Application of Deep Learning LSTM in Online Power Prediction on Three-Phase Power Transformer

Destra Andika Pratama; Sinta Nabila

doi:10.47065/bits.v4i3.2690

Destra Andika Pratama * Politeknik Negeri Sriwijaya, Palembang, Indonesia
Sinta Nabila Politeknik Negeri Sriwijaya, Palembang, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v4i3.2690

Keywords: Electrical Energy; Power Transformer 3 Phase; Deep Learning; LSTM

Abstract

Electrical energy plays an important role in daily life, especially companies. The 3 Phase Power Transformer is one of the important electrical components that is very influential in distributing electrical energy to companies as is the case in PT. Semen Baturaja (Persero). 3-phase power transformers require attention because they are one of the components that are prone to interference, this interference can hinder the effectiveness of using electrical energy as a company support for employees to work. As one of the disturbances for 3-phase power transformers is overload or excessive power usage, overload can raise the temperature at the winding and reduce its service life. Artificial intelligence can be one of the keys to predict the use of power transformers in the future, especially deep learning by utilizing the LSTM algorithm. Optimal power prediction requires a lot of maximum input variables so that in this study, it not only adds an offline learning mode but adds a learning mode that can directly access the company's Power Quality Monitoring (PQM) website online with an average accuracy value of 86.62%.

Downloads

Download data is not yet available.

References

R. S. De Andrade Ferreira, P. Picher, H. Ezzaidi, and I. Fofana, “Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model,” IEEE Access, vol. 9, pp. 67051–67063, 2021, doi: 10.1109/ACCESS.2021.3076154.

I. Soedibyo and M. Mt, “Evaluasi Penentuan Kelayakan Minyak Transformator Daya Berdasarkan Metode Interval Fuzzy Type 2 B-115 B-116,” vol. 7, pp. 115–120, 2015.

Z. Kazemi, F. Naseri, M. Yazdi, and E. Farjah, “An EKF-SVM machine learning-based approach for fault detection and classification in three-phase power transformers,” IET Sci. Meas. Technol., vol. 15, no. 2, pp. 130–142, 2021, doi: 10.1049/smt2.12015.

C. Zhang, Y. He, B. Du, L. Yuan, B. Li, and S. Jiang, “Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning,” Futur. Gener. Comput. Syst., vol. 108, pp. 533–545, 2020, doi: 10.1016/j.future.2020.03.008.

R. B. ROSLI, “Parameter Identification for Fault Detection of Power,” 2015.

A. A. Ningrum, I. Syarif, A. I. Gunawan, E. Satriyanto, and R. Muchtar, “Algoritma Deep Learning-LSTM untuk Memprediksi Umur Transformator,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 3, p. 539, 2021, doi: 10.25126/jtiik.2021834587.

R. Soni and K. Chaudhari, “An approach to diagnose incipient faults of power transformer using dissolved gas analysis of mineral oil by ratio methods using fuzzy logic,” Int. Conf. Signal Process. Commun. Power Embed. Syst. SCOPES 2016 - Proc., pp. 1894–1899, 2017, doi: 10.1109/SCOPES.2016.7955775.

A. Tavakoli, L. de Maria, B. Valecillos, D. Bartalesi, S. Garatti, and S. Bittanti, “A Machine Learning approach to fault detection in transformers by using vibration data,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 13656–13661, 2020, doi: 10.1016/j.ifacol.2020.12.866.

M. I. Dahlan, “Analisa Respon Tegangan Uji Surja,” 2017.

I. El Naqa and M. J. Murphy, “What Is Machine Learning ?,” pp. 3–11, doi: 10.1007/978-3-319-18305-3.

M. Ukkelberg, “Utilisation of Machine Learning in Power Transformer Asset Management,” no. June, 2018.

“Project Report On Condition Monitoring of Distribution Transformer Using Machine Learning Submitted in partial fulfilment of the requirement for the award of the Degree of BACHELOR OF TECHONOLOGY In ELECTRICAL ENGINEERING By AHMAD HASAN ( 1615105001 ) AJI,” no. 1615105002.

L. Malviya, J. Shreemali, R. Ojha, P. Chakrabarti, and S. Poddar, “Transformer prediction in the supply chain using machine learning,” Mater. Today Proc., no. xxxx, 2021, doi: 10.1016/j.matpr.2020.12.625.

F. Valencia, H. Arcos, and F. Quilumba, “Comparison of Machine Learning Algorithms for the Prediction of Mechanical Stress in Three-Phase Power Transformer Winding Conductors,” J. Electr. Comput. Eng., vol. 2021, 2021, doi: 10.1155/2021/4657696.

K. Mahmudah et al., “Pengembangan Sistem Informasi Dissolved Gas Analysis ( Dga ),” vol. 12, pp. 54–59, 2016.

S. . Arismunandar, Artono M.A, Teknik Tenaga Listrik Jilid III, no. 0806365412. 2004.

Y. Sun, X. Wang, and J. Yang, “Modified Particle Swarm Optimization with Attention-Based LSTM for Wind Power Prediction,” Energies, vol. 15, no. 12, 2022, doi: 10.3390/en15124334.

X. Zhang, S. Wang, Y. Jiang, F. Wu, and C. Sun, “Prediction of dissolved gas in power transformer oil based on LSTM-GA,” IOP Conf. Ser. Earth Environ. Sci., vol. 675, no. 1, 2021, doi: 10.1088/1755-1315/675/1/012099.

J. Lin, L. Su, Y. Yan, G. Sheng, D. Xie, and X. Jiang, “Prediction method for power transformer running state based on LSTM-DBN network,” Energies, vol. 11, no. 7, pp. 1–14, 2018, doi: 10.3390/en11071880.

H. Song, J. Dai, L. Luo, G. Sheng, and X. Jiang, “Power transformer operating state prediction method based on an LSTM network,” Energies, vol. 11, no. 4, 2018, doi: 10.3390/en11040914.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Application of Deep Learning LSTM in Online Power Prediction on Three-Phase Power Transformer