SIMULASI PENGENDALIAN TEMPERATUR PADA HEAT EXCHANGER MENGGUNAKAN TEKNIK NEURO-FUZZY ADAPTIF
Keywords:
Neuro-fuzzy, heat exchanger, kendali cerdas, kendali predictiveAbstract
Makalah ini menyajikan simulasi suhu penukar panas mengendalikan menggunakan teknik neuro-fuzzy. Heat exchanger merupakan proses yang sangat non-linear. Oleh karena itu, teknik prediksi non-linear diharapkan dapat menjadi solusi yang lebih baik dalam strategi kontrol prediktif. Suatu sistem kendali berbasis neuro-fuzzy adaptif prediktif dirancang untuk mengendalikan suatu plant yaitu heat exchanger yang sangat dinamis. Keuntungan penggunaan jaringan saraf dan logika fuzzy untuk pemodelan proses yang dipelajari dan prediktor berbasis neuro-fuzzy dirancang, dilatih dan diuji sebagai bagian dari kontroler prediktif. Dinamika plant diidentifikasi menggunakan jaringan neural tipe backpropagation. Selanjutnya strategi kontrol prediktif berdasarkan model neuro-fuzzy dari plant tersebut diterapkan untuk mencapai set-point titik output dari plant yang diharapkan. Simulasi dilakukan menggunakan perangkat-lunak aplikasi Matlab-Simulink. Hasil yang diperoleh menunjukkan efektivitas dan keunggulan dari pendekatan yang diusulkan.
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References
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Chen, S.T., Yu, D.C., and Moghaddamjo, A.R., 1992, “ Weather Sensitive Short-Term Load Forecasting Using Nonfully Connected Artificial Neural Network”, IEEE Trans. on Power Systems, Vol. 7, No. 3, pp. 1098 - 1105
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Syahputra, R., Robandi, I., and Ashari, M., 2012a, “Reconfiguration of Distribution Network with DG Using Fuzzy Multi-objective Method”, International Conference on Innovation, Management and Technology Research (ICIMTR), May 21-22, 2012, Melacca, Malaysia.
Syahputra, R., 2012b, “Fuzzy Multi-Objective Approach for the Improvement of Distribution Network Efficiency by Considering DG”, IJCSIT, Vol. 4, No. 2, pp. 57-68.
Syahputra, R. 2012c, “Distributed Generation: State of the Arts dalam Penyediaan Energi Listrik”. LP3M UMY, Yogyakarta, 2012.
Syahputra, R., 2013, “A Neuro-Fuzzy Approach For the Fault Location Estimation of Unsynchronized Two-Terminal Transmission Lines”, IJCSIT, Vol. 5, No. 1, pp. 23-37.
Syahputra, R., Robandi, I., and Ashari, M., 2014a, “Optimization of Distribution Network Configuration with Integration of Distributed Energy Resources Using Extended Fuzzy Multi-objective Method”, International Review of Electrical Engineering (IREE), vol.9, no.3, pp. 629-639.
Syahputra, R., Robandi, I., and Ashari, M., 2014b, “Optimal Distribution Network Reconfiguration with Penetration of Distributed Energy Resources”, in Proceeding of ICITACEE 2014, Semarang, Indonesia.
Syahputra R, Robandi I, Ashari M., 2014c, “Distribution Network Efficiency Improvement Based on Fuzzy Multi-objective Method”. IPTEK Journal of Proceedings Series. 2014; 1(1): pp. 224-229.
Syahputra, R., Robandi, I., and Ashari, M., 2014d, “Performance Analysis of Wind Turbine as a Distributed Generation Unit in Distribution System”, IJCSIT, Vol. 6, No. 3, pp. 39-56.
Syahputra, R., Robandi, I., and Ashari, M., 2015a, “Performance Improvement of Radial Distribution Network with Distributed Generation Integration Using Extended Particle Swarm Optimization Algorithm”, International Review of Electrical Engineering (IREE), vol.10, no.2, 2015. pp.293-304.
Syahputra, R., Robandi, I., and Ashari, M., 2015b, “Reconfiguration of Distribution Network with DER Integration Using PSO Algorithm”, TELKOMNIKA, vol.13, no.3, 2015. pp.759-766.
Syahputra, R., Robandi, I., and Ashari, M., 2015c, “PSO Based Multi-objective Optimization for Reconfiguration of Radial Distribution Network”, International Journal of Applied Engineering Research (IJAER), vol.10, no.6, 2015. pp. 14573-14586.
Wang, J.S., Lee, C.S.G., 2002, "Self-Adaptive Neuro-Fuzzy Inference Systems for Classification Applications", IEEE Trans. on Fuzzy Systems, 10, 6, Dec.
