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Load Identification Using Harmonic Based on Probabilistic Neural Network Anggriawan, Dimas Okky; Amsyar, Aidin; Prasetyono, Eka; Wahjono, Endro; Sudiharto, Indhana; Tjahjono, Anang
EMITTER International Journal of Engineering Technology Vol 7, No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Due to increase power quality which are caused by harmonic distortion it could be affected malfunction electrical equipment. Therefore, identification of harmonic loads become important attention in the power system. According to those problems, this paper proposes a Load Identification using harmonic based on probabilistic neural network (PNN). Harmonic is obtained by experiment using prototype, which it consists of microcontroller and current sensor. Fast Fourier Transform (FFT) method to analyze of current waveform on loads become harmonic load data. PNN is used to identify the type of load. To load identification, PNN is trained to get the new weight. Testing is conducted To evaluate of the accuracy of the PNN from combination of four loads. The results demonstrate that this method has high accuracy to determine type of loads based on harmonic load

Studi Komparasi Fungsi Keanggotaan Fuzzy sebagai Kontroler Bidirectional DC-DC Converter pada Sistem Penyimpan Energi Prasetyono, Eka; Ashary, Wima; Tjahjono, Anang; Windarko, Novie Ayub
JURNAL NASIONAL TEKNIK ELEKTRO Vol 4, No 2: September 2015
Publisher : Jurusan Teknik Elektro Universitas Andalas

Bidirectional DC-DC converter is needed in the energy storage system. The converter topology used in this paper was a non-isolated bidirectional DC-DC buck-boost converter. This converter worked in two ways, which the charging mode stored energy into battery when load current was less than nominal main DC current (set point) and discharging mode transferred energy from battery to the load when its current exceeded set point value. Both of these modes worked automatically according to the load current. The charging and discharging currents were controlled by fuzzy logic controller which was implemented on microcontroller ARM Cortex-M4F STM32F407VG. This paper compares two types of fuzzy membership function (triangular and sigmoid) in controlling bidirectional DC-DC converter. The results showed that fuzzy logic controller with triangle membership function and sigmoid as control bidirectional DC-DC converter had no significant different response, both had an average error for charging and discharging process under 4% with ripple current on the main DC bus around 0.5%.Â The computing time of program for fuzzy logic controller with triangular membership functions had 19.01% faster than sigmoid, and fuzzy logic computation time on a microcontroller with hardware floating point was 60% faster than software floating point.Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Keywords : Bidirectional DC-DC converter, Fuzzy logic controller and MikorkontrolerAbstrakâ€”Bidirectional DC-DC converter merupakan converter yang diperlukan dalam sistem penyimpan energi. Topologi converter yang digunakan pada paper ini adalah non-isolated bidirectional DC-DC converter jenis buckâ€“boost converter, converter ini dapat bekerja dua arah yaitu mode charging untuk menyimpan energi ke dalam baterai apabila arus beban kurang dari nilai nominal (set point) kemampuan main DC bus dan mode discharging untuk menyalurkan energi dari baterai ke beban bila arus beban melebihi nilai set point. Kedua mode tersebut bekerja secara otomatis sesuai dengan besarnya beban yang digunakan. Besarnya arus charging dan discharging dikontrol oleh kontrol logika fuzzy yang diimplemanetasikan pada mikrokontroler ARM Cortex-M4F STM32F407VG. Paper ini membandingkan dua jenis fungsi keanggotaan fuzzy (segitiga dan sigmoid) dalam mengontrol bidirectional DC-DC converter. Hasil yang diperoleh menunjukkan kontrol logika fuzzy dengan fungsi keanggotaan segi tiga dan sigmoid sebagai kontrol bidirectional DC-DC converter memiliki perbedaan respon yang tidak signifikan, keduanya memiliki rata-rata error untuk proses charging dan discharging dibawah 4% dengan ripple pada main DC bus 0.5%. Ditinjau dari waktu komputasi program, kontrol logika fuzzy dengan fungsi keanggotaan segitiga 19.01% lebih cepat komputasinya dibanding dengan sigmoid dan waktu komputasi logika fuzzy pada mikrokontroler dengan floating point hardware 60%Â cepat dibanding dengan floating point secara software.Kata Kunci : Bidirectional DC-DC converter, Fuzzy logic controller dan Mikorkontroler.

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, Universitas Malikussaleh, Indonesia

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

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 Yohanes Yohanie Fridelin Panduman; Sritrusta Sukaridhoto; Anang Tjahjono; Rizqi Putri Nourma Budiarti
JOIV : International Journal on Informatics Visualization Vol 4, No 4 (2020)
Publisher : Politeknik Negeri Padang

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

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.

Perbaikan MPPT Incremental Conductance menggunakan ANN pada Berbayang Sebagian dengan Hubungan Paralel MUHAMMAD NIZAR HABIBI; DIMAS NUR PRAKOSO; NOVIE AYUB WINDARKO; ANANG TJAHJONO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 3 (2020): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektro
Publisher : Institut Teknologi Nasional, Bandung

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

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

Load Identification Using Harmonic Based on Probabilistic Neural Network Dimas Okky Anggriawan; Aidin Amsyar; Eka Prasetyono; Endro Wahjono; Indhana Sudiharto; Anang Tjahjono
EMITTER International Journal of Engineering Technology Vol 7 No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Due to increase power quality which are caused by harmonic distortion it could be affected malfunction electrical equipment. Therefore, identification of harmonic loads become important attentionÂ in the power system. According to those problems, this paper proposes a Load Identification using harmonic based on probabilistic neural network (PNN). Harmonic is obtained by experiment using prototype, which it consists of microcontroller and current sensor. Fast Fourier Transform (FFT) method to analyze of current waveform on loads become harmonic load data. PNN is used to identify the type of load. To load identification, PNN is trained to get the new weight. Testing is conducted To evaluate of the accuracy of the PNN from combination of four loads. The results demonstrate that this method has high accuracy to determine type of loads based on harmonic load

