Showing 20 results for Type of Study: Only For Articles of ELECRiS 2024
Hanim Suraya Mohd Mokhtar, Aimi Salihah Abdul Nasir, Mohammad Faridun Naim Tajuddin, Muhammad Hafeez Abdul Nasir, Kumuthawathe Ananda Rao,
Volume 21, Issue 2 (6-2025)
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.
Jia Wen Tang, Chin Leong Wooi, Wen Shan Tan, Nur Hazirah Zaini, Yuan Kang Wu, Syahrun Nizam Bin Md Arshad@hashim,
Volume 21, Issue 2 (6-2025)
Abstract
Photovoltaic (PV) energy is increasingly recognized as an environmentally friendly source of renewable energy. Integrating PV systems into power grids involves power electronic inverters, adding complexity and evolving traditional grids into smarter systems. Ensuring the reliability of decentralized PV generation is crucial, particularly as PV systems are often exposed to extreme weather conditions. This study investigates the impact of temperature and solar radiation on the performance of a PV array, focusing on key characteristics such as open-circuit voltage (VOC), short-circuit current (ISC), and maximum power (PMAX). Using PSCAD/EMTDC simulations, the study analyses these characteristics under varying temperatures (5°C to 45°C) and radiation levels (200 W/m² to 1200 W/m²). Results indicate that VOC increases with higher irradiance but decreases with higher temperatures. ISC increases with both higher radiation and temperature, while PMAX is optimized at high irradiance and low temperatures. The impulse withstand voltage (Vimp), a critical factor for PV system reliability, is assessed according to the PD CLC/TS 50539-12 standard. Findings reveal that at low temperatures and high radiation, the Vimp requirement is highest, emphasizing the need for robust voltage protection in PV systems. These insights underscore the importance of considering local climate conditions and implementing effective thermal management to enhance the performance and reliability of PV systems.
Muhammad Syafiq Sheik Azmi, Muhamad Hisyam Rosle, Muhammad Nazrin Shah Shahrol Aman, Ali Akbar Abd Aziz, Chandran Tetegre,
Volume 21, Issue 2 (6-2025)
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 (6-2025)
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 (6-2025)
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 (6-2025)
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.
Yanawati Yahya, Nor Shafiqin Shariffuddin, Muhammad Khairul Hisyam Jarail, Dina Maizana, Phd Ibrahim Alhamrouni, Mohd Khairil Rahmat,
Volume 21, Issue 2 (6-2025)
Abstract
Induction motors are highly favored in industrial applications for their ease of operation, compactness, lightweight, efficiency, low maintenance, and cost-effectiveness. They are widely used in conveyors, compressors, crushers, drills, fans, escalators, refrigerators, and electric vehicles. In Malaysia, industrial motors account for about 48% of energy consumption. This research introduces an improved rotor design with optimized rotor bars. Using MotorSolve (IM) software and theoretical calculations, the study found that the new design boosts energy efficiency. The new rotor bar design achieved an energy efficiency of 76.92%, compared to 74% for the current design. In terms of energy efficiency, this research found that adopting high-efficiency motors in industrial applications can save a significant amount of energy. These motors can also be used in a variety of horsepower ranges. The research suggests a maintenance plan for malfunctioning motors that attempts to reduce energy consumption, motor losses, and CO2 emissions in any apparatus. These results offer valuable insights for policymakers to refine energy policies for induction motors. In the future, real-time estimation of the motor's actual operating loss will be required to properly predict the trend in motor efficiency loss under various failure scenarios, which is consistent with the research goal of reducing energy losses in induction motors.
Malik Khalid , Baharuddin Ismail , Chanuri Charin, Arnawan Hasibuan , Abd Alazeez Almaleeh,
Volume 21, Issue 2 (6-2025)
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 (6-2025)
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.
Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Elmi Abu Bakar, Ahmad Faizul Hawary,
Volume 21, Issue 2 (6-2025)
Abstract
Vehicle detection in satellite images is a challenging task due to the variability in scale and resolution, complex background, and variability in object appearance. One-stage detection models are currently state-of-the-art in object detection due to their faster detection times. However, these models have complex architectures that require powerful processing units to train while generating a large number of parameters and achieving slow detection speed on embedded devices. To solve these problems, this work proposes an enhanced lightweight object detection model based on the YOLOv4 Tiny model. The proposed model incorporates multiple modifications, including integrating a Mix-efficient layer aggregation network within its backbone network to optimize efficiency by reducing parameter generation. Additionally, an improved small efficient layer aggregation network is adopted in the modified path aggregation network to enhance feature extraction across various scales. Finally, the proposed model incorporates the Swish function and an extra YOLO head for detection. The experimental results evaluated on the VEDAI dataset demonstrated that the proposed model achieved a higher mean average precision value and generated the smallest model size compared to the other lightweight models. Moreover, the proposed model achieved real-time performance on the NVIDIA Jetson Nano. These findings demonstrate that the proposed model offers the best trade-offs in terms of detection accuracy, model size, and detection time, making it highly suitable for deployment on embedded devices with limited capacity.
Syazwan Ahmad Sabri, Siti Rafidah Abdul Rahim, Azralmukmin Azmi, Syahrul Ashikin Azmi, Muhamad Hatta Hussain, Ismail Musirin,
Volume 21, Issue 2 (6-2025)
Abstract
The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. MPA simulates marine predators' foraging strategies through Lévy and Brownian movements, while OOA models the hunting and survival tactics of ospreys, known for their remarkable fishing skills. Effective placement and sizing of DG units are crucial for minimizing network losses and ensuring cost efficiency. Improper configurations can lead to overcompensation or undercompensation in the network, increasing operational costs. Different DG technologies, such as photovoltaic (PV), wind, microturbines, and generators, vary significantly in cost and performance, highlighting the importance of selecting the right models and designs. This study compares MPA and OOA in optimizing the placement of multiple DGs with two types of power injection which are active and reactive power. Simulations on the IEEE 69-bus reliability test system, conducted using MATLAB, demonstrated MPA’s superiority, achieving a 69% reduction in active power losses compared to OOA’s 61%, highlighting its potential for more efficient DG placement in power distribution systems. The proposed approach incorporates a DG model encompassing multiple technologies to ensure economic feasibility and improve overall system performance.