Chen, S.T., Yu, D.C., and Moghaddamjo, A.R., 1992, “ Weather Sensitive Short-Term Load Forecasting Using Nonfully Connected Artificial Neural Network”, IEEE Trans. on Power Systems, Vol. 7, No. 3, pp. 1098 - 1105
Fausett, L., 1994, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Prentice Hall, Englewood Cliffs, New Jersey.
Henriques, L.O.A.P., Rolim, L.G.B., Suemitsu,W.I., 2002, "Proposition of an Offline Learning Current Modulation for Torque-Ripple Reductionin Switched Reluctance Motors: Design and Experimental Evaluation", IEEE Transactions on Industrial Electronics, 49, 3, June.
Jamal, A., Syahputra, R., 2011, “Model Power System Stabilizer Berbasis Neuro-Fuzzy Adaptif”, Jurnal Ilmiah Semesta Teknika, Vol. 14, No. 2, 139-149, 2011, pp. 139-149.
Jang, J.S.R., 1993, "ANFIS: Adaptive-Network-based Fuzzy Inference System", IEEE Trans. Syst., Man, Cybern., 23, 665-685, June.
Paraskevopoulos, P.N., 1996, Digital Control Systems, Prentice Hall, New Jersey.
Syahputra R, Robandi I, Ashari M. “Modeling and Simulation of Wind Energy Conversion System in Distributed Generation Units”. International Seminar on APTECS. 2011; pp. 290-296.
Syahputra, R., Robandi, I., and Ashari, M., 2012a, “Reconfiguration of Distribution Network with DG Using Fuzzy Multi-objective Method”, International Conference on Innovation, Management and Technology Research (ICIMTR), May 21-22, 2012, Melacca, Malaysia.
Syahputra, R., 2012b, “Fuzzy Multi-Objective Approach for the Improvement of Distribution Network Efficiency by Considering DG”, IJCSIT, Vol. 4, No. 2, pp. 57-68.
Syahputra, R. 2012c, “Distributed Generation: State of the Arts dalam Penyediaan Energi Listrik”. LP3M UMY, Yogyakarta, 2012.
Syahputra, R., 2013, “A Neuro-Fuzzy Approach For the Fault Location Estimation of Unsynchronized Two-Terminal Transmission Lines”, IJCSIT, Vol. 5, No. 1, pp. 23-37.
Syahputra, R., Robandi, I., and Ashari, M., 2014a, “Optimization of Distribution Network Configuration with Integration of Distributed Energy Resources Using Extended Fuzzy Multi-objective Method”, International Review of Electrical Engineering (IREE), vol.9, no.3, pp. 629-639.
Syahputra, R., Robandi, I., and Ashari, M., 2014b, “Optimal Distribution Network Reconfiguration with Penetration of Distributed Energy Resources”, in Proceeding of ICITACEE 2014, Semarang, Indonesia.
Syahputra R, Robandi I, Ashari M., 2014c, “Distribution Network Efficiency Improvement Based on Fuzzy Multi-objective Method”. IPTEK Journal of Proceedings Series. 2014; 1(1): pp. 224-229.
Syahputra, R., Robandi, I., and Ashari, M., 2014d, “Performance Analysis of Wind Turbine as a Distributed Generation Unit in Distribution System”, IJCSIT, Vol. 6, No. 3, pp. 39-56.
Syahputra, R., Robandi, I., and Ashari, M., 2015a, “Performance Improvement of Radial Distribution Network with Distributed Generation Integration Using Extended Particle Swarm Optimization Algorithm”, International Review of Electrical Engineering (IREE), vol.10, no.2, 2015. pp.293-304.
Syahputra, R., Robandi, I., and Ashari, M., 2015b, “Reconfiguration of Distribution Network with DER Integration Using PSO Algorithm”, TELKOMNIKA, vol.13, no.3, 2015. pp.759-766.
Syahputra, R., Robandi, I., and Ashari, M., 2015c, “PSO Based Multi-objective Optimization for Reconfiguration of Radial Distribution Network”, International Journal of Applied Engineering Research (IJAER), vol.10, no.6, 2015. pp. 14573-14586.
Wang, J.S., Lee, C.S.G., 2002, "Self-Adaptive Neuro-Fuzzy Inference Systems for Classification Applications", IEEE Trans. on Fuzzy Systems, 10, 6, Dec.
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Published
2015-12-30
How to Cite
Syahputra, R. (2015). SIMULASI PENGENDALIAN TEMPERATUR PADA HEAT EXCHANGER MENGGUNAKAN TEKNIK NEURO-FUZZY ADAPTIF. Jurnal Teknologi, 8(2), 161–168. Retrieved from https://ejournal.akprind.ac.id/index.php/jurtek/article/view/1127
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