Advanced winding models and ontology-based fault diagnosis for power transformers

Lu, Chen
Advanced winding models and ontology-based fault diagnosis for power transformers. Master of Philosophy thesis, University of Liverpool.

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Power transformer plays an important role in a power system, and its fault diag- nosis has been recognised as a matter of most considerable interest in maintaining the reliable operation of a power system. In practise, operation and fault diagno- sis of the power transformer are based on knowledge and experience of electrical power engineers. There are several on-line diagnosis methods to monitor the power transformer, such as dissolved gasses analysis (DGA), partial discharge (PD), and frequency response analysis (FRA). In order to reduce the cost and increase fault diagnosis efficiency, new techniques and expert-systems are required, which can provide power transformer failure knowledge representation, automated data analy- sis and decision-making. Power transformer failure modes and diagnostic methods have been reviewed in Chapter 1. Then, ontology has been employed in establishing the power fail- ure models system. Ontology is a mechanism that describes the concepts and their systematic relationships. In order to develop ontology system for the power failure models system, numerous concepts and their relationships between faults exhibited for power transformers are analysed. This system uses a software called Prote ́ge ́, which is based on ontology to provide a semantic model for knowledge representa- tion and information management. The relationship between electrical failure mod- els has been illustrated successfully, and the system can correctly provide a query searching function. Partial discharge (PD) is a common fault in power transformer, it may causes gradual degradation of power transformer insulation material, which may finally lead to a full break down. Localisation of PD source is vital for saving in mainte- nance time and costs, but it is not a simple task in application due to noise signal iv and interference. The multi-conductor transmission model (MTL) is one of the most suitable models for PD propagation study in transformers. Chapter 3 shows an ini- tial study of MTL model and tests its effectiveness of PD faults locations. Then, the transfer function from all possible PD locations to line-end and neutral-end were calculated. The results proved that this method can estimate the location of PD very effectively. FRA is a diagnosis method for detecting winding deformation based on varia- tion of power transformer AC impedance. In chapter 4, a lumped parameter winding model of single phase power transformer is introduced. However, the FRA fre- quency range of original lumped model is only available up to 1MHz. In order to improve frequency response range, an advanced lumped model has been proposed by adding a negative-value capacitive branch with inductance branch in the original model. It significantly enhances the valid range of frequency up to 3MHz. In chapter 5, three optimisation methods, particle swarm optimisation (PSO), genetic algorithms (GA), and simulated annealing (SA) are subsequently applied for transformer parameter identification based on FRA measurements. The simulation results show that PSO, GA, and SA can accurately identify the parameters, partial significance of the deviation between simulation with reference is acceptable. The model with the optimised parameters ideally describes the magnetic and electrical characteristics of the given transformer. The comparison of results from the opti- misation methods shows that converge time of PSO is shorter than others’ and the GA provides the best FRA outputs, which is more closer to reference in a limited number of iterations.

Item Type: Thesis (Master of Philosophy)
Additional Information: Date: 2014-07 (completed)
Subjects: ?? TK ??
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 19 Feb 2015 15:27
Last Modified: 17 Dec 2022 01:28
DOI: 10.17638/00019593
  • Mu, Tingting