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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology JURNAL NASIONAL TEKNIK ELEKTRO KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization JURNAL INTEGRASI Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Rekayasa elektrika
Anang Tjahjono, Anang
Program Studi Teknik Elektro Industri, Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Other

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Journal : JURNAL INTEGRASI

 1 
EFFICIENT MAXIMUM POWER POINT ESTIMATION MONITORING OF PHOTOVOLTAIC USING FEED FORWARD NEURAL NETWORK Hasnira Hasnira; Novie Ayub Windarko; Anang Tjahjono; Mochammad Ari Bagus Nugroho; Mentari Putri Jati
JURNAL INTEGRASI Vol 12 No 2 (2020): Jurnal Integrasi - Oktober 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v12i2.2161

Abstract

The development of the utilization of solar panels in the future will continue to increase. One characteristic form of solar panels is the I-V curve which can be used to analyze the amount of solar panel output power. By knowing the I-V curve, we can get Maximum Power Point Estimation (MPPE) value that can be supported by solar panels. Information about the estimated value of the maximum solar panel power is an important part in determining the loading capacity, while maintaining the life of the equipment used. Feed Forward Neural Network with Back Propagation Algorithm (FFBP) has proven to be able to provide MPPE value information on solar panel output. The input values ​​in ANN are the voltage and current of the solar panel, while the output of ANN is in the form of an estimated power value. MPPE simulation results obtained an average error of 0.04 points between actual power (MPP) and estimated power (MPPE).
ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER MENGGUNAKAN BACK PROPAGATION NEURAL NETWORK Mohammad Imron Dwi Prasetyo; Hasnira Hasnira; Novie Ayub Windarko; Anang Tjahjono
JURNAL INTEGRASI Vol 12 No 2 (2020): Jurnal Integrasi - Oktober 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v12i2.2163

Abstract

The battery is an important component in the context of implementing renewable energy. The type of battery that has a density in energy storage is lithium polymer. The parameter in the battery that must be considered is the State of Charge (SOC) estimation. In general, the SOC battery estimation uses the coloumb counting method because the difficulty level is low. However, there are weaknesses in the dependence on the utility of the current sensor which is used as an accumulation of the integral of the incoming and outgoing currents over time. In this study presents Back Propagation Neural Network (BPNN) as an algorithm for estimating SOC based on OCV-SOC characteristic curves. The OCV-SOC characteristic curve of the battery is obtained from the battery pulse test. Battery voltage, current and discharging time are used as the first BPNN input layer for the estimation of Open Circuit Voltage (OCV). OCV will be learned as the second BPNN input layer for estimating battery SOC. The results of SOC estimation simulations obtained an average error of 0.479% against the real SOC based on the characteristic curve of OCV - SOC.
Co-Authors Achmad Luki Satriawan Ahmad Ridwan Aidin Amsyar Amsyar, Aidin Anang Budikarso Ashary, Wima Budiarti, Rizqi Putri Nourma Dimas Nur Prakoso Dimas Okky Anggriawan Eka Prasetyono, Eka El Gazaly, Aejelina Endro Wahjono Endro Wahjono, Endro Firdaus, Aji Akbar Habibi, Muhammad Nizar Hasnira Hasnira Indhana Sudiharto Mas Sulung Wisnu Jati Mentari Putri Jati Mochammad Ari Bagus Nugroho Mohammad Imron Dwi Prasetyo Mohammad Imron Dwi Prasetyo Mokhamad Firdaus Karyapraja Novie Ayub Windarko Panduman, Yohanes Yohanie Fridelin Wima Ashary