Reliable engineering
takes many forms

Multi-layer visualization and time-space correlation for risk-based asset management

20 May 2015by admin
by F. Azevedo¹, J. Gomes-Mota², S. Cordeiro², J. Casaca³, L. Campos Pinto³; Universidade Nova de Lisboa¹, Albatroz Engenharia², REN – Rede Elétrica Nacional³
presented at ERIAC Conference – Encuentro Regional Iberoamericano de Cigré, Argentina, 17-21 May 2015, ERIAC


Following the work on the identification of environmental factors impact in the reliability and remaining lifetime of transmission electric lines, the authors present a visualisation tool that allows combining grid incidents data with spatio-temporal distributions of risk factors, allowing spatiotemporal risk index calculations (or other equivalent heuristics) associated to the electric circuits. This tool is It is an integral part of the project designated as “Grid Intelligence and Optimisation”, resulting from a national (Portugal) collaboration between 3 distinct entities: 1) a University IT Department; 2) the national electric grid operator (transmission); e 3) a private R&D company working in signal processing.

The tool shows the correlations between the causes observed on the field or on the order of records, risk indicators that are intended to model the risk factors and phenomena and descriptions of assets that characterize the transmission grid and the ecosystem in which it operates.

case studies are presented for environmental factors such as forest fires, storks and other birds, insulators laundering, forest cover and land use, vegetation growth rates, land register, lightning.

However, correlation does not mean causality and there is the need to validate the possible relations between causes and consequences being that many of the correlated data translate physical realities and dependent phenomena (forest fires depend on the temperature, insulators washing depends on the type insulators  and existing pollution, storks depend on hydrographic basins   and soil occupation, etc.).

The paper initiates with the introduction to the several factors to consider for the reliability and lifetime of the lines and the models that are behind the risk indexes creation. Following is  a description of the correlation tools and the relation with the spatio temporal referencing data base infrastructure which is the infrastructure that connects to the geographical information system and to the asset management system. The user interface and its visualisation ways of the stored data are presented after.

The tool discussion is illustrated with the description of field examples starting with evident and easy to interpret correlations (and that inspire the passage from correlation to causality) until the cases where there are multiple correlations of phenomena apparently independents and that require a better analysis to reduce the ambiguity space and eventually to find other phenomena that are primordial causes or different from those that are already represented on the data base.

It ends with a discussion of the limitations found – mostly on the reporting and spatio temporal modulation of the data – and with the extension proposals to other grids and other analysis causes and with reference to the impact this methodology had by REN from the factors that affect the reliability and the remaining lifetime of their overhead lines grid.



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