Article Sidebar
Main Article Content
Abstract
Pemanfaatan energi angin sebagai Pembangkit Listrik Tenaga Bayu di wilayah Indonesia perlu pengolahan dengan cermat, karena kecepatan angin rata-rata harian yang berkisar antara 2,5 – 6 m/s merupakan kategori kecepatan angin kelas rendah hingga menengah. Penelitian tentang prediksi kecepatan angin pada Vertical-Axis Wind Turbine (VAWT) menggunakan klasifikasi fuzzy time series ini dimaksudkan untuk menggantikan sensor kecepatan dalam mendeteksi dan mendapatkan potensi kecepatan angin terbaik untuk menghasilkan tegangan listrik maksimal sepanjang catur wulan pertama dalam satu tahun. Algoritma fuzzy time series Chen mampu melakukan prediksi kecepatan angin untuk menghasilkan tegangan listrik pada sistem VAWT 800 Watt sehingga sistem dapat beroperasi dengan mode tanpa sensor namun tetap dapat mengukur kecepatan angin dengan akurasi hingga 70%.
Keywords
Article Details
Copyright (c) 2024 Oktriza Melfazen, Denda Dewatama, M. Taqijuddin Alawiy
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
- Badan Pusat Statistik Indonesia. (2018). Statistical Yearbook of Indonesia 2018.
- Kementrian ESDM. (2018). Inilah Konsumsi Listrik Nasional.
- Rahmat, M. H. (2018). Potensi Pengembangan PLTB di Indonesia.
- Yurtsven, Kaan., Karatepe, Engin., & Deniz, E. (2021). Sensorless fault detection method for photovoltaic systems through mapping the inherent characteristics of PV plant site: Simple and practical. Solar Energy, 216, 96–110. https://doi.org/https://doi.org/10.1016/j.solener.2021.01.011
- Hata, Katsuhiro., Hanajiri, Kensuke., Imura, Takehiro., Hori, Yoichi., Sato, Motoki., & Gunji, D. (2018). Driving Test Evaluation of Sensorless Vehicle Detection Method for In-motion Wireless Power Transfer. International Power Electronics Conference (IPEC-Niigata -ECCE Asia), 663–668. https://doi.org/https://doi.org/10.23919/IPEC.2018.8508025
- Wang, Qiong., Wang, Shuanghong., & Chen, C. (2019). Review of sensorless control techniques for PMSM drives. IEEJ Transactions on Electrical and Electronic Engineering. https://doi.org/https://doi.org/10.1002/tee.22974
- Yao, Yihui., Shen, Yichao., Lu, Yan., & Zhuang, C. (2020). Sensorless Collision Detection Method for Robots with Uncertain Dynamics Based on Fuzzy Logics. 2020 IEEE International Conference on Mechatronics and Automation (ICMA), 413–418. https://doi.org/10.1109/ICMA49215.2020.9233749
- Sreeram, K. (2018). Design of Fuzzy Logic Controller for Speed Control of Sensorless BLDC Motor Drive. 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT), 18–24. https://doi.org/10.1109/ICCPCCT.2018.8574280
- Wagner, H.-J. (2020). Introduction to wind energy systems. EPJ Web of Conferences, 00004. https://doi.org/https://doi.org/10.1051/epjconf/202024600004
- Twidell, J. (2022). Renewable Energy Resources (4th ed.).
- Nelson, V., & Starcher, K. (2018). Wind energy: renewable energy and the environment (3rd ed.). CRC press.
