All classes Distributed Generation Optimization PSO-GWO Syllabus · Episode 6 of 6

Class Optimasi Distributed Generation PSO-GWO #6: Analysis Results: Loss Reduction, Voltage Profile & Algorithm Comparison

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Tutorial on analysis and comparison of PSO, Grey Wolf Optimizer, and hybrid PSO-GWO for distributed generation placement using Python: 20 experiments, convergence, boxplot. Episode 6, closing the series.

Software: Python (NumPy, Metaheuristik)
Level: Advanced
Duration: ± 9 min
Series total: 1 h · 6 videos
Published: October 25, 2026

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Introduction

Here is the final result, a fair comparison of three algorithms from 20 trials. All three were able to find the best solution, but the most consistent one was the hybrid PSO-GWO, and there was an interesting finding about the speed of PSO. Let's start. Hello, welcome to the final episode of the Distributed Generation optimization series. Meta heuristics are random, so one trial is not enough to conclude which algorithm is better. Good research runs each algorithm multiple times with different SID, then reports its statistics. Today, we do this for PSO, GWO, and hybrid PSO-GWO.

Step 1: Twenty trials per algorithm

Create analysis.py file All three algorithms were run 20 times with SID 0-19, with the same settings, 30 agents and 100 iterations. For each trial, we save the final power loss, convergence curve, and the solution. Then we print the best, average, standard deviation, worst, and average percentage of loss reduction. Run, 60 optimizations completed in about half a minute.

Step 2: " statistics

The results show that all three algorithms found the same best loss, around 71.5 kW at bus 14, 24, and 30. The difference lies in consistency. Hybrid PSO-GWO has the lowest average, around 72 kW, and the smallest standard deviation, around 1.1. PSO average is around 72.5, While GWO, which excels in benchmark functions, performs the weakest here, average around 73.1 with the largest deviation.

Step 3: Convergence & boxplot

Let's draw. The average convergence curve shows that PSO decreases the fastest at the beginning, but then flattens out. The hybrid approach is slower at first, but continues to decrease and ends at the lowest point. The box plot on the right shows the distribution, GWO has a wide box because many trials got stuck in a local optimum around 76 kW, While the hybrid is the closest. In conclusion, The hybrid PSO-GWO is the most reliable for this problem, While PSO is suitable when the number of iterations is limited. Results are saved to the comparison_results.json file for the report.

Done!

Done, and this series is complete. We have already built a complete optimization study for distributed generation. data IEEE 33 bus, power flow backward forward sweep, objective function with penalty, PSO and gray wolf optimizer from scratch, hybrid algorithm, up to statistical analysis of 20 trials. For further research, Try Digi with a power factor not one, IEEE 69 bus system, multi-objective loss and cost, or 24-hour load profile with solar panels. Thank you for following this series. Currently working on a thesis, dissertation, or research in the field of power systems and renewable energy? Cerdas Inovasi Digital helps implement power system optimization, from backward forward sweep power flow, placement and capacity of distributed generation, PSO, gray wolf optimizer, up to hybrid algorithms, as well as opening Python programming courses for electrical engineering. Want to master the entire series, from IEEE 33 bus data to algorithm comparison analysis? All six episodes, complete with PDF step-by-step guides and source code, are available for premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or one-time payment of Rp999.000 for lifetime access. Want just one class? Buy a single class with lifetime access. Subscribe now on the premium page cerdasinovasidigital.com. Contact our WhatsApp or visit cerdasinovasidigital.com. Thank you for watching, see you in the next episode.

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Transcript

0:03Introduction

0:47Step 1: Twenty trials per algorithm

4:33Step 2: " statistics

5:07Step 3: Convergence & boxplot

7:05Done!

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Transcript

0:03Introduction

0:47Step 1: Twenty trials per algorithm

4:33Step 2: " statistics

5:07Step 3: Convergence & boxplot

7:05Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Twenty trials per algorithm

Step 2: " statistics

Step 3: Convergence & boxplot

Done!

Introduction

Step 1: Twenty trials per algorithm

Step 2: " statistics

Step 3: Convergence & boxplot

Done!