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All Journal Jurnal Teknologi Elektro SMARTICS Journal
Wahyu Setyo Pambudi
Institut Teknologi Adhi Tama Surabaya (ITATS)
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Grasshopper Optimization Algorithm For Optimizing Microgrid Networks To Achieve Economic Dispatch Wahyu Setyo Pambudi; Novian Patria Uman Putra; Primawan Rezky Pangayom; Aji Akbar Ferdaus
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.13747

Abstract

In recent years, energy management has become an essential issue in the growing integration of microgrids, enabling more efficient and sustainable energy use. This research focuses on optimizing microgrid networks to achieve economic dispatch using the Grasshopper Optimization Algorithm (GOA). Economic dispatch aims to minimize total generation costs while meeting load demand. Traditional methods such as Particle Swarm Optimization (PSO), Priority List (Merit Order), and the Lagrange method have been widely used. Still, GOA offers superior performance potential in cost efficiency and computational effectiveness. This study compares the efficacy of PSO, GOA, Priority List, and the Lagrange method in optimizing microgrid operations through a series of simulations. Key performance metrics include total generation costs, convergence rates, and computation time. The results showed that GOA was superior to PSO and Priority List in reducing generation costs, with differences of $3.8 per hour of generation compared to PSO and $39.8 per hour of generation compared to Priority List. Additionally, GOA has a more adaptive convergence rate. However, the Lagrangian method does not apply to all objective functions, making it less effective for solving economic dispatch problems. In conclusion, GOA could significantly improve the economic efficiency of microgrid systems, contributing to more sustainable and cost-effective energy management. The combination of various optimization methods, such as GOA, PSO, and others, offers a more comprehensive approach to facing future energy management challenges.
Analisa Kondisi Dan Prediksi Umur Transformator Daya Menggunakan Metode Health Index Berbasis Artificial Neural Network (ANN) Wahyu Setyo Pambudi; Wahyu Setyawan; Misbahul Munir; Titiek Suheta; Yuliyanto Agung Prabowo
Jurnal Teknologi Elektro Vol. 17 No. 1 (2026)
Publisher : Electrical Engineering, Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jte.2026.v17i1.001

Abstract

Transformator daya merupakan salah satu komponen utama yang mempunyai peran penting pada sistem transmisi dan distribusi listrik. Untuk beroperasi secara optimal dibutuhkan pemantauan kondisi transformator secara berkala untuk mencegah terjadinya kerusakan dan memperpanjang umur transformator daya. Salah satu metode yang digunakan untuk menilai kondisi transformator daya adalah health index, yaitu metode yang mengintegrasikan berbagai hasil pengujian, seperti dissolved gas analysis (DGA), analisis minyak transformator, dan pengujian furan, guna memberikan gambaran menyeluruh mengenai kondisi kesehatan transformator. Penelitian ini bertujuan untuk menganalisis kesehatan transformator daya berdasarkan hasil uji laboratorium ketiga parameter utama tersebut. Dengan memanfaatkan metode artificial neural network (ANN), penelitian ini juga mengevaluasi kemampuan ANN dalam memprediksi kondisi kesehatan serta umur transformator. Hasil penelitian menunjukkan bahwa metode health index berbasis ANN efektif dalam mengidentifikasi kondisi dan memprediksi umur transformator daya secara komprehensif.
Co-Authors Aji Akbar Ferdaus Misbahul Munir Novian Patria Uman Putra Primawan Rezky Pangayom Titiek Suheta Wahyu Setyawan Yuliyanto Agung Prabowo