- Eriksson, Sandra., Bernhoff, Hans., & Leijon, M. (2008). Evaluation of different turbine concepts for wind power. Renewable and Sustainable Energy Reviews, 12(5), 1419–1434. https://doi.org/https://doi.org/10.1016/j.rser.2006.05.017
- Al-Bahadly, I. (2009). Building a wind turbine for rural home. Energy for Sustainable Development, 13(3), 159–165. https://doi.org/https://doi.org/10.1016/j.esd.2009.06.005
- Kim, Y., Kang, M., Muljadi, E., Park, J. W., & Kang, Y. C. (2017). Power Smoothing of a Variable-Speed Wind Turbine Generator in Association With the Rotor-Speed-Dependent Gain. IEEE Transactions on Sustainable Energy, 8(3), 990–999. https://doi.org/https://doi.org/10.1109/TSTE.2016.2637907
- Schaffarczyk, A. P. (2020). Introduction to Wind Turbine Aerodynamics. In Green Energy and Technology book series (pp. 7–25)
- Dilimulati, Aierken., Stathopoulos, Ted., & Paraschivoiu, M. (2018). Wind turbine designs for urban applications: A case study of shrouded diffuser casing for turbines. Journal of Wind Engineering and Industrial Aerodynamics, 175, 179–192. https://doi.org/https://doi.org/10.1016/j.jweia.2018.01.003
- Abo-Khalil, A. G., Eltamaly, A. M., RP, P., Alghamdi, A. S., & Tlili, I. (2020). A Sensorless Wind Speed and Rotor Position Control of PMSG in Wind Power Generation Systems. Sustainability, 12(20), 8481. https://doi.org/https://doi.org/10.3390/su12208481
- Shotorbani, A. M., Mohammadi-Ivatloo, B., Wang, Liwei., Marzband, Mousa., & Sabahi, M. (2019). Application of finite-time control Lyapunov function in low-power PMSG wind energy conversion systems for sensorless MPPT. International Journal of Electrical Power & Energy Systems, 106, 169–182. https://doi.org/https://doi.org/10.1016/j.ijepes.2018.09.039
- Du, Wenjuan., Wang, Xubin., & Wang, H. (2018). Sub-synchronous interactions caused by the PLL in the grid-connected PMSG for the wind power generation. International Journal of Electrical Power & Energy Systems, 98, 331–341. https://doi.org/https://doi.org/10.1016/j.ijepes.2017.11.018
- Chatri, Chakib., & Ouassaid, M. (2021). Chapter 11 - Advanced control of PMSG-based wind energy conversion system applying linear matrix inequality approach (N. A. K. Ahmad Taher Azar (ed.)). Renewable Energy Systems, Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-820004-9.00002-4
- Song, Q., & Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series—part I, . Fuzzy sets and systems, 54(1).
- Wei, W. W. S. (2019). Multivariate Time Series Analysis and Applications. John Wiley & Sons.
- Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series, Fuzzy Sets Syst. 81.
- Arnita, Afnisah, N., & Marpaung, F. (2019). A Comparison of The Fuzzy Time Series Methods of Chen, Cheng and Markov Chain in Predicting Rainfall in Medan. Journal of Physics: Conference Series, Volume 1462, The 6th Annual International Seminar on Trends in Science and Science, Conf. Ser. 1462 012044 Education. https://doi.org/10.1088/1742-6596/1462/1/012044
- Bose, Mahua., & Mali, K. (2019). Designing fuzzy time series forecasting models: A survey. International Journal of Approximate Reasoning, Volume 111, 78–99.
- Severiano, C. A., Silva, P. C. L., Sadaei, H. J., & Guimarães, F. G. (2017). Very short-term solar forecasting using fuzzy time series. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–6. https://doi.org/10.1109/FUZZ-IEEE.2017.8015732
References
Badan Pusat Statistik Indonesia. (2018). Statistical Yearbook of Indonesia 2018.
Kementrian ESDM. (2018). Inilah Konsumsi Listrik Nasional.
Rahmat, M. H. (2018). Potensi Pengembangan PLTB di Indonesia.
Yurtsven, Kaan., Karatepe, Engin., & Deniz, E. (2021). Sensorless fault detection method for photovoltaic systems through mapping the inherent characteristics of PV plant site: Simple and practical. Solar Energy, 216, 96–110. https://doi.org/https://doi.org/10.1016/j.solener.2021.01.011
Hata, Katsuhiro., Hanajiri, Kensuke., Imura, Takehiro., Hori, Yoichi., Sato, Motoki., & Gunji, D. (2018). Driving Test Evaluation of Sensorless Vehicle Detection Method for In-motion Wireless Power Transfer. International Power Electronics Conference (IPEC-Niigata -ECCE Asia), 663–668. https://doi.org/https://doi.org/10.23919/IPEC.2018.8508025
Wang, Qiong., Wang, Shuanghong., & Chen, C. (2019). Review of sensorless control techniques for PMSM drives. IEEJ Transactions on Electrical and Electronic Engineering. https://doi.org/https://doi.org/10.1002/tee.22974
Yao, Yihui., Shen, Yichao., Lu, Yan., & Zhuang, C. (2020). Sensorless Collision Detection Method for Robots with Uncertain Dynamics Based on Fuzzy Logics. 2020 IEEE International Conference on Mechatronics and Automation (ICMA), 413–418. https://doi.org/10.1109/ICMA49215.2020.9233749
Sreeram, K. (2018). Design of Fuzzy Logic Controller for Speed Control of Sensorless BLDC Motor Drive. 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT), 18–24. https://doi.org/10.1109/ICCPCCT.2018.8574280
Wagner, H.-J. (2020). Introduction to wind energy systems. EPJ Web of Conferences, 00004. https://doi.org/https://doi.org/10.1051/epjconf/202024600004
Twidell, J. (2022). Renewable Energy Resources (4th ed.).
