Reliability analysis for distribution networks combined with transformer condition assessment
Research output: Collection of articles › Doctoral Thesis
|Place of Publication||Tampere|
|Publisher||Tampere University of Technology|
|Number of pages||68|
|ISBN (Electronic)||978-952-15-2406-6 |
|State||Published - 5 Feb 2010|
|Publication type||G5 Doctoral dissertation (article)|
|Name||Tampere University of Technology. Publication|
|Publisher||Tampere University of Technology|
Most electricity distribution companies have an ICT based network information system, which is used to manage operational and network component related information. Therefore, computer based tools and data are already widely available for network planning, operation and control. However, even in the state of the art systems there is still valuable data, which is can be utilized further to support network asset management. The information exists for example in kWh-meter data management systems, power quality measurement systems, SCADA and distribution management systems. Available data gives good opportunities to develop automatic ICT based asset management functionalities to give more precise information for decision-making process.
This thesis concentrates on developing electricity network asset management principles. The asset management framework is described and risk-based asset management approach is presented. The procedure, how risks in electricity distribution networks can be recognised and used as part of maintaining and developing network assets is described. Risk evaluation in this thesis is based on a reliability based network analysis including life cycle cost evaluation.
As a basis of reliability based network analysis, detailed information from a electricity distribution network component is needed. This includes the structural information and information from the surrounding environment of a component, which is used to evaluate external failure probability and consequences of a failure of a component. Furthermore, the condition of a component can be used to evaluate the risk of the internal failure of a component. This thesis presents methodology to evaluate both the external and internal conditions of a component.
The work concentrates on defining possibilities to utilise available transformer related data as part of the reliability based network analysis. In the analysis, external failure probabilities are based on the structural information of the component including inspection information combined with environmental factors. At a more detailed level the thesis concentrates to develop and evaluate novel methods for a transformer condition assessment. The presented condition assessment methodology for a transformer utilizes available structural and loading and temperature measurement information. The methodology is based on a calculated temperature behaviour and aging of a transformer adapted from the IEC and IEEE loading guides. The usability of artificial neural networks (ANN) for temperature and aging calculations are discussed and evaluated. The results achieved with loading guides and ANN are verified using various measurements including loading, condition and temperature measurements. As a results of the thesis, novel methods to utilise available transformer loading and temperature information for distribution transformer condition monitoring is presented.
The pilot and commercially developed software implementations for reliability based network analysis and transformer condition assessment are presented. The usability of the reliability analysis and the condition assessment methodology of transformers is verified using several case studies with real and fictive networks. Commercialised applications for transformer condition assessment and reliability based network analysis are described and the usability of the tools in every day practice is discussed. As a conclusion of the studies, the results indicate that the developed condition assessment approach for transformers can be used for distribution transformer condition monitoring and also as input information for the reliability based network analysis.
As a result of the studies, the comprehensive approach to utilise available information for distribution transformer condition assessment as an input of a reliability based network analysis is presented. Based on the approach, it is possible to optimise maintenance and monitoring actions as part of the life cycle cost evaluation. This is elucidated with a simplistic case study, which evaluates costs and benefits for distribution transformer condition monitoring system for in-house installed transformers.