Showing 103 results for Ali
Zahra Mobini-Serajy, Mehdi Radmehr, Alireza Ghorbani,
Volume 21, Issue 1 (March 2025)
Abstract
Microgrids harness the benefits of non-inverter and inverter-based Distributed Energy Resources (DER) in grid-connected and island environments. Adoption of them with the various types of electric loads in modern MGs has led to stability and power quality issues. In this paper, a two-level control approach is proposed to overcome these problems. A state-space dynamic model is performed for Micro-Grids, for this goal, the state-space equations for generation, network, and load components are separately developed in a local DQ reference frame, and after linearization around the set point, then combining them into a common DQ reference frame. In the first level, the control of inverter-based DERs and some types of loads with fast response are activated, and in the second level, the control of synchronous diesel generator resources with slower response is used. In order to validate and evaluate the effectiveness of the proposed control approach, numerical studies have been established on a standard test MG under normal and symmetrical three-phase fault conditions. Finally, the simulation results are summarized.
Mohammadreza Alizadeh Aliabadi, Mohsen Karimi, Zahra Karimi, Mehrdad Soheili Fard,
Volume 21, Issue 1 (March 2025)
Abstract
Photoplethysmography (PPG) signals provide a non-invasive means of monitoring cardiovascular status during physical exercise; however, they are prone to noise, especially motion artifacts (MA). For specific telemedicine applications, compression is necessary for tasks such as PPG signal generation and secure data transmission. In this study, the investigation focused on determining whether it is better to perform compression before or after noise removal by applying a noise removal method and various compression methods. To achieve the aim, the study explored a subspace-based denoising method called "Maximum Uncorrelated PPG Denoising." Additionally, signal compression methods were examined in nine distinct steps. Compression quality is evaluated using various criteria, such as compression rate (CR) and Percentage Root Mean Square Difference (PRD). The results showed that regardless of the type of compression method, it is better not to remove noise before the compression process because doing so reduces CR and increases PRD.
Mostafa Jalalian-Ebrahimi, M. A. Shamsi-Nejad,
Volume 21, Issue 1 (March 2025)
Abstract
This paper proposes an inductive power transfer (IPT) system to maintain stable power transfer and improve efficiency for battery charging performance across a wide range of coupling coefficient variations. The proposed IPT system uses series-series (S-S) and series-inductor-capacitor-inductor (S-LCL) compensation. In both compensation configurations, the rectifier operates in half-bridge (HB) and full-bridge (FB) modes. By using the correct switching pattern between compensation networks and the rectifier, four transfer power-coupling coefficient (P-k) curves are created. A 400 W prototype simulated in MATLAB demonstrates that, with the proposed method, output power fluctuation is limited to only 3% for coupling coefficients varying from 0.1 to 0.4, with system efficiency ranging from 80% to 95.9%. Compared to other methods, the proposed structure provides stable power transfer over an ultra-wide coupling variation and does not require special coil design, clamp circuit design, or complex control.
Mehrdad Kamali, Behrooz Rezaeealam, Farhad Rezaee-Alam,
Volume 21, Issue 1 (March 2025)
Abstract
This paper investigates the operational performance of a novel Double-Rotor Hybrid Excitation Axial Flux Switching Permanent Magnet (DRHE-AFSPM) machine, combining the strengths of Flux-Switching Machines and Hybrid Excitation Synchronous Machines. The study analyzes the machine's structure and magnetic field adjustment principles, including inductance and flux linkage characteristics. A mathematical model is derived and a vector control-based drive system is established. The loading capacity of the DRHE-AFSPM motor is examined at low speeds using an id = 0 control approach based on a stage control strategy. For high-speed operation, a field-weakening control strategy is implemented, with the field-weakening moment determined based on the voltage difference. Simulations and experimental results demonstrate the DRHE-AFSPM motor's ability to fully utilize its torque with id = 0 control, highlighting its strong load capacity. Compared to speed-based field-weakening control strategies, the voltage difference-based approach offers improved inverter output voltage utilization and a broader speed regulation range. These findings suggest that the DRHE-AFSPM motor is a promising candidate for in-wheel motor applications in electric vehicles (EVs).
