Sep 2018
Electricity Distribution
CALISTA - Cable Asset Life by Integrating Statistical Failure Models
NIA_SPEN_033
Live
Sep 2018
Jun 2022
SP Energy Networks
Andrew McDiarmid
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Network Innovation Allowance
ED - Network improvements and system operability, ED - New technologies and commercial evolution, ED - Customer and stakeholder focus, ED - Safety and health and environment
LV & 11kV Networks
£320,000.00
This Project will, through research, develop an analytical model to predict cable asset lifespan through analysis of the cable parameters.
(a)     Establish an analytical cable insulation loss-of-life or aging model via developing analytical models for all failure and correlate the PD trend with past failure events.

(b)     Developing a top-down approach, based on the Weibull model and Proportional Hazard Model (PHM), to allow systematic analysis of failure behaviour and to identify the main influencing factors on failure. The approach, based on historical life and failure data, will (i) formulate a methodology for systematic collection of cable failure, life and condition monitoring data, (ii) develop a method for dealing with small and incomplete datasets; and (iii) apply the PHM to generate knowledge on the effect of main influencing factors such as historical loading, climate conditions, frequency of circuit switching and installation methods on cable failures.

(c)     Applying a bottom-up approach, based on physics-based analytical models which will quantify the aging mechanisms and the routes to failure of cable insulation, under various stressing conditions. The work will based on theoretical investigation and on-site partial discharge measurement results of the PILC cable circuits. It is supposed that the GCU research team will use their PD monitoring system to take PD measurements 4 times a year over the project life on selected circuits.

(d)     Developing a systematic model which can combine the top-down and bottom-up approaches to forecast cable life for individual cable circuits under in-service conditions. Factors will include electrical, mechanical, thermal, environmental stresses including steady state and short term events (switching and lightning impulse, cable dig-up etc.). Effects of future load increase and changes in power quality will be considered.

(e)     Based on the outcomes of the above research and with studies on the effect of maintenance on the insulation lifespan, an optimal integrated condition assessment, maintenance and replacement model will be developed.

(f)      Development of software packages to implement the research results.