The electric power industry is in the midst of fundamental change. Traditionally a highly regulated and monopolistic industry with structured utilities, it is evolving rapidly into a marketplace defined by increasing competition and deregulation. Independent power producers (IPPs) and utility operations executives alike are being challenged to increase Return On Assets (ROA) and asset availability with fewer resources. This task is further complicated by corporate mandates to reduce both capital and human resource expenditures. As the industry evolves, so too must business practices, particularly maintenance activities and the acceptance of unplanned downtime as “normal.”
Power generators of all types should be focused on maximizing efficiencies. Perhaps the most obvious, and costly, drain on operating efficiency is asset unavailability due to a forced outage, sometimes referred to as “unplanned downtime.”
Placing a cost on downtime is never easy. When dealing with widely varying situations and overlapping impacts, it becomes particularly difficult using a “bottom-up” approach. However, it is possible to estimate downtime costs using a simple “top-down” approach based on financial statement ratios and widely accepted practices.
The greatest impact of unplanned downtime is revenue loss. This can occur due to myriad factors, but it is typically the result of demand exceeding supply. The loss of revenue due to downtime is especially egregious, because the cost is not just the loss of the typical 3%–10% profit margin on the lost revenue—it is actually the value of the total revenue lost, less the direct avoided costs of
production (generally materials or energy).
This logic applies to plant downtime. If everything produced can be sold, when a plant breaks down, the only costs that are avoided are the raw inputs that are purchased or inventoried (e.g., energy, materials). The personnel, overhead, financing, and all the other myriad costs of running the business does not stop. The lost revenue, however, cannot be made up, resulting in significant bottom line impact. In this worst-case example, it is easy to calculate the magnitude of this potential cost from financial statement ratios and some simple assumptions.
Since companies recognize, either explicitly or implicitly, that the cost of lost revenue is very high, they develop strategies to deal with the 95% ceiling. An obvious means is to anticipate unplanned downtime and to carry excess capacity. This excess capacity allows revenue to be recouped at a different time, or spare equipment to be substituted.
However, this built-in excess capacity also carries a cost.
A typical strategy to address the 95% barrier is carrying excess production capacity. This may entail building a plant slightly larger than necessary so product can be inventoried to cover unplanned downtime, or carrying spare units to replace those that fail. Both “solutions” have costs: capital to purchase that additional capacity and additional maintenance expenses associated with a larger facility.
While typical manufacturers have excess stock and/or buffers to protect themselves against short-term outages, power generation is one of the few industries where a buffer strategy is not possible. By its very nature, electricity cannot be stockpiled. As a result, asset downtime for a power generation plant has a direct and immediate impact on revenues. With limited ability to generate electricity, a generator will have to absorb lost revenues, along with the high costs of unplanned repair and maintenance. Even worse, if the generator has a contractual commitment to provide power, the costs of purchasing electricity on the spot market may also be incurred, further eroding revenues.
Yesterday’s Solution: “Blind” Preventative Maintenance
Not surprisingly, in an effort to maximize asset availability, power generation companies have embarked upon intensive preventative maintenance programs. However, the downside of excessive preventative maintenance is increased maintenance costs.
The industry desperately needs a solution that not only provides early warning of potential failures, to reduce downtime, maintenance and associated costs, but one that also gives managers a clear picture of generating capacity.
Equipment Condition Monitoring and Predictive Maintenance Solutions
Unlike preventative maintenance practices, which recommend maintenance based on failure statistics for a class of equipment problems over time, an entirely new solution, Predictive Maintenance and Equipment Condition Monitoring (ECM), has emerged to provide condition-based early warning. Predictive Maintenance and Equipment Condition Monitoring provide advanced equipment-specific warning of deteriorating conditions that lead to failure or poor performance. In the power generation industry, these technologies provide early failure warning of assets such as combustion turbines, steam turbines, generators, boiler feed water pumps and cooling water pumps.
Acting upon specific, condition-based warning enables companies to reduce catastrophic failure frequency, mitigate the impact of deteriorating equipment conditions, increase asset availability, decrease maintenance expenditures and recapture what has been called the “Hidden Cost” of downtime.
Insight into the value of early warning requires an understanding that most failures exhibit some deviations symptomatic of incipient failure. Both catastrophic equipment failures and gradual deterioration exhibit abnormal behavior prior to the incident. For example, gradually rising blade path temperatures often precede catastrophic combustion turbine trips due to a transition piece failure. The temperatures will rise over hours and even days until they reach a threshold level that activates a machine trip. This subtle drift typically goes unnoticed when assessing asset health through simple maximum and minimum alarm thresholds or preventative maintenance measures.
Unlike conventional threshold alarming systems, which alert when a set point is passed, the higher sensitivity of these ECM and Predictive Maintenance solutions is able to identify a fault while the equipment is still well within the normal operating range.
As Figures 1 and 2 illustrate, by leveraging the invaluable information currently invisible to existing threshold-based monitoring approaches, early warning systems can deliver $1 million in annual benefit to a 600 MW power plant. This is made possible by: (1) validating sensor data and detecting performance degradation early so planned maintenance can address potential problems, and (2) reducing forced outages, planned downtime, and spending on maintenance and diagnostic services.
In summary, a new class of early warning solutions have emerged allowing power operations executives to increase ROA through early warning of equipment failure. In the increasing competitive power generation business, this capability can provide operators with a significant advantage.
For the first time, companies can greatly reduce unplanned downtime and the potential for catastrophic failure by implementing Predictive Maintenance and ECM technologies. Developing problems can be addressed as part of a coordinated and efficient condition-based maintenance strategy. As a result, power generators can significantly reduce the costs associated with downtime, maintenance, and diagnostic services. With a highly favorable cost/benefit ratio and a payback period typically less than one year, these solutions are a viable solution to maximizing asset availability.
