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Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi Zaini Zaini; Dwi Mutiara Harfina; Agung P Iswar
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 1 No. 2 (2021): November 2021
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Measurement of electric charge on the battery in real-time cannot be separated from external noise and disturbances such as temperature and interference. An optimal State of Charge (SoC) estimator model is needed to make the estimation more accurate. To obtain the model, the battery was tested under room temperature conditions and at a temperature of 40oC to obtain a second-order RC model for the Li-Ion battery used. Based on the test data obtained, the data will be tested first using the Kalman Filter method to get an estimate of the State of Charge (SoC). Tests were carried out using MATLAB software. After the method was tested, the online SoC Estimator design began using the Raspberry Pi Single Board Computer (SBC). After that, the estimator will be tested first using data from offline measurements and then used in real-time (online) SoC estimation measurements. The Voc before the battery discharge test was 13.16 V and after the test, the measured Voc was 11.58 V. During the discharge the Voc was reduced by 1.58 V. While the discharge data from the battery manufacturer showed the reduced Voc during the discharge was 1.2V.

Design of Monitoring System for Hazardous Gas and Fire Detection In Building Based On Internet of Things Zaini Zaini; Taffany Hudalil Alvy
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 2 No. 1 (2022): May 2022
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Fires and gas leaks are events that still occur frequently. This incident is usually caused by various factors including leakage of LPG gas cylinders, cigarette butts that are disposed of carelessly, short circuits of electric current and so on. Generally, fires and gas leaks can only be detected if the fire has already grown or a lot of smoke comes out of the building. Therefore, a monitoring system for detecting dangerous gases and fires in buildings based on the Internet of Things was created that can monitor the condition of the building through a website as well as send notifications to the Telegram application on smartphones. The detection system implemented uses a flame sensor as a fire detector, an MQ-2 gas sensor as a detector of hazardous gases (CO, CO2, and CH4), and NodeMCU as a module to transmit data. The system will work continuously in real time, if gas is detected that exceeds the threshold or a fire is detected, the system will send a notification to Telegram and the website will display the value and status of the sensor and a map of the area where the fire or gas leak occurred. The results of the detection system created to be able to provide solutions so that cases of fire and gas leaks can be handled early by detecting signs of fire or gas leaks and sending the information to users via the website and notifications.

The Use of Artificial Neural Networks in Agricultural Plants Roza Susanti; Riko Nofendra; Zaini Zaini; Muhammad Syaiful Amri bin Suhaimi; Muhammad Ilhamdi Rusydi
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 2 No. 2 (2022): November 2022
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v2i2.32

Artificial Neural Networks use high-performance computing and big data technology, opportunities for science to create new opportunities in agriculture. The purpose of writing this article is to analyze the use of artificial neural networks on (a) plant diseases based on plant leaf diseases, (b) plant pests, (c) growth or quality, and (d) agricultural products. The writing method used is a literature study of the research that has been done. The keywords used in the search for references include ANN, plant, diseases, pests, growth or quality, and agricultural products. Publishers for the reference in this article are ScienceDirect and IEEE. The years of publication of the references are restricted from 2015 to 2022. Based on the literature study results, it was concluded that Artificial Neural Networks' deep learning models are accurate for detecting and classifying leaf diseases and pests, detecting growth, and application to agricultural plant products.

Analysis of Exhaust Gas Heat Utilization in Waste Heat Recovery Power Generator at Indarung V Factory PT Semen Padang Mayang Safira; Melda Latif; Zaini Zaini; Aulia Aulia; Mumuh Muharam; Waweru Njeri
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 1 (2023): May 2023
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v3i1.34

Increasing energy efficiency in the cement production process at PT Semen Padang is carried out by reusing exhaust gas to produce electricity using Waste heat recovery power generation (WHRPG) with a capacity of 8.5 MW. WHRPG is a technology for utilizing exhaust gas heat as a source of heat energy to heat feed water into steam by using a suspension preheater (SP) boiler and air quenching cooler (AQC) boiler. This study aims to calculate the power potential of the steam heat influenced by the steam temperature and the mass flow rate of the steam produced by the boiler, to calculate the efficiency of the boiler using the direct method by comparing the boiler output heat against the boiler input heat, to calculate the turbine efficiency based on the difference between the steam enthalpy enter the turbine against the steam enthalpy out of the turbine and the isotropic enthalpy of the steam out of the turbine and to calculate the power generated by WHRPG at PT Semen Padang. The results obtained in this study are the total potential power of steam heat is 19.778 MW, the boiler AQC efficiency is 70.30%, the boiler SP efficiency is 94.04% and the turbine efficiency is 78.64%. The electricity generated by PT Semen Padang's WHRPG is 3.70 MW.

The Use of Artificial Neural Networks in Agricultural Plants Roza Susanti; Riko Nofendra; Zaini Zaini; Muhammad Syaiful Amri bin Suhaimi; Muhammad Ilhamdi Rusydi
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 2 No. 2 (2022): November 2022
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v2i2.32

Artificial Neural Networks use high-performance computing and big data technology, opportunities for science to create new opportunities in agriculture. The purpose of writing this article is to analyze the use of artificial neural networks on (a) plant diseases based on plant leaf diseases, (b) plant pests, (c) growth or quality, and (d) agricultural products. The writing method used is a literature study of the research that has been done. The keywords used in the search for references include ANN, plant, diseases, pests, growth or quality, and agricultural products. Publishers for the reference in this article are ScienceDirect and IEEE. The years of publication of the references are restricted from 2015 to 2022. Based on the literature study results, it was concluded that Artificial Neural Networks' deep learning models are accurate for detecting and classifying leaf diseases and pests, detecting growth, and application to agricultural plant products.

Analysis of Exhaust Gas Heat Utilization in Waste Heat Recovery Power Generator at Indarung V Factory PT Semen Padang Mayang Safira; Melda Latif; Zaini Zaini; Aulia Aulia; Mumuh Muharam; Waweru Njeri
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 1 (2023): May 2023
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v3i1.34

Increasing energy efficiency in the cement production process at PT Semen Padang is carried out by reusing exhaust gas to produce electricity using Waste heat recovery power generation (WHRPG) with a capacity of 8.5 MW. WHRPG is a technology for utilizing exhaust gas heat as a source of heat energy to heat feed water into steam by using a suspension preheater (SP) boiler and air quenching cooler (AQC) boiler. This study aims to calculate the power potential of the steam heat influenced by the steam temperature and the mass flow rate of the steam produced by the boiler, to calculate the efficiency of the boiler using the direct method by comparing the boiler output heat against the boiler input heat, to calculate the turbine efficiency based on the difference between the steam enthalpy enter the turbine against the steam enthalpy out of the turbine and the isotropic enthalpy of the steam out of the turbine and to calculate the power generated by WHRPG at PT Semen Padang. The results obtained in this study are the total potential power of steam heat is 19.778 MW, the boiler AQC efficiency is 70.30%, the boiler SP efficiency is 94.04% and the turbine efficiency is 78.64%. The electricity generated by PT Semen Padang's WHRPG is 3.70 MW.

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