Optimalisasi Wind Speed Energy Harvesting dengan Wind Speed Estimation dan Modifikasi Maximum Power Point Tracking

Putri, Andi Nur (2025) Optimalisasi Wind Speed Energy Harvesting dengan Wind Speed Estimation dan Modifikasi Maximum Power Point Tracking. Doctoral thesis, Institut Teknologi Sepuluh Nopember.

Text 07111960010022-Dissertation.pdf - Accepted Version Restricted to Repository staff only until 1 April 2027. Download (3MB) | Request a copy

Abstract

Integrasi energi terbarukan pada sistem kelistrikan memberikan dampak signifikan dalam meningkatkan pasokan daya sekaligus mengurangi dampak lingkungan. Energi angin adalah salah satu sumber energi terbarukan yang tidak terbatas, tetapi memiliki sifat intermitten yang memengaruhi stabilitas frekuensi sistem dan kualitas daya. Penelitian ini mengkaji dampak sifat intermittent angin terhadap stabilitas sistem kelistrikan dan mengusulkan metode optimalisasi energi yang dihasilkan. Tujuan penelitian ini adalah mengembangkan model estimasi kecepatan angin berbasis hybrid CNN-LSTM-PSO untuk meningkatkan akurasi prediksi kecepatan angin di wilayah tropis. Selain itu, penelitian ini juga mengusulkan modifikasi algoritma Maximum Power Point Tracking (MPPT) menjadi Automatic Selecting Step Size (ASS-PO MPPT) dengan pembagian wilayah operasi turbin angin ke dalam beberapa sektor. Hasil simulasi menunjukkan bahwa pendekatan hybrid CNN-LSTM-PSO memiliki nilai R² tertinggi (0.81) dan RMSE terendah (0.41), menunjukkan keakuratan prediksi yang jauh lebih baik dibandingkan metode konvensional serta mempercepat waktu konvergensi MPPT dengan waktu 33 milidetik pada kecepatan angin 8 m/s, jauh lebih cepat daripada P&O konvensional dan VSS-P&O. Kontribusi utama dari penelitian ini adalah pengembangan algoritma ASS-PO MPPT yang lebih efisien dalam meminimalkan kesalahan perturbasi dan mempertahankan daya maksimum pada kondisi angin yang fluktuatif. Dengan hasil tersebut, penelitian ini diharapkan dapat memberikan solusi praktis untuk optimalisasi wind energy harvesting dengan meningkatkan efisiensi sistem tenaga angin sekaligus menjaga stabilitas daya.
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The integration of renewable energy into power systems has a significant impact on increasing power supply while reducing environmental effects. Wind energy is an unlimited renewable energy source; however, its intermittent nature affects system frequency stability and power quality. This study examines the impact of wind intermittency on power system stability and proposes an optimization method for the generated energy. The objective of this research is to develop a wind speed estimation model based on a hybrid CNN-LSTM-PSO to improve wind speed prediction accuracy in tropical regions. Additionally, this study proposes a modification of the Maximum Power Point Tracking (MPPT) algorithm into Automatic Selecting Step Size (ASSPO MPPT) by dividing the wind turbine operating region into multiple sectors. Simulation results show that the hybrid CNN-LSTM-PSO approach achieves the highest R² value (0.81) and the lowest RMSE (0.41), demonstrating significantly improved prediction accuracy compared to conventional methods. Furthermore, it accelerates MPPT convergence time to 33 milliseconds at a wind speed of 8 m/s, which is considerably faster than conventional P&O and VSS-P&O methods. The main contribution of this research is the development of the ASS-PO MPPT algorithm, which is more efficient in minimizing perturbation errors and maintaining maximum power under fluctuating wind conditions. With these results, this study is expected to provide a practical solution for optimizing wind energy harvesting by improving wind power system efficiency while ensuring power stability.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Energi Angin, intermittent, Wind speed estimation, Maximum Power Point Tracking, CNN-LSTM-PSO
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy or power T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1322.6 Electric power-plants T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK152.A75 Electrical engineering--Safety measures
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20001-(S3) PhD Thesis
Depositing User:	Andi Nur Putri
Date Deposited:	03 Feb 2025 02:46
Last Modified:	03 Feb 2025 02:46
URI:	http://repository.its.ac.id/id/eprint/117642

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