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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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Maximum Power Point Tracking Menggunakan Algoritma Artificial Neural Network Berbasis Arus Hubung Singkat Panel Surya Muhammad Nizar Habibi; Mas Sulung Wisnu Jati; Novie Ayub Windarko; Anang Tjahjono
Jurnal Rekayasa Elektrika Vol 16, No 2 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1434.609 KB) | DOI: 10.17529/jre.v16i2.14860

Abstract

The conversion of solar energy into electrical can be utilized by using the solar panel, but the energy conversion ratio is still low. Maximum Power Point Tracking (MPPT) is a method used to increase energy production in the process of converting electrical to the solar panel. Artificial Neural Network (ANN) is one of the soft-computing methods that can be applied as MPPT with the advantage of having a learning process, very stable, fast, doesn’t require complicated mathematical modeling, and has good performance. ANN is proposed with input from the short circuit current of the solar panel and is used as a reference for the ANN to reach the maximum power. The process of detecting a short circuit current is indicated by a momentary decrease of the power by the solar panel. The results show the proposed algorithm can reach the maximum power operating point of the solar panel despite the change of radiation. When at maximum power operating point, ANN can hold the value, so the resulting value doesn’t change and doesn’t generate ripple. At radiation of 1000 W/m2 and using 100 WP, ANN can produce a maximum power of 99.97 Watts with a time of 0.063 seconds. 
Maximum Power Point Tracking Menggunakan Algoritma Artificial Neural Network Berbasis Arus Hubung Singkat Panel Surya Muhammad Nizar Habibi; Mas Sulung Wisnu Jati; Novie Ayub Windarko; Anang Tjahjono
Jurnal Rekayasa Elektrika Vol 16, No 2 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i2.14860

Abstract

The conversion of solar energy into electrical can be utilized by using the solar panel, but the energy conversion ratio is still low. Maximum Power Point Tracking (MPPT) is a method used to increase energy production in the process of converting electrical to the solar panel. Artificial Neural Network (ANN) is one of the soft-computing methods that can be applied as MPPT with the advantage of having a learning process, very stable, fast, doesn’t require complicated mathematical modeling, and has good performance. ANN is proposed with input from the short circuit current of the solar panel and is used as a reference for the ANN to reach the maximum power. The process of detecting a short circuit current is indicated by a momentary decrease of the power by the solar panel. The results show the proposed algorithm can reach the maximum power operating point of the solar panel despite the change of radiation. When at maximum power operating point, ANN can hold the value, so the resulting value doesn’t change and doesn’t generate ripple. At radiation of 1000 W/m2 and using 100 WP, ANN can produce a maximum power of 99.97 Watts with a time of 0.063 seconds. 
Pendeteksian Harmonisa Arus Berbasis Feed Forward Neural Network Secara Real Time Endro Wahjono; Dimas Okky Anggriawan; Achmad Luki Satriawan; Aji Akbar Firdaus; Eka Prasetyono; Indhana Sudiharto; Anang Tjahjono; Anang Budikarso
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i1.15093

Abstract

The development of power electronics converters has been widespread in the industrial, commercial, and home applications. The device is considered to produce harmonics in non-linear loads. Harmonics cause a decrease in power quality in the electric power system. To prevent a decrease in power quality caused by harmonics in the power system, the detection of harmonics has an important role. Therefore, this paper proposed feed forward neural network (FFNN) for harmonic detection. The design of harmonic detection device is designed with a feed forward neural network method that it has two stages of information processing, namely the training stage and the testing stage. FFNN has input harmonics and THDi as output. To detect harmonics, frst training is conducted to recognize waveform patterns and calculate the fast fourier transform (FFT) process offline. Prototype using the AMC1100DUB current sensor, microcontroller and display. To validate the proposed algorithm, compared by standard measurement tool and FFT. The results show the proposed algorithm has good performance with the average percentage error compared by standard measurement tool and FFT of 5.33 %.
Perbaikan MPPT Incremental Conductance menggunakan ANN pada Berbayang Sebagian dengan Hubungan Paralel HABIBI, MUHAMMAD NIZAR; PRAKOSO, DIMAS NUR; WINDARKO, NOVIE AYUB; TJAHJONO, ANANG
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 3: Published September 2020
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i3.546

