An optimal deep belief with buffalo optimization algorithm for fault detection and power loss in grid-connected system

Alam, Manawwer; Haque, Ahteshamul; Hakami, Jabir; Khan, Asif Irshad; Pasha, Amjad Ali; Kasim, Navin; Islam, Saiful; Khan, Mohammad Amir; Zahmatkesh, Sasan; Hajiaghaei–Keshteli, Mostafa; Irshad, Kashif

doi:10.1007/s00500-023-08558-2

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…However, the major disadvantage of frequency regulation techniques that utilise ESSs is their high purchasing and maintenance costs. A mathematical model of ternary hydro nanofluid for improving the transfer of heat has been provided in [25], a buffalo optimisation-based algorithm was coined in [26] for fault detection and regulation in grid connected PV systems. [27] Has reviewed the several thermal ESSs for improving the thermal performance of buildings, these systems aim at effective cooling in the modern buildings.…”

Section: Introductionmentioning

confidence: 99%

Analysing active power reserve strategies for photovoltaic systems under varying shading scenarios: a comparative study

Verma,

Katal

2023

Prog. Energy

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The installed capacity of grid-connected solar photovoltaic (PV) systems is increasing rapidly; therefore, in the near future, the total system inertia may possibly decrease. Reserving some active power in PV systems is crucial to manage the problem of low inertia. In this paper, we critically analyse and compare the performances of several active power reserve and frequency regulation techniques for PV systems. The discussed techniques do not use energy storage devices and are aimed at enabling the inertial capabilities of PV systems. These techniques are broadly classified according to the shading conditions and methodologies adopted for reserve generation. In this paper, we closely investigate different deloading techniques used in in PV systems. In deloading, PV systems are operated at a voltage distant from the maximum power point. Further, we also address the effectiveness and suitability of the aforementioned techniques under different operating conditions. Through this review paper, we aim to provide a one-stop reference for PV researchers to select appropriately from the available reserve techniques for designing flexible DC–DC controls for PV systems.

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Section: Introductionmentioning

confidence: 99%

Analysing active power reserve strategies for photovoltaic systems under varying shading scenarios: a comparative study

Verma,

Katal

2023

Prog. Energy

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Fault in Converter Interfaced Micro Grid Using Detection and Identification of Hybrid Technique

Mani,

Vengadakrishnan,

Moorthy

2024

Adaptive Control & Signal

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This paper introduces a novel hybrid approach, termed ZOA‐SNN, for fault detection and identification in converter‐interfaced microgrids. By integrating the Zebra Optimization Algorithm (ZOA) with Spiking Neural Network (SNN) technology, the proposed method provides a comprehensive solution suitable for both grid‐connected and autonomous microgrid operation scenarios. The technique effectively isolates faults in the microgrid while maintaining operation continuity, particularly in islanded conditions. When operating in grid‐connected mode, distributed generators (DGs) provide electricity as needed. When the grid is not available, power sharing amongst DGs is controlled by voltage angle droop control. By isolating malfunctioning portions, the proposed protection system reduces load shedding, while DG control guarantees smooth islanding and resynchronization. Evaluation on the MATLAB platform demonstrates the superior performance of the proposed technique compared to existing algorithms such as Augmented Lagrangian Particle Swarm Optimization (ALPSO), Graph Convolutional Network (GCN), and Buffalo Optimization (BO). With an accuracy, recall, precision, and F1‐score reaching 98.5%, 99.2%, 99.1%, and 99.1%, respectively, the ZOA‐SNN approach excels in fault detection and classification. Additionally, it significantly reduces computation times for parameter calculation, enhancing efficiency in microgrid control systems. These results highlight the innovation and advantages of the ZOA‐SNN approach in enhancing the reliability and efficiency of fault detection systems in microgrid environments.

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Optimal detection and classification of grid connected system using MSVM-FSO technique

Stallon,

Anand,

Kannan

et al. 2024

Environ Sci Pollut Res

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An optimal deep belief with buffalo optimization algorithm for fault detection and power loss in grid-connected system

Cited by 3 publications

References 41 publications

Analysing active power reserve strategies for photovoltaic systems under varying shading scenarios: a comparative study

Analysing active power reserve strategies for photovoltaic systems under varying shading scenarios: a comparative study

Fault in Converter Interfaced Micro Grid Using Detection and Identification of Hybrid Technique

Optimal detection and classification of grid connected system using MSVM-FSO technique

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