Analisa Sisa Umur Transformator Berdasarkan Pengaruh Pembebanan Menggunakan Metode Probabilistik Neural Network (PNN) - Ilham Maulana Jatmiko; Misbahul Munir; Novian Patria Uman Putra; Nasyith Hananur Rohiem; Ilmiatul Masfufiah (Institut Teknologi Adhi Tama Surabaya, 2022)
Load conditions that go up and down can affect the life of the transformer. Therefore, it is very important to know the loss of life of the transformer every day when experiencing an increase in load because it can help when the transformer will stop functioning or stop being reliable and stable. For this transformer age analysis, using the Probabilistic Neural Network (PNN) method, how PNN works is based on calculating the value of the probability density function (fi(x)) for each data point (vector). For normalized data (vector) x and xij, the function (f(x)) is a Bayesian decision-making function (g(x)). The highest aging rate occurs at night due to the use of peak loads in the electric power system at night, so it will have an impact on the capacity of the transformer, which causes the transformer to break down faster or the life of the transformer to decrease faster. The remaining life of transformer 1 is 23.957 years, with an aging rate of 0.248 hours/day