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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.

Community service program is a mandatory activity for higher eduacation in community empowerment and development. These activities include training programs and application of appropriate technology including the utilization of solar energy for water pumps in orphanages in Rokan Hulu regency and the street lighting program in Simpang Petai village, Kampar regency in Riau province. This activity also considers to the obstacles faced by the community with offering and implementing appropriate technology to them as beneficiaries. This activity takes place with the involvement of students in work and lecture program (KKN) that integrated with community service activities.It is shown that the community as beneficiaries have joined diligently and received assistance with this program by implementation of solar water pump and street lighting. The evaluation of this activity shows that collaboration continues between the community and the Department of Electrical Engineering, Faculty of Engineering, University of Riau and the communitis feel helped and there is an improvement in the electricity infrastructure in their village. This activity was carried out in a fostered village by the Faculty of Engineering, University of Riau.

The power transmission system is essential for the power scheme to transfer the energy from generators to consumers. The short circuit problem repeatedly occurs in the transmission system, and the main problem is to separate the sources from users. This research has applied two hybrid techniques to predict fault location. The first hybrid technique has involved the Discrete Wavelet Transformation (DWT) and Adaptive Neuro-Fuzzy Inference System (ANFIS), while the second hybrid technique is for DWT grouping and Support Vector Machine (SVM). These hybrid techniques are intended to estimate the fault location of each fault category in a transmission system. The DWT was applied to both D8 and D9 level at the 50 kHz sample frequency. The root mean square (RMS) values of the D8 and D9 coefficients were used for training using ANFIS and SVM techniques. After that, ANFIS and SVM were utilised to detect faults in the phase and ground lines. Several types of fault have been simulated, i.e. fault location, fault resistance, and original point of view. The RMS results from the two hybrid techniques were compared to find the best results. The tests of error estimation were performed for the three bus systems. The comparison of error estimation of the two methods shows that both hybrid techniques can be applied to predict fault locations.

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.