Power transformer is one of the electrical equipment that has a central and critical role in the power system. In order to avoid power transformer failure, information system that provides the transformer condition is needed. This paper is present the information system for diagnostic transformer condition based on health index. Health Index method provides a comprehensive transformer condition assessment. This method is resulting power transformer condition that divided into several categories according to the life time prediction and degradation level of transformer components. Analysis on dissolved gas, oil, and furan (insulation paper) are subjected to detect the faults types that could be occurs in transformer. The outputs of the analysis are life time prediction, types of possible faults, and recommendations for future maintenance action. For practical application, this method is applied to the transformer data test that provided by Indonesia Power Electric Company or PT PLN. Results of the analysis found that the smaller Transformer Health Index means greater number of population failure probability. This relationship is quite strong with the value of the correlation coefficient r is -0.63.

Projected global energy demand has been on the rise, with a 2.3% increase per year forecast for the 2018?2050 period. Electricity, vehicles, and industries are the dominant consumer sectors, while natural gas, crude oil, and gas are the dominant producer sectors. Renewable energy sources other than hydro are set to be in the top three supplies, with photovoltaics (PVs) and wind turbines (WTs) as the alternatives. Global scenarios for renewable energy include a target 26% increase, while the goal for WTs and PVs is 37% and 23.4%, respectively. In Indonesia, the use of renewable energy is set to rise 18% from the installed capacity in 2018 to 23% for new and renewable energy in 2025 and 31% in 2050. When the penetration of renewable energy grows significantly, electricity prices will decrease, and electric vehicles (EVs) will be the cheaper option compared to internal combustion engines (ICEs). The EV market share will rise after the price of batteries goes down.

This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1–3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.

In this study, the lightning protection system and grounding system of one plant of the petrochemical industry were investigated, evaluated, and improved. The methods used in this study were rolling sphere and angle protection methods. The grounding system of the building under study was modeled and simulated using ATP/EMTP (Alternative Transient Program/Electromagnetic Transient Program) software. The results show that the external lightning protection system of the prilling tower studied does not adhere to IEC 62305 and IEC 1024-1-1 standards. Moreover, the grounding configuration of the DCS building was not appropriate for protecting sensitive equipment inside. Lightning causes an enormous potential difference between lightning ground rods in the grounding system. Additionally, disabling the existing surge protective device (SPD) causes an increase in the magnitude of Ground Potential Rise at the DCS building. Improvement of the lightning protection system of the prilling tower and DCS building on Plant 1 of this petrochemical company as well as some other recommendations for improvements are proposed. This paper also shows evidence that external lightning protection, internal lightning protection, and the grounding system need to be connected to make an exemplary lightning protection system.

Ring distribution network system has a complex problem. Besides that, the addition of distributed generators (Distributed Generation) would make the changes in network topology configurations and affected the value of short-circuit current that supply the load on each relay. The difficulty to define the setting of relay operation setting that localize the fault would be increase. From these problems, it requires a method to reduce the complexity of coordination protection in ring distribution system with zone selective interlocking method which is modelled and adapted by using artificial neural network. In this paper, adaptive methods would be used to localize the short circuit which is faster than the manual method (conventional). The output of the system is locating the short circuit current and the operation system relay which is modelled by a status system. The status can produce a relay setting that capable in every configuration on the system.