Installing passive sensing for condition monitoring of a 400 kV cable

HV cables are particularly susceptible to failures at joints and terminations, but can be challenging to instrument at these key locations using conventional monitoring technologies. This paper demonstrates the deployment and learnings from a passive sensing approach for a critical 400 kV cable circuit in Spain. The cable is 12.8 km long and has cross-bonded joints. Passive optical sensors have been retrofitted to 4 out of 16 sections of the circuit, but the sensors do not require power or other infrastructure at the monitoring locations. This enables continuous and cost-effective monitoring of phase current, sheath current, and cable joint temperature. A central optical interrogation device processes the optical signals from all sensors connected via a single-mode fibre, and it outputs measured waveform samples from these sensors at 4 kHz using the IEC 61850-9-2 Sampled Values protocol. A substation computer has been installed to collect data over a long period of time and perform processing and analysis. Practical installation guidance and learning experiences from the installation of the sensing platform are documented in this paper, including the complexities of installing sheath current sensors at the correct locations and issues experienced with CT saturation.

The paper describes opportunities for leveraging the sensing platform for data analysis purposes. The goal is to develop automated early warning signs of asset degradation or stress and identify installation issues, such as broken connections or insulation damage. Several methods are discussed in the paper, which broadly fall into two categories: 1) analytical and 2) statistical. The analytical methods involve using knowledge of the cross-bonded configuration with the real-time measurements to derive cable health metrics. For example, it is possible to use the ratio of sheath-to-phase current to readily identify common installation problems. There are several further techniques in the literature for automatically detecting additional failure modes with this type of cable circuit, which are described in the paper. A key strategy with the statistical methods is to remove “seasonality” effects from the data trends (such as daily cyclic variations) so that true deviations can be revealed and analysed. Furthermore, by monitoring sheath currents and temperatures at multiple sections along the cable, it is possible to perform analysis methods that cross-correlate data at different locations to find outliers – which may indicate asset health degradation or other issues. This visibility will be used to optimise the cable maintenance strategy (i.e. avoiding routine manual inspections) and provide advance warning of possible damage. At the time of writing, the system is awaiting final commissioning, but future work will report on the findings of the analysis methods once sufficient data has been collected.