At the University of Queensland we have always focused on higher degree research training. In the recent past, a number of our students have graduated with their PhDs and MPhils in power transformer research area. Currently they are working in many countries including Australia, India, Oman, Malaysia, and Sri Lanka. In this document abstracts of some of the most recent PhD projects and information on ongoing PhD projects are provided.
1. Dr. Chi Ho Chan (Jeffery): Advanced Signal Processing Techniques for Online Power Transformer Insulation Diagnosis
Abstract: Power transformer is one of the most important assets in an electric utility. However, a large number of existing power transformers worldwide have already approached or even exceeded their designed lifetimes. Any failure of a transformer can be disastrous. Therefore, the conditions of transformers need to be continuously monitored and evaluated. Since a transformer’s condition is largely dependent on its insulation system, a number of diagnostic methods have been developed for assessing transformer insulation conditions over the past decades. Among these methods, partial discharge (PD) measurement is widely adopted due to its capability of providing continuously online monitoring and diagnosis of a transformer without disturbing its normal operation. PD is a rather complicated phenomenon and stochastic in nature. Properly performing online PD measurements of a transformer, effectively analysing the measured PD signals, and subsequently making an informed condition assessment on a transformer’s insulation system are still challenging. This thesis is aimed at developing advanced signal processing techniques for online PD monitoring and diagnosis of power transformer insulation systems.
Journal Papers:
[1] J. Chan, H. Ma, and T. Saha, “Time-frequency sparsity map on automatic partial discharge sources separation for power transformer condition assessment,” IEEE Transactions on Dielectrics and Electrical Insulation, 22 4: 2271-2283, doi:10.1109/TDEI.2015.004836, 2015.
[2] J. Chan, H. Ma, and T. Saha, “Hybrid method on signal de-noising and representation for online partial discharge monitoring of power transformers at substations,” IET Science, Measurement and Technology, 9 7: 890-899, doi:10.1049/iet-smt.2014.0358, 2015.
[3] J. Chan, H. Ma, and T. Saha, “Automatic blind equalization and thresholding for partial discharge measurement in power transformer,” IEEE Transactions on Power Delivery, 29 4: 1927-1938, doi:10.1109/TPWRD.2014.2322114, 2014.
[4] J. Chan, H. Ma, T. Saha, and C. Ekanayake, “Self-adaptive partial discharge signal denoising based on ensemble empirical mode decomposition and automatic morphological thresholding,” IEEE Transactions On Dielectrics and Electrical Insulation, 21 1: 294-303, doi:10.1109/TDEI.2014.6740752, 2014.
[5] H. Ma, J. Chan, T. Saha, and C. Ekanayake, “Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources,” IEEE Transactions on Dielectrics and Electrical Insulation, 20 2: 468-478, doi:10.1109/TDEI.2013.6508749, 2013.
2. Dr. Kapila Senarath Bandara: Condition Monitoring of Biodegradable Oil Filled Transformer
Abstract: Natural and synthetic ester insulating oils have higher fire points and excellent biodegradable characteristics. Therefore, in order to reduce the adverse environmental impact and to improve the fire safety of transformers, there is an increasing demand for natural and synthetic ester insulating liquids as a transformer insulating oil. However, present understanding on ageing behaviour of ester oil-paper composite insulation system and knowledge on application of existing condition monitoring tools for ester based insulation systems are inadequate. This impedes the cost effective and reliable field applications of ester insulating oils, particularly application of natural esters. To reduce this knowledge gap, series of controlled ageing experiments are performed in this research project to provide a better and comprehensive understanding on ageing behaviour of ester oil-paper insulation systems. Furthermore, applicability of existing chemical and electrical based condition monitoring techniques for ester oil-paper insulation systems is systematically investigated in this research project.
Journal Papers:
[1] Kapila Bandara, Chandima Ekanayake, Tapan Saha, Hui Ma “Performance of Natural Ester as a Transformer Oil in Moisture-Rich Environments”, Energies, 9 4: 258. doi:10.3390/en9040258 2016.
[2] Kapila Bandara, Chandima Ekanayake, Tapan .K. Saha, “Analysis of Frequency Domain Dielectric Response of Pressboard Insulation Impregnated with Different Insulating Liquids”, IEEE Transactions on Dielectrics and Electrical Insulation, 23 4: 2042-2050, doi:10.1109/TDEI.2016.7556477, 2016.
[3] Kapila Bandara, Chandima Ekanayake, Tapan .K. Saha, Pratheep Kumar, “Understanding the Ageing Aspects of Natural Ester Based Insulation”, IEEE Transactions On Dielectrics and Electrical Insulation, 21 1: 369-379. doi:10.1109/TDEI.2014.6740761, 2014.
