Research Interests

Condition Monitoring

Condition monitoring is important in high voltage equipment because it can provide vital information about the health and condition of the equipment. Failure to detect earlier sign of fault may cause further degradation of the equipment, leading to breakdown and consequently cause loss of power supply. In condition monitoring, we actively perform research on partial discharges, leakage current, breakdown voltage, dielectric loss, thermal imaging and fault location in medium and high voltage power cables, surge arresters, insulator strings, circuit breaker, transformer oil and dielectric materials.

Modelling and Simulation

Measurement is an important diagnostic tool for high voltage insulation systems as obtained results can be used to assess the condition of the system. However, modelling of the electrical phenomena allows a better understanding on the physical mechanism of the phenomena. Comparison between measurement and simulation results allows physical parameters affecting the phenomena to be identified. Design of high voltage equipment can also be evaluated through simulation model. Our interests in modelling and simulation include lightning phenomena, partial discharge, breakdown in gases, thermal and electric field distribution and leakage current in high voltage power cables, dielectric insulation material, circuit breakers, surge arresters and insulator strings.

Artificial Intelligence

Artificial intelligence methods have been widely used in high voltage engineering applications. These methods are very useful particularly in classifying the output with certain sets of input. The most popular methods are artificial neural network (ANN) and support vector machine (SVM). In our group, we apply artificial intelligence methods on classification of partial discharge types, noise segregation from signals, transformer fault type based on dissolved gas analysis data and hydrophobicity level of insulator string based on image processing.

Optimisation Techniques

Optimisation techniques are also widely used in high voltage engineering applications. The techniques allow the optimum results of particular problems to be achieved without the needs of trial and error method. Some popular optimisation methods are particle swarm optimisation (PSO), genetic algorithm (GA), evolutionary programming (EP) and up to the recent methods, which include imperialist competitive algorithm (ICA) and gravitational search algorithm (GSA). Our research applies optimisation techniques in optimising the performance of artificial intelligence methods and design parameters of high voltage equipment and also minimising the errors between measurement and simulation results.

Dielectric Material Characterisation

New dielectric materials for the applications of high voltage insulation are widely explored in the world nowadays. It is aimed that new dielectric materials can improve the specifications of the insulation system and prolong the lifetime of the equipment. We actively explore different types of solid and liquid dielectric materials through characterisation processes, which include permittivity, capacitance and dielectric loss.