A Novel Sea Horse Optimizer Based Load Frequency Controller for Two-Area Power System with PV and Thermal Units

Andiç, Cenk; Ozumcan, Sercan; Varan, Metin; Öztürk, Ali

doi:10.20944/preprints202304.0368.v1

Cited by 4 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 27) with Eq. (32), which corresponds to the updating position mechanisms of the SCA. Then, in the second trial, referred to as Hybrid SHOSCA (Breeding), the SHO's mating behavior was replaced by the SCA algorithm.…”

Section: Sensitivity Analysis For the Proposed Ms Detection Methodsmentioning

confidence: 99%

See 1 more Smart Citation

AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans

Khattap,

Abd Elaziz,

Hassan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This study aims to develop an AI-enhanced methodology for the expedited and accurate diagnosis of Multiple Sclerosis (MS), a chronic disease affecting the central nervous system leading to progressive impairment. Traditional diagnostic methods are slow and require substantial expertise, underscoring the need for innovative solutions. Our approach involves two phases: initially, extracting features from brain MRI images using first-order histograms, the gray level co-occurrence matrix, and local binary patterns. A unique feature selection technique combining the Sine Cosine Algorithm with the Sea-horse Optimizer is then employed to identify the most significant features. Utilizing the eHealth lab dataset, which includes images from 38 MS patients (mean age 34.1 ± 10.5 years; 17 males, 21 females) and matched healthy controls, our model achieved a remarkable 97.97% detection accuracy using the k-nearest neighbors classifier. Further validation on a larger dataset containing 262 MS cases (199 females, 63 males; mean age 31.26 ± 10.34 years) and 163 healthy individuals (109 females, 54 males; mean age 32.35 ± 10.30 years) demonstrated a 92.94% accuracy for FLAIR images and 91.25% for T2-weighted images with the Random Forest classifier, outperforming existing MS detection methods. These results highlight the potential of the proposed technique as a clinical decision-making tool for the early identification and management of MS.

show abstract

Section: Sensitivity Analysis For the Proposed Ms Detection Methodsmentioning

confidence: 99%

“…( 30) and (31) with the trigonometric functions of the SCA using Eq. (32). The outcomes of the two trials, in comparison to the developed SHOSCA algorithm which employs the RF classifier, are displayed in Table 5.…”

Section: Sensitivity Analysis For the Proposed Ms Detection Methodsmentioning

confidence: 99%

AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans

Khattap,

Abd Elaziz,

Hassan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…As a direct consequence of this, soft computing technologies have been included into the process of tuning various controllers. Multiple optimisation strategies, such as particle swarm optimisation [27], whale optimisation algorithm [28], cuckoo search algorithm [29], marine predators algorithm [30], sine cosine adopted dingo optimisation algorithm [31], colliding bodies optimizer [32], cohort intelligence optimization [33], and sea horse optimizer [34] have been used to the design of LFC in order to conduct more research and development on the technology. Using PID controllers, several LFC strategies have been suggested as potential solutions for multi-area linked power systems.…”

Section: Literature Reviewmentioning

confidence: 99%

First-of-Its-Kind Frequency Enhancement Methodology Based on an Optimized Combination of FLC and TFOIDFF Controllers Evaluated on EVs, SMES, and UPFC-Integrated Smart Grid

Alghamdi,

Alqarni,

Hammad

et al. 2023

Fractal Fract

View full text Add to dashboard Cite

The most recent advancements in renewable energy resources, as well as their broad acceptance in power sectors, have created substantial operational, security, and management concerns. As a result of the continual decrease in power system inertia, it is critical to maintain the normal operating frequency and reduce tie-line power changes. The preceding issues sparked this research, which proposes the Fuzzy Tilted Fractional Order Integral Derivative with Fractional Filter (FTFOIDFF), a unique load frequency controller. The FTFOIDFF controller described here combines the benefits of tilt, fuzzy logic, FOPID, and fractional filter controllers. Furthermore, the prairie dog optimizer (PDO), a newly developed metaheuristic optimization approach, is shown to efficiently tune the suggested controller settings as well as the forms of the fuzzy logic membership functions in the two-area hybrid power grid investigated in this paper. When the PDO results are compared to those of the Seagull Optimization Algorithm, the Runge Kutta optimizer, and the Chaos Game Optimizer for the same hybrid power system, PDO prevails. The system model incorporates physical constraints such as communication time delays and generation rate constraints. In addition, a unified power flow controller (UPFC) is put in the tie-line, and SMES units have been planned in both regions. Furthermore, the contribution of electric vehicles (EVs) is considered in both sections. The proposed PDO-based FTFOIDFF controller outperformed many PDO-based traditional (such as proportional integral derivative (PID), proportional integral derivative acceleration (PIDA), and TFOIDFF) and intelligent (such as Fuzzy PID and Fuzzy PIDA) controllers from the literature. The suggested PDO-based FTFOIDFF controller has excellent performance due to the usage of various load patterns such as step load perturbation, multi-step load perturbation, random load perturbation, random sinusoidal load perturbation, and pulse load perturbation. Furthermore, a variety of scenarios have been implemented to demonstrate the advantageous effects that SMES, UPFC, and EV units have on the overall performance of the system. The sensitivity of a system is ascertained by modifying its parameters from their standard configurations. According to the simulation results, the suggested PDO-based FTFOIDFF controller can improve system stability despite the multiple difficult conditions indicated previously. According to the MATLAB/Simulink data, the proposed method decreased the total fitness function to 0.0875, representing a 97.35% improvement over PID, 95.84% improvement over PIDA, 92.45% improvement over TFOIDFF, 83.43% improvement over Fuzzy PID, and 37.9% improvement over Fuzzy PIDA.

show abstract

Design of cascade P-P-FOPID controller based on marine predators algorithm for load frequency control of electric power systems

Hussain,

Zou,

Akhtar

et al. 2024

Electr Eng

View full text Add to dashboard Cite

A Novel Sea Horse Optimizer Based Load Frequency Controller for Two-Area Power System with PV and Thermal Units

Cited by 4 publications

References 34 publications

AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans

AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans

First-of-Its-Kind Frequency Enhancement Methodology Based on an Optimized Combination of FLC and TFOIDFF Controllers Evaluated on EVs, SMES, and UPFC-Integrated Smart Grid

Design of cascade P-P-FOPID controller based on marine predators algorithm for load frequency control of electric power systems

Contact Info

Product

Resources

About