Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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Voltage Instability and Voltage Regulating Distribution Transformer Assessment Under Renewable Energy Penetration For Low Voltage Distribution System
Get PDFThe Voltage Regulating Distribution Transformer (VRDT) is a tap-changing transformer that regulates the voltage across all three phases. However, its application in the context of renewable energy penetration into low-voltage grids remains understudied. This paper addresses this research gap by presenting a refined voltage drop model tailored for the International Islamic University Malaysia (IIUM) distribution network. Based on a derived mathematical equation, the model is validated and analyzed using Simulink's modeling platform. Simulations are performed without and with the VRDT, revealing that renewable energy penetration can cause instability, leading to voltage deviations proportional to the injected renewable energy. Incorporating the VRDT in the low-voltage grid allows for voltage adjustment under loaded conditions, ensuring uninterrupted renewable energy injection. Voltage stability analysis is conducted using actual load consumption data from the IIUM network for 2020 and 2021, offering valuable insights despite assuming equal energy consumption across buildings. Most hostels exhibit stable distribution systems with solar energy, but instability arises when solar energy comprises 100% of the input for the Safiyyah and Zubair hostels' 11kV distribution transformers. Implementing the VRDT regulates this instability, restoring system stability. This study highlights the importance of VRDT integration in high renewable energy proportion low-voltage grids, enabling voltage regulation and stability under variable renewable energy injection scenarios. The findings demonstrate that VRDTs mitigate voltage instability caused by renewable energy, providing a reliable solution for incorporating renewables into low-voltage distribution networks. It contributes to understanding renewable energy's impact on distribution system stability and offers guidance for VRDT implementation in similar contexts.
Performance Analysis of Fiber Attenuation in Passive Optical Networks
Get PDFThe introduction of Fiber Optics cables in broadband Internet distribution has been a game changer in bulk capacity delivery, speed, reliability and penetration. However, the uncurbed incessant existence of cuts and failures have threatened the growth of Internet connectivity as a whole. In this work, the impact of fiber cuts is investigated using a hybrid approach, encompassing both real-world data from a live GPON network and simulations using OptiSystem 12 for FTTH GPON scenarios. Fiber cuts and failures are emulated by introducing varying attenuation levels in the simulated network's feeder cable section within OptiSystem 12, while in the live GPON network, the attenuation is induced by introducing wrap bends in the last-mile patch cord. The findings reveal a consistent pattern in both simulated and live data for both downstream and upstream traffic scenarios. As attenuation levels increased, there was a corresponding decline in Q-factor, Eye Height, and optical power, coupled with a concurrent rise in the minimum BER. Thus, in the most severe scenario, fiber cuts can result in service degradation and eventual service outage. To mitigate this issue, the implementation of a typeB PON protection system with a wireless auto-failover technique is proposed. Adoption and deployment of the proposed technique and deliberate maintenance measures alongside thorough supervision are suggested to be possible solutions to fiber cuts in metropolitan parlance
Design and Realization of 2.4 GHz Bowtie Antenna for Ground Penetrating Radar (GPR)
Get PDFIn this research, a microstrip antenna with a bowtie hole was constructed. The proposed antenna is designed and fabricated to operate in the 2.4 GHz frequency band. Arc antennas are a popular choice due to their flat structure, lightweight design, wide bandwidth, and high gain characteristics for GPR applications. The antenna was designed as a microstrip antenna in the size of 58 mm x 69 mm. using an FR4 duplex printed circuit board with a material thickness of 1.6 mm, a dielectric constant of 4.3 and a transverse dielectric loss tangent of 0.02. The design and simulation were performed using CST Studio Suite programming. The results of the simulation and measurements antenna were tested for resonant frequency, return loss, VSWR, bandwidth, impedance, and polarization, and the simulation results were compared. The measurements carried out with a Vector Network Analyzer, showed a return loss of -18, a VSWR of 1.29, a bandwidth of 100 MHz, an impedance of 47ohms, and a high gain of 18 dB at 2.42 GHz. Both the simulation and measurement results demonstrated good agreement, with frequency bands of interest that were very close and stable with high-gain omnidirectional radiation characteristics. Thus, the antenna is well-suited to meet the requirements of GPR applications
