Peningkatan Kestabilan Sistem Kelistrikan Sulselrabar Akibat Penetrasi Energi Terbarukan Berbasis Model Kecerdasan Buatan

Djalal, Muhammad Ruswandi (2025) Peningkatan Kestabilan Sistem Kelistrikan Sulselrabar Akibat Penetrasi Energi Terbarukan Berbasis Model Kecerdasan Buatan. Doctoral thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penelitian ini bertujuan untuk meningkatkan stabilitas sistem kelistrikan Sulselrabar (Sulawesi Selatan, Tenggara dan Barat) setelah penetrasi Energi Baru Terbarukan (EBT) Pembangkit Listrik Tenaga Bayu (PLTB). Penelitian ini menganalisis dan mendesain performansi kestabilan sistem akibat penetrasi PLTB berbasis metode kecerdasan buatan. Penetrasi pembangkit baru dapat menyebabkan terjadinya ketidakstabilan pada sistem yang telah exsisting, dan mempengaruhi stabilitas sistem secara keseluruhan. Perubahan beban dalam sistem tenaga listrik menimbulkan osilasi pada poros rotor generator yang diakibatkan dari perilaku dinamik. Terdapat beberapa peralatan tambahan yang dapat digunakan untuk meredam osilasi dan meningkatkan stabilitas sistem, salah satu diantaranya adalah Flexible AC Transmission System (FACTS) seperti Static Var Compensator (SVC) yang dikoordinasi dengan Multi-Band Power System Stabillizer (MB-PSS) yang telah terbukti dapat meredam osilasi dengan baik. Metode kecerdasan buatan swarm intelligence berbasis Mayfly Optimization Algorithm (MOA) digunakan untuk optimasi penempatan dan penalaan optimal FACTS dan MB-PSS. MOA terinspirasi dari perilaku terbang dan kawin mayfly dewasa. MOA menggabungkan proses seperti crossing, mutasi, pengumpulan kawanan, tarian kawin, dan random walking, yang berkontribusi pada kemampuan eksplorasinya. Hasil identifikasi stabilitas sistem Sulselrabar sebelum dan setelah integrasi PLTB Sidrap, menghasilkan osilasi yang menyebabkan ketidakstabilan pada generator. Peningkatan stabilitas sistem Sulselrabar diusulkan penerapan kontrol tambahan berbasis SVC dan MB-PSS. Pada optimasi penempatan dan penalaan SVC dan MB-PSS2B berbasis MOA menunjukkan nilai fitness function minimum sebesar 75.1222 yang konvergen pada iterasi ke-16th. Penerapan SVC menghasilkan performansi load flow yang lebih optimal. Hal ini ditunjukkan dengan peningkatan profil tegangan yang sebelumnya mengalami marginal dan critical condition, dan pengurangan losses pada saluran transmisi menjadi 0.49%. Optimasi MB-PSS2B diperoleh lokasi pemasangan dan penalaan optimal pada 14 generator menghasilkan ratio damping tertinggi yaitu 0.722521481. Sedangkan pada optimasi penempatan dan penalaan MB-PSS3C berbasis MOA pada sistem terintegrasi PLTB yaitu sebesar 80.4372791 yang konvergen pada iterasi ke-13th. Optimasi MB-PSS3C diperoleh ratio damping tertinggi yaitu 0.7498925. Hal ini menunjukkan peningkatan stabilitas sistem Sulselrabar diindikasikan dengan peningkatan damping, eigenvalue sistem, pengurangan osilasi overshoot, settling time yang cepat, dan respon field voltage generator yang mengalami perbaikan signifikan khususnya saat terrjadi gangguan.
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This study aims to enhance the stability of the Sulselrabar (South, Southeast, and West Sulawesi) electricity system following the integration of renewable energy from wind power plants (WPP). It analyzes and designs the system's stability performance in response to WPP integration, utilizing artificial intelligence methods. The addition of new power plants may lead to instability within the existing system, impacting overall system stability. Load variations in the power system induce oscillations in the generator rotor shaft due to dynamic behavior. Several auxiliary devices can be employed to dampen these oscillations and enhance system stability. One such device is the Flexible AC Transmission System (FACTS), specifically the Static Var Compensator (SVC), which is coordinated with a Multi-Band Power System Stabilizer (MB-PSS) that has proven effective in damping oscillations. The Mayfly Optimization Algorithm (MOA), a swarm intelligence-based artificial intelligence method, is utilized to optimize the placement and tuning of FACTS and MB-PSS. MOA is inspired by the flying and mating behavior of adult mayflies and combines processes such as crossing, mutation, swarm gathering, mating dances, and random walking, which enhance its exploration capabilities. The results of the stability assessment of the Sulselrabar system, both before and after the integration of the WPP Sidrap, indicate oscillations that contribute to instability in the generators. This study proposes enhancing the stability of the Sulselrabar system through the implementation of additional controls based on SVC and MB-PSS. The optimization results for the placement and tuning of SVC and MB-PSS2B using the MOA indicate a minimum fitness function value of 75.1222, converging at the 16th iteration. The implementation of SVC led to improved load flow performance, as evidenced by voltage profiles that had previously experienced marginal and critical conditions, along with a reduction in transmission losses to 0.49%. The optimization of MB-PSS2B resulted in optimal placement and tuning across 14 generators, achieving the highest damping ratio of 0.722521481. Meanwhile, the optimization of placement and tuning for MB-PSS3C, based on the MOA within the integrated WPP system, yielded a fitness function value of 80.4372791, converging at the 13th iteration. This optimization achieved the highest damping ratio of 0.7498925. This demonstrates an increase in the stability of the Sulselrabar system, as evidenced by higher damping, improved system eigenvalues, reduced overshoot oscillations, faster settling times, and a significant enhancement in the generator field voltage response, particularly during disturbances.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Stabilitas, Sulselrabar, MB-PSS, FACTS, MOA Stability, Sulselrabar, MB-PSS, FACTS, MOA
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1007 Electric power systems control
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20001-(S3) PhD Thesis
Depositing User:	Muhammad Ruswandi Djalal
Date Deposited:	23 Jan 2025 01:00
Last Modified:	23 Jan 2025 01:00
URI:	http://repository.its.ac.id/id/eprint/116643

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