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VRLA battery state of health estimation based on charging time Akhmad Zainuri; Unggul Wibawa; Mochammad Rusli; Rini Nur Hasanah; Rosihan Arby Harahap
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.12241

Battery state of health (SoH) is an important parameter of the battery’s ability to store and deliver electrical energy. Various methods have been so far developed to calculate the battery SoH, such as through the calculation of battery impedance or battery capacity using Kalman Filter, Fuzzy theory, Probabilistic Neural Network, adaptive hybrid battery model, and Double Unscented Kalman Filtering (D-UKF) algorithm. This paper proposes an approach to estimate the value of battery SoH based on the charging time measurement. The results of observation and measurements showed that a new and used batteries would indicate different charging times. Unhealthy battery tends to have faster charging and discharging time. The undertaken analysis has been focused on finding out the relationship between the battery SoH and the charging time range. To validate the results of this proposed approach, the use of battery capacity method has been considered as comparison. It can be concluded that there is a strong correlation between the two discussed SoH estimation methods, confirming that the proposed method is feasible as an alternative SoH estimation method to the widely known battery capacity method. The correlation between the charging-disharging times of healthy and unhealthy batteries is very prospective to develop a battery charger in the future with a prime advantage of not requiring any sensor for the data acquisition.

Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple Linear Regression Methods Hadi Suyono; Rini Nur Hasanah; R. A. Setyawan; Panca Mudjirahardjo; Anthony Wijoyo; Ismail Musirin
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method.

Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple Linear Regression Methods Hadi Suyono; Rini Nur Hasanah; R. A. Setyawan; Panca Mudjirahardjo; Anthony Wijoyo; Ismail Musirin
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method.

Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple Linear Regression Methods Hadi Suyono; Rini Nur Hasanah; R. A. Setyawan; Panca Mudjirahardjo; Anthony Wijoyo; Ismail Musirin
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method.

PENGENDALIAN MPPT BERBASIS METODE P&O MENGGUNAKAN BOOST CONVERTER Frediawan Yuniar; Rini Nur Hasanah; Onny Setyawati
Jurnal Arus Elektro Indonesia Vol 3 No 1 (2017)
Publisher : Fakultas Teknik, Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jaei.v3i1.5497

Sinar matahari merupakan sumber energi terbarukan yang tersedia secara gratis dan dapat menjadi alternatif sumber energi listrik selain yang disediakan oleh Perusahaan Listrik Negara (PLN). Dalam bentuk photovoltaic (PV), penyediaan energi listrik bersumber sinar matahari diharapkan mampu memberikan suplai daya yang sama handalnya dengan PLN. Untuk memaksimalkan daya yang dihasilkan sistem PV, diterapkan teknik maximum power point tracking (MPPT). Berbagai algoritme pengendalian dicari untuk memaksimalkan daya sistem PV. Pada penelitian ini dikaji penggunaan metode perturb and observe (P&O) yang diimplementasikan pada modulasi lebar pulsa (pulse-width modulation/PWM) untuk mengendalikan boost converter. Untuk mengatasi timbulnya osilasi di sekitar titik daya maksimum karena penggunaan metode P&O, pada penelitian ini diusulkan modifikasi dengan menambahkan pengukuran variabel arus saat perubahan iradiasi. Hasil simulasi menunjukkan bahwa hilangnya potensi daya yang dibangkitkan dapat dikurangi, yang tidak dapat dilacak jika menggunakan metode P&O konvensional.

