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INTRODUCTIONElectrical technology for the Renewable Energy (RE) uses solar energy source as an alternative source of reliable energy. Some of the problems arising in the photovoltaic system (PV) among others: prediction of PV configuration and battery size, energy consumption prediction, maximum power system and performance analysis of PV system. The PV system modeling is an initial phase to be considered, such as size, identification and simulation applications. In some literature, models have been proposed for modeling of different components on the stand-alone PV. Some methods are based on software simulation namely by using the program such as; PSpice, Matlab Simulink and Labview (Wahid and Hambali, 2015;Ramenah et al., 2014;Mizuki et al., 2014). This article will introduce a model of solar radiation based on weather forecasting system. It produces short-term photovoltaic output predictions from the forecasting information from the meteorological department. Based on the output results of solar radiation and distribution network, then an algorithm can be generated. The prediction model of produced photovoltaic power can predict the output and algorithm in computational speed (Bastidas-Rodríguez et al., 2018;Mai et al., 2017;Lin, 2012). Meng et al. (2014) have proposed to utilize solar power plant (photovoltaic) on a micro grid system with DC voltage source. The battery charging and discharging system is connected to the traction system through a DC bus. For tracking the maximum power point is adopted on the solar power plant. Consequently, if applied for locomotives on working condition and regenerative braking produce a more stable voltage.Akos Baldauf (2015) has developed an RE

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This paper presents a fault diagnosis for long transmission lines using Adaptive Neuro-Fuzzy Inference System (ANFIS). The electric power transmission system is a link power generation and distribution. If a failure occurs as long the transmission line could be estimation caused of undesired fault power delivery to consumer come not go well. Therefore, it would need to provide an alternative solution to solve this problem. The objectives of this paper are classification and estimate of a fault into the transmission line by using application of ANFIS. The systems have been put forward and tested on simulated data transmission lines into different faults. The results test given to contribute to an alternate technique where it has good performance for fault diagnosis in the transmission lines.

The feasibility study is one of the principal documents in building a hydropower plant consisting of technical, economic, and financial aspects. Contained technical studies on civil, mechanical, and electrical. This requires data on hydrologic, geology, land contours, river discharge, water catchment areas, and so on. Economic and financial studies include cost and financial parameters such as; BEP, IRR, NVP, BCR, and others. The installed capacity of a hydropower plant is given in optimization based on the Flow Duration Curve (FDC) and the Capacity Factor (CF) used the Newton Interpolation Method. The results showed that the installed power capacity was 11.99 MW. The water discharge was 31.603 m3/secs and the effective head was 37.5 meters. Annual income is around IDR 103.026 billion. Finally, HPP Lubuak Gadang is technically, economically, and financially feasible, so it is feasible to carry out the next process.

A power transformer is an electrical machine that converts electrical power at different voltage levels. Faults, occur in power transformers, inhibit electrical power distribution to the consumer. Protection, therefore, of the power transformers is essential in power systems reliability. The power system can be reliable if the protection devices work well when there is a fault. A hybrid intelligent technique, which is a combination of Artificial Neural Network (ANN) and Fuzzy known as Adaptive Neuro-Fuzzy Inference Systems (ANFIS), was used in this research. The objective of this paper is the simulation of differential relays as a protection device on power transformers using Matlab/Simulink. Performance of differential relays for power transformers protection is carried out with internal and external fault scenarios. The input data were classified into three different input for ANFIS such as internal and external 1, internal and external 2, internal, external 1, and external 2, respectively. The error results of ANFIS training for the type of fault internal and external 1 is 9.46*10-7, and types of fault internal and external 2 is 1.09*10-6 internal, external 1 and external 2 are 8.59*10-7. The results obtained from the simulation were accurate and shows that the ANFIS technique is an efficient method that gives less error and a great value. Finally, the technique can minimize faults with power transformers. Finally, to prove this method can reduce faults in the power transformer, the assess of this model has been carried out through the RMSE that has been generated which is zero.

Loading between phases must always be considered in order to reduce the imbalance, which results in an increase in neutral current and loss of conductor power. Non-linear loads can cause harmonics that affect the increase in copper losses in the transformer. Harmonics can cause distortion of the voltage and current waveforms, so attention must be paid to the specified limits so as not to interfere with the performance of electrical equipment. In addition, these parameters can affect the main electrical parameters, so they must be considered at the specified limits. This study aims to analyze the condition of load imbalance, harmonic content, and the effect on power loss in distribution transformers at Universitas Bangka Belitung. Parameters were measured using a Meterel Power Meter Analyzer type MI 2592 Power Q4 for ten days with the comparative method as a data analysis method. The average current unbalanced percentage is 9.91%, with the highest value at 24.31%. Energy due to power loss in the neutral conductor is 0.46 kWh/10 days, and the total conductor loss is 18.122 kWh/10 days.