2023 began with one of the largest near-complete grid failures in SEA, government incentives to reduce energy usage, emission regulations on coal power plants, and a stronger focus on making the shift to renewable energy. Each of these trends will require legacy power industry players to shift their spend into new areas to mitigate unprecedented challenges amidst rising customer expectations.

However, traditional cost centers will not dissipate with these challenges. Aging infrastructure in the non-renewable sector, and rising cost of maintenance in the renewable sector has turned Operations & Maintenance (O&M) costs into a key area of focus for nearly every power utility business. While reductions in operating costs will require significant transformative measures, lowering maintenance costs with the use of predictive analytics will enable businesses to score a quick win on their balance sheets.

But this is not the only reason why predictive maintenance technologies are turning into an implicit industry mandate. Check out what’s propelling this trend, and how predictive maintenance can help reduce the downtime and maintenance costs for power plants.

The need for predictive maintenance in power plants

Here are the key factors that are propelling the adoption of predictive maintenance solutions across all sectors in the power industry:

  1. Maintenance costs: In the power industry, maintenance costs form a significant fraction of the overall operating costs. In some sectors, these costs are as high as $140/kw per year, and represent a key cost reduction opportunity.
  2. Regulatory shifts: As regulatory bodies roll out sustainability directives for non-renewable sectors, investing in sustainability technologies will become a necessity. Predictive maintenance can help create these funds by generating positive ROI in months.
  3. Safety hazards: While power plant failures can lead to risky situations of their own accord, the teams that investigate the cause of failures can also be exposed to unprecedented risks – both of which can be mitigated by predictive maintenance solutions.
  4. Aging infrastructure: In some countries, more than half of the infrastructure used by the power industry is nearly 5 decades old. Aging infrastructure contributes to higher maintenance costs and a higher risk of failures and downtime – which can be lowered by predictive maintenance.

Navigating the maze of failure points with predictive maintenance

Power plants are highly complex systems which deploy a variety of critical machinery such as gas and steam turbines, generators, transformers, and cooling systems. Each of these machines can fail due to a number of causes, whereas each failure contributes to a massive lost production cost. In a reactive maintenance strategy, discovery of the root cause of failure adds further to the downtime.

Predictive technologies can reduce maintenance costs by up to 40%, and positively impact the bottomline across the following key areas:

  • Mitigate cost of lost production during downtime
  • Reduce the cost of root cause discovery (in the form of man hours)
  • Reduce operating expenses by maximizing remaining useful life (RUL)
  • Eliminating excess maintenance and optimizing maintenance schedules

Finally, by getting ahead of the failures, predictive maintenance can help power plant operations teams address pressing challenges like high lead times of complex equipment, and high inventory costs. These problems, along with over-maintenance, are named by the IAEA as the greatest challenges in nuclear power plant operations – but to an extent, are the norm across all sectors in the industry.

Fail-proofing power plants with predictive analytics

Advanced predictive maintenance systems will usually detect the failure of critical equipment well in advance, and point to the probable cause of failure at the same time. Considering that pumping systems alone, which are used in boiler feed, cooling towers, blow-down mechanisms, and heater drain – can fail due to 11 unique reasons, predictive maintenance solutions can pinpoint the root cause beforehand, and enable faster closure of work orders.

Predicting pumping systems failures

Common hydraulic faults in centrifugal pumps, like cavitation, pressure pulsation, and axial thrust can be inferred through vibration, temperature, and pressure data feeds by machine learning algorithms. Similarly, mechanical failures can be detected through stress waves and vibration analysis, and power consumption anomalies can be spotted by observing changes in impeller speed.

Predicting generator failures

One of the most common causes of failure in a generator is insulation wear due to thermal, electrical, and mechanical stress. That’s why stator and rotor windings are monitored with insulation resistance testing, partial discharge testing, and vibration analysis techniques to mitigate a failure with corrective action in time.

Forecasting transformer failures

Transformers are usually subject to issues such as winding faults, bushing problems, and oil degradation, and mechanical damage to transformer windings. These can be detected by monitoring parameters like temperature, load, and insulation resistance. While higher accuracy predictions can be achieved through dissolved gas analysis techniques using ANN, the cost of gas sensors outweighs the marginal improvement.

Predicting turbine degradation

Despite being crucial and lasting assets in hydropower and wind power plants, turbines can suffer structural collapse due to excessive loads or fatigue of blade elements. Gas turbines can also fail due to compressor fouling and combustion problems. However, turbine failure can be predicted nearly a month in advance, by analyzing  temperature anomalies in the gearbox, transformer, and generator, monitoring generator speed and power output, and vibration analysis.

Next steps

New predictive maintenance techniques are continuously being devised in the power industry. These techniques are not only experimenting with easing the costs and complexity factors associated with IoT (Internet of Things) sensor installation, but also with new data analytics techniques to achieve better accuracy in established use-cases.

But in its current state, predictive maintenance has matured adequately to garner widespread adoption. Organizations in the business of power and energy are best positioned to jump the gun on predictive maintenance now. As cash flows become tighter over the course of the year, identifying low-investment, quick ROI pathways to implementing predictive maintenance will be the key.

Across most organizations in the industry, IT expertise is slim – which means that low-touch, proven predictive maintenance platforms like Uptime AI are the best way ahead.

Let us show you how Uptime AI is fail-proofing power plants across the globe with its industry-leading machine learning platform for predictive maintenance. Contact us now.