The Study of the Effect of Capacity Increase and Photovoltaic Placement on Power Loss, Voltage Profile by Considering THDv

The application of Photovoltaic (PV) is one solution to the increasing demand for electrical energy. However, the application of photovoltaic (PV) must be in the right location and capacity so that the power loss you want to reduce is large and the voltage profile is good. Photovoltaic (PV) generates DC voltage which is then required by an inverter to convert it to AC. The inverter is a non-linear load that produces harmonics. Harmonics in an electric power system can be known from Total Harmonic Distortion (THD). The purpose of this study is to determine the optimal location of placement (PV) and its maximum capacity so that the power loss is smaller. The resulting voltage and THD profile conform to the permitted standards. The methods used in determining the optimal location of photovoltaic (PV) are Loss Sensitivity Factor (LSF) and Voltage Sensitivity Index (VSI). ETAP 16 software is used for power and harmonic flow simulation. From this research, the most optimal photovoltaic (PV) placement is on bus 10 (bus 283 T) with a maximum capacity of 3255 kVA. This placement location provides minimal power loss and a good voltage profile taking into account the permitted standard THDv.

Digital Stethoscope to Examine Patients According to Technical Guidelines of Covid-19

Health professionals have to use Personal Protective Equipment (PPE) that could cover the entire body to prevent the transmission of the SARS-CoV-2 virus while examining patients. The current stethoscope is not compatible with the need of health professionals. Therefore, this project aims to create a digital stethoscope that can fulfill the requirement of health professionals to examine patients according to health service guidance in Covid-19 era. Three main objectives in designing the stethoscope are to meet the needs of health professionals, easy and convenient to use. The designed stethoscope has an amplifier circuit with 100 gain and a bandpass filter with 20 Hz and 700 Hz for low and high cut-off frequencies. It is also able to calculate the heart rate and displays it on the stethoscope screen. The stethoscope has a record function that lets the users replay sound as much as they need for the analysis. This digital stethoscope has function tests, performance tests and user satisfaction tests. Seven health professionals who are well-experienced in handling Covid-19 patients auscultate and provide an assessment of the three objectives design. The test results showed that this stethoscope satisfies the health professional’s requirements to examine the patient, grade 4.69 of 5. Health professionals also give a perfect rating for the objective of being easy to use. This stethoscope is the right choice for examining patients according to health service guidance in the Covid-19 era.

Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method

To identify the fault, wavelet method is used in solving complex protection problems. This study uses a new approach, namely the wavelet multi resolution analysis method with its application where multi resolution analysis works to analyze signals at different frequencies with the same resolution. In this study, the classification of fault types that occur in the 150 kV transmission line quickly and accurately is carried out using the wavelet multi resolution analysis method. This research is included in applied research and was designed using computer simulation software, namely ATP and MATLAB. The data transmission system used is the Maninjau Hydroelectric Power Plant transmission line to Pauh Limo Substation. The modeled transmission system is given 1-phase to ground, 2-phase to ground, 2- phase, 3-phase and lightning faults. To determine the accuracy of this classification, the fault is varied according to the distance and impedance of the disturbance. From the analysis of the simulation results and calculations, based on the wavelet multi resolution analysis method used in fault classifying, the average value of the approximation coefficient used to classify the type of fault is obtained. Based on the results of the study, it can be said that all types of fault analyzed in this study have met the classification requirements using the wavelet multi resolution analysis method

Digital Stethoscope to Examine Patients According to Technical Guidelines of Covid-19

