Predictive Duty Cycle of Maximum Power Point Tracking Based on Artificial Neural Network and Bootstrap Method for Hybrid Photovoltaic/ Wind Turbine System Considering Limitation Voltage of Grid

Feby Agung Pamuji, Nurvita Arumsari, Mochamad Ashari, Hery Suryoatmojo, Soedibyo Soedibyo


In this paper, we propose a new control-based the neural network and bootstrap method to get the predictive duty cycle for the maximum power point of hybrid Photovoltaic (PV) and Wind Turbine generator system (WTG) connected to 380 V grid. The neural network is designed to be controller by learning the data control of multi-input DC/ DC converter. The artificial neural network (ANN) needs many data for training then the ANN can give the predictive duty cycle to multi input DC/ DC converter. To get much data, we can use the bootstrap method to generate data from the real data. From Photovoltaic characteristic, we can get 344 real data after the data are made by bootstrap method we can get 8000 data. The 8000 data of PV can be used for training artificial neural network (ANN) of PV system. From wind turbine characteristic we can get 348 real data after the data are made by bootstrap method we can get 6000 data. The 6000 data of WT can be used for training artificial neural network of WT system. This new control has two responsibilities, are to shift the voltage of PV and WTG to optimum condition and to maintain the stability of grid system. From the simulation results those can be seen that the power of hybrid PV / WTG system using MPPT controller is in maximum power and has constant voltage and constant frequency of grid system.

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Feby Agung Pamuji, Hajime Miyauchi, “Maximum Power Point Tracking of Multi-input Inverter for connected Hybrid PV/Wind Power System Considering Voltage Limitation in Grid, “International Review on Modelling and Simulations (I.REMOS), Vol.11, No.3, June ,2018

Y.-M. Chen, S.-C Hung, C.-S. Cheng, and Y.-C. Liu, “ Multi Input Inverter For Grid – Connected Hybrid PV/Wind Power System, “ IEEE, 2005.

Nasif Mahmud, A.Zahedi, “ Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation, “Elsevier, 2016

Pablo Martı´nez-Camblor, NorbertoCorral,”A general bootstrap algorithm for hypothesis testing,” Elsevier, 2011.

Mohammad Hemmat Esfe, Seyfolah Saedodin, Nima Sina , Masoud Afrand , Sara Rostami, “Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid,”Elsevier, 2015

Amir Abolfazl Suratgar, Mohammad Bagher Tavakoli, and Abbas Hoseinabadi, “Modified Levenberg-Marquardt Method for Neural Networks Training,” World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:1, No:6, 2007

Hao Yu, Bogdan M. Wilamowski, “Intelligent System,” Auburn University, 2010.

Efron B. and Tibshirani, “An Introduction of the Bootstrap”. Chapman and Hall/CRC: New York, R.J (1993).

Efron, “The Annals of Statistics”, Bootstrap Methods: Another Look At the Jackknife , 7, 1–26, B. (1979).

S. Vinoth John Prakash, P. K. Dhal, “A Review: Solar Tracking System with Grid Used in Kurnool Ultra Mega Solar Park, “International Review of Electrical Engineering (IREE), Vol.14, No.3, 2019.

Abraham Akhikpemelo, Ma-Riekpen J. E. Evbogbai, Michael S. Okundamiya, “Fault Detection on a 132kV Transmission Line Using Artificial Neural Network, “International Review of Electrical Engineering (IREE), Vol.14, No.3, 2019.

Panya Khemmook, Surin Khomfoi, “Transient Stability Improvement Using Coordinated Control of Solar PVs and Solid State Transformers “International Review of Electrical Engineering (IREE), Vol.13, No.6, 2018.


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