PV panel system modelling method based on neural network

2022 ◽  
Author(s):  
Fadi M. Khaleel ◽  
Ibtisam A. Hasan ◽  
Mohammed J. Mohammed
Keyword(s):  
Neural Network ◽  
System Modelling ◽  
Pv Panel ◽  
Modelling Method

Research on the Hybrid Modelling Method Based on Mechanism and Identification for Marine Diesel

2012 ◽  
Vol 241-244 ◽  
pp. 1514-1519 ◽  
Author(s):  
Qing Fu Kong ◽  
Fan Ming Zeng ◽  
Jia Ming Wu ◽  
Jie Chang Wu
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
System Modelling ◽  
Hybrid Modelling ◽  
Mechanism Model ◽  
Modelling Method ◽  
Marine Diesel ◽  
Network Identification ◽  
Accuracy Performance ◽  
Identification Model

Mechanism method and neural network identification method are widely used for system modelling. Due to the complexity of actual investigated objects, models built with individual method are usually not good enough for the problems to be solved. Therefore, a hybrid modelling method based on mechanism and identification is presented in the paper. In the hybrid model, the mechanism model is applied as the fundamental model in order to ensure the overall coincidence performance and the identification model is adopted as the compensated model in order to ensure the accuracy performance. The developing process of the hybrid model of a marine turbo-charged diesel is demonstrated in detail in the paper. Testing result shows that the hybrid modelling method is very suitable for modelling complex investigated objects.

PV Panel - Wind Turbine Hybrid System Modelling

2017 ◽  
Author(s):  
Mihaela Puianu ◽  
Ramona-Oana Flangea ◽  
Nicoleta Arghira ◽  
Sergiu Stelian Iliescu
Keyword(s):  
Wind Turbine ◽  
Hybrid System ◽  
System Modelling ◽  
Pv Panel

scholarly journals Optimal Power Point on the I-V Curve of a Photovoltaic Solar System (Modelling and Analysis)

2020 ◽  
pp. 91-104
Author(s):  
Baba Alfa ◽  
Yakubu Adamu ◽  
Daniel Alberto Pena Perez
Keyword(s):  
Pulse Width Modulation ◽  
Electronic Device ◽  
System Modelling ◽  
Point Tracking ◽  
Pv Panel ◽  
Power Point Tracking ◽  
Optimal Power ◽  
Perturb And Observe ◽  
Pv Cell ◽  
Power Point

Motivated by recent interest in improving the performance of PV cells, we explored the optimal power point in Photovoltaic (PV) cells by using three different topologies to compare its function and efficacy. Firstly, we investigate the consequence of connecting a PV directly to the load. Secondly, the efficacy of an electronic device that generates a pulse width modulation (PWM) to control a boost converter connected to the PV panel and the load and finally the maximum power point tracking (MPPT) method by using the Algorithm Perturb and Observe (P\&O), with the implementation of the DC-DC converter between the PV panel and the load. In doing so, a mathematical model of the PV cell was employed and using MATLAB Simulink, the behaviour of the voltage and current signals acquired were analysed, this helps in understanding the performance of the solar cell under different meteorological circumstances, and its effect on the power generated by the PV cell. Finally, based on the performance simulations on the three methods implemented, the results were tested for the response time of the MPPT under different load conditions in order to ascertain it performance.

scholarly journals Mathematical Modeling of Closed Loop Pulsating Heat Pipe by Using Artificial Neural Networks

2021 ◽  
Vol 39 (3) ◽  
pp. 955-962
Author(s):  
Santhi Sree Nerella ◽  
Sudheer V.V.S. Nakka ◽  
Bhramara Panitapu
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Heat Pipe ◽  
Closed Loop ◽  
Analytical Models ◽  
System Modelling ◽  
Main Function ◽  
Pulsating Heat Pipe ◽  
Condenser Section ◽  
Artificial Neural

Pulsating heat pipe is one of the prominent technology for thermal management of electronic devices. It consists of three sections namely evaporator, adiabatic and condenser section. PHP is a two phase passive device having efficient and quick ability of transferring heat from evaporator section to condenser section. At first an 8 turn pulsating heat pipe of closed loop ends (CLPHP) with copper tube capillary dimensions is investigated experimentally for different fill ratios and for different inclinations by varying range of heat inputs. Different working fluids viz Water, Acetone, Ethanol and Methanol are considered for the experimentation. One of the recent analytical technology for modelling of CLPHPs is Artificial Neural Network (ANN) approach. The analytical models are having limited scope of applicability and they are simple in nature. The present paper describes Validation of experimental data by training prediction model ANN with available data. Three input nodes such as input heat, fill ratio and angle of inclination and one output node corresponding to PHP that is thermal resistance are considered. The feed forward neural network (FFNN) architecture is adopted for predictions. By using the physical phenomena of the system modelling are clearly known for obtaining feasible results which is main function of ANN. The predicted data validates experimental data in a satisfactory range and the results are found to be in good agreement with in the range of ± 10 percent.

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