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Journal : Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Design and Simulation of Utilization of Solar Cells as Battery Chargers CC-CV (Constant Current-Constant Voltage) Method with Fuzzy Control Indhana Sudiharto; Endro Wahjono; Lugiana Nur Fitriah Rhamadani Lugiana
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 2, May 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i2.1448

Abstract

In a country with a tropical climate, the use of the sunlight is very important. Thus, to be able to apply, a solar power conversion system is needed into a source of electrical energy. The use of electrical equipment that is quite high will increase the consumption of electrical power so that people spend more and more on electricity costs. A battery is a device consisting of electrochemical cells that can store electrical energy. Overcharging the battery causes the battery to be susceptible to damage. So that the process of charging the battery becomes important, to get maximum attention and good efficiency. In this study, the use of solar cells with battery chargers using the CC-CV (Constant Current-Constant Voltage) Fuzzy Control method uses a solar cell to convert sunlight into electrical energy. The specifications of the solar cell used are 100 WP, while the charging process uses a DC-DC Sepic Converter. DC-DC Sepic Converter can increase efficiency and output polarity that is not reversed. This system is used to charge the lead-acid battery of 12 Volt 20 Ah. The charging method used is constant current-constant voltage (CC-CV) using Fuzzy Logic Control to adjust the duty cycle so that the converter output is by the constant current - constant voltage (CC-CV) planning. The constant current - constant voltage (CC-CV) method was chosen because it can provide good efficiency in charging time and the addition of the Constant Voltage method after Constant Current is enabled to keep the voltage at the setpoint and avoid overvoltage during the charging process. Sepic Converter is used to maintain the value of the voltage set point at 14.4 Volts and 6 Ampere for battery charging current.
A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-Fuzzy Inference System Algorithm Indhana Sudiharto; Eka Prasetyono; Anang Budikarso; Safira Fitria Devi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1452

Abstract

Renewable energy is being used to lessen the consumption of fossil fuels. Solar energy is a common source of renewable energy. Solar energy is the most promising source of energy due to its long-term sustainability and availability. The output power of solar panels is strongly influenced by the intensity of sunlight and the temperature of the solar panels. Maximum Power Point Tracking (MPPT) control, which aims to optimize the output power of solar panels, is commonly used to increase the efficiency of solar panels. However, MPPT control often causes overvoltage disturbance in systems directly connected to the load. To limit the output power of solar panels, additional Constant Power Generation (CPG) control is required. In this research, a solar panel system will be created to supply submersible DC pumps without any energy storage devices. DC-DC SEPIC Converter is designed with MPPT control combined with CPG control to limit the output power of the converter using the Adaptive Neuro-Fuzzy Inference System method by 150 watts. When the output power of the solar panel is less than the power limit, then MPPT mode will work. While CPG mode works when the PV output power is greater than the limit power. The results of this research showed that the system can provide optimal power generated by solar panels in MPPT mode by increasing efficiency by up to 33.04% and CPG mode can limit power to 150 Watts to avoid overvoltage disturbance at load.
High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS) Indhana Sudiharto; Farid Dwi Murdianto; Anang Budikarso; Putri Taufika
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1453

Abstract

Nowadays, water heater is a common household appliance. Water heater can be divided into three types, based on fuel sources: gas, diesel, and electric. Electric water heater is the most common due to its ease of use. The problems that often occur on electric water heater are over-temperature due to user error in setting up the thermostat and inaccurate readings caused by a conventional system control. These problems will cause a surge in power consumption. Over-temperature and conventional control inaccuracies can be overcome using the Artificial Intelligence (AI) control algorithm in the form of an adaptive neuro-fuzzy inference system (ANFIS). The proposed algorithm acts as a control by maintaining the stability of the temperature to obtain more accurate results. An accurate temperature reading can lower power consumption in electric water heater. This study tries to simulate Electric Water Heater temperature control using the ANFIS algorithm until stable readings can be achieved in all temperature settings. Results from disturbance tests in the form of external condition that causes sudden temperature change show that the system can maintain stability with an average error margin of 0.045% and the rate of accuracy of 99.955%.
Design MPPT with Anfis Method on Zeta Converter With DC Load Epyk Sunarno; Indhana Sudiharto; Dian Yolanita
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 1, February 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i1.1629

Abstract

Maximum power point tracking (MPPT) for PV (Photovoltaic) systems is provided in this research using artificial intelligence-based control. The design of MPPT system with Anfis Method on the Zeta Converter with DC Load is used to optimize the work of the Photovoltaic which will be used for DC load sources. The MPPT process consists of four main stages, namely module training data, determining input and output data, determining the number and type of membership functions and ANFIS training data. Zeta converter works like a buck boost, which can increase or decrease the voltage which is an advantage in designing systems with very volatile Photovoltaic sources. Zeta Converter is used to get higher efficiency, smaller input and output current ripple values and smaller core losses in the inductor. To improve the efficiency of system performance, An MPPT algorithm for the adaptive neuro fuzzy inference system (ANFIS) that is programmed into a microcontroller controls the zeta converter. ANFIS control is used because the response is faster and more effective. The combined simulation's findings demonstrate that the ANFIS control was successful, and the system can now produce the best possible power from Photovoltaic ipanelsiiniMPPT mode by boosting efficiency by up to 19.96%.