Quasi‐Oppositional Whale Optimization Optimized Load Frequency Stabilization of Hybrid Power Systems Integrated with Electric Vehicle

Mudi, Joyti; Shiva, Chandan Kumar; Mukherjee, V.

doi:10.1002/adts.202100510

Cited by 6 publications

(1 citation statement)

References 26 publications

(40 reference statements)

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“…Inclusion of the multi-objective optimization technique is an efficient step to improving the performance of association rule mining. After surveying the literature, we obtained some recently developed multi-objective optimization techniques that were presented by Mudi et al (2019), Mudi et al (2021a), Mudi et al (2021b), Mudi et al, (2022), Ganguly et al (2018), Ganguly et al (2017), and Ganguly and Mukherjee (2020). In this article, we developed multi-objective optimized variable cutoff-based association rule mining (MOOVARM) for multi-omics profiles based on the minimum distance from the positive ideal solution (PIS) and that from the negative ideal solution (NIS).…”

Section: Introductionmentioning

confidence: 99%

Optimal ranking and directional signature classification using the integral strategy of multi-objective optimization-based association rule mining of multi-omics data

Mallik

Seth

et al. 2023

Front. Bioinform.

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Introduction: Association rule mining (ARM) is a powerful tool for exploring the informative relationships among multiple items (genes) in any dataset. The main problem of ARM is that it generates many rules containing different rule-informative values, which becomes a challenge for the user to choose the effective rules. In addition, few works have been performed on the integration of multiple biological datasets and variable cutoff values in ARM.Methods: To solve all these problems, in this article, we developed a novel framework MOOVARM (multi-objective optimized variable cutoff-based association rule mining) for multi-omics profiles.Results: In this regard, we identified the positive ideal solution (PIS), which maximized the profit and minimized the loss, and negative ideal solution (NIS), which minimized the profit and maximized the loss for all gene sets (item sets), belonging to each extracted rule. Thereafter, we computed the distance (d +) from PIS and distance (d −) from NIS for each gene set or product. These two distances played an important role in determining the optimized associations among various pairs of genes in the multi-omics dataset. We then globally estimated the relative closeness to PIS for ranking the gene sets. When the relative closeness score of the rule is greater than or equal to the pre-defined threshold value, the rule can be considered a final resultant rule. Moreover, MOOVARM evaluated the relative score of the rule based on the status of all genes instead of individual genes.Conclusions:MOOVARM produced the final rank of the extracted (multi-objective optimized) rules of correlated genes which had better disease classification than the state-of-the-art algorithms on gene signature identification.

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Section: Introductionmentioning

confidence: 99%

Optimal ranking and directional signature classification using the integral strategy of multi-objective optimization-based association rule mining of multi-omics data

Mallik

Seth

et al. 2023

Front. Bioinform.

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Frequency regulation in a hybrid renewable power grid: an effective strategy utilizing load frequency control and redox flow batteries

Elkasem,

Kamel,

Khamies

et al. 2024

Sci Rep

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Renewable energy sources (RESs) have become integral components of power grids, yet their integration presents challenges such as system inertia losses and mismatches between load demand and generation capacity. These issues jeopardize grid stability. To address this, an effective approach is proposed, combining enhanced load frequency control (LFC) (i.e., fuzzy PID- T $${I}^{\lambda }{D}^{\mu }$$ I λ D μ ) with controlled energy storage systems, specifically controlled redox flow batteries (CRFBs), to mitigate uncertainties arising from RES integration. The optimization of this strategy's parameters is achieved using the crayfish optimization algorithm (COA), known for its global optimization capabilities and balance between exploration and exploitation. Performance evaluation against conventional controllers (PID, FO-PID, FO-(PD-PI)) confirms the superiority of the proposed approach in LFC. Extensive testing under various load disturbances, high renewables penetration, and communication delays ensures its effectiveness in minimizing disruptions. Validation using a standardized IEEE 39-bus system further demonstrates its efficiency in power networks grappling with significant renewables penetration. In summary, this integrated strategy presents a robust solution for modern power systems adapting to increasing renewable energy utilization.

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Assessment of Power System Resiliency with New Intelligent Controller and Energy Storage Systems

Murali

Shankar

Sharma

et al. 2023

Electric Power Components and Systems

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Quasi‐Oppositional Whale Optimization Optimized Load Frequency Stabilization of Hybrid Power Systems Integrated with Electric Vehicle

Cited by 6 publications

References 26 publications

Optimal ranking and directional signature classification using the integral strategy of multi-objective optimization-based association rule mining of multi-omics data

Optimal ranking and directional signature classification using the integral strategy of multi-objective optimization-based association rule mining of multi-omics data

Frequency regulation in a hybrid renewable power grid: an effective strategy utilizing load frequency control and redox flow batteries

Assessment of Power System Resiliency with New Intelligent Controller and Energy Storage Systems

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