Perancangan Fault Detection Pada Proton Exchange Membrane Fuel Cell (PEMFC) Menggunakan Adaptive Extended Kalman Filter

Christnantasari, Tasya Yusiasfa (2021) Perancangan Fault Detection Pada Proton Exchange Membrane Fuel Cell (PEMFC) Menggunakan Adaptive Extended Kalman Filter. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

PEM fuel cell merupakan sel bahan bakar dengan prinsip pertukaran proton. Namun pada penggunaannya, PEM fuel cell dapat mengalami kesalahan. Salah satu bentuk kesalahan tersebut adalah drying. Dalam Tugas Akhir ini, dampak kesalahan drying diteliti secara simulasi numerik. Dari hasil simulasi, disimpulkan bahwa drying dapat menurunkan water uptake, konduktivitas proton, koefisien electro-osmotic drag, serta koefisien thermal-osmotic. Penelitian Tugas Akhir ini juga mengajukan perancangan fault detection menggunakan adaptive extended Kalman filter (AEKF) untuk mendeteksi kesalahan drying tersebut. Dalam hal ini ada empat state yang ditinjau, yaitu temperature, RH, tekanan parsial dari hydrogen dan oksigen. Sedangkan output state AEKF adalah tegangan output PEM fuel cell. AEKF berfungsi sebagai estimator keempat state dan tegangan output untuk kondisi normal. Perbedaan antara hasil estimasi tegangan dengan pengukuran tegangan disebut sebagai residual dan digunakan sebagai variabel penentu terjadinya kesalahan menggunakan teknik perbandingan threshold (yaitu empat kali standar deviasi). Fault detection diujikan pada beberapa variasi kesalahan drying. Dari hasil pengujian diperoleh kesimpulan bahwa AEKF yang dirancang memiliki eror estimasi sebesar 0,00303%. Sedangkan fault detection mampu mendeteksi kesalahan drying dalam waktu 0.01 s untuk kesalahan drying terkecil (T > 357.15 K dan RH < 75%). ===================================================================================================== PEM fuel cell is a fuel cell that implements proton exchange principle Unfortunately, PEM fuel cell might encounter some faults. One of faults is drying. This final project, Effects of drying were observed through numerical simulation. Results showed that drying could cause reduction of membrane and catalyst water uptake, membrane and catalyst proton conductivity, electro-osmotic drag coefficient, and thermal osmotic coefficient. This final project also proposed fault detection design using adaptive extended Kalman filter (AEKF) to detect drying fault. In this case, AEKF used four states, they were temperature, relative humidity, partial pressure of hydrogen and oxygen and voltage as output state. Discrepancy of true voltage and estimated voltage was called as voltage residual which became variable that determined drying fault using threshold comparison method (used four-times sigma threshold). Fault detection was tested using some scenarios of drying. The results showed that adaptive extended Kalman filter that was designed had 0,00303% estimation errors. Fault detection could detect drying fault within 0,01 second for smallest drying fault scenarios (T>357.15 K and RH<75%).

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: adaptive extended Kalman filter, drying, fault detection, PEM fuel cell, adaptive extended Kalman filter, drying, fault detection, PEM fuel cell
Subjects: Q Science > QA Mathematics > QA402.3 Kalman filtering.
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2931 Fuel cells
Divisions: Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User: Tasya Yusiasfa Christnantasari
Date Deposited: 05 Mar 2021 01:27
Last Modified: 05 Mar 2021 01:27
URI: https://repository.its.ac.id/id/eprint/83137

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