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OHL Assessment and Risk Assessment Based on Environmental Data and Inspection

26 September 2012by admin
by J. Gomes-Mota¹, F. Azevedo¹‘², L. Campos Pinto³, N. Machado³, S.A.B. de Almeida de Graaffª; Albatroz Engenharia¹, CENTRIA Universidade Nova de Lisboa², REN – Rede Eléctrica Nacional³ , TenneT TSO B.V.ª
presented at the Conference of CIGRÉ Canadá, Montréal, 24-26 September, 2012, ,


Electrical grid operators have been struggling in recent years to deploy new lines to meet increasing demands for transmission capacity and quality of service. Simultaneously, management rules are becoming stronger in order to reduce costs and make an optimal use of resources and assets along their life span. In this context of cost reduction, higher efficiency and productivity, together with current environmental concerns, REN – Rede Eléctrica Nacional, S.A., Portuguese Transmission System Operator (TSO), has been using LiDAR technology in their aerial inspections since 2007. The acquired three-dimensional data in this Power Line Maintenance System are automatically processed, with support of the video inspections and geo-referenced systems. The national grid was thus fully characterized at span level with urbanization and vegetation growth rate indexes.

The collection of historical data, together with the knowledge of asset characteristics and system operation conditions, is structured in a generalized relational database. Data collection and organization becomes crucial to obtain better results and it represents a major step for a TSO. For example, some ‘number crunching’ analysis tools and statistical methods to infer valuable information, such as common cause failure and probability of failure under different conditions. Assets lifeline can then be more precisely estimated. Additionally, this model is being extended with a comprehensive set of different environmental, meteorological and geographic data over the years.

With such a system, more precise risk indexes can be calculated based on the probability of failure for each span and aggregated for a whole line. Moreover, the criticality of each line depends on its risk index, as well as on the grid topology. Having such information reliably calculated allows then to develop a number of optimisation tools to reduce costs or to improve the quality of service by an appropriate use of resources, such as an intelligent maintenance policy or inspection planning. Equally important, is the ability of more accurate asset life line estimation, to fully exploit its use with less risk, and better plan future maintenance and investments, reducing uncertainty.

A methodology to evaluate operational risk for a short-period of time was developed inside REN, taking into account the probabilistic nature of contingencies and their severity under specific operating conditions. This new methodology aims to provide control room operators with risk-based security indices for the following period (such as 1 hour). Risk indices provide an insight to operators of the network state and of the constraints that operators face.



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