Application of the Average Based Fuzzy Time Series Model in Predictions Seeing the Use of Travo Substations

Mutammimul Ula; Ivan Satriawan; Rizky Putra Fhonna; Arnawan Hasibuan

doi:10.25077/aijaset.v3i01.74

Authors

Mutammimul Ula Universitas Malikussaleh
Ivan Satriawan Universitas Malikussaleh
Rizky Putra Fhonna Universitas Malikussaleh
Arnawan Hasibuan Universitas Malikussaleh

DOI:

https://doi.org/10.25077/aijaset.v3i01.74

Abstract

PT PLN expects the delivery of information quickly in predicting the capacity of transformer substations in each region in view of population growth in industrial areas. Unbalanced and overloaded electricity that is not suitable for the capacity of the transformer substation so that the results of this study are more optimal in predicting the usage load at the transformer substation using the methodaverage based fuzzy time series. The application of this method can provide fast and accurate information in accordance with consumer expectations in predicting each need in each area and the number of managed substations. The capacity of the substation can be seen in 1 phase and 3 phase with the percentage of loading quickly and precisely with the current transformer card system. The purpose of the transformer distribution in this study is to look at reducing high voltage to low voltage, so that the voltage used is in accordance with the customer's electrical equipment rating or the load rating used by all consumers in each region. The research methodology is to determine the placement of distribution transformer locations that are not suitable which can affect the end voltage drop on consumers or the drop/drop in consumer line end voltage and view complete data from the specifications of the distribution transformers along with the locations of distribution transformers that can be managed through Development of a mobile device-based Distribution Transformer Recording System at PT PLN. The results of this study in transformer power 100 consumption 99.43, unbalanced 28%, fuzzification A8, FLRG G8, forecasting results 10.69 with a Mape forecast value of 0.57%. Furthermore, power consumption of transformer 50 is 36.70, unbalanced 78%, fuzzification A6, FLRG G6, forecasting results 23.91 with Mape 1.11%. Results with the smallest mape with each travo travo 50 in each usage area 28.43, unbalanced 26%, fuzzification A5, FLRG G5, forecasting results 23.91 with Mape 0.28%. The results of this study can determine the location of the transformer along with unbalance, overload and the estimated amount of power consumption load for the use of transformer substations in an area, especially the ULP PT.PLN area for each region in PT.PLN (Persero) Krueng Geukuh. Then the results of this study can be used as a reference for monitoring population growth with excessive transformer power loads (overload) so that later you can install new transformer substations with a capacity according to the number of customers

Author Biographies

Mutammimul Ula, Universitas Malikussaleh

Faculty of Engineering, Universitas Malikussaleh, Reuleut, Aceh Utara, Indonesia

Ivan Satriawan, Universitas Malikussaleh

Faculty of Engineering, Universitas Malikussaleh, Reuleut, Aceh Utara, Indonesia

Rizky Putra Fhonna, Universitas Malikussaleh

Faculty of Engineering, Universitas Malikussaleh, Reuleut, Aceh Utara, Indonesia

Arnawan Hasibuan, Universitas Malikussaleh

Faculty of Engineering, Universitas Malikussaleh, Reuleut, Aceh Utara, Indonesia

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