Pengaturan Injeksi Arus pada Coilgun dengan PWM menggunakan Metode Algoritma Genetika Basuki Winarno; Wijono Wijono; Rini Nur Hasanah
Jurnal Arus Elektro Indonesia Vol 1 No 3 (2015)
Publisher : Fakultas Teknik, Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Paper ini membahas tentang pengaturan injeksi arus pada coilgun untuk menghasilkan suatu profil kecepatan tertentu. Rancangan konstruksi koil yang diperlukan disusun dari 3 bagian area. Pergerakan proyektil dalam tiap area dibatasi oleh energi maksimal yang disediakan masing-masing koil sesuai kemampuan hantar arus konduktornya. Pengaturan duty cyclepada pulse-width modulation (PWM) digunakan untuk menghasilkan besar tegangan keluaran yang menentukan besar arus injeksi ke dalam koil. Arus yang diinjeksikan ke dalam koil diubah setiap 1 ms selama 24 ms, yaitu waktu tempuh proyektil dalam melewati 3 area, sesuai dengan kebutuhan energi yang berubah terhadap jarak. Jumlah layer pada masing-masing koil ditentukan secara iteratif untuk mempertimbangkanketerkaitan antar parameter koil. Injeksi arus pada ketiga koil dilakukan secara terpisah pada saat yang bersamaan, sehingga untuk setiap posisi proyektil tertentu dalam koil diperlukan pengaturanduty cycleyang berbeda.Algoritma genetika digunakan untuk keperluan tersebut. Setiapduty cycledikodekan menggunakan bilangan biner 8 bit, sehingga untuk setiap injeksi diperlukan pengkodean menggunakan 3x8 bit, yang membentuk populasi duty cycle. Hasil penelitian menunjukkan untuk mencapai kesesuaian yang baik antara profil kecepatan gerak proyektil menggunakan metode yang diusulkan dan profil kecepatan yang digunakan sebagai acuan. Besar arus maksimal untuk koil 1 adalah 1460 A, koil 2 adalah 1752 A, dan koil 3 adalah 1898 A dengan kecepatan akhir sebesar 54,5 m/s.

Empowering Traditional Education Institution Through the Implementation of Potable Water Provision System Rini Nur Hasanah; Ainul Hayat; Moh. Farid Rahman; Zainul Abidin; Hadi Suyono
JOURNAL OF SCIENCE AND APPLIED ENGINEERING Vol 4, No 2 (2021): JSAE
Publisher : Widyagama University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jsae.v4i2.2975

The activity reported in this journal was aimed to empower the traditional education institutions commonly found and not formally financed by government of Indonesia. Empowering was carried out through the implementation of an action plan to ensure the potable water provision. Clean as well as potable water is indispensable for daily life and activities of students and teachers, especially those who were living and boarding in the school complex. The action was also potential to improve the financial independence of organization and to develop the entrepreneurship skills of students. The obtained tangible results of the action were in the form of production facility with ready-to-drink water quality of less than 10 ppm (parts per million) of total dissolved solids.

Analisis Turbin Darrieus Tipe V-Shaped Blade Untuk Aplikasi Konverter Energi Arus Laut Menggunakan Software QBlade Rizki Mendung Ariefianto; Rini Nur Hasanah; Wijono Wijono
Jurnal Kelautan Nasional Vol 17, No 2 (2022): AGUSTUS
Publisher : Pusat Riset Kelautan

Turbin tipe Darrieus merupakan salah satu jenis turbin sumbu vertikal yang memiliki prospek menjanjikan dalam pengembangan turbin hidrokinetik, salah satunya dalam aplikasi untuk pembangkit arus laut. Berbagai penelitian telah dilakukan untuk meningkatkan performa turbin Darrieus yang pada umumnya memiliki performa efisiensi dan self-starting lebih rendah dibandingkan jenis turbin sumbu horisontal. Tujuan penelitian ini adalah untuk mengevaluasi performa turbin Darrieus yang ditinjau dari aspek efisiensi dan kemampuan self-starting. Skenario pengujian berupa penerapan bentuk foil dan blade swept angle (γ) pada desain turbin dipertimbangkan dalam penelitian ini. Pada evaluasi pengaruh bentuk foil, diterapkan foil NACA 634021 sebagai foil utama kemudian dibandingkan dengan foil lain seperti NACA 0018. Sedangkan evaluasi pengaruh blade swept angle, dipertimbangkan nilai γ = 30° agar menghasilkan turbin dengan bentuk V-shaped blade yang kemudian dibandingkan dengan turbin Straight blade dengan γ = 0°. Software QBlade digunakan untuk mensimulasikan turbin V-shaped blade selama kondisi kerja. Hasil simulasi menunjukkan bahwa turbin V-shaped blade yang berbasis foil NACA 634021 mampu mencapai efisiensi terbesar yaitu 0,425 dan memiliki self-starting yang baik pada cut-in speed arus laut sebesar 1,765 m/s. Selain itu, turbin ini juga mampu menghasilkan daya sebesar 27,64 kW pada kecepatan ratingnya dengan rata-rata peningkatan daya tiap 1 m/s arus laut sebesar 2,51 kW.

