In this paper, the efficiency of photovoltaic panels is improved by adding a sun tracking system. The solar tracking system is used for tracking the sun so that photovoltaic always faces the sun. This system uses a dual axis consisting of horizontal rotation axis and a vertical rotation axis. The horizontal rotational axis motion is to follow the azimuth angle of the sun from north to south. Then, to follow the sun's azimuth angle from east to west is the vertical axis motion. Both types of movements are controlled using a PID controller that is optimized with an artificial intelligence approach, namely particle swarm optimization (PID-PSO), firefly algorithm (PID-FA), imperialist competitive algorithm (PID-ICA), bat algorithm (PID-BA), and ant colony optimization (PID-ACO). Experiments of various approaches were carried out and the corresponding performance compared. The experimental results show that PID-BA performs best in terms of settling time and overshoot. The results also allow the comparison of different PID controller and the calculation of the fastest completion time.

Photovoltaic (PV) is a source of electrical energy derived from solar energy and has a poor level of efficiency. This efficiency is influenced by PV condition, weather, and equipments like Maximum Power Point Tracking (MPPT). MPPT control is widely used to improve PV efficiency because MPPT can produce optimal power in various weather conditions. In this paper, MPPT control is performed using the Fuzzy Logic-Particle Swarm Optimization (FL-PSO) method. This FL-PSO is used to get the Maximum Power Point (MPP) and minimize the output power oscillation from PV. From the simulation results using FL-PSO, the values of voltage, and output power from the boost converter are 183.6 V, and 637.7 W, respectively. The ripple of output power from PV with FL-PSO is 69.5 W. Then, the time required by FL-PSO reaches MPP is 0.354 s. Compared with MPPT control based on the PSO method, the MPPT technique using FL-PSO indicates better performance and faster than the PSO.

Literasi merupakan seperangkat kemampuan dan keterampilan individu dalam membaca, menulis, berbicara, menghitung, dan memecahkan masalah pada tingkat keahlian tertentu. Terdapat enam literasi yang harus dikuasai peserta didik, orang tua, dan seluruh warga masyarakat menurut World Economic Forum yaitu baca tulis, literasi numerasi, literasi finansial, literasi sains, literasi budaya dan kewarganegaraan, serta literasi teknologi informasi dan komunikasi atau digital. Namun kemampuan ini tidak hanya berupa literasi secara umum, tetapi juga literasi dalam berbagai bidang keilmuan seperti literasi keuangan, literasi kesehatan, literasi sosial, dan literasi saintifik serta pengaruh antar bidang ilmu pengetahuan yang lain. Oleh karena itu, kuisioner tentang pengaruh literasi diberbagai bidang ilmu pengetahuan diperlukan. Berdasarkan hasil kuisioner yang telah dilakukan, dapat disimpulkan mahasiswa UNAIR semester 2 tahun angkatan 2021 sudah memiliki pemahaman mengenai kemampuan literasi dan pentingnya peran membaca dalam kemampuan literasi. Namun, kemampuan literasi mahasiswa semester 2 Universitas Airlangga terbilang masih kurang karena 62% responden masih melakukan kegiatan literasi hanya tergantung dari bacaannya saja, serta 86% responden masih mengalami kesulitan dalam memahami informasi yang dibaca.

This paper considered maximum power extraction in wind turbines. However, the importance of constant power output of a wind turbine is increasing as the contribution of wind energy to the distribution network / grid grows. This may be considered as a guide of the future pitch angle control problems. The permanent magnetic synchrounous generator (PMSG) wind turbine variable pitch system is done through changing the electrical characteristics of the generator and changing the pitch angle. Also PMSG wind turbine variable pitch system is equipped with systems for regulating the orientation angles of the axis of the turbine against the wind and against the skyline. The design and simulation of Neural Network Predictive Controller (NNPC)-pitch angle involved in PMSG wind turbine variable pitch system is outlined. Its performance design is simulated to baseline linear controller through simulation in the range of wind speed is 7- 10 m/s. To test the designed NNPC-pitch angle performance, PMSG wind turbine variable pitch system has been simulated and results are constant power output.

The oscillation of frequency can cause the generator to run out of sync in a power system. Therefore, load frequency control (LFC) is needed to reduce frequency oscillation and prevent out of sync operation. The LFC regulates the governor to balance the turbine speed with changes in the load of existing. In this paper, a proportional integral differential (PID) controller and a battery energy storage system (BESS) are added to an LFC to improve the frequency stability of a power system. The parameters of the PID controller and BESS are optimized using the Bat algorithm (BA) to attain a good coordination. The frequency performance analysis is done by introducing disturbance in the form of changes in load power. The simulation results show that the frequency deviation of the system with the PID controller and BESS, has a faster settling time and smaller overshoot value. The system performs better than those with only the PID controller or the BESS. In conclusion, the BA algorithm can be used to find optimal parameter values of the PID controller and BESS for a synchronized coordination of an LFC.

This research endeavors to increase the lifespan of a battery utilized in a standalone microgrid system, a self-sufficient electrical system that consists of multiple generators that are not connected to the main power grid. This type of system is ideal for use in remote locations or areas where the grid connection is not possible. The sources of energy for this system include photovoltaic panels, wind turbines, diesel generators, and batteries. The state of charge (SOC) of the battery is used to determine the amount of energy stored in it. The particle swarm optimization (PSO) method is applied to minimize energy generation costs and maximize battery life. The results show that battery optimization can decrease energy generation costs from Rp 5,271,523.03 ($338.64 in USD) to Rp 13,064,979.20 ($839.30 in USD) while increasing the battery's lifespan by 0.42%, with losses of 7.22 kW and 433.29 kVAR, and also a life loss cost of Rp 5,499/$0.35.