Search Results - (( panel optimization swarm algorithm ) OR ( wave optimization based algorithm ))

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  1. 1

    Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition by Yew W.H., Fat Chau C., Mahmood Zuhdi A.W., Syakirah Wan Abdullah W., Yew W.K., Amin N.

    Published 2024
    “…In this study, MATLAB models of a DRL-based MPPT algorithm were developed, tested, and compared to simulation based on two established MPPT algorithms-the Particle Swarm Optimization (PSO), and the Perturb and Observe (P&O). …”
    Conference Paper
  2. 2

    Performance analysis of PSO MPPT for photovoltaic (PV) system during irradiance changes / Kharismi Burhanudin by Burhanudin, Kharismi

    Published 2018
    “…The MPPT method applied to track maximum power from PV panel is particle swarm optimization (PSO). Particle swarm optimization is soft computing methods which follow the bird swarm to track maximum power from PV panel. …”
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    Thesis
  3. 3

    PARTICLE SWARM OPTIMIZATION MAXIMUM POWER POINT TRACKING FOR PARTIALLY SHADED SOLAR PV by Alvin, Ngu Tien Leong

    Published 2023
    “…This study proposes a particle swarm optimization (PSO) algorithm based on MPPT for the PGS to operate under PSC. …”
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    Final Year Project Report / IMRAD
  4. 4

    Hybrid MPPT algorithm for mismatch photovoltaic panel application / Muhammad Iqbal Mohd Zakki by Mohd Zakki, Muhammad Iqbal

    Published 2019
    “…On the other hand, the implementation of conventional direct MPPT technique causes oscillation in MPP tracking due to the perturbative nature of the algorithms. Otherwise, the soft-computation MPPT methods by evolutionary algorithms such as Particle Swarm Optimization (PSO) algorithm require longer tracking time to prevent the false MPP tracking convergence. …”
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    Thesis
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    Ant colony optimization for controller and sensor-actuator location in active vibration control by Md Nor, Khairul Affendy, Abdul Muthalif, Asan Gani, Wahid, Azni N.

    Published 2013
    “…The main focus is to find the optimal location of the collocated sensor-actuatorand controller gains using a swarm intelligent algorithm called Ant Colony Optimization (ACO) which later verified with Genetic Algorithm (GA). …”
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    Article
  8. 8

    Optimizing the efficiency of Oscillating Water Column (OWC) wave energy converter using genetic algorithm by Nallagownden, P., Alhaj, H.M.M., Sarwar, M.B.

    Published 2015
    “…This paper, describes a method to maximize the pneumatic system efficiency using optimization technique based on Genetic algorithm. This method involves an extraction of maximum incident wave energy corresponding to the wave height, determining of the best deep water length and maximizing the applied damping ratio which can lead to an increase in the pneumatic system efficiency. …”
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    Article
  9. 9

    ANT colony optimization for controller and sensor-actuator location in active vibration control by Md Nor, Khairul affendy, Abdul Muthalif, Asan Gani, Walid, Azni N.

    Published 2013
    “…The main focus is to find the optimal location of the collocated sensor-actuator and controller gains using a swarm intelligent algorithm called Ant Colony Optimization (ACO) which later verified with Genetic Algorithm (GA). …”
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    Article
  10. 10

    Healthcare Data Analysis Using Water Wave Optimization-Based Diagnostic Model by Kaur, Arvinder, Kumar, Yugal

    Published 2021
    “…In the proposed diagnostic model, a water wave optimization (WWO) algorithm was implemented for improving the diagnosis accuracy. …”
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    Article
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    Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G by Salh, Adeb, Audah, Lukman, Abdullah, Qazwan, Aydoğdu, Ömer, A. Alhartomi, Mohammed, Alsamhi, Saeed Hamood, A. Almalki, Faris, M. Shah, Nor Shahida

    Published 2021
    “…We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). …”
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    Article
  15. 15

    Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G by Salh, Adeb, Audah, Lukman, Abdullah, Qazwan, Aydoğdu, Ömer, A. Alhartomi, Mohammed, Alsamhi, Saeed Hamood, A. Almalki, Faris, M. Shah, Nor Shahida

    Published 2021
    “…We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). …”
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    Article
  16. 16

    Multi-objective optimization of stand-alone hybrid renewable energy system by genetic algorithm by Nejad, Mohsen Fadaee

    Published 2013
    “…Among these methods, Genetic Algorithm and Particle Swarm Optimization are known as two most effective methods for HRESs. …”
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    Thesis
  17. 17

    Enhanced weight-optimized recurrent neural networks based on sine cosine algorithm for wave height prediction by Alqushaibi, A., Abdulkadir, S.J., Rais, H.M., Al-Tashi, Q., Ragab, M.G., Alhussian, H.

    Published 2021
    “…Therefore, the wind plays an essential role in the oceanic atmosphere and contributes to the formation of waves. This paper proposes an enhanced weight-optimized neural network based on Sine Cosine Algorithm (SCA) to accurately predict the wave height. …”
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    Article
  18. 18

    NSGA-II and MOPSO Based Optimization for Sizing of Hybrid PV/ Wind / Battery Energy Storage System by Hlal, Mohamed Izdin, Ramachandaramurthya, Vigna K., Padmanaban, Sanjeevikumar, Kaboli, Hamid Reza, Pouryekta, Aref, Tuan Abdullah, Tuan Ab Rashid

    Published 2019
    “…The appropriate sizing of each component was accomplished using Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) techniques. …”
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    Article
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    NSGA-II and MOPSO based optimization for sizing of hybrid PV / wind / battery energy storage system by Mohamad Izdin Hlal A., Ramachandaramurthya V.K., Sanjeevikumar Padmanaban B., Hamid Reza Kaboli C., Aref Pouryekta A., Tuan Ab Rashid Bin Tuan Abdullah D.

    Published 2023
    “…The appropriate sizing of each component was accomplished using Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) techniques. …”
    Article