Andalas Journal of Electrical and Electronic Engineering Technology
Electrical power and energy: Transmission and distribution, high voltage, electrical energy conversion, power electronics and drive. Telecomunication and Signal Processing: Antenna and wave propagation, network and systems, Modulation and signal processing, Radar and sonar, Radar imaging; Radio, multimedia content, Routing protocols, Wireless communications, Signal Processing, Image Processing, Voice Processing. Control automation and Robotic: Robotics, Automation, Pattern Recognition, Biosignal Engineering, Control Theory, Applied Control, System Design, Optimization, Process Control, Sensor. Research in Electrical and Electronic Engineering Education.
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<![CDATA[DC-DC Buck and Boost Converter Design for Energy Control in Hybrid PV Systems ]]>
<![CDATA[Zaini Zaini]]>;
<![CDATA[Wisnu Joko Wulung]]>
<![CDATA[ Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[Electrical Engineering Dept, Engineering Faculty, Universitas Andalas ]]>
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DOI: 10.25077/ajeeet.v3i2.41
<![CDATA[The intermittent nature of photovoltaic (PV) power generation due to weather conditions and time of day can affect the ability of PV systems to satisfy load demand. An effective PV system must store excess electrical energy when generation exceeds demand and discharge stored energy when demand is greater than generation. This study utilizes MATLAB simulations to design and evaluate DC-DC converter circuits for battery charging and discharging in PV systems. For charging, a buck converter with a fixed 45 V source is able to reduce voltage to a range of 33.99 V to 1.46 V by decreasing the duty cycle. For discharging, a boost converter with a fixed 12.8V source can increase voltage to 16.90 V–33.49 V by raising the duty cycle. Furthermore, under equal comparison, the open-loop buck converter operating at a 35% duty cycle demonstrates worse overshoot of 14.36% versus 0.24% for the closed-loop PID controlled buck converter. Similarly, the open-loop boost converter at 70% duty cycle exhibits slightly higher overshoot of 0.47% compared to negligible overshoot for the closed-loop PID controlled boost converter.]]>
<![CDATA[A Hybrid Technique for Fault Classification and Location in a Jointed Overhead-Underground Distribution Line ]]>
<![CDATA[Dorothy W. Gicheru]]>;
<![CDATA[Edwell T. Mharakurwa]]>;
<![CDATA[Waweru Njeri]]>
<![CDATA[ Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[Electrical Engineering Dept, Engineering Faculty, Universitas Andalas ]]>
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DOI: 10.25077/ajeeet.v3i2.51
<![CDATA[The electrical distribution system is crucial for the utility grid to transmit power from generators to consumers. Considering the intricate structure of distribution systems and their significant role in power networks, establishing a robust fault classification and location scheme is vital. Due to ageing, distribution systems are often prone to faults from factors like poor operational conditions and wear and tear. The line faulting rate and the pertinent restoration epochs influence the frequency and duration of power disruptions. Thus, precisely locating the fault section is essential to minimize power restoration timeframes. This paper presents a hybrid fault classification and location technique in a combined continuous overhead and underground distribution line. A simulation of the hybrid model was designed in Simulink for an 11 kV combined continuous overhead and underground electrical distribution line, considering short circuit faults as they are the most predominant and cause massive damage in distributed systems. The proposed technique first classifies the fault using Discrete Wavelet Transforms (DWT) and Multi-layer Perceptron-Artificial Neural Networks (MLP-ANN). Next, the impedance and Adaptive Neuro-Fuzzy System (ANFIS) based technique is employed for fault location. At a sample rate of 50 kHz, the DWT was applied to current signals and the coefficients used for ANN training, while phase impedance values were used as input to the ANFIS for training. The simulation results showed accuracy of 96.6% for fault classification and 99.17% for fault location. The developed models can significantly enhance fault location for speedier outage resolution by promptly repairing the affected distribution lines.]]>
