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All Journal Mechatronics, Electrical Power, and Vehicular Technology Perfecting a Video Game with Game Metrics PROCEEDING IC-ITECHS 2014 ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) Energi & Kelistrikan INTEK: Jurnal Penelitian JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Techno Bahari Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Renny Rakhmawati, Safira Nur Hanifah, Renny Rakhmawati,
Politeknik Elektronika Negeri Surabaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Transportation

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Journal : ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi,

 1 
Safety Design using ATS by Identifying Voltage Interference based on Fuzzy Logic RENNY RAKHMAWATI; LUCKY PRADIGTA SETIYA RAHARJA; MANICHA MIFTACHUL HUSNAH
Jurnal Elkomika Vol 11, No 2 (2023): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektr
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i2.510

Abstract

ABSTRAKSumber energi listrik PLN merupakan sumber utama bagi masyarakat. Namun kenyataan saat ini PLN masih belum maksimal dalam menyalurkan energi seperti dapat terjadi pemadaman, dan fluktuasi tegangan yang merupakan gangguan tegangan AC. Gangguan tegangan sendiri dapat menyebabkan kerusakan pada beban rumah tangga. Oleh karena itu, dalam jurnal ini membuat suatu inovasi dengan memanfaatkan sumber dari Baterai yang berfungsi untuk membackup ketika sumber PLN mengalami pemadaman dan gangguan. Dengan mengembangkan sistem yaitu automatic transfer switch (ATS) yang dapat membuat beban dalam kondisi aman dan mendapatkan aliran energi dari sumber cadangan. Sistem ini bekerja dengan mendeteksi 6 gangguan tegangan AC dengan metode fuzzy logic. Gangguan tersebut antara lain Interruption, Sag, Swell, Sustained Interruption, Undervoltage, dan Overvoltage. Dan sistem ATS bekerja saat jenis gangguan Durasi Panjang yaitu Sustained Interruption, Undervoltage, dan Overvoltage. Sistem automatic transfer switch ini mampu melakukan perpindahan sumber dalam waktu 5ms.Kata kunci: Automatic Transfer switch, Fuzzy Logic, Gangguan AC, PLN ABSTRACTThe primary source of electricity is PLN. However, the current situation shows that PLN is still not doing its best to distribute energy, as seen by blackouts and voltage fluctuations, which are disturbances of the AC voltage. Household loads can damaged by voltage disruptions. As a result, an innovation is made in this journal by utilizing the battery source, which serves as a backup when the PLN source encounters a blackout and other disruption. By developing a system, namely an automated transfer switch (ATS), that can maintain loads in a safe condition and allow energy to flow from backup sources The fuzzy logic method is used to identify six AC voltage disturbances in this system. There are six of AC voltage disturbances: interruption, sag, swell, sustained interruption, undervoltage, and overvoltage. The ATS system functions when there is a long duration disturbance, such as a sustained interruption, undervoltage, or overvoltage. This automatic transfer switch system has a switching time of 5 milliseconds.Keywords: Automatic Transfer switch, Fuzzy Logic, Voltage Disruption, PLN
Distribution Transformer Secondary Bushing Temperature Detection Device using Feed Forward Neural Network GALIH FEBRYANTA ASWA YUDHISTIRA; SUTEDJO SUTEDJO; RENNY RAKHMAWATI
Jurnal Elkomika Vol 11, No 4 (2023): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektr
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i4.983