Mohd Zulhisham Mohd Radzi, Baharuddin Ismail, Muhammad Mokhzaini Azizan,
Volume 21, Issue 2 (6-2025)
Abstract
The rise of nonlinear and unbalanced loads in modern electrical systems poses challenges to power quality management. These loads, prevalent in electronic devices and industrial equipment, induce harmonic distortions and unbalance, adversely affecting the neutral conductor in three-phase systems. This study investigates these effects through modeling and simulation using MATLAB/Simulink and symmetrical components theory for detailed power quality analysis. The research focuses on three scenarios: nonlinear loads, unbalanced loads, and combined nonlinear-unbalanced loads. Simulation results show that nonlinear loads significantly increase harmonic content, while unbalanced loads lead to notable power quality deviations. When combined, these conditions exacerbate harmonic distortions and unbalance, resulting in higher neutral current magnitudes. Key findings highlight the severe impact of combined load conditions on the neutral conductor, emphasizing the need for accurate modeling and analysis. This research provides valuable insights and practical recommendations for addressing the challenges of nonlinear and unbalanced loads, contributing to improved power system design and management.
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 (6-2025)
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.
Sharulnizam Mohd Mukhtar, Muzamir Isa, Azremi Abdullah Al-Hadi,
Volume 21, Issue 2 (6-2025)
Abstract
The development of advanced diagnostic tools is critical for the effective monitoring and management of electrical insulation systems. This paper presents the development of an Ultra High Frequency (UHF) sensor designed for the detection of partial discharges (PD) within high-voltage substations. The study focuses on the sensor’s technical development, encompassing design considerations, fabrication processes, and initial performance evaluations in laboratory settings. The engineering principles underlying the sensor design are detailed, including the selection of innovative materials that enhance sensitivity and frequency response. The sensor configuration is tailored to optimize the detection of PD signals, with adjustments made based on simulated PD scenarios. Initial testing results demonstrate the sensor’s capability to detect a range of PD activities, showcasing its potential effectiveness in real-world applications. The sensor's performance is analyzed through a series of controlled lab experiments, which confirm its high sensitivity and broad operational frequency range. This paper not only illustrates the technical specifications and capabilities of the newly developed UHF sensor but also discusses its practical implications for improving the reliability and efficiency of PD monitoring systems in electrical substations.
Julie Roslita Rusli, Muhamad Syahirin Danial Noor Shahrin, Nurul Izzati Binti Che Abdu Patah, Izanoordina Ahmad, Siti Marwangi Mohamad Maharum, Sairul Izwan Safie,
Volume 21, Issue 2 (6-2025)
Abstract
Digital stethoscopes represent a significant advancement in medical diagnostics, addressing the limitations of traditional auscultation methods, which often suffer from diagnostic delays and inefficient workflows. This digital stethoscope facilitates real-time diagnosis through machine learning and remote monitoring, utilizing the ESP32’s ADC and Wi-Fi capabilities to wirelessly send audio data to a remote server for comprehensive analysis. By integrating modern technologies such as the ESP32 microcontroller and the MAX9814 microphone module, these devices capture and transmit high-fidelity respiratory sounds, overcoming the challenges of imprecision and time lag in conventional methods. Initial tests have demonstrated the device's ability to capture clear respiratory sounds, underscoring its potential for effective remote health monitoring and telemedicine. These improvements aim to enhance diagnostic accuracy, facilitate early diagnosis, and ultimately improve patient outcomes, showcasing the significant potential of digital stethoscopes to transform respiratory diagnostics and patient care, particularly in remote and telemedicine settings. In this research, a prototype of a digital stethoscope for respiratory diagnostics was developed and evaluated. The obtained results from the prototype measurements demonstrated that the proposed system could be a solid starting point for the actual implementation of an advanced respiratory monitoring system.
Nurul Hidayah Rodzuan, Ili Najaa Aimi Mohd Nordin, Ahmad ‘athif Mohd Faudzi, Noraishikin Zulkarnain, Muhammad Rusydi Muhammad Razif, Nik Normunira Mat Hassan, Muhamad Hazwan Abdul Hafidz,
Volume 21, Issue 2 (6-2025)
Abstract
Rehabilitation devices like assistive gloves require bending-type soft actuators for controlled, repetitive finger movements essential for therapy. However, non-segmented actuators often struggle to replicate natural finger articulation, which can cause discomfort and reduce patient compliance. This paper presents the design and assembly of a segmented bending pneumatic soft actuator to achieve index finger flexion, aiming to improve comfort and support natural finger movement at low pressure. The actuator is integrated into a glove with a flexible bend sensor to measure the flexion angle of the metacarpophalangeal joint. Ecoflex 0-50 A-B silicone rubber is used in the fabrication, with air bubbles removed to ensure consistent actuator performance. The study investigates the actuator's performance and the sensor's ability to accurately measure joint flexion. The results, presented through detailed graphs, analyze the actuator’s flexibility, bending, and elongation under different pressure scenarios, offering insights into its effectiveness in improving patient comfort, joint articulation, and rehabilitation outcomes.
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 (6-2025)
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 (6-2025)
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 (6-2025)
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 (6-2025)
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.