<![CDATA[Analyzing Troubleshooting of BTS Transmit Power and 4G LTE Coverage Area via VSWR Value Measurement ]]>
<![CDATA[Nadila Khairanisa]]>;
<![CDATA[Siska Aulia]]>;
<![CDATA[Sri Yusnita]]>;
<![CDATA[Yulindon Yulindon]]>
<![CDATA[ Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[Electrical Engineering Dept, Engineering Faculty, Universitas Andalas ]]>
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DOI: 10.25077/ajeeet.v3i2.57
<![CDATA[The Voltage Standing Wave Ratio (VSWR) serves as a comparative measure between transmitter and receiver voltages, impacting site transmit power. VSWR values at a Radio Base Station (RBS) are evident in feeder cable losses, jumpers, combiners, and radio antenna losses. This study aims to assess the impact of poor VSWR values on BTS transmit power and perform troubleshooting analysis on 4G signal quality and Coverage Area. The study commenced with a literature review and VSWR measurements in the Gurun Laweh Aia Pacah region. The process involved identifying VSWR issues at the BTS site, conducting a Drive Test using pocket Tems, and troubleshooting the VSWR problem. Drive Test findings encompassed areas with weak signals, BTS site data, and problematic spots. The analysis utilized applications like Tems Discovery and MapInfo Pro. Results along the Gurun Laweh Aia Pacah road showed signal strength affected by a high VSWR value of 1.6. RSRP data rated 50% in the good category, SINR at 31% in the good category, and throughput at 5% in the good category. Elevated VSWR values diminish signal range, subsequently impacting traffic metrics. Immediate VSWR troubleshooting becomes imperative; low VSWR promotes higher transmit power efficiency, while normal VSWR ensures optimal transmit power efficiency.]]>
<![CDATA[Design of Power Factor Monitoring System Based on Android Application ]]>
<![CDATA[Fadhillah Hazrina]]>;
<![CDATA[Inu Yuni Erawati]]>;
<![CDATA[Galih Mustiko Aji]]>;
<![CDATA[Devi Taufiq Nurrohman]]>
<![CDATA[ Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[Electrical Engineering Dept, Engineering Faculty, Universitas Andalas ]]>
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DOI: 10.25077/ajeeet.v3i2.62
<![CDATA[Electrical energy is an essential resource for human needs. The prolific utilization of electrical devices accounts for high energy consumption patterns. Resistive and inductive loads characterize conventional electrical equipment. In practice, the properties of electrical loads impact energy demand and system efficiency. Thus, power factor correction presents a viable strategy to improve electrical energy efficiency. This research aims to develop an Internet of Things-integrated power factor monitoring system. When connected to Wi-Fi, the system employs a PZEM-004T sensor to monitor current, voltage, power, and power factor measurements from the load in the absence of active monitoring. The ESP32 microcontroller processes the sensor data. Then, control programs running on the microcontroller instruct a relay to engage capacitive banks accordingly. The system displays output metrics on a Liquid Crystal Display and Android application. Experimental results indicate that a single-phase electric motor operates at a baseline power factor of 0.31. However, integration of the factor correction tool detailed herein improves the power factor to 0.98 for the given load.]]>
<![CDATA[Fabrication of Smart Meter for Accurate Use in Home and Industry ]]>
<![CDATA[Nicholas Kirui]]>;
<![CDATA[Charles Kagiri]]>;
<![CDATA[Titus Mulembo]]>
<![CDATA[ Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[Electrical Engineering Dept, Engineering Faculty, Universitas Andalas ]]>
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DOI: 10.25077/ajeeet.v3i2.63
<![CDATA[This study addresses the challenges posed by conventional energy meters, which rely on manual readings, leading to human errors and inefficiencies. In response to this, a battery-powered smart meter was developed utilizing an STM32 microcontroller, ADE7758 and STPM32 metering integrated circuits (ICs), SIM and ESP32 communication modules, along with a MYSQL database. Real-time energy data from both single and three-phase appliances were collected, and their energy consumption, errors, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were quantified. The model demonstrated an acceptable accuracy level, with an estimated MAE of approximately 2.912 units and an estimated RMSE of around 4.048 units, particularly in predicting motor power consumption. Additionally, ARIMA forecasting was applied to a three-phase asynchronous motor, revealing an average active motor power of 250.95 watts, indicating precise results over time.]]>
