WITHDRAWN: Transformer prediction in the supply chain using machine learning

Malviya, Lokesh; Shreemali, Jitendra; Ojha, Rahul; Chakrabarti, Prąsun; Poddar, Sandeep

doi:10.1016/j.matpr.2020.12.625

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…The LSTM algorithm can be used to predict the use of power consumption to protect the transformer from excess power otherwise known as overload power. [6] [13] Data on Power Quality Monitoring (PQM) in PT. 3-phase power transformer.…”

Section: Introductionmentioning

confidence: 99%

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

Pratama

Nabila

2022

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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%.

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Section: Introductionmentioning

confidence: 99%