Aida Gholami, Masume Khodsuz, Valiollah Mashayekhi,
Volume 21, Issue 1 (March 2025)
Abstract
Ensuring the protection of all components within power systems from lightning-induced overvoltage is crucial. The issue of power interruptions caused by both direct and indirect lightning strikes (LS) presents significant challenges in the electrical sector. In medium voltage distribution feeders, the relatively low dielectric strength makes them susceptible to insulation degradation, which can ultimately lead to failures in the distribution system. Therefore, implementing effective protective measures against LS is vital for maintaining an acceptable level of reliability in distribution systems. This paper presents an analytical assessment of LS-induced system overvoltage through high-frequency modeling of components within a 20kV distribution system. The study utilizes EMTP-RV software for precise component modeling, including the grounding system, surge arresters, and distribution feeders. Additionally, the operational impacts of protective devices, such as ZnO surge arresters, shield wires, and lightning rods, are evaluated to mitigate LS-induced overvoltage. A frequency grounding system is implemented using the method of moments (MOM) to analyze the grounding system's influence on LS-induced overvoltage. Furthermore, eight different scenarios are explored to assess the anti-LS capabilities of the 20kV distribution system. Each scenario involves evaluating dielectric breakdown and overvoltage across the insulator chain while proposing suitable protective solutions. The results indicate that the absence of shielding wires and surge arresters leads to higher breakdown voltages, with the lowest breakdown voltage occurring when surge arresters are installed during LS events. Additionally, the use of a frequency grounding system, due to its accurate modeling, yields more precise results compared to a static resistor approach. The MOM simulation reveals a 50% reduction in breakdown voltage under the worst-case scenario, and overall overvoltage experiences a 2% decrease.
Arizadayana Zahalan, Samila Mat Zali, Ernie Che Mid, Noor Fazliana Fadzail,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various fault scenarios. An open circuit, a short circuit, and a degrading fault are the three types of faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and fault conditions. This training enables the network to learn the distinctive patterns associated with each fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of faults.
Hanim Suraya Mohd Mokhtar, Aimi Salihah Abdul Nasir, Mohammad Faridun Naim Tajuddin, Muhammad Hafeez Abdul Nasir, Kumuthawathe Ananda Rao,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
The rapid growth of photovoltaic (PV) systems has highlighted the need for efficient and reliable defect detection to maintain system performance. Electroluminescence (EL) imaging has emerged as a promising technique for identifying defects in PV cells; however, challenges remain in accurately classifying defects due to the variability in image quality and the complex nature of the defects. Existing studies often focus on single image enhancement techniques or fail to comprehensively compare the performance of various image enhancement methods across different deep learning (DL) models. This research addresses these gaps by proposing an in-depth analysis of the impact of multiple image enhancement techniques on defect detection performance, using various deep learning models of low, medium, and high complexity. The results demonstrate that mid-complexity models, especially DarkNet-53, achieve the highest performance with an accuracy of 94.55% after MSR2 enhancement. DarkNet-53 consistently outperformed both lower-complexity models and higher-complexity models in terms of accuracy, precision, and F1-score. The findings highlight that medium-depth models, enhanced with MSR2, offer the most reliable results for photovoltaic defect detection, demonstrating a significant improvement over other models in terms of accuracy and efficiency. This research provides valuable insights for optimizing defect detection systems in photovoltaic applications, emphasizing the importance of both model complexity and image enhancement techniques for robust performance.
