Hybrid Quadratic Programming - Enhanced Genetic Algorithm untuk Menyelesaikan Masalah Dynamic Optimum Power Flow

Wati, Trisna (2017) Hybrid Quadratic Programming - Enhanced Genetic Algorithm untuk Menyelesaikan Masalah Dynamic Optimum Power Flow. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Enhanced Genetic Algorithm (E-GA) merupakan penyempurnaan dari metode Genetic Algorithm. Dalam penelitian terdahulu E-GA menggabungkan Particle Swarm Optimization (PSO) dan fuzzy untuk menyelesaikan Optimum Power Flow (OPF) [9] tanpa mempertimbangkan valve point effect. Dalam penelitian ini E-GA disempurnakan dengan penambahan operator kromosom, dengan inisialisasi awal menggunakan Quadratic Programming (QP) untuk menyelesaikan permasalahan OPF dengan pembebanan selama 24 jam dan mempertimbangkan fungsi biaya valve point effect (VPE) dengan memperhatikan ramp-rate. Fungsi tujuan dari OPF adalah untuk meminimalkan biaya pembangkitan dengan tidak melanggar batasan-batasan sistem seperti batasan tegangan, ramp rate, batasan tap trafo, dan batasan generator. Dengan menerapkan E-GA diharapkan mampu menyelesaikan OPF dengan pembebanan selama 24 jam dengan biaya pembangkitan yang minimum, dibandingkan dengan metode E-GA [9] sebelumnya ======================================================================================================== Enhanced Genetic Algorithm (E-GA) is a refinement of the Genetic Algorithm method. In a previous, E-GA combine Particle Swarm Optimization (PSO) and fuzzy to complete Optimum Power Flow (OPF) [9] without valve point effect. In this study, E-GA was enhanced by the addition of chromosome operators, with initialization using Quadratic Programming (QP) to solve OPF problems with 24-hour loading with valve point effect (VPE). The objective function is to minimize generating costs by not breaking system boundaries such as voltage limits, street level, tap transformer limits, and generator limits. E-GA implementation to solve OPF with 24-hour loading on minimum generation costs, and compared with previous E-GA methods [9]

Item Type: Thesis (Masters)
Additional Information: RTE 621.319 Wat h-1 3100018074162
Uncontrolled Keywords: Dynamic Optimum Power Flow, Genetic Algorithm, optimasi biaya
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK201 Electric Power Transmission
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3030 Electric power distribution systems
Divisions: Faculty of Industrial Technology > Electrical Engineering > 20101-(S2) Master Thesis
Depositing User: Trisna Wati
Date Deposited: 19 Feb 2018 02:23
Last Modified: 17 Apr 2020 04:52
URI: http://repository.its.ac.id/id/eprint/49133

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