Perancangan Fault Detection pada Solid Oxide Fuel Cell Menggunakan Extended Kalman Filter

Dhana, I. G. P. Arya William Wiranata Purusa (2026) Perancangan Fault Detection pada Solid Oxide Fuel Cell Menggunakan Extended Kalman Filter. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Solid Oxide Fuel Cell (SOFC) merupakan teknologi konversi energi yang memiliki potensi tinggi untuk mendukung energi bersih karena memiliki eifisiensi tinggi, emisi rendah, fleksibilitas bahan bakar, dan potensi umur pemakaian yang panjang. Namun, pada operasinya, SOFC rentan mengalami kesalahan operasional, terutama kebocoran gas reaktan, yakni hideogen dan oksigen. Hal tersebut dapat menyebabkan perubahan tekanan parsial yang berdampak pada penurunan tegangan output melalui penurunan tegangan Nernst, pada akhirnya dapat berpotensi memicu fuel starvation dan air starvation apabila tidak dapat diketahui lebih dini. Penelitian ini bertujuan untuk menganalisis pengaruh kebocoran hidrogen dan oksigen terhadap performa SOFC, memanfaatkan Extended Kalman Filter untuk fault detection berbasis model, serta mengevaluasi kemampuan algoritma tersebut dalam mendeteksi dan mengisolasi kesalahan operasional. Penelitian ini dilakukan berbasis simulasi pada perangkat lunak MATLAB Simulink pada jenis SOFC planar sel tunggal dengan arus konstan 300 A. Simulasi dibangun berdasarkan dinamika tekanan parsial, persamaan tegangan Nernst, rugi aktivasi, rugi ohmik, rugi konsentrasi, serta parameter degradasi α dan β yang merepresentasikan peningkatan resistansi ohmik dan penurunan limiting current. Fault kebocoran direpresentasikan sebagai penurunan bertahap laju alir molar reaktan hingga 25% untuk suplai gas hidrogen dan 50% untuk suplai gas oksigen. Algoritma fault detection dirancang menggunakan residual tekanan parsial dengan batas -0,10 untuk fault code. Hasil pengujian menunjukkan sistem dapat mendeteksi dan mengisolasi jenis fault yang berbeda tanpa adanya false alarm.
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Solid Oxide Fuel Cells (SOFCs) are an energy conversion technology with high potential to support clean energy due to their high efficiency, low emissions, fuel flexibility, and potential for a long service life. However, during operation, SOFCs are prone to operational errors, particularly leaks of reactant gases—namely hydrogen and oxygen. These leaks can cause changes in partial pressure, leading to a decrease in output voltage due to a reduction in the Nernst voltage; if not detected early, this can potentially trigger fuel starvation and air starvation. This study aims to analyze the effect of hydrogen and oxygen leaks on SOFC performance, utilizing an Extended Kalman Filter for model-based fault detection, and to evaluate the algorithm’s ability to detect and isolate operational faults. This study was conducted via simulation using MATLAB Simulink software on a single-cell planar SOFC with a constant current of 300 A. The simulation was based on partial pressure dynamics, the Nernst voltage equation, activation losses, ohmic losses, concentration losses, and degradation parameters α and β, which represent increases in ohmic resistance and decreases in limiting current. Leakage faults are represented as a gradual decrease in the molar flow rate of reactants to 25% for the hydrogen gas supply and 50% for the oxygen gas supply. The fault detection algorithm was designed using partial pressure residual with a limit of -0,10 for the fault code. Test results show that the system can detect and isolate different types of errors without any false alarm.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Solid Oxide Fuel Cell, Extended Kalman Filter, fault detection, kebocoran gas, gas leakage
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA402.3 Kalman filtering. Q Science > QC Physics
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	I. G. P. Arya William Wiranata Purusa Dhana
Date Deposited:	04 Aug 2026 06:25
Last Modified:	04 Aug 2026 06:25
URI:	http://repository.its.ac.id/id/eprint/141376

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