Optimal Load Frequency Control for Interconnected Power Systems using Optimized PI-PD Controller with TCSC and HVDC Integration
Get PDFThe aim of this study is to develop a consistent load frequency control system for a multi-area power system (PS) network that uses a variety of energy sources. The research focuses on improving frequency regulation and ensuring stability in the face of uncertainties and disturbances. This article introduces load frequency control (LFC) analysis for multi-area multi-source (MAMS) interconnected power systems (IPS). Each area comprises thermal reheat turbines, a hydro unit, and gas turbines as generating units. The effect of AVR after TCSC and HVDC is extensively investigated in this research article. The suggested hybrid PI-PD controller is tuned using the PSO optimization technique, presenting a novel and efficient approach to addressing the complexities of multi-area power systems. Another significant advancement is the incorporation of high-voltage direct current (HVDC) and thyristor-controlled series compensator (TCSC) as auxiliary parameters. This dual integration improves system robustness by managing power flow variations and enhancing transient response, ultimately contributing to the overall stability of the multi-area power grid. Four different power system models are studied. To determine the best value for controllers, all four performance metrics are employed to define the optimal parameters of the PI-PD and PID controller. Eigenvalues analysis is also conducted to find the stability of the MAMS power system. The robustness of the proposed PI-PD controller at different loading conditions is tested, and the superiority of the suggested controller is determined by executing it in four different cases. A step-load of 10% is applied for each area. Non-linearities such as governor dead band (GDB), governor rate constraints (GRC), and the boiler are also included in the MAMS-IPS. Based on settling-time (ST) and rise-time (RT), the performance of the proposed controller is compared with various PID controllers optimized with novel evolutionary algorithms from recent published literature. MATLAB 2018@ software is used for simulation purposes
Personal Assistant Development by CED (Canine Eye-disease Detection)
Get PDFIn this paper, we develop a deep learning-based canine eye disease detection and utilize it to create a dog health management system. With the recent surge in the number of pet dogs, ensuring their well-being has become crucial. We achieve this by applying lightweight deep learning methods like MobileNet and SqueezeNet to mobile devices, enabling regular monitoring of a pet's eye health. Additionally, we provide a GPS-based search feature for nearby hospitals, facilitating swift response to diseases. The validity of the developed method is demonstrated through experiments on 5 eye diseases. The results confirm the importance of considering appropriate recognition rates and recognizability metrics, as outcomes may vary depending on the applied deep learning approach
Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion as Preliminary Study for Smart Prosthetic Hand
Get PDFEMG signals have random, non-linear, and non-stationary characteristics that require the selection of the suitable feature extraction and classifier for application to prosthetic hands based on EMG pattern recognition. This research aims to implement EMG pattern recognition on an embedded Raspberry Pi system to recognize hand motion as a preliminary study for a smart prosthetic hand. The contribution of this research is that the time domain feature extraction model and classifier machine can be implemented into the Raspberry Pi embedded system. In addition, the machine learning training and evaluation process is carried out online on the Raspberry Pi system. The online training process is carried out by integrating EMG data acquisition hardware devices, time domain features, classifiers, and motor control on embedded machine learning using Python programming. This study involved ten respondents in good health. EMG signals are collected at two lead flexor carpi radialis and extensor digitorum muscles. EMG signals are extracted using time domain features (TDF) mean absolute value (MAV), root mean square (RMS), variance (VAR) using a window length of 100 ms. Supervised machine learning decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) are chosen because they have a simple algorithm structure and less computation. Finally, the TDF and classifier are embedded in the Raspberry Pi 3 Model B+ microcomputer. Experimental results show that the highest accuracy is obtained in the open class, 97.03%. Furthermore, the additional datasets show a significant difference in accuracy (p-value <0.05). Based on the evaluation results obtained, the embedded system can be implemented for prosthetic hands based on EMG pattern recognition
