The power sector drives nearly every aspect of economic activity, and performs a critical function in nearly every aspect of our lives. Energy demands have seen an upward trend over the last decade, and electricity demands grew by a massive 1 200 TWh over 2021 alone. However, the power sector also remains a key contributor to GHG emissions, as 61% of electricity generation still comes from high-emission technologies – which needs to be reduced to 30% by 2030 to keep the sector on track to NetZero targets.

It is therefore not surprising that the power industry is under the regulatory spotlight. Regulating GHG emissions requires new investments. However, the power industry doesn’t rank high in terms of profit margins, and some companies have continued to log sub-12% margins over the last decade. To comply with emerging regulations a few have been forced to resort to offsetting strategies, which are becoming expensive amidst growing cost of carbon credits.

Because maintenance and operations constitute a significant chunk of power plant operating expenses, optimizing them is turning into a key priority. Take a look at some of the challenges of power plant operations and maintenance, and how they are being tackled with data analytics and artificial intelligence technologies today.

Power plant maintenance and operations: key challenges

Each type of power plant has its own unique set of maintenance and operations challenges. Here are a few of them.

  • Reducing reliability levels of aging power plant fleet

Machinery breakdown remains the top contributor of utility loss by both amount (42%) and frequency (58%) in power plants. Because more power plants have aging components by now, their reliability levels continue to reduce, causing unforeseen outages. 

  • Resource management in nuclear power plants

Nuclear power plants are always under a high burden of maintenance activities, which leads to the accumulation of a backlog of non-critical maintenance tasks. Because each system is interconnected with the rest and often at varying distances, co-ordinating maintenance schedules, tasks, and resources can prove challenging for operators.

  • Parts management in gas turbine power plants

The thermal efficiency of a gas turbine varies directly with its operating temperature. However, parts that operate under high temperature, like the combustor and rotor stator, typically have a shorter lifespan than the turbine itself. Paper-based tracking of these parts, and lack of insight into changes in operating conditions can lead to unprecedented failures, leading to downtime. 

  • Outage due to manual inspections of critical parts

Manual inspection of critical parts remains a status quo in the power industry. However, some parts like water wall tubes in boilers are often found in narrow spaces where manual inspection proves challenging. Yet, failures associated with water wall leakage account for 60% of outage hours. To achieve reliability, plant operators often conduct premature maintenance which contributes to higher costs.

  • Optimizing usage of fuel in multi-fuel power plants

Multi-fuel power plants provide better reliability with their ability to adapt to multiple types of fuels based on availability. However, to achieve efficiency, it is crucial to optimize multiple factors like accolation of fuel, steam power smoothing, and load allocation. Conducting these tasks manually based on intuition leads to suboptimal operation.

  • High lead times of critical parts leading to inflated inventories

With the lack of foresight into the remaining life of critical parts, power plant operators are often forced to maintain an inflated inventory because some items may have a long lead time. This chokes funds in the inventory, and reduces financial agility.

Most of these challenges are now being tackled by leading power plants with the use of data and analytics technologies. See how in the next section.

Modernizing power plant maintenance and operations with data analytics

Neural networks for operations optimization of single and multi-fuel power plants

Power plant operation requires operators to track hundreds of variables like fan speeds, temperatures, pressure levels, and oxygen levels. Use of multi-layered neural networks has saved $60mn annually for thermal power plant operators, and least square support vector machine (LS-SVMs) for operation control has enabled up to 1.8% increase in generator power for coal power-plants.

Power plant maintenance planning with prescriptive analytics systems

Power plant maintenance operations are resource-intensive. Prescriptive maintenance systems can help plant operators optimize maintenance periods for in-service maintenance, outage maintenance, refurbishment maintenance, and emergent maintenance, while taking inventory, resource, and supply chain considerations like lead times into account. They are able to model complex situations, predict task delays, and examine their impact on plant operations months in advance.

Mitigating backlogs and reducing dependence on manual resources with sensor-based inspections

Power plants require frequent manual inspections in which highly trained resources look for corrosion, cracking, abnormal wear, leaks, coating wear, nozzle deflections, and recording readings. Most of these tasks can now be performed by installing sensors and cameras that take snapshots and record readings at predefined intervals. These are then analyzed by artificial intelligence and computer vision models to notify plant operators of any deviations – thereby reducing operational burden and cognitive load on plant operators.

Advanced analytics techniques for complex plant maintenance tasks

In some plant operations scenarios, advanced analytics techniques may be required. While they may be costly to implement upfront, they pay off within months and generate significant RoI over time. Some examples include infrared analysis for detecting bearing temperature or inferring insulation condition, electronic signature analysis for predicting faults in electric components or motors, and vibration analysis to predict faults in moving parts like fuel conveyor belts or failure modes of gas turbines.

What next?

In the past, power plant maintenance and operations have been conducted by large teams of technicians and field engineers. Today, however, power companies need to adapt their operating model to stay relevant amidst changing industry dynamics. With growing use of rooftop solar plants, demand for renewables, and increasing regulatory pressure to cut emissions, demand and supply dynamics are undergoing rapid shifts. In addition, geopolitical factors have also contributed to fluctuations in fuel prices.

Power plant operators must unleash an operating model transformation with the use of data analytics technologies like AI and ML to cut operating costs and increase uptime, reliability and efficiency of their power plants. 

Start your journey by grabbing the lowest hanging fruit – slash your maintenance costs and eliminate unplanned downtime with UptimeAI. Contact us today.