Analisis Pola Asosiasi Gangguan Berulang Jaringan Distribusi PLN Menggunakan Algoritma Apriori

Dwina Kuswardani; Muhamad Habibi

doi:10.36080/idealis.v9i2.3853

Authors

Dwina Kuswardani Pendidikan Profesi Insinyur, Sekolah Pasca Sarjana, Institut Teknologi PLN, Jakarta, Indonesia
Muhamad Habibi Pendidikan Profesi Insinyur, Sekolah Pasca Sarjana, Institut Teknologi PLN, Jakarta, Indonesia

DOI:

https://doi.org/10.36080/idealis.v9i2.3853

Keywords:

Apriori Algorithm, Association Rule Mining, CRISP-DM, Distribution Network, Preventive Maintenance, Power Distribution Disturbance

Abstract

Repeated disturbances in power distribution networks can reduce system reliability and increase maintenance costs. However, maintenance activities are often performed reactively because recurring disturbance patterns have not been systematically identified from historical operational data. This study aims to identify recurring disturbance patterns in the distribution network of PT PLN (Persero) by applying the Apriori algorithm within the CRISP-DM framework. The dataset consisted of 29,446 historical disturbance records containing disturbance type, cause, and location. After the data preparation stage, which included data cleaning, standardization, and transformation, 10,274 valid transactions were obtained for association rule mining. The Apriori algorithm was implemented using a minimum support threshold of 0.5%, a minimum confidence threshold of 70%, and lift values greater than 1 as the evaluation criterion. The analysis produced 57 frequent 1-itemsets, 180 frequent 2-itemsets, 35 frequent 3-itemsets, and 64 association rules. The results indicate that Core FO Problem, FOC – Putus Core, and Putus Kabel FO are the most frequently occurring disturbance patterns. The strongest association rule, Banten and FOT – Software → Konfigurasi Di Router Problem, achieved a support of 0.993%, a confidence of 81.6%, and the highest lift value of 30.936, indicating a strong positive relationship between the antecedent and consequent. These findings demonstrate that the Apriori algorithm is effective in discovering recurring disturbance patterns from historical operational data and can provide maintenance-oriented knowledge to support inspection prioritization and preventive maintenance planning in power distribution networks.

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References

[1] M. K. da Silva, R. S. Lopes, M. E. Fontana, and J. K. B. de Almeida, "A Data-Driven Approach to Maintenance-Preventable Causes of Failure Analysis in Power Distribution Systems," Brazilian Journal of Operations & Production Management, vol. 22, no. 3, Art. no. 2702, 2025, doi: 10.14488/BJOPM.2702.2025.

[2] S. Barja-Martinez, M. Aragüés-Peñalba, Í. Munné-Collado, P. Lloret-Gallego, E. Bullich-Massagué, and R. Villafafila-Robles, "Artificial Intelligence Techniques for Enabling Big Data Services in Distribution Networks: A Review," Renewable and Sustainable Energy Reviews, vol. 150, Art. no. 111459, 2021, doi: 10.1016/j.rser.2021.111459.

[3] M. Aghahadi, A. Bosisio, M. Merlo, A. Berizzi, A. Pegoiani, and S. Forciniti, "Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges," Applied Sciences, vol. 14, no. 11, Art. no. 4528, 2024, doi: 10.3390/app14114528.

[4] Karngala, A. K., and Singh, C., "Reliability Assessment Framework for the Distribution System Including Distributed Energy Resources," IEEE Transactions on Sustainable Energy, vol. 12, no. 3, pp. 1539–1548, Jul. 2021, doi: 10.1109/TSTE.2021.3053911.

[5] M. Klerx, J. Morren, and H. Slootweg, "Analyzing Parameters That Affect the Reliability of Low Voltage Cable Grids and Their Applicability in Asset Management," IEEE Transactions on Power Delivery, vol. 34, no. 4, pp. 1432–1441, Aug. 2019, doi: 10.1109/TPWRD.2019.2903928

[6] M. A. Mahmoud, N. R. M. Nasir, M. Gurunathan, P. Raj, and S. A. Mostafa, "The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault's Types, Causes, and Prediction Methods—A Systematic Review," Energies, vol. 14, no. 16, Art. no. 5078, 2021, doi: 10.3390/en14165078.

[7] N. Zhou, Y. Xu, S. Cho, and C. T. Wee, "A Systematic Review for Switchgear Asset Management in Power Grids: Condition Monitoring, Health Assessment, and Maintenance Strategy," IEEE Transactions on Power Delivery, vol. 38, no. 5, pp. 3296–3311, 2023, doi: 10.1109/TPWRD.2023.3272883.

[8] X. Zhang, Y. Tang, Q. Liu, G. Liu, X. Ning, and J. Chen, "A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit," Applied Sciences, vol. 11, no. 11, Art. no. 5221, 2021, doi: 10.3390/app11115221.

[9] H. Kuang, R. Qin, M. He, X. He, R. Duan, C. Guo, and X. Meng, "An Association Rules-Based Method for Outliers Cleaning of Measurement Data in the Distribution Network in Smart Grids," Frontiers in Energy Research, vol. 9, Art. no. 730058, 2021, doi: 10.3389/fenrg.2021.730058.

[10] Q. Sun, "Enhancing Power Grid Data Analysis with Fusion Algorithms for Efficient Association Rule Mining in Large-Scale Datasets," International Journal of Computers Communications & Control, vol. 19, no. 3, 2024, doi: 10.15837/ijccc.2024.3.6232.

[11] H. Rezapour, S. Jamali, and A. Bahmanyar, "Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks," Energies, vol. 16, no. 12, Art. no. 4636, 2023, doi: 10.3390/en16124636.

[12] M. Bindi, M. C. Piccirilli, A. Luchetta, and F. Grasso, "A Comprehensive Review of Fault Diagnosis and Prognosis Techniques in High Voltage and Medium Voltage Electrical Power Lines," Energies, vol. 16, no. 21, Art. no. 7317, 2023, doi: 10.3390/en16217317.

[13] Y. Cao, J. Tang, S. Shi, D. Cai, L. Zhang, and P. Xiong, "Fault Diagnosis Techniques for Electrical Distribution Network Based on Artificial Intelligence and Signal Processing: A Review," Processes, vol. 13, no. 1, Art. no. 48, 2025, doi: 10.3390/pr13010048

[14] D. B. Saputra, V. Atina, and F. E. Nastiti, "Penerapan Model CRISP-DM pada Prediksi Nasabah Kredit Menggunakan Algoritma Random Forest," Idealis: Indonesia Journal Information System, vol. 7, no. 2, pp. 240–247, Jul. 2024. doi.org/10.36080/idealis.v7i2.3244

[15] F. Prasetyo and H. Hasugian, "Analisis Pola Pembelian Produk Makanan Menggunakan Algoritma FP-Growth untuk Strategi Penjualan," Jurnal Idealis, vol. 7, no. 1, pp. 11–20, Jan. 2023, doi: org/10.36080/idealis.v7i1.3085.