Muhammad Syafiq Sheik Azmi, Muhamad Hisyam Rosle, Muhammad Nazrin Shah Shahrol Aman, Ali Akbar Abd Aziz, Chandran Tetegre,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
The automation of Printed Circuit Board (PCB) assembly using robotic arms is increasingly essential in the electronics manufacturing industry, driven by the need for high precision and efficiency. A significant challenge in this process is the delicate handling and accurate placement of various types of PCB boards, such as SATA M.2, mSATA, and SATA Slim. This research aims to design and evaluate a vacuum-based robotic gripper using a vacuum generator and soft suction cup for the pick-and-place operations of electronic PCB boards. The methodology involves the design, fabrication, and experimental testing of the vacuum gripper, analyzing its performance across different feed pressures and vacuum levels. The principal results show that the vacuum gripper is highly effective in securely handling different PCB types, with success rates improving significantly at higher feed pressures, particularly at 0.3 MPa where all three PCB types attained perfect success rates of 100%. Specifically, the vacuum flow rates at a vacuum level of 80 kPa were 0.0010 NL/s, 0.002 NL/s, and 0.0030 NL/s for feed pressures of 0.1 MPa, 0.2 MPa, and 0.3 MPa, respectively. These findings confirm the vacuum gripper's capability to enhance automation in PCB assembly, offering a scalable and adaptable solution that meets the industry's demands for precision, efficiency, and reliability. Overall, the vacuum gripper demonstrated a 100% success rate for all tested PCB types at optimal feed pressure, significantly improving. This study provides a foundation for future improvements in robotic handling systems for delicate electronic components.
Wan Ismail Ibrahim, Nasiruddin Sadan, Noorlina Ramli , Mohd Riduwan Ghazali Riduwan Ghazali , Ilham Fuad,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Hydrokinetic energy harnessing has emerged as a promising renewable energy that utilizes the kinetic energy of moving water to generate electricity. Nevertheless, the variation and fluctuation of water velocity and turbulence flow in a river is a challenging issue, especially in designing a control system that can harness the maximum output power with high efficiency. Besides, the conventional Hill-climbing Search (HCS) MPPT algorithm has weaknesses, such as slow tracking time and producing high steady-state oscillation, which reduces efficiency. In this paper, the Variable-Step Hill Climbing Search (VS-HCS) MPPT algorithm is proposed to solve the limitation of the conventional HCS MPPT. The model of hydrokinetic energy harnessing is developed using MATLAB/Simulink. The system consists of a water turbine, permanent magnet synchronous generator (PMSG), passive rectifier, and DC-DC boost converter. The results show that the power output achieves a 28 % increase over the system without MPPT and exhibits the lowest energy losses with a loss percentage of 0.9 %.
Edy Victor Haryanto S, Aimi Salihah Abdul Nasir, Mohd Yusoff Mashor, Bob Subhan Riza, Zeehaida Mohamed,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Malaria is a parasitic disease that causes significant morbidity and mortality worldwide. Early diagnosis and treatment are crucial for preventing complications and improving patient outcomes. Microscopic examination of blood smears remains the gold standard for malaria diagnosis, but it is time-consuming and requires skilled technicians. Deep learning has emerged as a promising tool for automated image analysis, including malaria diagnosis. In this study, we propose a novel approach for identifying malaria parasites in microscopic images using the GoogLeNet. Our method includes enhancement with the AGCS method, color transformation with grayscale, adaptive thresholding for segmentation, extraction, and GoogLeNet-based classification. We evaluated our method on a dataset of malaria blood smear images and achieved an accuracy of 95%, demonstrating the potential of GoogLeNet for automated malaria diagnosis.