<![CDATA[DC-DC Buck and Boost Converter Design for Energy Control in Hybrid PV Systems ]]>
<![CDATA[Zaini Zaini]]>;
<![CDATA[Wisnu Joko Wulung]]>
<![CDATA[ Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[Electrical Engineering Dept, Engineering Faculty, Universitas Andalas ]]>
Show Abstract
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Original Source
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DOI: 10.25077/ajeeet.v3i2.41
<![CDATA[The intermittent nature of photovoltaic (PV) power generation due to weather conditions and time of day can affect the ability of PV systems to satisfy load demand. An effective PV system must store excess electrical energy when generation exceeds demand and discharge stored energy when demand is greater than generation. This study utilizes MATLAB simulations to design and evaluate DC-DC converter circuits for battery charging and discharging in PV systems. For charging, a buck converter with a fixed 45 V source is able to reduce voltage to a range of 33.99 V to 1.46 V by decreasing the duty cycle. For discharging, a boost converter with a fixed 12.8V source can increase voltage to 16.90 V–33.49 V by raising the duty cycle. Furthermore, under equal comparison, the open-loop buck converter operating at a 35% duty cycle demonstrates worse overshoot of 14.36% versus 0.24% for the closed-loop PID controlled buck converter. Similarly, the open-loop boost converter at 70% duty cycle exhibits slightly higher overshoot of 0.47% compared to negligible overshoot for the closed-loop PID controlled boost converter.]]>
<![CDATA[A Hybrid Technique for Fault Classification and Location in a Jointed Overhead-Underground Distribution Line ]]>
<![CDATA[Dorothy W. Gicheru]]>;
<![CDATA[Edwell T. Mharakurwa]]>;
<![CDATA[Waweru Njeri]]>
<![CDATA[ Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[Electrical Engineering Dept, Engineering Faculty, Universitas Andalas ]]>
Show Abstract
|
Download Original
|
Original Source
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Check in Google Scholar
|
DOI: 10.25077/ajeeet.v3i2.51
<![CDATA[The electrical distribution system is crucial for the utility grid to transmit power from generators to consumers. Considering the intricate structure of distribution systems and their significant role in power networks, establishing a robust fault classification and location scheme is vital. Due to ageing, distribution systems are often prone to faults from factors like poor operational conditions and wear and tear. The line faulting rate and the pertinent restoration epochs influence the frequency and duration of power disruptions. Thus, precisely locating the fault section is essential to minimize power restoration timeframes. This paper presents a hybrid fault classification and location technique in a combined continuous overhead and underground distribution line. A simulation of the hybrid model was designed in Simulink for an 11 kV combined continuous overhead and underground electrical distribution line, considering short circuit faults as they are the most predominant and cause massive damage in distributed systems. The proposed technique first classifies the fault using Discrete Wavelet Transforms (DWT) and Multi-layer Perceptron-Artificial Neural Networks (MLP-ANN). Next, the impedance and Adaptive Neuro-Fuzzy System (ANFIS) based technique is employed for fault location. At a sample rate of 50 kHz, the DWT was applied to current signals and the coefficients used for ANN training, while phase impedance values were used as input to the ANFIS for training. The simulation results showed accuracy of 96.6% for fault classification and 99.17% for fault location. The developed models can significantly enhance fault location for speedier outage resolution by promptly repairing the affected distribution lines.]]>
<![CDATA[Analyzing Troubleshooting of BTS Transmit Power and 4G LTE Coverage Area via VSWR Value Measurement ]]>
<![CDATA[Nadila Khairanisa]]>;
<![CDATA[Siska Aulia]]>;
<![CDATA[Sri Yusnita]]>;
<![CDATA[Yulindon Yulindon]]>
<![CDATA[ Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[Electrical Engineering Dept, Engineering Faculty, Universitas Andalas ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.25077/ajeeet.v3i2.57