[4] Kapila Bandara, Chandima Ekanayake, Tapan .K. Saha, “Modelling the dielectric response measurements of transformer oil”, IEEE Transactions on Dielectrics and Electrical Insulation, 22 2: 1283-1291, doi:10.1109/TDEI.2015.7076832, 2015.
3. Dr Yi Cui: Investigation of Data Centric Diagnostic Techniques for Transformer Condition Assessment
Abstract: Power transformer is one of the most important and expensive equipment in a power system. Its reliability directly affects a power system. To ensure the reliable operation of a power transformer, its condition needs to be continuously monitored and evaluated. Over the past two decades, a number of diagnostic techniques have been developed for transformer condition
assessment such as dissolved gas analysis (DGA), degree of polymerization (DP) measurement, polarization and depolarization current (PDC) measurement, frequency domain spectroscopy (FDS), frequency response analysis (FRA), and partial discharge (PD) detection. However, the interpretations of measurement results acquired from these diagnostics are usually based upon the empirical models, which are sometimes inaccurate and incomplete especially in abnormal transformer operation scenarios. Therefore, accurate interpreting on the measurement data obtained by the above techniques and subsequently making explicit condition assessment of transformers is still a challenge task.
To integrate every piece of data and information obtained from different transformer diagnostic measurements and subsequently evaluating the overall health condition of a transformer, this thesis proposes a data and information fusion framework based on Bayesian Network (BN). Within the Bayesian Network, Monte Carlo and Bootstrap methods are employed to extract the most informative characteristics regarding transformer condition from different diagnostic measurements. Results of case studies demonstrate that the proposed data and information fusion framework can evaluate the effectiveness of combinations of different diagnostic measurements and subsequently facilitate determining optimal diagnostic strategies involved in transformer condition assessment. It is expected that the data centric diagnostic approaches developed in this thesis can provide an accurate modelling and reliable assessment of transformer’s health condition.
Journal Papers:
[1] Yi Cui; Hui Ma; Tapan Saha; Chandima Ekanayake, “Understanding Moisture Dynamics and Its Effect on Dielectric Response of Transformer Insulation,” IEEE Transactions on Power Delivery, 30 5: 2195-2204. doi:10.1109/TPWRD.2015.2426199, 2015.
[2] Yi Cui; Hui Ma; Tapan Saha, “Improvement of Power Transformer Insulation Diagnosis Using Oil Characteristics Data Pre-processed By SMOTEBoost Technique,” IEEE Transactions on Dielectrics and Electrical Insulation, 21 5: 2363-2373, doi:10.1109/TDEI.2014.004547, 2014.
[3] Yi Cui; Hui Ma; Tapan Saha; Chandima Ekanayake; Guangning Wu, “Multi-physics Modelling Approach for Investigation of Moisture Dynamics in Power Transformers,” IET Generation Transmission and Distribution, 10 8: 1993-2001, doi:10.1049/iet-gtd.2015.1459, 2016.
[4] Yi Cui; Hui Ma; Tapan Saha; Chandima Ekanayake; Daniel Martin, “Particle Tracing Modelling on Moisture Dynamics of Oil-impregnated Transformer,” IET Science, Measurement and Technology, 10 4: 335-343, doi:10.1049/iet-smt.2015.0196, 2016.
[5] Yi Cui; Hui Ma; Tapan Saha, “Pattern Recognition Techniques for Power Transformer Insulation Diagnosis – A Comparative Study Part 1: Framework, Literature, and Illustration”, International Transactions on Electrical Energy Systems, 25 10: 2247-2259, doi:10.1002/etep.1959, 2015.
[6] Yi Cui; Hui Ma; Tapan Saha, “Pattern Recognition Techniques for Power Transformer Insulation Diagnosis – A Comparative Study Part 2: Implementation, Case Study, and Statistical Analysis”, International Transactions on Electrical Energy Systems, 25 10: 2260-2274, doi:10.1002/etep.1963, 2015.
[7] Daniel Martin; Yi Cui; Chandima Ekanayake; Hui Ma; Tapan Saha, “An Updated Model to Determine the Life Remaining of Transformer Insulation”, IEEE Transactions on Power Delivery, 30 1: 395-402, doi:10.1109/TPWRD.2014.2345775, 2014.
[8] Atefeh Dehghani Ashkezari; Hui Ma; Tapan Saha; Yi Cui, “Investigation of Feature Selection Techniques for Power Transformer Insulation Condition Assessment,” IEEE Transactions on Dielectrics and Electrical Insulation, 21 2: 836-844, doi:10.1109/TDEI.2013.004090, 2014.
[9] Yi Cui; Hui Ma; Tapan Saha; Chandima Ekanayake; Daniel Martin, “ Moisture Dependent Thermal Modelling of Transformers Filled With Vegetable Oil,” IEEE Transactions On Power Delivery, 31 5: 2140-2150, doi:10.1109/TPWRD.2016.2569123, 2015.
