Shamshul Bahar Yaakob

This paper presents the modeling of glass insulators, specifically focusing on the effects of contamination with and without Room Temperature Vulcanizing (RTV) coating. The study selected 132 kV cap and pin glass suspension insulators because of their popularity in transmission systems. The glass insulator has strong dielectric strength to ensure the reliability in transmission system. Despite their advantages, glass insulators can suffer significant performance degradation when exposed to contaminants, leading to increased maintenance and instability in transmission systems. To address these challenges, this research employs Finite Element Modeling (FEM) using Ansys Maxwell in a 3D simulation environment. The study examines the electrical performance of clean uncoated insulator, polluted uncoated, and RTV-coated insulators to evaluate the effectiveness of RTV coatings in mitigating contamination effects. Results demonstrate that polluted insulators experience increased in electric field and current density distributions, which can compromise performance and increase the possibility of flashover and arcing. In contrast, RTV-coated insulators exhibit enhanced performance under polluted conditions, showing significant reductions in both electric field and current density distributions. These findings underscore the importance of RTV coatings in improving the reliability of glass insulators in contaminated environments, thereby reducing maintenance and enhancing the overall performance of transmission lines.

This paper presents the modelling of a glass type of insulator with a voltage rating of 132 kV. The insulators play a critical role in transmission lines to ensure the stability and reliability of the system. Glass insulators are still widely used in overhead transmission systems because of their dielectric strength capability. Whenever faults occur in the transmission line or system, a considerable cost for maintenance and repair is required. The outdoor application of the insulator has resulted in exposure to various service conditions such as weather, pollution and lightning. Indeed, the inclination effect under the wind load is one of the significant challenges for outdoor insulators. Therefore, this paper studied the electrical performance of the 132 kV glass insulators under the inclination effects through Finite Elementary Modelling (FEM). In this simulation, the model of the glass insulator is constructed in four different inclination angles using the modelling software Ansys Maxwell 3D. From the results, the electric field distribution for the insulator became uneven when the insulator was inclined. With the reduction of clearance distance under inclination effects, the localised electric field form may increase the electrical discharge and arching likelihood. In a worst-case scenario, the flashover may cause faulty transmission equipment. In either case, the electrical performance of the insulator is reduced under the inclination effects.

Direct torque control is one type of vector control used to operate an induction motor. Efficient control rules for induction motor drives offer a great chance to save energy. In comparison to DC batteries, supercapacitor (SC) technology is commonly recognised as one of the most promising and energy-efficient technologies for next-generation energy storage systems. Rotating equipment generates vibration, which causes rotor dynamic. Battery applications also provide high torque current for induction motor start-up. The goal of these investigations and analyses is to improve the performance of the electrical device system for six sector and twelve sector direct torque control methods employing DC battery and supercapacitor. The direct torque control model is separated into seven sections: input energy storage, voltage source inverter, flux and torque estimate, sector detection, flux and torque controller, vector selection table, and induction motor. The paper focuses on the improvement of based sector for six sector and twelve sector technique of induction motor in MATLAB/Simulink simulation framework by employing different energy storage (supercapacitor and DC battery). The output of the system designed in MATLAB Simulink has been examined in terms of rotor speed and electromagnetic torque. In terms of rotor speed, the twelve-sector approach requires 0.04 second speed transition to reach steady state response, but the six-sector method requires 0.06 second when a load disturbance is added for DC battery and supercapacitor. When compared to the six-sector approach for DC battery and supercapacitor, the electromagnetic torque that employs the twelve-sector method provides more smoothly following the load torque. When a load disturbance is applied, the electromagnetic torque in the twelve-sector approach produces a spiky torque of around 15.5N, whereas the electromagnetic torque in the six-sector method produces 18N. In terms of electromagnetic torque, the usage of a supercapacitor generates 15N, whereas the use of a battery provides around 50N for the six sector and twelve sector methods.

Diabetic retinopathy is one of the most significant retinal diseases that can lead to blindness. As a result, it is critical to receive a prompt diagnosis of the disease. Manual screening can result in misdiagnosis due to human error and limited human capability. In such cases, using a deep learning-based automated diagnosis of the disease could aid in early detection and treatment. In deep learning-based analysis, the original and segmented blood vessels are typically used for diagnosis. However, it is still unclear which approach is superior. In this study, a comparison of two deep learning approaches (Inception v3 and DenseNet-121) was performed on two different datasets of colored images and segmented images. The study’s findings revealed that the accuracy for original images on both Inception v3 and DenseNet-121 equaled 0.8 or higher, whereas the segmented retinal blood vessels under both approaches provided an accuracy of just greater than 0.6, demonstrating that the segmented vessels do not add much utility to the deep learning-based analysis. The study’s findings show that the original-colored images are more significant in diagnosing retinopathy than the extracted retinal blood vessels.

This paper opens up an issue regarding to power system investment planning. In order to obtain a sustainable electricity supply, the power outage should be minimized. Maintenance cost plays as an essential element in investment planning, therefore misdirect investment should be avoided. Since soft computing can solved this problem, this paper offers a review in solving investment planning problem through such approaches. The main objective of this paper is to examine the concept and findings by the researchers in power system investment planning problem. Besides, this paper will highlight the most relevant contributions and limitations that can be improve in the other research. Finally, this paper will be pointed out some trends in power system investment planning problem.

This paper proposes an effective technique to solve Distribution System Expansion Planning (DSEP) problem by using the artificial neural network. The proposed technique will be formulated by using mean-variance analysis (MVA) approach in the form of mixed-integer quadratic programming problem. It consists of two layers neural network which combine Hopfield network and Boltzmann machine (BM) in upper and lower layer respectively named as Modified BM. The originality of the proposed technique is it will delete the unit of the second layer, which is not selected in the first layer in its execution. Then, the second layer is restructured using the selected units. Due to this feature, the proposed technique will improve time consuming and accuracy of solution. Referring to the case study demonstrated in this paper, the significance outputs obtained are the improvement in computational time and accuracy of solution provided. As the solution provided various of options, the proposed technique will help decision makers in solving DSEP problem. As a result, the performance of strategic investment planning in DSEP certainly enhanced.