Asset Management and Smart HV Systems

Every chapter so far has examined a thing — a gap, a dielectric, an arrester, a cable. Asset management looks instead at the population: the thousands of transformers, hundreds of thousands of kilometres of line, and uncounted items of switchgear that a utility must run as one fleet, extracting the most reliability and value from them across their whole lives at acceptable cost and risk. The pressure to do this well has never been greater, because much of the world's grid was built in a post-war boom and is now at or beyond its design life — a vast inventory of ageing assets that cannot all be replaced at once.

So asset management answers three linked questions for every item and for the fleet as a whole: what is its condition? (the diagnostics of Part 6), what is the risk it poses? (how likely is failure, and how bad would it be?), and when and how should we act? (run on, refurbish, or replace). The discipline turns the physics of the earlier chapters into decisions about money, reliability and safety — the point where high-voltage engineering meets management.

An asset is managed from cradle to grave. It is specified and designed, manufactured and tested (the routine and type tests of Part 6), installed and commissioned, then enters the long phase of operation and maintenance, before eventually being refurbished to extend its life or retired and recycled. Decisions taken early — the insulation level, the quality bought — echo through the whole life, and the cheapest purchase is rarely the cheapest over forty years, so good practice weighs the whole-life cost rather than the purchase price alone.

Across that life the failure rate follows the famous bathtub curve. Early on, manufacturing defects cause infant-mortality failures that fall away as weak units are weeded out; then comes a long useful life of low, roughly constant random failures; and finally the failure rate climbs as wear-out sets in — insulation ageing, contacts eroding, paper depolymerising. The whole purpose of condition monitoring and asset management is to see that wear-out climb coming and act on the right units before it turns into outages.

How an owner maintains the fleet has evolved along a clear spectrum of sophistication, each step using more information to time intervention better.

Reactive maintenance fixes things when they break — cheap until the break is catastrophic. Time-based preventive maintenance services everything on a calendar — safer, but it disturbs healthy units and may still miss a sick one. Condition-based maintenance, the heart of Part 6, acts on the evidence of the diagnostics, intervening only when an asset's measured condition warrants. The most refined approaches — reliability-centred and risk-based maintenance — go further, directing scarce maintenance effort to the assets where it most reduces overall risk, accepting run-to-failure for cheap, non-critical items while lavishing attention on the critical few. The trend across the industry is steadily up this spectrum, from the calendar toward the sensor and the risk model.

Condition- and risk-based maintenance run on data, and the modern grid is being rebuilt to supply it. The digital substation replaces the copper wiring of conventional control with a fibre-optic network governed by the standard IEC 61850. Measurements are taken by non-conventional instrument transformers (optical current and voltage sensors that are compact, safe and wide-band), digitised at the equipment by merging units, and streamed as sampled values over a process bus to protection and control devices, which exchange fast status messages over the network as well.

The pay-off for asset management is that the condition data of Chapter 21 — dissolved gas, bushing tan δ, partial discharge, temperature — now flows continuously and natively across the same digital backbone, rather than being collected by hand on periodic visits. The substation becomes a source of rich, real-time data about its own health, the raw material for everything that follows.

Data alone is not insight. The streams from a digital substation are turned into decisions by analytics: simple trending to watch a quantity creep toward a limit, anomaly detection to flag a departure from normal behaviour, and increasingly machine learning trained on fleets of historical failures to recognise the early signature of an emerging fault. The goal is prognostics and health management (PHM) — not just diagnosing a present fault but predicting a future one and estimating the remaining useful life (RUL) of the asset, so intervention can be scheduled before failure rather than after.

The most ambitious expression of this is the digital twin: a living virtual model of a real asset or substation, continuously updated with its sensor data, that mirrors the real thing's state and can be used to ask "what if?". Run the twin forward under a forecast loading and it predicts the hot-spot temperature and the insulation ageing that loading will cause; push a hypothetical fault through it and it shows the consequence — all without touching the physical asset. The twin fuses the physics of the whole course with live data into a tool for foreseeing, rather than merely observing, an asset's future.

To act across a fleet, the many condition indicators must collapse into something comparable between assets. The asset health index (HI) does this, aggregating the scored diagnostics — DGA, furan, tan δ, partial discharge, oil quality, age — into a single weighted number from healthy to end-of-life. But condition is only half the decision. A health index says how likely an asset is to fail (its probability of failure, PoF); the other half is how much it would matter (its consequence of failure, CoF — the cost, the outage, the safety and environmental impact). The two combine into risk:

The risk matrix is what makes asset management rational. A unit may be quite likely to fail yet sit in a low-risk corner because its failure is cheap and easily covered; another may be unlikely to fail but command urgent attention because its failure would black out a city. Ranking the whole fleet by \(R = \text{PoF}\times\text{CoF}\) tells the owner where the next pound of maintenance or replacement budget does the most good — turning the diagnostics of Part 6 and the health index into a defensible, fleet-wide investment plan.

Asset management is one facet of a broader digitalisation that is reshaping the whole power system into a smart grid. Beyond the substation, wide-area monitoring using phasor measurement units (synchronised across the network by GPS) watches the dynamic state of the grid in real time, catching instabilities that local measurements miss. Dynamic line rating lets a line carry more power when weather allows by sensing its real thermal state rather than assuming the worst. The flexible AC and DC devices of Chapter 26 steer power flows actively, integrating volatile renewables and HVDC links. All of this rests on pervasive sensing, fast communication and computation — and so brings a new vulnerability: cybersecurity. A grid that is controlled through its data network can also be attacked through it, so securing the digital infrastructure has become as much a part of high-voltage engineering as securing the insulation.

The road ahead is already visible. Artificial intelligence will increasingly read the flood of condition data, spotting faults earlier and estimating life more accurately than human analysts can. Robots and drones will inspect lines and substations that are dangerous or tedious for people to reach. And sustainability is reshaping the materials themselves: the search for replacements for \(\mathrm{SF_6}\) — an excellent insulating gas but an extraordinarily potent greenhouse gas — and for recyclable, eco-designed insulation, is now a central concern, driven by the same energy transition that is filling the grid with renewables, HVDC and the pulsed-power and PEF technologies of this part.

And so the course closes where it is fitting to close — by looking back. Every chapter, however different its subject, has been a variation on a single theme: the unrelenting contest between electric stress and the strength of an insulating medium. We met that contest first as a field concentrating on a sharp edge; then as the breakdown of gases, liquids and solids when the stress wins; as the generation and measurement of the high voltages that apply the stress; as the testing and diagnostics that judge the insulation's strength; as the overvoltages that threaten it and the coordination and protection that defend it; and finally as the modern materials, the direct-current and pulsed systems, and the smart, data-driven management that carry the whole enterprise into its future. From a single electric field to a continent of intelligently managed apparatus, it is one story — the lifelong duel between stress and strength, and the engineering that keeps strength, just, ahead.

For each item, first identify what it tests — the fleet view, the lifecycle and bathtub curve, the maintenance strategies, the digital substation, analytics, or the health-index and risk picture — then apply it. Difficulty rises down the list.