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Course on Optimization of Location & Capacity of Distributed Generation with Hybrid PSO-GWO

Optimize location and capacity of distributed generation on radial IEEE 33-bus network: backward-forward sweep power flow, objective function loss & voltage profile, PSO and Grey Wolf Optimizer from scratch, then hybrid PSO-GWO, complete with statistical comparison.

✅ 6 Video + PDF🐍 Python🎓 Suitable for Thesis & Dissertation🔒 Customer-Specific Dataset & Project Files

Course on DG Placement Optimization, kurikulum dan hasil tiap modul, gambar 1

Course on DG Placement Optimization, kurikulum dan hasil tiap modul, gambar 2

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Course on DG Placement Optimization, kurikulum dan hasil tiap modul, gambar 4

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Geser untuk melihat kurikulum dan hasil setiap modul.

Lihat Paket & Daftar Kursus Tonton Modul 1 Gratis

±65%

Power loss reduction with three optimal DG

202,68 kW

Base IEEE 33-bus loss, suitable for literature

Experiment for statistical comparison

3 algorithms

PSO, GWO, and hybrid PSO-GWO

Apa yang Akan Kamu Pelajari?

Six consecutive modules follow the flow of a real distributed generation placement research, each module produces working code and ready-to-use graphs for reports.

🔌

IEEE 33-Bus System

Line data, load, and single-line diagram of radial distribution network.

BFS Power Flow

Backward-forward sweep for power loss and voltage profile.

Objective Function & Constraints

Power loss, voltage limits, DG capacity, and sensitivity analysis.

PSO & Grey Wolf from Scratch

Two popular metaheuristics written from scratch and tested on benchmark functions.

🔀

Hybrid PSO-GWO

Combining GWO exploration and PSO speed for location & size of DG.

Analysis & Statistics

Voltage profile before-after and comparison of 20 trials.

Kurikulum: 6 Modul Video

Video 8–12 minutes per modul, lengkap dengan PDF panduan. Modul 1 bisa ditonton gratis.

Siapa yang Cocok Mengikuti?

S1/S2 Students (skripsi & tesis)

Complete research framework for distributed generation placement: comparative method, proposed method, experiments, and evaluation ready for chapter 4.

Researchers & lecturers

Neat Python code easily replaceable for data, parameters, or method for publication.

Practitioners & engineers

Understand how distributed generation placement works from theory to implementation that can be tried yourself.

Developers & data scientists

Python practice on real case studies, not just toy examples.

Butuh Pendampingan Distribution System Research?

Kami membantu implementasi model, eksperimen, evaluasi, dan penulisan hasil untuk skripsi, tesis, maupun publikasi.

Lihat Jasa Distribution System Research

Pertanyaan Umum

Is it suitable for thesis or dissertation?+

Yes. The workflow follows real research: comparative method, proposed method, experiment, evaluation, and analysis results that can directly become the framework for chapter 3 and chapter 4.

What data is used?+

IEEE 33-bus test system (Baran & Wu, 1989). The data used is included in the project file so the results can be reproduced.

Are source code and dataset available?+

Yes. Premium Class subscribers and class buyers receive a zip file containing the dataset and all source code from the six modules, downloadable via a secure link from the class page.

What are the prerequisites?+

Python basics, Python 3.10+, and VS Code. Libraries used: numpy, matplotlib.

Is there a free module?+

Yes. Module 1 can be watched for free on the class page; subsequent modules are for Premium Class subscribers or class buyers.

Is there support for my research?+

Yes, live online classes and research assistance services for distribution systems are available for thesis, dissertation, or publication.

Mulai Distribution System Research Anda

Konsultasikan topik penelitian Anda, kami bantu pilihkan paket belajar yang paling sesuai.

Konsultasi Gratis