Nelson, V., & Starcher, K. (2018). Wind energy: renewable energy and the environment (3rd ed.). CRC press.
Eriksson, Sandra., Bernhoff, Hans., & Leijon, M. (2008). Evaluation of different turbine concepts for wind power. Renewable and Sustainable Energy Reviews, 12(5), 1419–1434. https://doi.org/https://doi.org/10.1016/j.rser.2006.05.017
Al-Bahadly, I. (2009). Building a wind turbine for rural home. Energy for Sustainable Development, 13(3), 159–165. https://doi.org/https://doi.org/10.1016/j.esd.2009.06.005
Kim, Y., Kang, M., Muljadi, E., Park, J. W., & Kang, Y. C. (2017). Power Smoothing of a Variable-Speed Wind Turbine Generator in Association With the Rotor-Speed-Dependent Gain. IEEE Transactions on Sustainable Energy, 8(3), 990–999. https://doi.org/https://doi.org/10.1109/TSTE.2016.2637907
Schaffarczyk, A. P. (2020). Introduction to Wind Turbine Aerodynamics. In Green Energy and Technology book series (pp. 7–25)
Dilimulati, Aierken., Stathopoulos, Ted., & Paraschivoiu, M. (2018). Wind turbine designs for urban applications: A case study of shrouded diffuser casing for turbines. Journal of Wind Engineering and Industrial Aerodynamics, 175, 179–192. https://doi.org/https://doi.org/10.1016/j.jweia.2018.01.003
Abo-Khalil, A. G., Eltamaly, A. M., RP, P., Alghamdi, A. S., & Tlili, I. (2020). A Sensorless Wind Speed and Rotor Position Control of PMSG in Wind Power Generation Systems. Sustainability, 12(20), 8481. https://doi.org/https://doi.org/10.3390/su12208481
Shotorbani, A. M., Mohammadi-Ivatloo, B., Wang, Liwei., Marzband, Mousa., & Sabahi, M. (2019). Application of finite-time control Lyapunov function in low-power PMSG wind energy conversion systems for sensorless MPPT. International Journal of Electrical Power & Energy Systems, 106, 169–182. https://doi.org/https://doi.org/10.1016/j.ijepes.2018.09.039
Du, Wenjuan., Wang, Xubin., & Wang, H. (2018). Sub-synchronous interactions caused by the PLL in the grid-connected PMSG for the wind power generation. International Journal of Electrical Power & Energy Systems, 98, 331–341. https://doi.org/https://doi.org/10.1016/j.ijepes.2017.11.018
Chatri, Chakib., & Ouassaid, M. (2021). Chapter 11 - Advanced control of PMSG-based wind energy conversion system applying linear matrix inequality approach (N. A. K. Ahmad Taher Azar (ed.)). Renewable Energy Systems, Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-820004-9.00002-4
Song, Q., & Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series—part I, . Fuzzy sets and systems, 54(1).
Wei, W. W. S. (2019). Multivariate Time Series Analysis and Applications. John Wiley & Sons.
Chen, S. M. (1996). Forecasting enrollments based on fuzzy time series, Fuzzy Sets Syst. 81.
Arnita, Afnisah, N., & Marpaung, F. (2019). A Comparison of The Fuzzy Time Series Methods of Chen, Cheng and Markov Chain in Predicting Rainfall in Medan. Journal of Physics: Conference Series, Volume 1462, The 6th Annual International Seminar on Trends in Science and Science, Conf. Ser. 1462 012044 Education. https://doi.org/10.1088/1742-6596/1462/1/012044
Bose, Mahua., & Mali, K. (2019). Designing fuzzy time series forecasting models: A survey. International Journal of Approximate Reasoning, Volume 111, 78–99.
Severiano, C. A., Silva, P. C. L., Sadaei, H. J., & Guimarães, F. G. (2017). Very short-term solar forecasting using fuzzy time series. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–6. https://doi.org/10.1109/FUZZ-IEEE.2017.8015732