CD Skripsi

Optimasi Mppt Pada Sistem Pv Array Dan Boost Converter Berbasis Artificial Neural Network

---

ABSTRACT
This study demonstrates that the Artificial Neural Network (ANN) algorithm can effectively adapt to varying operating conditions, both under Standard Test Condition (STC) and under dynamic irradiation and temperature scenarios. The simulation was conducted using MATLAB/Simulink, modeling a PV power system comprising a Trina Solar TSM-250PA05A.08 solar panel, a boost converter, and a load.Under STC testing, ANN maintained a stable output voltage of 35.30 V with constant output power of 222.54 W, achieving 88.97% efficiency. The Perturb and Observe (P&O) method maintained a stable output voltage of 34.05 V with constant output power of 207.09 W (82.78% efficiency), while the system without MPPT delivered 33.21 V and 196.95 W (78.77% efficiency). In non-STC testing with varying irradiance and temperature, ANN achieved an average power output of 75.57 W, higher than P&O (67.52 W) and the no-MPPT system (62.58 W). ANN also maintained output voltage stability within 36.60–37.98 V with relatively low voltage ripple. These results indicate that ANN can consistently maintain the operating point near the Maximum Power Point (MPP), minimize power fluctuations, and improve system efficiency. Therefore, ANN is suitable for real-time control implementation to maximize PV system performance under diverse environmental conditions..
Keywords: Artificial Neural Network, MPPT, Boost Converter, PV Array, Simulink, P&O, Energy Efficiency.

---

Ketersediaan

Informasi Detail

Judul Seri

-

No. Panggil

2007110627

Penerbit

Pekanbaru : Universitas Riau – F.TEKNIK – Elektro., 2025

Deskripsi Fisik

-

Bahasa

Indonesia

ISBN/ISSN

-

Klasifikasi

2007110627

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

TEKNIK ELEKTRO

Info Detail Spesifik

-

Pernyataan Tanggungjawab

daus

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar