An adaptive neuro fuzzy inference system model for studying free in plane and out of plane vibration behavior of curved beams

Mohanty, Nupur; Mishra, Uttam Kumar; Sahu, S. K.

doi:10.1016/j.istruc.2022.12.021

Cited by 10 publications

(1 citation statement)

References 37 publications

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“…Fuzzy systems have linguistic rules that can be understood, can make decisions with imperfect data, and are good at explaining their choices, but they can't instantly learn new rules. Due to these limits, hybrid intelligent systems have been created [10].…”

Section: Proposed Schemementioning

confidence: 99%

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Nireekshana,

Ramachandran,

Narayana

2024

E3S Web Conf.

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The main objective of Load Frequency Control (LFC) is to effectively manage the power output of an electric generator at a designated site, in order to maintain system frequency and tie-line loading within desired limits, in reaction to fluctuations. The adaptive neuro-fuzzy inference system (ANFIS) is a controller that integrates the beneficial features of neural networks and fuzzy networks. The comparative analysis of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Proportional-Integral-Derivative (PID)-based methodologies demonstrates that the suggested ANFIS controller outperforms both the PID controller and the ANN controller in mitigating power and frequency deviations across many regions of a hybrid power system. Two systems are analysed and represented using mathematical models. The initial system comprises a thermal plant alongside photovoltaic (PV) grid-connected installations equipped with maximum power point trackers (MPPT). The second system comprises hydroelectric systems. The MATLAB/Simulink software is employed to conduct a comparative analysis of the outcomes produced by the controllers.

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Section: Proposed Schemementioning

confidence: 99%