4. Dr. Mohd Fairouz Bin Mohd Yousof: Frequency Response Analysis for Transformer Winding Condition Monitoring
Abstract: The large power transformer is one of the most expensive assets in a power system network. Special attention needs to be taken to monitor this expensive asset. Among the most critical aspect of a transformer that needs to be monitored is the mechanical condition of the windings and core. One of the best approaches to achieve this is by performing the Frequency Response Analysis (FRA) test on the transformer. The test measures the transfer function response of the transformer winding. If any physical changes occur, it will affect the original response, which can be used to detect any abnormality. However, the critical challenge in this technique is to correctly interpret the measured response in determining the transformer status. Although various investigations have focussed on this issue, the interpretation aspect of FRA is still not fully established. This thesis studies several winding deformations, which includes tilting and bending of conductors and inter-disc fault. These three faults are examined in terms of their severity of damage and location of the fault. Statistical analysis is applied to determine the overall condition of the winding. On the other hand, transfer function based analysis is proposed to extract further iii information if the winding is found to be faulty. This includes using the pole plot and Nyquist plot, in which the latter proved to be useful for all winding failure modes. The transfer function is achieved by applying vector fitting algorithm.
Journal Papers:
1. M. Fairouz M. Yousof, C. Ekanayake and T. K. Saha, “Examining the Ageing of Transformer Insulation Using FRA and FDS Techniques”, IEEE Transactions on Dielectrics and Electrical Insulation, 22 2: 1258-1265, doi:10.1109/TDEI.2015.7076829, 2015.
2. M. Fairouz M. Yousof, C. Ekanayake and T. K. Saha, “Frequency Response Analysis to Investigate Deformation of Transformer Winding”, IEEE Transactions on Dielectrics and Electrical Insulation, 22 4: 2359-2367, doi:10.1109/TDEI.2015.004750, 2015.
5. Dr. Lakshitha Bandara Naranpanawe: Understanding the Moisture, Temperature and Ageing Dependency of Power Transformer Winding Clamping Pressure
Abstract: Progressive loss of winding clamping pressure is a failure mode that reduces the short circuit withstand capability of power transformers. Short circuit faults exert strong electromagnetic forces on the transformer winding structure, which could deform and displace its conductors.
Failure to maintain the structural integrity of the winding structure during short circuit faults often leads to a catastrophic transformer failure, which could cause a disastrous network failure. On the other hand, due to grid expansion both short circuit current magnitude and the frequency of fault occurrences have been increasing. Early identification of loose clamping conditions of field aged power transformers through non-invasive measurements is therefore, an attractive solution to overcome the above challenges. However, the accuracy of existing clamping pressure estimation techniques is low, and hence are rarely applied for clamping pressure detection of substation transformers. To estimate loose clamping conditions of substation transformers accurately, available knowledge on the changes in power transformer winding clamping pressure under different operating conditions is insufficient. To fulfil the above knowledge gap, in this thesis, a set of comprehensive laboratory investigations were conducted to measure the through-thickness compression behaviour of pressboard under various transformer-operating conditions. In addition, the influence of moisture, temperature and ageing dependency of pressboard mechanical properties on a static clamping system was investigated.
Journal Papers:
[1] Naranpanawe, Lakshitha, Ekanayake, Chandima and Saha, Tapan K. “Measurements on pressboard to understand the effect of solid insulation condition on monitoring of power transformer winding clamping pressure,” IET Science, Measurement and Technology, 13 2: 186-192. doi:10.1049/iet-smt.2018, 2018.
[2] Naranpanawe, Lakshitha, Ekanayake, Chandima, Annamalai, Pratheep K. and Saha, Tapan Kumar, “Influence of moisture dependency of pressboard on transformer winding clamping pressure,” IEEE Transactions on Dielectrics and Electrical Insulation, 24 5: 3191-3200. doi:10.1109/TDEI.2017.006206, 2017.
Our Current PhD students in transformer research areas:
1. Mr Md Abdul Hafeez Ansari, Fibre Optics Based Condition Monitoring of Transformer Insulation
2. Mr Sameera Samarasinghe, Preventing Transformer Failures Caused by Silver Sulphide
3. Mr Junhyuck Seo, Intelligent Monitoring and Diagnosis of On-Load Tap-Changer (OLTC) and Bushing of Power Transformer
4. Mr. Jakob Pallot, Transformer Vibro-Acoustic Characteristic Modelling and Vibro-Acoustic Based Transformer Condition Monitoring
5. Three new students are to join shortly.
For any further information: Please contact Prof Tapan K Saha
(Email: saha@itee.uq.edu.au)
