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All Journal International Journal of Engineering Continuity
Yuli Rodiah
Department of Electrical Engineering, Universitas Bengkulu, Bengkulu, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Genetic Algorithm-Based Contingency Ranking for the 500 kV JAMALI Interconnection System Irnanda Priyadi; Novalio Daratha; Yuli Rodiah; Ika Novia Anggraini; Tri Sutradi; Ade Sri Wahyuni; Makmun Reza Razali
International Journal of Engineering Continuity Vol. 4 No. 2 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i2.332

Abstract

The performance of an electric power system is strongly tied to how well it can handle disturbances. In daily operation, one of the most frequent and serious disturbances is the loss of a transmission line. When a line trips, its load must be shared by the rest of the network. Sometimes this redistribution is harmless, but in stressed conditions it can create overloads and trigger further outages. To reduce this risk, system operators rely on contingency analysis. The (N-1) criterion, which considers the effect of losing a single component, is the most common standard. However, when applied to a large network, the number of cases becomes very high, and the analysis can be time-consuming. In this work, contingency ranking using a Genetic Algorithm (GA) is studied for two systems: the IEEE 30-bus test grid and the 500 kV Java–Madura–Bali (JAMALI) interconnection in Indonesia. The GA follows the usual cycle of initialization, selection, crossover, mutation, and fitness evaluation, with the Voltage Performance Index (VPI) used to measure severity. Different parameter settings were tested. The results show that line 36 (bus 28–27) is most critical in the IEEE 30-bus system with a VPI of 56.5915, while line 35 (Bangil–Paiton) is most critical in the JAMALI system with a VPI of 95.3947. These outcomes highlight the usefulness of GA in identifying vulnerable transmission lines.
Comparative Evaluation of Artificial Neural Networks and Monte Carlo Simulation for Transformer Insulating Oil Lifetime Prediction Irnanda Priyadi; Yuli Rodiah; Makmun Reza Razali; Shara Alya Gifani Muhyisunah
International Journal of Engineering Continuity Vol. 5 No. 1 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i1.604

Abstract

Transformer insulating oil is an important factor for the reliability and service life of power transformers. It provides electrical insulation and heat dissipation. Accurate lifetime prediction is essential for asset management and condition-based maintenance. In this study, the comparison of Artificial Neural Network (ANN) and Monte Carlo Simulation (MCS) techniques is presented to predict transformer insulating oil lifetime based on three physicochemical parameters such as acid content, moisture content and breakdown voltage. The model was developed and validated on an experimental dataset of 18 transformer insulating oil samples. The ANN model was based on a multilayer perceptron architecture with three hidden layers (80-80-40 neurons). The MCS model was run for 3000 simulation iterations to include the input uncertainty. The model performance was assessed using mean Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and coefficient of determination (R2). The ANN model produced better results with a MAPE of 8.20%, an RMSE of 4.15 months and an R2 of 0.98, surpassing the MCS model, which achieved a MAPE of 11.50%, an RMSE of 9.40 months and an R2 of 0.89. The results presented show that ANN is a more accurate and reliable methodology for the prediction of transformer insulating oil lifetime that allows efficient condition-based maintenance and transformer asset management.
Co-Authors Ade Sri Wahyuni Ika Novia Anggraini Irnanda Priyadi IRNANDA PRIYADI Makmun Reza Razali Makmun Reza Razali Novalio Daratha Shara Alya Gifani Muhyisunah Tri Sutradi