Abstract

ABSTRAKAlgoritma IncrementaL Conductance (IC) adalah algoritma yang bisa diimplementasikan pada sistem Maximum Power Point Tracking (MPPT) untuk mendapatkan daya maksimum dari panel surya. Akan tetapi algoritma MPPT IC tidak bisa bekerja dikondisi berbayang sebagian, karena menimbulkan daya maksimum lebih dari satu. Artificial Neural Network (ANN) bisa mengidentifikasi kurva karakteristik pada kondisi berbayang sebagian dan dapat mengetahui posisi daya maksimum yang sebenarnya. Masukan dari ANN merupakan nilai arus hubung singkat serta tegangan buka dari panel surya, dan keluaran dari ANN adalah nilai duty cycle yang digunakan sebagai posisi awal tracking dari MPPT IC. Data learning didapatkan dari perubahan nilai duty cycle secara manual pada sistem MPPT di berbagai kondisi radiasi. Hasil pengujian menunjukkan algoritma yang diajukan dapat menaikkan energi 5.79% - 13.32% dibandingkan dengan ANN-Perturb and Observe dan ANN-Incremental Resistance dengan durasi 0.6 detik.Kata kunci: MPPT, Incremental Conductance, Artficial Neural Network, Berbayang Sebagian, Hubungan Paralel ABSTRACTThe Incremental Conductance (IC) algorithm is an algorithm that can be implemented on Maximum Power Point Tracking (MPPT) systems to get maximum power from solar panels. However, the MPPT IC algorithm cannot work in partial shading conditions because it causes more than one maximum power. Artificial Neural Network (ANN) can identify characteristic curves under partial shading conditions and can know the actual maximum power position. The input from ANN is the short circuit current and the open voltage of the solar panel. The output of ANN is the duty cycle value that is used as the initial tracking position of the MPPT IC. Learning data is obtained from manually changing the duty cycle value in the MPPT system in various radiation conditions. The test results show the proposed algorithm can increase energy 5.79% - 13.32% when compared with ANN-Perturb and Observe and ANN-Incremental Resistance with a duration of 0.6 seconds.Keywords: Maximum Power Point Tracking, Incremental Conductance, Artficial Neural Network, Partial Shading, Parallel Connection
Analysis of Load Flow and Short Circuit Against the Addition of Distributed Generation (DG) in Distribution Networks Ridwan, Ahmad; El Gazaly, Aejelina; Tjahjono, Anang
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 2 No. 1 (2022): March 2022
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v2i1.6807

Abstract

This study tries to determine the level of change in short-circuit fault currents on certain buses in the Andalas University distribution network due to the installation of a new generator. Simulation of load flow and short circuit faults uses a 20 kV Andalas University distribution network system model to which a renewable generator with a capacity of 200 kW will be added. The simulation results of the load flow on a 20 kV distribution system paralleled with DG show that the voltage drop is still in accordance with the provisions of PT. PLN, this is due to the voltage drop in the distribution system is not up to 10% of the nominal 20 kV. While the short circuit simulation results, the largest single-phase and three-phase short-circuit current values occur at the Nursing_P location of 9.362 kA. However, the short circuit capacity has not yet reached a maximum voltage of 20 kV 500 MVA or 14.4 kA. So that the amount of short circuit current contributed by Nursing_P is within normal limits and does not require additional equipment to protect the fault current.
Implementation SEMAR-IoT-Platform for Vehicle as a Mobile Sensor Network Panduman, Yohanes Yohanie Fridelin; Sukaridhoto, Sritrusta; Tjahjono, Anang; Budiarti, Rizqi Putri Nourma
JOIV : International Journal on Informatics Visualization Vol 4, No 4 (2020)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.4.4.425

Abstract

With the rapid development of IoT technology in various fields such as smart cities and industry 4.0, the need for wireless sensor network-based systems has increased, one of which is the concept of using a vehicle as a mobile sensor network or known as VaaMSN. Many developers use the IoT platform as a cloud computing service in developing the VaaMSN system. However, not all IoT platform service providers provide monitoring features on every device and provide information such as device location, purpose, condition. Therefore, this research aims to develop an IoT Platform that can receive data and provide information on each device, making it easier to process data and control devices.  Therefore, this research aims to develop an IoT platform called the SEMAR-IoT-Platform that able to received data and provide information on each device for easier data processing and control devices.  The SEMAR-IoT-Platform integrates Big Data, Data Analytics, Machine Learning, using the principles of Extract, Transfer, and Load (ETL) for data processing and provides communication services using HTTP-POST, MQTT, and NATS.  The test results show that the system has been successfully implemented to complement a simple IoT system with an average delay time of HTTP, NATS, and MQTT communications of less than 150ms for the data storage process, and for the data visualization process has an average delay time of less than 300ms.
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