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"ABSTRAKThe interaction of cationic surfactant (CTAB, Cationic surfactant hexadecyltrimethyl ammonium bromide ((C₁₆H₃₃)N(CH₃)₃BR and TTAB, Tetradecyltrimethyl ammonium bromide ( )), a prevalent chemical in the industrialand natural processes, with water ( ) has been studied using GROMACSprogram and VMD program. In the following project, to simulate the CTAB andTTAB at air-water interface, GROMACS (Groningen Machine for ChemicalSimulations) software is used. GROMACS is a molecular dynamics packagedesigned for primary use of simulation of biochemical molecules like proteins,lipids, and nucleic acids that have a many complicated bonded interactions(GROMACS, 2010). Furthermore, to model and display the simulation, VMD(Visual Molecular Dynamics) program is used. VMD is intended for modeling,visualization and analysis of biological systems such as proteins, nucleic acids,lipid bilayer assemblies, etc. In this research project 13 simulations, 6 of these aresuccessive simulations and the other 7 simulations have been included in theappendix. The simulations have been simulated to prove the following 3hypotheses which are ssurfactants has an amphiphilic nature, surfactants adsorbon the interface between oil and water, lowering the interfacial tension andpromoting mixing and surface potential measurement at the air-water interfaceincreases surfactant-dependent manner in the air-water expanded transition region.Therefore with the addition of surfactant to the air-water interface, there will be asudden increase of surface potential. The first seven simulations that have beenincluded in the appendix were simulated to find the right charge distribution. Allthe observed results shown by these 13 simulations are not yet predictable orreliable; this is due to not the right amount of simulation time or the chargedistribution of the cationic surfactant. The three kinds of observations (the densityprofile of the cationic surfactant, the surface tension of the cationic surfactant withwater, and the surface potential of cationic surfactant with water) are veryuncertain and therefore many more simulations are required to be completed inthe future."

"Microgrid dituntut untuk dapat mendeteksi kondisi islanding dalam waktu yang cepat untuk dapat mencegah memburuknya gangguan. Dan salah satu metode deteksinya yaitu deteksi pasif yang dapat mendeteksi secara cepat dan sistemnya sederhana tapi mengorbankan akurasi deteksinya. Sehingga digunakan machine learning pada metode pasif untuk meningkatkan akurasi deteksinya. Metode deteksi yang diajukan ini membaca sinyal tegangan yang kemudian diolah dengan Variational Mode Decomposition (VMD) untuk menjadi fitur masukkan machine learning Long Short Term Memory (LSTM) yang digunakan untuk mendeteksi keadaan Islanding. Simulasi metode deteksi ini yang dilakukan pada model microgrid PV-baterai menghasilkan deteksi dengan akurasi sebesar 97.6% dengan waktu deteksi sekitar 0.04 detik

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Microgrid is required to be able to detect islanding conditions as fast as possible to prevent worsening the situation. And one of the detection methods is passive detection which can detect quickly and simple but have low detection accuracy and a non-detection zone (NDZ). Hence, machine learning is used in the passive method to improve the detection accuracy and remove NDZ. The proposed detection method reads grid voltage signal which is then processed with Variational Mode Decomposition (VMD) to become an input feature for Long Short-Term Memory (LSTM) machine learning which is used to detect the Islanding state. The simulation of this detection method on the PV-battery microgrid model resulted in detection with an accuracy of 97.6%, a detection time about 0.04 seconds and almost zero NDZ."