Implementasi Algoritme Evolusioner NSGA-II pada Optimasi Daya Wireless Power Transfer Multi-Penerlima

Penulis

  • Sabriansyah Rizqika Akbar Program Studi Teknik Komputer Universitas Brawijaya Malang
  • Eko Setiawan Universitas Brawijaya, Malang
  • Agung Setiabudi Universitas Brawijaya, Malang

DOI:

https://doi.org/10.25126/jtiik.2022976780

Abstrak

Wireless Power Transfer (WPT) merupakan teknologi yang dapat menghantarkan energi listrik tanpa kabel. Teknologi WPT terus dikembangkan untuk mencapai tujuan yaitu meningkatkan kemampuan sistem dalam memaksimalkan daya yang diserap pada beban di sisi penerima. Teknologi WPT saat ini banyak digunakan dan diterapkan untuk pengisian daya pada perangkat elektronik dengan memanfaatkan konsep koil induktif kopling. Induktif WPT memanfaatkan pasangan koil antara koil primer sebagai sumber, dan koil sekunder sebagai penerima. Salah satu tantangan desain sistem WPT adalah mendapatkan daya optimal dengan batasan-batasan rekayasa yang ada. Untuk mendapatkan daya optimal, diperlukan beberapa langkah sistematis dimulai dengan analisis sistem dan rangkaian, penentuan variabel state-space, dan model matematis. Parameter utama (sumber tegangan, kapasitansi, induktansi, dan mutual inductance) tercantum dalam model matematis untuk selanjutnya diformulasikan sebagaidaya pada multi-penerima sebagai multi objective-function. Proses optimasi dilakukan dengan mempertimbangkan batasan rekayasa nilai kapasitansi yang ada di pasaran dan limitasi nilai frekuensi yang dapat beroperasi pada rangkaian listrik. Algoritme evolusioner NSGA-II digunakan untuk menyelesaikan permasalahanmulti objective-function dan batasannya untuk memperoleh nilai parameter optimal. Selanjutnya, Perolehan nilai parameter optimal dibandingkan dengan hasil aplikasi simulator LTSPICE untuk melakukan validasi nilai daya pada penerima. Dalam proses optimasi dengan NSGA-II, jumlah generasi akan menentukan kecepatan proses optimasi. Untuk itu, evaluasi running-metric dilakukanagar dapat mengetahui jumlah generasi yang menunjukkan hasil konvergen pada nilai objective-function. Penelitian ini menunjukkan hasil implementasi NSGA-II dapat digunakan untuk menyelesaikan permasalahan optimasi daya WPT multi penerima, dan mempertimbangkan jumlah generasi, untuk meningkatkan kecepatan dalam melakukan proses optimasi.


Abstract

Wireless Power Transfer (WPT) can transmit electrical energy without wires. Research on WPT technology is mainly conducted to achieve maximum power absorbed at the load on the receiving side. The current WPT technology is widely used and applied to charging electronic devices using the concept of the inductive coil. Inductive WPT utilizes a pair of coils between the primary coil as a source and the secondary coil as a receiver. The design challenge of the WPT system is obtaining optimal power within existing engineering constraints. In order to obtain optimal power, systematic steps are needed. It starts with the system and circuit analysis, determining state-space variables, and mathematical models. The main parameters (voltage source, capacitance, inductance, and mutual inductance) are used in the mathematical model and further formulated as power in receivers as a multi-objective function. The optimization is conducted by considering engineering constraints on capacitance values on the market and limitations on frequency values that can operate in electrical circuits. The NSGA-II evolutionary algorithm is used to solve multi-objective-function problems and their constraints to obtain optimal parameter values. The obtained optimal parameter values are compared with the results of the LTSPICE simulator application to validate the power value of the receiver. In the optimization process with NSGA-II, the number of generations will determine the speed of the optimization process. For this reason, a running-metric evaluation was carried out to find the number of generations that show convergent results on the objective-function value. This study shows that the implementation of NSGA-II  can optimize power in multi-receiver WPT. The running metric analysis can analyze and obtain the number of generations that can get the optimal solution faster.