Nurul Syahirah Mohd Ideris, Hasimah Ali, Mohd Shuhanaz Zanar Azalan, Tengku Sarah Tengku Amran,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
GPR (Ground Penetrating Radar) is well-known as an effective non-invasive imaging approach for shallow nature underground discovery, like finding and locating submerged objects. Although GPR has achieved some success, it is difficult to automatically process GPR images because human experts must interpret GPR images of buried objects. This can happen due to the possibility of a variety of mediums or underground noises from the environment, especially rocks and roots of trees. Thus, detecting hyperbolic echo characteristics is critical. As a result, Viola Jones detection is used to determine whether the presence of a hyperbolic signature underground indicates a pipe or not. GPR can also be used in the public works department because it is a non-destructive tool. Workers, for example, should be aware of the pipe size that must be replaced when it leaks. The original GPR image already shows hyperbolic image distortion due to pipe refraction. The current method is unreliable due to its lack of flexibility. As a result, there is another method for resolving this issue. Thus, the image will be pre-processed to eliminate or reduce background noise in the GPR input image. The results of this project demonstrate that the Viola Jones algorithm can accurately detect hyperbolic patterns in GPR images.
Malik Khalid , Baharuddin Ismail , Chanuri Charin, Arnawan Hasibuan , Abd Alazeez Almaleeh,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
This paper presents a comprehensive research endeavor focused on evaluating the influence of renewable energy, particularly wind power, on power quality within the context of Jordan's electrical grid. The escalating global demand for energy, coupled with the imperative to curb greenhouse gas emissions, has propelled the rapid adoption of renewable energy sources. Against this backdrop, the study aims to meticulously analyze the effects of wind energy projects on power quality parameters such as voltage fluctuations, harmonics, and power factor. Through an extensive methodology comprising data collection, rigorous analysis, and advanced simulation techniques, actionable insights are provided into the seamless integration of renewable energy into existing grid infrastructures. In this work, power quality parameters like Total Harmonic Distortion, flickers, power frequency, Crest factor, and voltage unbalance are measured at Al-Tafilah Governorate, Jordan. The significance of this study lies in its contribution to the development of strategies and guidelines essential for policymakers, engineers, and stakeholders. By fostering a deeper understanding of the interplay between renewable energy and power quality, the findings aim to facilitate the establishment of a sustainable and resilient energy system in Jordan. Beyond mitigating climate change and enhancing energy security, this research underscores the pivotal role of renewable energy in ushering in a greener, cleaner future for generations to come.
Noor Fazliana Fadzail, Samila Mat Zali, Ernie Che Mid,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
The activation function has gained popularity in the research community since it is the most crucial component of the artificial neural network (ANN) algorithm. However, the existing activation function is unable to accurately capture the value of several parameters that are affected by the fault, especially in wind turbines (WT). Therefore, a new activation function is suggested in this paper, which is called the double sigmoid activation function to capture the value of certain parameters that are affected by the fault. The fault detection in WT with a doubly fed induction generator (DFIG) is the basis for the ANN algorithm model that is presented in this study. The ANN model was developed in different activation functions, namely linear and double sigmoid activation functions to evaluate the effectiveness of the proposed activation function. The findings indicate that the model with a double sigmoid activation function has greater accuracy than the model with a linear activation function. Moreover, the double sigmoid activation function provides an accuracy of more than 82% in the ANN algorithm. In conclusion, the simulated response demonstrates that the proposed double sigmoid activation function in the ANN model can effectively be applied in fault detection for DFIG based WT model.
Murni Nabila Mohd Zawawi, Zainuddin Mat Isa, Baharuddin Ismail, Mohd Hafiz Arshad, Ernie Che Mid, Md Hairul Nizam Talib, Muhammad Fitra Zambak,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
This study introduces a pioneering method to enhance the efficiency and effectiveness of three-phase five-level reduced switch cascaded H-bridge multilevel inverters (CHB MLI) by employing the Henry Gas Solubility Optimization (HGSO) algorithm. Targeting the selective harmonic elimination (SHE) technique, the research emphasizes the optimization of switching angles to significantly reduce total harmonic distortion (THD) and align the fundamental output voltage closely with the reference voltage. Central to this exploration are three distinct objective functions (OFs), meticulously designed to assess the HGSO algorithm’s performance across various modulation indices. Simulation results, facilitated by PSIM software, illustrate the impactful role these objective functions play in the optimization process. OF1 demonstrated a superior ability in generating low OF values and maintaining a consistent match between reference and fundamental voltages across the modulation index spectrum. Regarding the reduction of THD, it is crucial to emphasize that all OFs can identify the most effective switching angle to minimize THD and eliminate the fifth harmonic to a level below 0.1%. The findings highlight the potential of HGSO in solving complex optimization challenges within power electronics, offering a novel pathway for advancing modulation strategies in CHB MLIs and contributing to the development of more efficient, reliable, and compact power conversion systems.
