A modified sine cosine algorithm for improving wind plant energy production

Suid, Mohd Helmi; Tumari, Mohd Zaidi Mohd; Ahmad, Mohd Ashraf

doi:10.11591/ijeecs.v16.i1.pp101-106

Cited by 25 publications

(12 citation statements)

References 23 publications

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“…The fundamental characteristic of the SSCO algorithm is that the algorithm's procedure is slightly simple mechanism where the design variable is updated using only the mathematical modeling of the sine cosine functions to guide the population to search for global optimal solutions. In SSCO algorithm, the position's updating rule of an agent's population in the design space is formulated in accordance to the following equation [19][20][21]:…”

Section: Ssco Algorithmmentioning

confidence: 99%

Controller design for gantry crane system using modified sine cosine optimization algorithm

Abbas

Mahmood

2021

TELKOMNIKA

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The objective of this research paper is to design a control system to optimize the operating works of the gantry crane system. The dynamic model of the gantry crane system is derived in terms of trolley position and payload oscillation, which is highly nonlinear. The crane system should have the capability to transfer the material to destination end with desired speed along with reducing the load oscillation, obtain expected trolley position and preserving the safety. Proposed controlling method is based on the proportional-integral-derivative (PID) controller with a series differential compensator to control the swinging of the payload and the system trolley movement in order to perform the optimum utilization of the gantry crane. Standard sine cosine optimization algorithm is one of the most-recent optimization techniques based on a stochastic algorithm was presented to tune the PID controller with a series differential compensator. Furthermore, the considered optimization algorithm is modified in order to overcome the inherent drawbacks and solve complex benchmark test functions and to find the optimal design's parameters of the proposed controller. The simulation results show that the modified sine cosine optimization algorithm has better global search performance and exhibits good computational robustness based on test functions. Moreover, the results of testing the gantry crane model reveal that the proposed controller with standard and modified algorithms is effective, feasible and robust in achieving the desired requirements.

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Section: Ssco Algorithmmentioning

confidence: 99%

Controller design for gantry crane system using modified sine cosine optimization algorithm

Abbas

Mahmood

2021

TELKOMNIKA

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“…Recently, a considerable literature has grown up around the theme of multi-agent-based optimization for wind plant. This include, spiral dynamics algorithm [7], particle swarm optimization [7], Bayesian ascent (BA) [8], sine-cosine algorithm [9] and moth flame optimization [10].…”

Section: Introductionmentioning

confidence: 99%

Optimal tuning of a wind plant energy production based on improved grey wolf optimizer

2021

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The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizing the total energy production. The proposed approach is compared with original GWO and previously published modified GWO. Finally, I-GWO produces the highest total energy production as compared to other methods, as shown in statistical performance analysis.

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“…[63,64] SCA is applied to solve a wide variety of engineering problems. [65][66][67][68] Pal et al investigated the performance of SCA on large-scale optimization problems. [69] To enhance exploitation ability of the solutions and to decrease overflow of diversity existing in the search equations of standard SCA, Gupta and Deep developed an improved version of SCA.…”

Section: Introductionmentioning

confidence: 99%

Multiverse optimizer for structural damage detection: Numerical study and experimental validation

Ghannadi

Kourehli

2020

Structural Design Tall Build

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New optimization algorithms have been increasingly developed in the course of the recent years. The accuracy of identified results in optimization-based damage detection methods depends on the objective function and optimization algorithm. This paper employs multiverse Optimizer (MVO) to solve the optimization-based damage identification problem. Statistical results obtained by MVO are compared with those of sine cosine algorithm (SCA) and Harris hawks optimization (HHO) in order to perform a comparative study. Two objective functions are used in this optimization problem. The first one is based on the modal assurance criterion (MAC) and the second one on modified total modal assurance criterion (MTMAC). Numerical and experimental examples indicate that the combination of objective function based on MTMAC and MVO algorithm can provide accurate and reliable results in structural damage detection process. MAC is also rejected in competition with MTMAC. While using SCA-MTMAC, the accuracy of the results is acceptable in some cases. However, HHO-MTMAC gives weak results in most cases. MVO and SSA are the lowest, and HHO spends significantly more time in terms of computational cost.

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A modified sine cosine algorithm for improving wind plant energy production

Cited by 25 publications

References 23 publications

Controller design for gantry crane system using modified sine cosine optimization algorithm

Controller design for gantry crane system using modified sine cosine optimization algorithm

Optimal tuning of a wind plant energy production based on improved grey wolf optimizer

Multiverse optimizer for structural damage detection: Numerical study and experimental validation

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