Adaptive Swarm Intelligence-Based Optimization Approach for Smart Grids Power Dispatch

Alvi, Um-E-Habiba; Ahmed, Ijaz; Hasan, Syed Rizwan; Ashfaq, Babar; Raza, Muhammad; Mukhtar, Sana

doi:10.1109/icetecc56662.2022.10069682

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Cited by 7 publications

References 31 publications

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Enhancing EV charging predictions: a comprehensive analysis using K-nearest neighbours and ensemble stack generalization

Kumar,

Jyothi,

Singh

et al. 2024

Multiscale and Multidiscip. Model. Exp. and Des.

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Enhancing EV charging predictions: a comprehensive analysis using K-nearest neighbours and ensemble stack generalization

Kumar,

Jyothi,

Singh

et al. 2024

Multiscale and Multidiscip. Model. Exp. and Des.

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Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis

Mustafa,

Khan,

Samee

et al. 2023

IEEE Access

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Industrial processes are nonlinear and complicated, requiring accurate fault identification to minimize performance deterioration and respond quickly to emergencies. This work investigates industrial process defect identification and isolation, which is analytically difficult owing to their complexity. This paper carefully analyzes four design methods for flaw identification and isolation based on Principal Component Analysis (PCA), Fisher Discriminant Analysis (FDA), Kernel Fisher Discriminant Analysis (KFDA), and Sequential quadratic programming (SQP). Our study includes the Tennessee Eastman Process (TEP) and the Penicillin Fermentation Process (PFP), among other comparable methods. We assess the proposed fault detection and isolation methods through detailed analysis and comparison. The simulation findings from our extensive investigation provide remarkable insights. Simulation findings show that FDA and KFDA work well in fault identification and isolation, but PCA has certain limits. We also considered SQP as a TEP fault detection and isolation improvement tool. SQP is noted for its success in nonlinear and restricted optimization problems, making it ideal for fault identification and isolation in complicated industrial processes. Data-driven design approaches increase problem identification in complicated industrial processes with greater reliability and efficiency than PCA-based methods. This study also shows that advanced data-driven techniques can improve industrial fault diagnosis, improving operational safety and system performance by leveraging the FDA, KFDA, and SQP.

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Passivity-Based Nonlinear Control Approach for Efficient Energy Management in Fuel Cell Hybrid Electric Vehicles

Khalid

2024

IEEE Access

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Adaptive Swarm Intelligence-Based Optimization Approach for Smart Grids Power Dispatch

Cited by 7 publications

References 31 publications

Enhancing EV charging predictions: a comprehensive analysis using K-nearest neighbours and ensemble stack generalization

Enhancing EV charging predictions: a comprehensive analysis using K-nearest neighbours and ensemble stack generalization

Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis

Passivity-Based Nonlinear Control Approach for Efficient Energy Management in Fuel Cell Hybrid Electric Vehicles

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