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Referensi

AHMET, S., & KAVUT, S. 2018. A frequency-tuned magnetic resonance-based wireless power transfer system with A frequency-tuned magnetic resonance-based wireless power transfer system with. Turkish Journal of Electrical Engineering & Computer Sciences, 26, 3168 – 3180. doi:10.3906/elk-1803-98

AKBAR, S. R., & HODAKA, I. 2021. A Design Approach to Wireless High-Power Transfer to Multiple Receivers with Asymmetric Circuit. International journal of circuits, systems and signal processing, 125-134.

AKBAR, S., SETIAWAN, E., HIRATA, T., YAMAGUCHI, K., & HODAKA, I. 2021. The Frequency Response and Steady-State analysis on Wireless Power Transfer using Square Wave Input. SIET '21: 6th International Conference on Sustainable Information Engineering and Technology 2021. Malang.

BEH, T. C., KATO, M., IMURA, T., OH, S., & HORI, Y. (2013). Automated Impedance Matching System for Robust Wireless Power Transfer via Magnetic Resonance Coupling. IEEE Transactions on Industrial Electronics, 60(9), 3689-3698. doi:10.1109/TIE.2012.2206337

BLANK, J., & DEB, K. 2020. Pymoo: Multi-Objective Optimization in Python. IEEE Access, 89497-89509. doi:doi: 10.1109/ACCESS.2020.2990567

DEB, K., PRATAP, A., AGARWAL, S., & MEYARIVAN, T. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017

EKA WIJAYA SUSILO, H. N. 2019. Multi-Objective Optimization Untuk Container Scheduling Menggunakan Algoritma Ɛ-Nsga-Ii. Jurnal Infra.

GOURAV KUMAR SUMAN, J. M. 2022. Stability of microgrid cluster with Diverse Energy Sources: A multi-objective solution using NSGA-II based controller. Sustainable Energy Technologies and Assessments, 50(101834). doi:doi.org/10.1016/j.seta.2021.101834.

JAWAD, A. M., NORDIN, R., GHARGHAN, S. K., JAWAD, H. M., & ISMAIL, M. 2017. Opportunities and Challenges for Near-Field Wireless Power Transfer: A Review. Energies, 10(7).

JULIAN BLANK, K. D. 2020. A Running Performance Metric and Termination Criterion for Evaluating Evolutionary Multi- and Many-objective Optimization Algorithms. 2020 IEEE Congress on Evolutionary Computation (CEC) (hal. 1-8). Glasgow, UK: IEEE.

KURS, A., KARALIS, A., MOFFATT, R., JOANNOPOULOS, J. D., FISHER, P., & SOLJACIC, A. M. 2007. Wireless power transfer via strongly coupled magnetic resonances. science(317(5834)), pp.83-86.

TESLA, N. 1900, May 15. United States Paten No. US649621A.

TIKA MUSTIKA, N. S. 2013. Penerapan Metode Nsga-Ii Pada Masalah Optimisasi Multi-Objektif Porsi Bagi Hasil Dalam Investasi Syariah. Bandung: PERPUSTAKAAN DIGITAL ITB.

WEI, X., WANG, Z., & DAI, H. 2014. A Critical Review of Wireless Power Transfer via Strongly Coupled Magnetic Resonances. Energies, 7(7), 4316-4341. doi:10.3390/en7074316

YAMAGUCHI, K., HIRATA, T., YAMAMOTO, Y., & HODAKA, I. 2014. Resonance and efficiency in wireless power transfer system. WSEAS Transactions on Circuits and Systems, 13, hal. 218-223.

ZHANG, W., & MI, C. C. 2015. Compensation Topologies of High-Power Wireless Power Transfer Systems. IEEE Transactions on Vehicular Technology, 4768 - 4778. doi:10.1109/TVT.2015.2454292

Diterbitkan

29-12-2022

Cara Mengutip

Implementasi Algoritme Evolusioner NSGA-II pada Optimasi Daya Wireless Power Transfer Multi-Penerlima. (2022). Jurnal Teknologi Informasi Dan Ilmu Komputer, 9(7), 1715-1720. https://doi.org/10.25126/jtiik.2022976780