Studi Komparasi Fungsi Keanggotaan Fuzzy sebagai Kontroler Bidirectional DC-DC Converter pada Sistem Penyimpan Energi Eka Prasetyono; Wima Ashary; Anang Tjahjono; Novie Ayub Windarko
JURNAL NASIONAL TEKNIK ELEKTRO Vol 4 No 2: September 2015
Publisher : Jurusan Teknik Elektro Universitas Andalas

Bidirectional DC-DC converter is needed in the energy storage system. The converter topology used in this paper was a non-isolated bidirectional DC-DC buck-boost converter. This converter worked in two ways, which the charging mode stored energy into battery when load current was less than nominal main DC current (set point) and discharging mode transferred energy from battery to the load when its current exceeded set point value. Both of these modes worked automatically according to the load current. The charging and discharging currents were controlled by fuzzy logic controller which was implemented on microcontroller ARM Cortex-M4F STM32F407VG. This paper compares two types of fuzzy membership function (triangular and sigmoid) in controlling bidirectional DC-DC converter. The results showed that fuzzy logic controller with triangle membership function and sigmoid as control bidirectional DC-DC converter had no significant different response, both had an average error for charging and discharging process under 4% with ripple current on the main DC bus around 0.5%. The computing time of program for fuzzy logic controller with triangular membership functions had 19.01% faster than sigmoid, and fuzzy logic computation time on a microcontroller with hardware floating point was 60% faster than software floating point. Keywords : Bidirectional DC-DC converter, Fuzzy logic controller and MikorkontrolerAbstrak—Bidirectional DC-DC converter merupakan converter yang diperlukan dalam sistem penyimpan energi. Topologi converter yang digunakan pada paper ini adalah non-isolated bidirectional DC-DC converter jenis buck–boost converter, converter ini dapat bekerja dua arah yaitu mode charging untuk menyimpan energi ke dalam baterai apabila arus beban kurang dari nilai nominal (set point) kemampuan main DC bus dan mode discharging untuk menyalurkan energi dari baterai ke beban bila arus beban melebihi nilai set point. Kedua mode tersebut bekerja secara otomatis sesuai dengan besarnya beban yang digunakan. Besarnya arus charging dan discharging dikontrol oleh kontrol logika fuzzy yang diimplemanetasikan pada mikrokontroler ARM Cortex-M4F STM32F407VG. Paper ini membandingkan dua jenis fungsi keanggotaan fuzzy (segitiga dan sigmoid) dalam mengontrol bidirectional DC-DC converter. Hasil yang diperoleh menunjukkan kontrol logika fuzzy dengan fungsi keanggotaan segi tiga dan sigmoid sebagai kontrol bidirectional DC-DC converter memiliki perbedaan respon yang tidak signifikan, keduanya memiliki rata-rata error untuk proses charging dan discharging dibawah 4% dengan ripple pada main DC bus 0.5%. Ditinjau dari waktu komputasi program, kontrol logika fuzzy dengan fungsi keanggotaan segitiga 19.01% lebih cepat komputasinya dibanding dengan sigmoid dan waktu komputasi logika fuzzy pada mikrokontroler dengan floating point hardware 60% cepat dibanding dengan floating point secara software.Kata Kunci : Bidirectional DC-DC converter, Fuzzy logic controller dan Mikorkontroler.

FEED FORWARD NEURAL NETWORK SEBAGAI ALGORITMA ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER Mohammad Imron Dwi Prasetyo; Anang Tjahjono; Novie Ayub Windarko
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 1 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i1.290

Estimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus, dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.Keywords: SOC, BMS, Coloumb Counting, OCV, FFNNEstimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus, dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.Kata kunci: SOC, BMS, Coloumb Counting, OCV, FFNN

Identifikasi Gangguan Qualitas Daya Pada Transformer Distribusi Untuk Menjaga Kualitas Jaringan Mokhamad Firdaus Karyapraja; Anang Tjahjono; Eka Prasetyono
Jurnal Elektro dan Telekomunikasi Terapan (e-Journal) Vol 6 No 2 (2019): JETT Desember 2019
Publisher : Direktorat Penelitian dan Pengabdian Masyarakat, Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jett.v6i2.2956

Hampir dari seluruh transformator yang terpasang di indonesia belum adanya pemantauan secara langsung guna untuk menjaga kualitas energi listrik yang akan disalurkan menuju pelanggan. Dengan adanya pemantauan secara langsung pada transformator dapat dilakukannya pendeteksian gangguan secara langsung hal ini akan membuat pihak penyalur tenaga listrik membantu mempercepat menangani masalah yang terjadi. Makalah ini menyajikan pengidentifikasi gangguan dengan cara melakukan pemodelan dari gangguan yang akan di identifikasi. Dengan bantuan perangkat elektronik yang dapat melakukan pemodelan gangguan menggunakan Artficial Neural Network sehingga perangkat elektronik dapat mengenali gangguan yang terjadi pada transformator.

Analysis of Load Flow and Short Circuit Against the Addition of Distributed Generation (DG) in Distribution Networks Ahmad Ridwan; Aejelina El Gazaly; Anang Tjahjono
Journal of Renewable Energy, Electrical, and Computer Engineering Vol 2, No 1 (2022): March 2022
Publisher : Institute for Research and Community Service, Universitas Malikussaleh, Indonesia

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

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.

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