Abstract

ABSTRAKTransformator distribusi mengubah tegangan listrik tinggi menjadi rendah. Pada sekunder transformator, tegangan dan arus listrik cukup besar sehingga terjadi disipasi panas berlebihan karena timbulnya tahanan listrik pada titik koneksi sekunder transformator dengan kabel keluaran. Hal ini menyebabkan unbalance current dan overheat sehingga terjadi lost contact yang mengganggu pasokan listrik serta drop tegangan. Sayangnya pemeriksaan di lapangan dilakukan tiap enam bulan sekali padahal lost contact dapat terjadi sewaktu-waktu. Sehingga kami mengusulkan pengembangan alat deteksi overheat real-time pada bushing sekunder menggunakan metode klasifikasi suhu berbasis Feed Forward Neural Network (FFNN) yang dilengkapi dengan Internet of Things. FFNN berhasil mengklasifikasikan suhu dengan nilai 0 untuk suhu 30˚C-50˚C, nilai 0 untuk suhu 51˚C-90˚C yang memerlukan perbaikan, dan nilai satu untuk suhu di atas 90˚C dengan relay memutus, kemudian sistem mengirimkan notifikasi lost contact realtime. Sehingga alat ini meningkatkan keefektifan pemeriksaan dan dapat diterapkan guna mengurangi tindakan pemeriksaan secara langsung.Kata kunci: Transformator Distribusi, Lost Contact, Internet of Things, Feed Forward Neural Network ABSTRACTThe distribution transformer turns high voltage into low voltage. On the secondary transformator, the voltage and current are sufficiently large that excessive heat dissipation occurs due to the appearance of electric retention at the point of secondary connection of the transformator to the output cable. This causes current imbalance and overheating, resulting in lost contact that disrupts power supply and voltage drop. Unfortunately, field inspections are carried out every six months and lost contact can occur at any time. So we suggested developing a real-time overheat detection tool on secondary bushing using a temperature classification method based on the Feed Forward Neural Network (FFNN) equipped with the Internet of Things. With FFNN, the system successfully classifies the temperature with a value of 0 for a temperature of 30 ̊ C-50 ̊ C, a value 0 for the temperature of 51°C-90 ̊ C that requires repair, and a value 1 for a temperatur above 90 ̊ C with a relay disconnect, then the system sends a real-time lost contact notification. Thus this tool increases the effectiveness of inspection and can be applied to reduce inspection actions directly.Keywords: Distribution Transformer, Lost Contact, Internet of Things, Feed Forward Neural Network
Estimasi State of Charge pada Baterai Lead Acid menggunakan Elman Recurrent Neural Network RENNY RAKHMAWATI; SUTEDJO SUTEDJO; FITROTIN NAFISA OKTAVIANI; IRIANTO IRIANTO; DIAH SEPTI YANARATRI; AHMAD FIRYAL ADILA
Jurnal Elkomika Vol 11, No 4 (2023): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektr
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i4.864

Abstract

ABSTRAKPenggunaan panel surya sebagai sumber energi terbarukan membutuhkan baterai sebagai tempat penyimpanan energi. Penggunaan baterai secara terus menerus, dapat menyebabkan pengurangan kapasitas dan penurunan performa. Untuk mengatasi permasalahan tersebut, diperlukan sistem estimasi nilai State of Charge (SOC) pada baterai yang berfungsi untuk mengontrol kondisi charge, agar performa baterai tetap optimal. Pada penelitian dikembangan suatu sistem estimasi SOC pada baterai jenis lead acid, dengan metode algoritma Elman Recurrent Neural Network (ERNN). Keunggulan yang terkait dengan metode ERNN meliputi proses iterasi menjadi lebih cepat, peningkatan kecepatan pembaruan parameter, dan pencapaian konvergensi yang lebih cepat. Hasil dari penelitian estimasi SOC pada baterai lead acid 12V, 12Ah dengan menggunakan algoritma ERNN sebesar 0.101% sedangkan dengan algoritma Feedforward Backpropagation sebesar 0.767%. Sehingga dapat disimpulkan bahwa algoritma ERNN lebih efisien dalam mengestimasi nilai SOC pada baterai lead acid.Kata kunci: Baterai, Elman Recurrent Neural Network, Panel Surya, State of Charge; Lead Acid ABSTRACTUsing solar panels as a renewable energy source requires batteries as energy storage. Continuous use of batteries can result in reduced capacity and performance degradation. Based on these problems, a State of Charge (SOC) estimation system is needed for the battery to control charge conditions so that battery performance remains optimal. In this research, a SOC estimation system was developed for lead acid battery using the Elman Recurrent Neural Network (ERNN) algorithm. The advantage of the ERNN method is that the iteration process is faster, the parameter update speed is increased, and convergence is faster. The results of the SOC estimation for a 12V, 12Ah lead acid battery using the ERNN algorithm were 0.101%, while the Feedforward Backpropagation algorithm resulted in 0.767%. The ERNN algorithm is more efficient in estimating the SOC value of a lead acid battery.Keywords: Battery, Elman Recurrent Neural Network, Solar Panel, State of Charge, Lead Acid
Co-Authors Ahmad Firyal Adila Aura Aulia Azmil Anil Yamin Fajrus Sodiq Diah Septi Yanaratri Enggar Bima Ihza Febian Era Purwanto Farid Dwi Murdianto FITROTIN NAFISA OKTAVIANI GALIH FEBRYANTA ASWA YUDHISTIRA Hendik Eko Hadi Suharyanto Irianto Irianto Lucky Pradigta Setiya Raharja MANICHA MIFTACHUL HUSNAH Muhammad Abdul Haq Muhammad Aditya Ardiansyah Muhammad Wildan Alim Novie Ayub Windarko Putu Agus Mahadi Putra Rachma Prilian Eviningsih Rizal Nurdiansyah Safira Nur Hanifah Suhariningsih Suhariningsih Suhariningsih sutedjo Sutedjo Yahya Chusna Arif Zhafira Rana Khalisa Permana