Sharulnizam Mohd Mukhtar, Muzamir Isa, Azremi Abdullah Al-Hadi,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 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.
Suhail Mahmoud Abdullah, Thamir Hassan Atyia,
Volume 21, Issue 4 (December 2025)
Abstract
Optimal control of DC motors remains a critical research area in modern control systems, given their wide industrial applications and the need for accurate performance under variable conditions. This paper explores the application of genetic algorithms (GAs) to optimize the control parameters of DC motors, particularly PID controllers, with the goal of improving the dynamic response and robustness of DC motor systems. Compared to traditional constraint-based tuning methods, GAs, inspired by natural selection and evolution, offer comprehensive search capabilities that significantly improve parameter optimization, providing better speed regulation, reduced overshoot, and minimal steady-state error. This review highlights the key challenges faced when using GAs. Comparative results from various studies demonstrate that GA-based controllers consistently outperform traditional tuning methods in terms of stability, efficiency, and adaptability. Key findings related to energy consumption and stability are highlighted. It is essential to analyze the system performance in terms of rise time (tr), settling time (ts), overshoot ratio (Mp%), and steady-state error (Ess). A proportional-integral-differential (PID) controller provides a stable response by tuning its parameters according to a specific methodology using a genetic algorithm. This paper concludes by emphasizing the potential of genetic generators as a powerful and flexible optimization tool for intelligent control of DC motors.
