Ongoing Research Projects

Project Descriptions:
Condition monitoring on in-service generators and motors is important and necessary because deterioration and failure of stator windings is a major factor of equipment problems. Partial discharge (PD) measurement is commonly used to evaluate the condition and problem of stator winding. The effectiveness of prediction of individual winding problems based on high PD charge readings have been widely reported in the past. However, the interpretation of winding problems based on PD measurement readings is commonly based on experience personnel or expert judgment. This could result in variation of the interpretation depending on the person or hardship if the experts are not available. Therefore, in this project, methods that can automatically classify the type of defect within stator insulation of rotating machines with high accuracy are developed. This can minimise the time and cost of repair, maintenance and diagnosis.

Project descriptions:
 Lightning overvoltage causes outages of transmission lines (TL) in tropical countries. Identifying and analysing the key factors responsible for tripping the lines can improve the performance of the lines. In this work, the flashover patterns due to direct lightning strikes on 275 kV double circuit TLs and towers in Malaysia was evaluated with Electromagnetic Transient Program (EMTP-RV). Three parameters that include lightning current magnitude, power frequency angle and tower footing resistance were analysed on the line performance during direct lightning strikes..

Project Descriptions:
Localization techniques, ranging from conventional iterative algorithms to advanced artificial intelligence (AI) models, were developed for insulation defect localization in substations. The study examined the influence of environmental factors, such as fluctuating outdoor noise levels across different days, times, and weather conditions, on localization performance. It also analyzed the impact of sensor placement, particularly at varying heights, on algorithmic accuracy.

Project Descriptions:
Recent advancements in deep learning (DL) have significantly improved PD diagnostics, particularly in classifying known fault categories. However, most existing approaches operate under a closed-world assumption, where all fault categories are predefined. This assumption limits their effectiveness in real-world scenarios, where unseen fault types frequently arise due to the variability and vulnerabilities in high-voltage network insulation and electrical systems. Addressing this limitation requires integrating open-set recognition techniques into PD diagnostics. This work proposes a framework for PD classification with open-set recognition capabilities using data from five cross-linked polyethylene (XLPE) cable joints with artificial defects. 

Project Descriptions:
Medium voltage switchgear plays an important role in the power grid system, and ensuring its operation is the basis for a reliable power supply. However, monitoring partial discharge (PD) caused by different types of insulation defects in medium voltage switchgear is a huge challenge. Different PD signals have very similar characteristics and are difficult to distinguish, even for the most experienced experts. This study proposes a deep learning method for PD pattern recognition based on a convolutional neural network (CNN).

Project Descriptions:
Partial discharge (PD) is a crucial electrical phenomenon that initiates severe damage to high-voltage (HV) equipment. Among several methods of sensing PD, the ultra-high frequency (UHF) technique has recently gained popularity for its reliability, high sensitivity, and anti-interference capability. A UHF antenna can be used as a PD sensor to detect UHF signals generated by partial discharge to prevent further deterioration of the insulator of HV systems such as air-insulated switchgear (AIS) and gas-insulated switchgear (GIS). Due to the compact cylindrical shape of AIS and GIS, rigid antenna structures create installation problems, and hence, complex modifications are required to install them inside the system.

Project Descriptions:
Partial discharge (PD) monitoring is an important diagnostic tool for insulation systems because the obtained results can be used to assess the overall condition of a high voltage plant. One defect that generally exists within solid dielectric insulation system is the presence of a delamination defect. In this project, a physical model describing PD mechanisms within a delamination defect in a dielectric insulation material is proposed to investigate the influence of delamination defects and high AC and superimposed switching impulse voltages on the severity of PD mechanisms. Then, critical physical parameters affecting the PD mechanisms can be identified, which can enhance the knowledge on the insulation failure caused by delamination defects.

Project Descriptions:
A prototype of automated, high accuracy partial discharge (PD) fault diagnosis method for insulation system of power transformers for multi-source PD based on deep neural network will be developed. The advantages of this prototype compared to the existing products in the market are it will be able to automatically diagnose PD fault in power transformers with high accuracy and minimum time, cost and effort. It can also assist engineers or power industries in making decision for action needed to be taken on the equipment being diagnosed. Thus, the cost of repair on insulation system of power transformers can be saved compared to the existing practice by power industry via providing an early guide about the potential PD root cause.