Search Results - (( model evaluation modified algorithm ) OR ( panel optimization swarm algorithm ))
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Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition
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 -
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Performance analysis of PSO MPPT for photovoltaic (PV) system during irradiance changes / Kharismi Burhanudin
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 -
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PARTICLE SWARM OPTIMIZATION MAXIMUM POWER POINT TRACKING FOR PARTIALLY SHADED SOLAR PV
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 -
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Hybrid MPPT algorithm for mismatch photovoltaic panel application / Muhammad Iqbal Mohd Zakki
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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Ant colony optimization for controller and sensor-actuator location in active vibration control
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 -
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ANT colony optimization for controller and sensor-actuator location in active vibration control
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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Multi-objective optimization of stand-alone hybrid renewable energy system by genetic algorithm
Published 2013“…Among these methods, Genetic Algorithm and Particle Swarm Optimization are known as two most effective methods for HRESs. …”
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Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm
Published 2021“…This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc–Wen Model in magnetorheological damper application. …”
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NSGA-II and MOPSO Based Optimization for Sizing of Hybrid PV/ Wind / Battery Energy Storage System
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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NSGA-II and MOPSO based optimization for sizing of hybrid PV / wind / battery energy storage system
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. …”
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An intelligent maximum power point tracking algorithm for Photovoltaic System
Published 2023“…This work comprehensively demonstrates the performance analysis of Fuzzy Logic Controller (FLC) with Particle Swarm Optimization (PSO) Maximum Power Point Tracker (MPPT) algorithm on a stand-alone Photovoltaic (PV) applications systems. …”
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Modified word representation vector based scalar weight for contextual text classification
Published 2024“…To validate this algorithm, the modified word vectors are compared with original LLM-generated word vectors to evaluate their reflection of the intended context. …”
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Design a photovoltaic system based on maximum power point tracking under partial shading
Published 2019“…The voltage and current of MSX60 PV module are subjected to various insolation conditions. The Particle Swarm Optimization (PSO) algorithm based MPPT has been implemented to track maximum power partial shading condition. …”
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M-Factors Fuzzy Time Series for Forecasting Moving Holiday Electricity Load Demand in Malaysia (S/O 14589)
“…The modified algorithm, Weighted Subsethood Segmented Fuzzy Time Series (WeSuSFTS) consists of four main phases; data pre-processing, model development, model implementation and model evaluation. …”
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Monograph -
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Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane
Published 2021“…In the HMVOSCA algorithm, the new position updating mechanism of the traditional MVO method is modified based on the sine function and cosine function which is taken from the Sine Cosine Algorithm (SCA). …”
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Dynamic reconfiguration of large-scale PV plant using based on specified switching matrix and genetic algorithm to mitigate partial shading
Published 2023“…In the second stage, Genetic Algorithm (GA) is applied to optimize the output, via rearranging the columns in PV plants to find the optimal solution of reconfiguration. …”
text::Thesis -
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Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…The new proposed method (MBPSO+MKN+GK) Gustafson- Kessel algorithm (GK)integrated with modified of Kohonen Network algorithm (MKN)and modified binary particle swarm optimization (MBPSO) was used to classify the credit scoring data. …”
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Thesis -
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Modification of the CREAMS Nutrient submodel
Published 2011“…The CREAMS nutrient submodel was modified to improve the prediction, of the nitrogen loss from a flat agricultural field with a fluctuating water table.The CREAMS nutrient submodal was modified by incorporating a water function in the CREAMS denitrification algorithm.The capability of the CREAMS nutrient submodel and modified CREAMS nutrient submodel in predicting nitrogen loss was evaluated by using linear regression analysis, t-test on the slope and intercept of the regression equation, standard deviation of differences, absolute average differences, and percent error.Observed data from an experimental plot near Baton Rouge, Louisiana, USA were used in this study.The modified model underestimated the total nitrogen losses by 2% compared to 35% overestimation by the CREAMS model.Overall performance of the modified model in predicting nitrogen losses was satisfactory.…”
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