Predicting Shear Capacity of Panel Zone Using Neural Network and Genetic Algorithm

Vajdian, Mehdi; Zahrai, Seyed Mehdi; Mirhosseini, Seyed Mohammad; Zeighami, Ehsanollah

doi:10.5829/ije.2020.33.08b.09

Cited by 1 publication

(1 citation statement)

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“…Artificial neural networks (ANNs) are widely recognized as a fundamental, powerful, and efficient approach for creating control strategies in situations where systems are only partially understood and have complex mathematical models (9). ANNs possess the capability to learn and approximate the dynamics of nonlinear systems by utilizing available input and output information (10). They are commonly applied in the fields of identification, adaptive control, and observation (11).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wind Power Conversion System Control Under Wind Constraints Using Single Hidden Layer Neural Network

Mazari,

Ait Abbas,

Laroussi

et al. 2024

IJE

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In the realm of wind power generation, cascaded doubly fed induction generators (CDFIG) play a pivotal role. However, the classical proportional integral derivative (PID) controllers used within such systems often struggle with instability and inaccuracies arising from wind variability. This study proposes an enhancement to overcome these limitations by incorporating a single hidden layer neural network (SHLNN) into the wind power conversion systems (WPCS). The SHLNN aims to complement the PID controller by addressing its shortcomings in handling nonlinearities and uncertainties. This integration exploits the adaptive nature and low computational demand of SHLNNs, utilizing historical wind speed and power data to form a more resilient control strategy. Through Matlab/Simulink simulations, this approach is rigorously compared against traditional PID control methods. The results demonstrate a marked improvement in performance, highlighting the SHLNN's capacity to contend with the intrinsic variabilities of wind patterns. This contribution is significant as it offers a sophisticated yet computationally efficient solution to enhance CDFIG-based WPCS, ensuring more stable and accurate energy production.

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Section: Introductionmentioning

confidence: 99%