<![CDATA[The Voltage Standing Wave Ratio (VSWR) serves as a comparative measure between transmitter and receiver voltages, impacting site transmit power. VSWR values at a Radio Base Station (RBS) are evident in feeder cable losses, jumpers, combiners, and radio antenna losses. This study aims to assess the impact of poor VSWR values on BTS transmit power and perform troubleshooting analysis on 4G signal quality and Coverage Area. The study commenced with a literature review and VSWR measurements in the Gurun Laweh Aia Pacah region. The process involved identifying VSWR issues at the BTS site, conducting a Drive Test using pocket Tems, and troubleshooting the VSWR problem. Drive Test findings encompassed areas with weak signals, BTS site data, and problematic spots. The analysis utilized applications like Tems Discovery and MapInfo Pro. Results along the Gurun Laweh Aia Pacah road showed signal strength affected by a high VSWR value of 1.6. RSRP data rated 50% in the good category, SINR at 31% in the good category, and throughput at 5% in the good category. Elevated VSWR values diminish signal range, subsequently impacting traffic metrics. Immediate VSWR troubleshooting becomes imperative; low VSWR promotes higher transmit power efficiency, while normal VSWR ensures optimal transmit power efficiency.]]>
<![CDATA[Design of Power Factor Monitoring System Based on Android Application ]]>
<![CDATA[Fadhillah Hazrina]]>;
<![CDATA[Inu Yuni Erawati]]>;
<![CDATA[Galih Mustiko Aji]]>;
<![CDATA[Devi Taufiq Nurrohman]]>
<![CDATA[ Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[Electrical Engineering Dept, Engineering Faculty, Universitas Andalas ]]>
Show Abstract
|
Download Original
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Original Source
|
Check in Google Scholar
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DOI: 10.25077/ajeeet.v3i2.62
<![CDATA[Electrical energy is an essential resource for human needs. The prolific utilization of electrical devices accounts for high energy consumption patterns. Resistive and inductive loads characterize conventional electrical equipment. In practice, the properties of electrical loads impact energy demand and system efficiency. Thus, power factor correction presents a viable strategy to improve electrical energy efficiency. This research aims to develop an Internet of Things-integrated power factor monitoring system. When connected to Wi-Fi, the system employs a PZEM-004T sensor to monitor current, voltage, power, and power factor measurements from the load in the absence of active monitoring. The ESP32 microcontroller processes the sensor data. Then, control programs running on the microcontroller instruct a relay to engage capacitive banks accordingly. The system displays output metrics on a Liquid Crystal Display and Android application. Experimental results indicate that a single-phase electric motor operates at a baseline power factor of 0.31. However, integration of the factor correction tool detailed herein improves the power factor to 0.98 for the given load.]]>
<![CDATA[Fabrication of Smart Meter for Accurate Use in Home and Industry ]]>
<![CDATA[Nicholas Kirui]]>;
<![CDATA[Charles Kagiri]]>;
<![CDATA[Titus Mulembo]]>
<![CDATA[ Andalas Journal of Electrical and Electronic Engineering Technology Vol. 3 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[Electrical Engineering Dept, Engineering Faculty, Universitas Andalas ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.25077/ajeeet.v3i2.63
<![CDATA[This study addresses the challenges posed by conventional energy meters, which rely on manual readings, leading to human errors and inefficiencies. In response to this, a battery-powered smart meter was developed utilizing an STM32 microcontroller, ADE7758 and STPM32 metering integrated circuits (ICs), SIM and ESP32 communication modules, along with a MYSQL database. Real-time energy data from both single and three-phase appliances were collected, and their energy consumption, errors, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were quantified. The model demonstrated an acceptable accuracy level, with an estimated MAE of approximately 2.912 units and an estimated RMSE of around 4.048 units, particularly in predicting motor power consumption. Additionally, ARIMA forecasting was applied to a three-phase asynchronous motor, revealing an average active motor power of 250.95 watts, indicating precise results over time.]]>
