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

A Modified Maximum Power Point Tracking with Constant Power Generation Using Adaptive Neuro-Fuzzy Inference System Algorithm Sudiharto, Indhana; Prasetyono, Eka; Budikarso, Anang; Fitria Devi, Safira
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) Sudiharto, Indhana; Dwi Murdianto, Farid; Budikarso, Anang; Taufika, Putri
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 and Simulation of Battery Charging System with Constant Temperature–constant Voltage Method Sudiharto, Indhana; Wahjono, Endro; Sasetyo, Muhammad Yudha; Suryono; Budikarso, Anang
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2194

Abstract

Batteries are essential to many contemporary applications, including electric cars and portable electronics. Overheating and charging time efficiency are the two biggest issues with battery charging. Overheating presents safety hazards and hastens battery deterioration. Due to their inability to regulate temperature, conventional charging techniques like Constant Current - Constant Voltage (CC-CV) result in excessive temperature rises during battery charging, which shortens battery life. A novel approach that helps lessen excessive temperature rises is the Constant Temperature - Constant Voltage (CT-CV) method, according to researchers. In order to avoid excessive temperature increases during the initial charging, the CT technique initially regulates the applied temperature. Second, to guarantee full capacity without causing damage to the battery, the CV technique is used to maintain a steady voltage. A fuzzy logic controller (FLC) control system is used to regulate the temperature and current at the DC-DC converter's output. The FLC control system's goal is to control the duty cycle such that the buck converter's output is 65V 11.5A. The simulation results show that the CT-CV method can reduce the increase in temperature in the battery with an average temperature during the battery charging process of 23.57° C with fuzzy control and 23.71° C with PI control. In addition, by comparing two control systems with the CT-CV method, namely PI and fuzzy, it was found that the fuzzy method was able to accelerate battery charging by 4.16% compared to the PI control.