This research aims to design a flyback topology switch-mode power supply constant voltage and constant current light emitting diode (LED) driver to improve the performance of the LED. The flyback topology is chosen for its simple design and small size and uses high-frequency switching to lower the high DC voltage from the power source. The research aims to avoid blackouts caused by a drop in the power source voltage and to produce technological products that can be developed for other lighting applications. The constant-voltage feedback system ensures that the current flowing to the LED remains stable despite changes in load or other conditions. When there is a voltage drop from the power source, this topology can maintain the voltage and current stable and in accordance with the working voltage specifications of the LEDs. According to test results, the LED driver's output voltage ranges from 18.03 to 18.06 volts with an average message error value of 0.3%. In this system, switching occurs at frequencies ranging from 18.041 Hz to 38.230 Hz. With a constant current value of 0.58 ampere, the resulting lamp's lux value is stable and does not significantly change.

This study presents the strategy of controlling the air discharge in the prototype of small scale compressed air energy storage (SS-CAES) to produce a constant voltage according to the user set point. The purpose of this study is to simplify the control of the SS-CAES, so that it can be integrated with a grid based on a constant voltage reference. The control strategy in this study is carried out by controlling the opening of the air valve combined with a servo motor using three intelligence control systems (fuzzy logic, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS)). The testing scenario of this system will be carried out using two scenes, including changing the voltage set point and by switching the load. The results that were obtained indicate that ANN has the best results, with an average settling time of 2.05S in the first test scenario and 6.65S in the second test scenario.

Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS.

Research on energy storage technology is an interesting topic, especially in Small Scale Compressed Air Energy Storage (SS-CAES) which is considered more environmentally friendly and does not have degrade over time such as other energy storage technologies. To generate the power in SS-CAES, it is by adjusting the rotation speed of the generator to get the certain speed by the user. However, existing research shows that the speed control still utilizes electric torque that counteracts the mechanical torque of the turbine rotation, so this is considered to be wasteful of power. Therefore, this paper will provide control of the generator rotation speed by utilizing the air that passes through the generator (mechanical torque only). This speed control utilizes fuzzy logic to achieve the desired speed and reach steady-state in less than 10 seconds..

This study focuses on developing a prototype for a seawater desalination system powered by solarpanel. The desalination process is heated by a solar collector and 150 WP solar panel. The primaryobjective is to design, develop, and evaluate the prototype's efficacy, affordability, and scalability.The efficiency was measured by the quantity of freshwater produced per unit of solar energy. Ofthe three distillation tests, it was determined that the addition of a heater enhanced theperformance of the system. However, the overall efficiency was limited due to the solar panel andcollector's low heat output. A positive correlation was observed between irradiance andtemperature, but incomplete evaporation indicated the need for additional research to optimizethe process. The system consisted of a solar collector, a heater, and a distillation apparatus. Threedistillation tests revealed that the addition of a heater improved the system's performance,resulting in a maximum achievable efficiency of 0.99% and the production of 16 ml of fresh water.This study demonstrates the potential for renewable energy sources to power seawaterdesalination and lays the groundwork for future sustainable desalination technologies despite itslimitations.