Prediction of Solar Radiation Intensity using Extreme Learning Machine Hadi Suyono; Hari Santoso; Rini Nur Hasanah; Unggul Wibawa; Ismail Musirin
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp691-698

The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This paper describes a method of predicting the availability of solar radiation using the Extreme Learning Machine (ELM) method. It is based on the artificial intelligence methods and known to have a good prediction accuracy. To measure the performance of the ELM method, a conventional forecasting method using the Multiple Linear Regression (MLR) method has been used as a comparison. The implementation of both the ELM and MLR methods has been tested using the solar radiation data of the Basel City, Switzerland, which are available to public. Five years of data have been divided into training data and testing data for 6 case-studies considered. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) have been used as the parameters to measure the prediction results based on the actual data analysis. The results show that the obtained average values of RMSE and MAE by using the ELM method respectively are 122.45 W/m2 and 84.04 W/m2, while using the MLR method they are 141.18 W/m2 and 104.87 W/m2 respectively. It means that the ELM method proved to perform better than the MLR method, giving 15.29% better value of RMSE parameter and 24.79% better value of MAE parameter.

Analisis Stabilitas Transien pada Onshore Windfarm Terhubung VSC-HVDC Sistem Jawa Bali RAI PRAMESTI SUTEJA; HADI SUYONO; RINI NUR HASANAH
Jurnal Elkomika Vol 10, No 4 (2022): 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.v10i4.873

ABSTRAKGangguan hubung singkat 3 fasa di Pulau Bali dapat menyebabkan ketidakstabilan pada sistem tenaga listrik. Sistem kelistrikan Pulau Bali bergantung pada pasokan dari Pulau Jawa dengan penghantar kabel bawah laut. Untuk memenuhi kebutuhan energi listrik di masa depan, diperlukan pengembangan sumber energi terbarukan. Penelitian ini membahas tentang kestabilitan sistem pada onshore wind farm terhubung VSC-HVDC sistem kelistrikan Jawa-Bali dengan melibatkan pengaruh gangguan hubung singkat 3 fasa. Analisis kestabilan sistem menggunakan DIgSILENT dengan metode time domain simulation. Respon sistem kembali pada frekuensi 50 Hz pada detik ke 1,622 dan tegangan 1 p.u pada detik ke 1,902. Oleh karena itu, Onshore Windfarm terhubung sistem transmisi VSC-HVDC Java-Bali memiliki respon tegangan dan frekuensi yang cepat kembali steady-state setelah ada gangguan hubung singkat 3 fasa.Kata kunci: Gangguan Hubung Singkat, High Voltage DC, Stabilitas Transien, Time Domain Simulation, Voltage Source ConverterABSTRACTA three phase short circuit in Bali island makes instabilty in power system. Bali's electricity system the supply is from Java Island with sub marine cable crossings. To meet the demand for electrical energy, it is necessary to develop renewable energy sources. This study discusses system stability on the onshore wind farm connected to the VSC-HVDC Java-Bali electrical system by involving the effect of a three phase short circuit. Analysis system stabilty by using DIgSILENT and time domain simulation method. The system response returns to 50 Hz frequency at 1,622 seconds and a voltage of 1 p.u at 1,902 seconds. Therefore, onshore wind farm connected to the VSC-HVDC Java-Bali has fast voltage and frequency response can return steady state after 3-phase short circuit.Keywords: Short Circuit, High Voltage DC, Transient Stability, Time Domain Simulation, Voltage Source Converter

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