Nurvita Arumsari
Politeknik Perkapalan Negeri Surabaya

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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
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 4, No 2 (2020)
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25796216.v4.i2.119

Abstract

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.Keywords: bootstrap, maximum power tracking, neural network, stability.
Peramalan Irradiance Cahaya Matahari pada Sel Surya untuk Memenuhi Kebutuhan Energi Listrik dengan Metode Support Vector Regression (SVR) Nurvita Arumsari; Feby Agung Pamuji
JURNAL NASIONAL TEKNIK ELEKTRO Vol 6, No 1: Maret 2017
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v6n1.367.2017

Abstract

This paper suggests the use of support vector regression (SVR) method for forecasting irradiance of sunlight on solar cells so that the energy produced by the solar cells can be predicted to meet electricity needs. This prediction is very important because to provide electrical energy that is sustainable and has a good reliability which has the constant frequency and constant voltage. From the simulation results can be seen that the SVR method has not a fairly good prediction results. So that, the approximate energy of solar cell that can be transfered to meet the electricity needs of the next month still not accurate with this method. Future research will be tried SVR hybrid time series method.Keywords : Electrical Energy, Irradiance,Support vector regression (SVR).Abstrak— Pada tulisan ini digunakan metode Support Vector Regression (SVR) untuk peramalan irradiance cahaya matahari pada sel surya sehingga besar energi yang dihasilkan sel surya bisa diprediksi untuk memenuhi kebutuhan energi listrik. Prediksi ini sangat penting dikarena untuk menyediakan energi listrik yang berkelanjutan dan mempunyai keandalan yang baik yaitu mempunyai frekuensi konstan dan tegangan konstan. Dari hasil simulasi dapat dilihat bahwa metode SVR mempunyai hasil prediksi yang masih rendah. Sehingga perkiraan energi solar cell yang dapat dikirim untuk memenuhi kebutuhan listrik satu bulan ke depan masih belum cukup akurat dengan menggunakan metode ini. Pada penelitian mendatang, akan dicoba penggunaan metode SVR berbasis time series.Kata Kunci : Energi listrik, Irradiance, Support Vector Regression (SVR).