Health professionals have to use Personal Protective Equipment (PPE) that could cover the entire body to prevent the transmission of the SARS-CoV-2 virus while examining patients. The current stethoscope is not compatible with the need of health professionals. Therefore, this project aims to create a digital stethoscope that can fulfill the requirement of health professionals to examine patients according to health service guidance in Covid-19 era. Three main objectives in designing the stethoscope are to meet the needs of health professionals, easy and convenient to use. The designed stethoscope has an amplifier circuit with 100 gain and a bandpass filter with 20 Hz and 700 Hz for low and high cut-off frequencies. It is also able to calculate the heart rate and displays it on the stethoscope screen. The stethoscope has a record function that lets the users replay sound as much as they need for the analysis. This digital stethoscope has function tests, performance tests and user satisfaction tests. Seven health professionals who are well-experienced in handling Covid-19 patients auscultate and provide an assessment of the three objectives design. The test results showed that this stethoscope satisfies the health professional’s requirements to examine the patient, grade 4.69 of 5. Health professionals also give a perfect rating for the objective of being easy to use. This stethoscope is the right choice for examining patients according to health service guidance in the Covid-19 era.

Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method

To identify the fault, wavelet method is used in solving complex protection problems. This study uses a new approach, namely the wavelet multi resolution analysis method with its application where multi resolution analysis works to analyze signals at different frequencies with the same resolution. In this study, the classification of fault types that occur in the 150 kV transmission line quickly and accurately is carried out using the wavelet multi resolution analysis method. This research is included in applied research and was designed using computer simulation software, namely ATP and MATLAB. The data transmission system used is the Maninjau Hydroelectric Power Plant transmission line to Pauh Limo Substation. The modeled transmission system is given 1-phase to ground, 2-phase to ground, 2- phase, 3-phase and lightning faults. To determine the accuracy of this classification, the fault is varied according to the distance and impedance of the disturbance. From the analysis of the simulation results and calculations, based on the wavelet multi resolution analysis method used in fault classifying, the average value of the approximation coefficient used to classify the type of fault is obtained. Based on the results of the study, it can be said that all types of fault analyzed in this study have met the classification requirements using the wavelet multi resolution analysis method

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

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.

Home Industry Powered Grid-Connected PV System an Economic Feasibility Study

This paper presents an analysis and feasibility of grid connected PV System for small scale home industry of welding workshop in Kambang Pesisir Selatan, Indonesia. The purpose of this paper is to analyze the feasibility of the on-grid PV system and obtain a comparison of the simulation results between the PV connected and only grid. The optimization results show that a feasible and optimal design configuration is a gid-connected PV system consisting of a Grid, PV system 3.48 kW, and a 5 kW inverter because it has a large intensity of solar radiation, which produces an economical generating system with a COE of Rp. 829/kWh, smaller than the basic cost of electricity provision that has been determined by the Ministry of Energy and Mineral Resources, which is IDR 1,058/kWh, in accordance with the criteria, the project is feasible to build.

Real-Time SoC Estimation for Li-Ion Batteries using Kalman Filter based on SBC Raspberry-Pi

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.

Home Industry Powered Grid-Connected PV System an Economic Feasibility Study

This paper presents an analysis and feasibility of grid connected PV System for small scale home industry of welding workshop in Kambang Pesisir Selatan, Indonesia. The purpose of this paper is to analyze the feasibility of the on-grid PV system and obtain a comparison of the simulation results between the PV connected and only grid. The optimization results show that a feasible and optimal design configuration is a gid-connected PV system consisting of a Grid, PV system 3.48 kW, and a 5 kW inverter because it has a large intensity of solar radiation, which produces an economical generating system with a COE of Rp. 829/kWh, smaller than the basic cost of electricity provision that has been determined by the Ministry of Energy and Mineral Resources, which is IDR 1,058/kWh, in accordance with the criteria, the project is feasible to build.

Estimation of the Shoulder Joint Angle using Brainwaves

This paper presents the angle of the shoulder joint as basic research for developing a machine interface using EEG. The raw EEG voltage signals and power density spectrum of the voltage value were used as the learning feature. Hebbian learning was used on a multilayer perceptron network for pattern classification for the estimation of joint angles 0o, 90o and 180o of the shoulder joint. Experimental results showed that it was possible to correctly classify up to 63.3% of motion using voltage values of the raw EEG signal with the neural network. Further, with selected electrodes and power density spectrum features, accuracy rose to 93.3% with more stable motion estimation.