Solar energy is a potential energy source in Indonesia.A photovoltaic is needed to harvest this kind of energy, and to be able to gather the most, the PV must have a good efficiency. The maximum efficiency is achieved when PV works at its maximum power point which depends on irradiation and temperature. Since the irradiation and temperature always change with time, a PV system which able to track the maximum power point needs to be developed to produce more energy. This research was aimed to explore the performance of a maximum power point tracking system which implements Incremental Conductance (IC) method. The IC algorithm was designed to control the duty cycle of Buck Boost converter and to ensure the MPPT work at its maximum efficiency. The system performance of IC algorithm was compared to widely used algorithm -Perturb and Observe (P&O) on a Simulink environment. From the simulation, the IC method shows a better performance and also has a lower oscillation.

Wind energy conversion systems (WECSs) can extract maximum power by controlling the wind turbine rotational speed. This study presents a novel sensorless maximum power extraction control for small-scale WECS using a permanent magnet synchronous generator (PMSG), to improve the maximum power extraction. The proposed method uses the output voltage and current of a rectifier to determine the duty cycle of the boost converter, without requiring the wind speed information and turbine characteristics. The step size of the duty cycle is changed adaptively, based on the difference between the rectifier output power and the previous duty cycle to obtain fast convergence, until the maximum power point is attained. The performance of the proposed sensorless maximum power extraction control is evaluated both by simulation, using PowerSIM and laboratory experiments, for variable wind speed conditions. The proposed maximum power extraction controller has a simple structure, low cost, and a good response to wind speed variations. The proposed method can extract a higher maximum power and has a higher efficiency of 93.87%, than the conventional perturb and observe method.

Abstract. The increasing mobility has directly led to deteriorating traffic conditions, extra fuel consumption, increasing automobile exhaust emissions, air pollution and lowering quality of life. Apart from being clean, cheap and equitable mode of transport for short-distance journeys, cycling can potentially offer solutions to the problem of urban mobility. Many cities have tried promoting cycling particularly through the implementation of bike-sharing. Apparently the fourth generation bikesharing system has been promoted utilizing electric bicycles which considered as a clean technology implementation. Utilization of solar power is probably the development keys in the fourth generation bikesharing system and will become the standard in bikesharing system in the future. Electric bikes use batteries as a source of energy, thus they require a battery charger system which powered from the solar cells energy. This research aims to design and implement electric bicycle battery charging system with solar energy sources using fuzzy logic algorithm. It is necessary to develop an electric bicycle battery charging system with solar energy sources using fuzzy logic algorithm. The study was conducted by means of experimental method which includes the design, manufacture and testing controller systems. The designed fuzzy algorithm have been planted in EEPROM microcontroller ATmega8535. The charging current was set at 1.2 Amperes and the full charged battery voltage was observed to be 40 Volts. The results showed a fuzzy logic controller was able to maintain the charging current of 1.2 Ampere with an error rate of less than 5% around the set point. The process of charging electric bike lead acid batteries from empty to fully charged was 5 hours. In conclusion, the development of solar-powered electric bicycle controlled using fuzzy logic controller can keep the battery charging current in solar-powered electric bicycle to remain stable. This shows that the fuzzy algorithm can be used as a controller in the process of charging for a solar electric bicycle.

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Abstract-At present around the world including Indonesia have an energy crisis that is necessary to find renewable energy as a replacement. One of renewable energy is solar energy that use photovoltaic (PV) system to convert into electrical energy. The weakness of this PV system is the low energy conversion efficiency. . To increase the efficiency of PV panels, it must operate around the maximum power point which is influenced by cell temperature and sun irradiation. A controller therefore is needed to determine MPP and control PV output voltage according MPP voltage although there change in temperature and sun irradiation. The aim of this paper is design neural fuzzy controller for control the PV system output voltage using the buck converter to operate at the MPP although occur disturbance with MATLAB/SIMULINK. Neural fuzzy define MPP point and the MPPT controlling done by adjusting the duty cycle of converter so that the PV array voltage remains at MPP operating point. In particular, the simulation of neural fuzzy will be discussed.

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A Cart Inverted Pendulum System is an unstable, nonlinear and underactuated system. This makes a cart inverted pendulum system used as a benchmark for testing many control method. A cart must occupy the desired position and the angle of the pendulum must be in an equilibrium point. System modeling of a cart inverted pendulum is important for controlling this system, but modeling using assumptions from state-feedback control is not completely valid. To minimize unmeasured state variables, state estimators need to be designed. In this paper, the state estimator is designed to complete the state-feedback control to control the cart inverted pendulum system. The mathematical model of the cart inverted pendulum system is obtained by using the Lagrange equation which is then changed in the state space form. Mathematical models of motors and mechanical transmissions are also included in the cart inverted pendulum system modeling so that it can reduce errors in a real-time application. The state gain control parameter is obtained by selecting the weighting matrix in the Linear Quadratic Regulator (LQR) method, then added with the Leuenberger observer gain that obtained by the pole placement method on the state estimator. Simulation is done to determine the system performance. The simulation results show that the proposed method can stabilize the cart inverted pendulum system on the cart position and the desired pendulum angle.

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