Learnings

Outcomes

The literature review conducted by Kinectrics has shown that the published information related to historical developments and failure mechanisms of cut-outs is quite sparse. The information is either retained by manufacturers that are still operating to date or has disappeared with manufacturers that no longer exist. 

The literature review also showed that certain cut-out issues can be detected by authorised meter operatives during meter replacements, which are reported back to the DNO. However, these issues do not necessarily relate to cut-out failures. Further research on identifying the most common failure mechanisms experienced by cut-outs concluded that the most common failure modes are overheating and short circuits caused by overloading, poor electrical contact, and moisture ingress. 

The detailed output from this review is contained in the WP1 report on the Electricity North West website.

The project developed a statistical model to examine the failure rates of metallic, phenolic and GRP cut-outs. The model used:

• a consistent body of data relating to cut-out replacements from 2019 – 2022, where two of these years also enabled the ratio of the three technology failure rates to be established,
• a large smart meter (MOCOPA) database that conclusively showed that a majority of phenolic cut-outs were in good service. 

By assuming a single Weibull-like replacement distribution function, and treating both metallic and phenolics cut-outs on a year-by-year basis, where the constraints were offered by:

• the number of cut-outs in the Electricity North West area from 1920 through to 2022,
• the number of metallics in service in 1966, at the time of the technology change over to phenolic cut-outs,
• the total number of consumers in 1993, at the time of the technology change over to GRP cut-outs,
• the current replacements rates, and
• the current ratio between phenolic and metallic cut-outs, 

provided a constrained model to fit the population and replacement rate for these components based on perceived chronological age derived from the housing age distribution. The analysis showed that when years away from the technology change over, the tail of the Weibull distribution could be approximated by a linear proportional rate.

However, the model has not been able to assess an “effective age” based on asset condition, manufacturer and material composition. There is currently insufficient data to assess the effect of age- or condition-related on changing the failure rate.

As the model was “derived” from the Electricity North West cut-out population, increasing year-by-year, this same approach was applied to the number of increasing households by year by LA and LSOA. The latter is valuable as the variance in the number of households per LSOA is comparatively small, offering a normalised unit. 

When the Electricity North West-wide methods were applied to the LAs and LSOAs, there were significant differences across the LAs and LSOAs, clearly related to various new builds. By relating the relative populations to the Electricity North West mean, the areas where higher than average phenolic and metallic populations are present become clearly visible. 

Application of these models to Electricity North West datasets and to LSOA levels of granularity shows: 

• a predicted failure rate that corresponds to that observed,
• a failure intensity, that then enables a projection forward in time,
• a clustering of more vulnerable assets that require attention,
• an easy correspondence from postcode, to LSOA, to current excess of phenolic cut-outs over the Electricity North West mean for total number and rateable value of households. 

Currently, the model is consistent with total cut-out population, replacement rates of metallic and phenolic cut-outs and the MOCOPA data set. Additionally, the model of cut-out populations provides estimates of current and near-future replacement rates, “heat” maps of any particular technology at the LSOA level, a health index, using housing data at the LSOA level and hence, LSOAs that provide higher risk. 

To further improve the model Kinectrics recommend the continued acquisition of good quality cut-out replacement data ensuring that emergency, reactive and campaign replacements are captured and consolidated in a single asset database. With this increased data an algorithm can be developed that adjusts the failure rate parameters to “match” replacement location and re-evaluates the risk profile for a given location.

The WP3 and WP4 reports on the Electricity North West website provide the full details on the data analysis and model development.

Lessons Learnt

It is important to identify the available data and any data protection requirements early so appropriate measures can be but in place.

To improve the quality of the data available Asset Management Policy should be amended to require field staff replacing cut-outs to record:

• Manufacturer of the removed cut-out,
• Location the cut-out was removed from,
• cause of failure from visual examination,

before returning the cut-out to a central store. Additionally, a sample of the returned units should be examined to audit the inspection report and feed into the growing asset database.