Muhammad Naqib Mohd Shukri, Syed Muhammad Mamduh Syed Zakaria, Ahmad Shakaff Ali Yeon, Ammar Zakaria, Latifah Munirah Kamarudin,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Accurate 3D Localization is very important for a wide range of applications, such as indoor navigation, industrial robotics, and motion tracking. This research focuses on indoor 3D positioning systems using ultra-wideband (UWB) devices. Two localization experiments were conducted using the Least Squares Trilateration method. In the first experiment, anchors were at the same height, while in the second, they were at varying heights. The lowest percentage errors in the first experiment were 0% at the x-axis, 0.21% at the y-axis, and 19.75% at the z-axis. In the second experiment, the lowest percentage errors in the experiment were 1.98% at the x-axis, 0.68% at the y-axis, and 17.86% at the z-axis, demonstrating improved accuracy with varied anchor heights at the axis. This work shows the z-axis measurements are unreliable and noisy due to the limited intersection of signal waves of each anchor in a same height anchors setup.
Kumuthawathe Ananda-Rao, Steven Taniselass, Afifah Shuhada Rosmi, Aimi Salihah Abdul Nasir, Nor Hanisah Baharudin, Indra Nisja,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
This study presents a Fuzzy Logic Controller (FLC)-based Maximum Power Point Tracking (MPPT) system for solar Photovoltaic (PV) setups, integrating PV panels, a boost converter, and battery storage. While FLC is known for its robustness in PV systems, challenges in battery charging and discharging efficiency can affect performance. The research addresses these challenges by optimizing battery charging, preventing overcharging, and enhancing overall system efficiency. The FLC MPPT system is designed to regulate the battery's State of Charge (SOC) while evaluating system performance under varying solar irradiance and temperature conditions. The system is modeled and simulated using MATLAB/Simulink, incorporating the PV system, MPPT algorithm, and models for the PV module and boost converter. System efficiency is assessed under different scenarios, with results showing 97.92% efficiency under Standard Test Conditions (STC) at 1000 W/m² and 25°C. Additionally, mean efficiencies of 97.13% and 96.13% are observed under varying irradiance and temperature, demonstrating the effectiveness of the FLC MPPT in regulating output. The system also extends battery life by optimizing power transfer between the PV module, boost converter, and battery, ensuring regulated SOC.
Ahmad Syukri Abd Rahman, Mohamad Nur Khairul Hafizi Rohani, Nur Dini Athirah Gazata, Afifah Shuhada Rosmi, Ayob Nazmi Nanyan, Aiman Ismail Mohamed Jamil, Mohd Helmy Halim Abdul Majid, Normiza Masturina Samsuddin,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Partial discharge (PD) is a significant concern in the operation of rotating machines such as generators and motors, as it can lead to insulation degradation over time, reducing the reliability and lifespan of the machines. To monitor PD activity, coupling capacitors (CC) are widely used as sensors for online PD detection, as they can effectively capture PD pulses in high-voltage (HV) rotating machines. The primary objective of this research is to measure and analyze PD signals using a CC sensor for HV rotating machines under varying input voltages and frequencies, following the guidelines of the IEC 60270 standard and utilizing the MPD 600 device. The experimental setup includes performing insulation resistance (IR) testing, PD calibration, and PD measurement. Additionally, this paper provides a detailed study of PD signal characteristics, specifically focusing on phase-resolved partial discharge (PRPD) patterns, to understand the behavior of PD in HV rotating machines, enhancing fault diagnosis and preventive maintenance strategies.