Investigation of Photovoltaic Hosting Capacity Using Minimum Generation Operation Approach
Get PDFPhotovoltaic (PV) have become a priority renewable energy source to be developed in Indonesia to achieve new and renewable energy (NRE) target of 23% in 2025 and 31% in 2050. The operation of a significant number of rooftop PV can also change the type of power system operating configuration to Distributed Energy Generation (DEG). The majority of DEGs which are NRE generators are capable of causing new problems because of their intermittent nature. Hosting Capacity is a high penetration limit for NRE without causing problems and limits on operational violations. The hosting capacity method used is based on the generator's minimum operation. In the test system consisting of 3 power plants such as hydro power plant, coal power plant, and geothermal power plant, the PV capacity that can be injected into the system is 139.1 MW. With PV injection based on hosting capacity, the system becomes better with the same average voltage profile as before PV injection, namely 0.991 p.u. System stability by reviewing the frequency, rotor angle, and rotor speed, the system after PV injection is better than before PV injection
Handling Imbalanced Data through Re-sampling: Systematic Review
Get PDFHandling imbalanced data is an important issue that can affect the validity and reliability of the results. One common approach to addressing this issue is through re-sampling the data. Re-sampling is a technique that allows researchers to balance the class distribution of their dataset by either over-sampling the minority class or under-sampling the majority class. Over-sampling involves adding more copies of the minority class examples to the dataset in order to balance out the class distribution. On the other hand, under-sampling involves removing some of the majority class examples from the dataset in order to balance out the class distribution. It's also common to combine both techniques, usually called hybrid sampling. It is important to note that re-sampling techniques can have an impact on the model's performance, and it is essential to evaluate the model using different evaluation metrics and to consider other techniques such as cost-sensitive learning and anomaly detection. In addition, it is important to keep in mind that increasing the sample size is always a good idea to improve the performance of the model. In this systematic review, we aim to provide an overview of existing methods for re-sampling imbalanced data. We will focus on methods that have been proposed in the literature and evaluate their effectiveness through a thorough examination of experimental results. The goal of this review is to provide practitioners with a comprehensive understanding of the different re-sampling methods available, as well as their strengths and weaknesses, to help them make informed decisions when dealing with imbalanced data
Optimal Control Technique of an Induction Motor
Get PDFThe squirrel cage induction motor (IM) has many advantages over other types of electric technique (FOC), classical direct torque control (DTC), and direct torque control with space vector modulation (DTC-SVM) is carried out. The objective of this paper is to decouple the mechanical quantities such as torque and flux in a way similar to the DC motor control. And also to minimize the torque and flux modulation of the IM. Torque oscillations can cause mechanical resonances and consequently acoustic noise, hence damaging the machine. Reducing the switching frequency significantly minimizes switching losses. The DTC-SVM control technique improves the performance of conventional DTC, which is characterized by low torque and flux modulation as well as a fixed switching frequency. Simulation results in MATLAB show that torque and current ripples are reduced with the improved DTC. DTC-SVM used for the traction control system is easy to implement in digital systems and also allows to move the photovoltaic panels according to the position of maximum sunshine to extract the maximum energy with high efficiency from the system
Days of autonomy for optimal Battery Sizing in Stand-alone Photovoltaic Systems
Get PDFThe main purpose of our article is to optimize the battery sizing by identifying the most appropriate number of autonomy days. A case study has been established and simulated to define the optimal number. In the others current researches, only a small importance has been attributed to the battery autonomy. The objective is generally to ensure a continuous presence of energy especially for isolated systems while this is not always optimal nor economical and does not necessarily guarantee a safe supply. Nevertheless, an over dimensioning of the battery will lead to a consequent cost and a loss of energy. The results show that the number of days of autonomy must correspond to the minimum ratio linking the lack of energy to the surplus during a specific period