncertainties. The shipboard MG system consists of a photovoltaic, wind turbine generator, ship diesel generator, electrolyzer-based fuel cells, and battery energy storage system. A time-varying derivative fractional order controller (TVD-FOPID) has been incorporated into the shipboard MG system to attain the desired frequency stability. In this respect, the introduction of the time function to the derivative part of the TVD-FOPID controller provides appropriate damping at the proper instant to reduce the voltage spike sufficiently which improves the system response. Consequently, a modified sine cosine algorithm (MSCA) has been employed for the tuning of the TVD-FOPID controller's coefficient. There is a 13.43% improvement in fitness value with MSCA against the basic SCA-tuned TVD-FOPID controller with diversified unpredictable disturbances. About, 80% reduction is observed in the derivative kick and a 40% reduction is observed in the voltage spike with the use of a tuned TVDFOPID controller over MSCA-tuned TV-FOPID. Again, 86% reduction in a derivative kick and, a 99% reduction in voltage spike with the use of a TVD-FOPID controller than that of a regular FO-PID controller. The simulation results validate that the TVD-FOPID controller offers stable output in less time than TV-FOPID and FOPID controllers. Also, it is confirmed that the derivative kick, voltage spikes, and the peak overshoot of the response offered by the proposed approach are decreased, which will save the system components. Further, the viability of the controller has been evaluated through sensitivity analysis. To witness the real-time application of the simulated system results are examined in the OPAL-RT simulator. [ABSTRACT FROM AUTHOR]

This paper proposes frequency regulation of multi microgrid systems. The system under study consists of wind energy, solar energy and energy storage system operating in dissimilar combinations to establish equilibrium among the production and consumer demand. In this proposed work, stability analysis of the concerned model is studied using the Henkel matrix approach of the reduced‐order model. Again, an attempt is made to apply the adaptive fuzzy proportional integral and derivative (AFPID) controller for frequency control of the system. The optimal gains of the controllers are tuned by reducing the integral time absolute error using a hybrid dragonfly algorithm supported by the pattern search (hDAPS) algorithm. The efficacy of the proposed adaptive fuzzy controller is examined with fuzzy PID and PID for different cases of the concern system. The efficacy of the hDAPS algorithm is justified over the genetic algorithm, dragonfly algorithm and particle swarm optimization. The robustness study also reveals that the systems are offering reduced overshoot and undershoot under different scenarios of load and source environment. To demonstrate expediency of the proposed controller sensitivity analysis was carried out under different loading conditions. In addition, to show the efficiency of the hDAPS tuned AFPID controller, the outcomes were associated with other artificial intelligence methods reported in the literature. [ABSTRACT FROM AUTHOR]

This article has implemented a robust type-II fuzzy PID controller for automatic generation control (AGC) of a diverse energy source-based multi-area power system under various operating conditions. The AGC aims to make stability over frequency and tie-line power of the power system. The proposed controller is accessed with Type-II Fuzzy systems with trapezoidal-based membership functions. In controller design concern, a novel soft computing algorithm called an improved-grey wolf optimizer (I-GWO) is approached for this study. The performance of the proposed trapezoidal membership function-based type-II fuzzy controller (TM-T2FC) has been extensively compared with the existing triangular membership-function based fuzzy-II controller (TrM-T2FC), type-I fuzzy controller (T1FC) and PID controller for AGC of the multi-area power system. Furthermore, the viability and effectiveness of the anticipated I-GWO algorithm have been separately assessed through suitable comparative studies. The robustness of the proposed TM-T2FC controller is investigated through different sensitive analyses and further assessment has been carried out by extending the proposed controller to a five area power system. It is observed from extensive studies and performance assessment that the proposed TM-T2FC controller can be a potential candidate for the AGC of the multi-area power system. [ABSTRACT FROM AUTHOR]