Humairah Mansor, Shazmin Aniza Abdul Shukor, Razak Wong Chen Keng, Nurul Syahirah Khalid,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Building fixtures like lighting are very important to be modelled, especially when a higher level of modelling details is required for planning indoor renovation. LIDAR is often used to capture these details due to its capability to produce dense information. However, this led to the high amount of data that needs to be processed and requires a specific method, especially to detect lighting fixtures. This work proposed a method named Size Density-Based Spatial Clustering of Applications with Noise (SDBSCAN) to detect the lighting fixtures by calculating the size of the clusters and classifying them by extracting the clusters that belong to lighting fixtures. It works based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), where geometrical features like size are incorporated to detect and classify these lighting fixtures. The final results of the detected lighting fixtures to the raw point cloud data are validated by using F1-score and IoU to determine the accuracy of the predicted object classification and the positions of the detected fixtures. The results show that the proposed method has successfully detected the lighting fixtures with scores of over 0.9. It is expected that the developed algorithm can be used to detect and classify fixtures from any 3D point cloud data representing buildings.
Siti Marwangi Mohamad Maharum, Muhammad Aliff Azim Hamzah, Muhammad Ridzwan Ahmad Yusri, Izanoordina Ahmad,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
The Heating, Ventilation, and Air Conditioning (HVAC) system is commonly found in buildings such as industrial, commercial, residential, and institutional buildings. This HVAC system generates a significant speed of wind flow from its condenser unit. Surprisingly, this wind energy remains unexploited and thus dissipates into the surroundings. This project aims to leverage this unused wind energy from the condenser unit by developing an energy harvesting prototype that harnesses the HVAC system’s wind for a practical charging station. Specifically, a wind turbine is connected to a three-phase 12 VAC generator motor. This connection would efficiently convert wind energy into electrical power. An energy storage module is also incorporated to ensure uninterrupted functionality for the developed charging station prototype. The energy storage module has a substantial capacity of 25Ah, equivalent to a standard socket outlet. This ensures that the energy storage system can fully charge within three hours if there are no interruptions in the turbine's operation. An experimental validation was conducted by supplying different wind speeds to this project prototype, and it was observed that only when the wind speed is above 10 ms-1 does the energy storage system charge, and sockets provide a consistent output. The final output at the socket provided both 230VAC voltage and a USB charging option, making it versatile for users to charge commonly used electrical appliances such as smartphones and laptops. By repurposing this otherwise wasted wind energy, the developed system prototype contributes to cleaner and more sustainable energy utilization. It also converts unused energy into valuable, cleaner energy.
Ahmad Syukri Abd Rahman, Mohamad Nur Khairul Hafizi Rohani, Nur Dini Athirah Gazata, Afifah Shuhada Rosmi, Ayob Nazmi Nanyan, Aiman Ismail Mohamed Jamil, Mohd Helmy Halim Abdul Majid, Normiza Masturina Samsuddin,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Partial discharge (PD) is a critical phenomenon in electrical systems, particularly in high-voltage (HV) equipment like transformers, cables, switchgear, and rotating machines. In rotating machines such as generators and motors, PD is a significant concern as it leads to insulation degradation, potentially resulting in catastrophic failure. Effective and reliable diagnostic techniques are essential for detecting and analyzing PD to ensure the operational safety and longevity of such equipment. Various PD detection methods have been developed, including coupling capacitor (CC), high-frequency current transformer (HFCT), and ultra-high frequency (UHF) techniques, each offering unique advantages in assessing the condition of HV electrical systems. Among these, coupling capacitors have gained significant attention due to their ability to improve the accuracy, sensitivity, and efficiency of PD detection in rotating machines. This study focuses on the advancements in coupling capacitor-based techniques and their critical role in enhancing PD diagnostics for monitoring and maintaining high-voltage rotating machinery.