About the Author
Brad True is a general manager for power, process and energy industries with Chicago-based SmartSignal Corporation. Mr. True has extensive background in advanced condition monitoring and predictive maintenance solutions. True has a bachelor of science in mechanical engineering from the Georgia Institute of Technology (btrue@smartsignal.com).
Power generators of all types should be focused on maximizing efficiencies. Perhaps the most obvious, and costly, drain on operating efficiency is asset unavailability due to a forced outage, sometimes referred to as “unplanned downtime.”
Placing a cost on downtime is never easy. When dealing with widely varying situations and overlapping impacts, it becomes particularly difficult using a “bottom-up” approach. However, it is possible to estimate downtime costs using a simple “top-down” approach based on financial statement ratios and widely accepted practices.
The greatest impact of unplanned downtime is revenue loss. This can occur due to myriad factors, but it is typically the result of demand exceeding supply. The loss of revenue due to downtime is especially egregious, because the cost is not just the loss of the typical 3%–10% profit margin on the lost revenue—it is actually the value of the total revenue lost, less the direct avoided costs of
production (generally materials or energy).
This logic applies to plant downtime. If everything produced can be sold, when a plant breaks down, the only costs that are avoided are the raw inputs that are purchased or inventoried (e.g., energy, materials). The personnel, overhead, financing, and all the other myriad costs of running the business does not stop. The lost revenue, however, cannot be made up, resulting in significant bottom line impact. In this worst-case example, it is easy to calculate the magnitude of this potential cost from financial statement ratios and some simple assumptions.
Since companies recognize, either explicitly or implicitly, that the cost of lost revenue is very high, they develop strategies to deal with the 95% ceiling. An obvious means is to anticipate unplanned downtime and to carry excess capacity. This excess capacity allows revenue to be recouped at a different time, or spare equipment to be substituted.
However, this built-in excess capacity also carries a cost.
A typical strategy to address the 95% barrier is carrying excess production capacity. This may entail building a plant slightly larger than necessary so product can be inventoried to cover unplanned downtime, or carrying spare units to replace those that fail. Both “solutions” have costs: capital to purchase that additional capacity and additional maintenance expenses associated with a larger facility.
While typical manufacturers have excess stock and/or buffers to protect themselves against short-term outages, power generation is one of the few industries where a buffer strategy is not possible. By its very nature, electricity cannot be stockpiled. As a result, asset downtime for a power generation plant has a direct and immediate impact on revenues. With limited ability to generate electricity, a generator will have to absorb lost revenues, along with the high costs of unplanned repair and maintenance. Even worse, if the generator has a contractual commitment to provide power, the costs of purchasing electricity on the spot market may also be incurred, further eroding revenues.
| Assumptions | Annual |
| Average monthly capacity | 600MW |
| Capacity factor | 70% |
| Annual electricity sales | $3.7 Million MWH |
| Average planned maintenance outage rate — 2% of calendar hrs | $175 HR/Y |
| Average planned maintenance outage rate — 4% of calendar hrs | $350 HR/Y |
| Annual maintenance spend | $8 Million/YR |
| Annual diagnostic services spend | $200, 000/YR |
| Figure 1: Cost/Benefit Assumptions | |
| Typical 600 MW Plant Benefits | Annual |
| Shift unplanned maintenace to planned (10% of unplanned) | $33.0 HR/Y |
| Margin on hours shifted from unplanned to planned | $15/MWH |
| Reduction in planned maintenance (5% of planned) | 17.5 HR/Y |
| Margin on increased availability hours through reductions in planned maintenance | $10/MWH |
| eCM value generated by reducing planned maintenance outages | $100,000 |
| eCM value generated by shifting from unplanned outages to planned | $300,000 |
| eCM value generated by 5% reduction in annual spend on maintenance | $350,000 |
| eCM value generated by 25% reduction in spend on expert diagnostic services | $50,000 |
| eCM value generated by 1 saved catastrophic failure every 5 years @ $1 million per save | $200,000 |
| Total benefit value generated (before costs of an ECM/Predictive Maintenance solution) $MM | $1,000,000 |
| Figure 2: Value Generated by eCM | |
Yesterday’s Solution: “Blind” Preventative Maintenance
Not surprisingly, in an effort to maximize asset availability, power generation companies have embarked upon intensive preventative maintenance programs. However, the downside of excessive preventative maintenance is increased maintenance costs.
The industry desperately needs a solution that not only provides early warning of potential failures, to reduce downtime, maintenance and associated costs, but one that also gives managers a clear picture of generating capacity.
Equipment Condition Monitoring and Predictive Maintenance Solutions
Unlike preventative maintenance practices, which recommend maintenance based on failure statistics for a class of equipment problems over time, an entirely new solution, Predictive Maintenance and Equipment Condition Monitoring (ECM), has emerged to provide condition-based early warning. Predictive Maintenance and Equipment Condition Monitoring provide advanced equipment-specific warning of deteriorating conditions that lead to failure or poor performance. In the power generation industry, these technologies provide early failure warning of assets such as combustion turbines, steam turbines, generators, boiler feed water pumps and cooling water pumps.
Acting upon specific, condition-based warning enables companies to reduce catastrophic failure frequency, mitigate the impact of deteriorating equipment conditions, increase asset availability, decrease maintenance expenditures and recapture what has been called the “Hidden Cost” of downtime.
Insight into the value of early warning requires an understanding that most failures exhibit some deviations symptomatic of incipient failure. Both catastrophic equipment failures and gradual deterioration exhibit abnormal behavior prior to the incident. For example, gradually rising blade path temperatures often precede catastrophic combustion turbine trips due to a transition piece failure. The temperatures will rise over hours and even days until they reach a threshold level that activates a machine trip. This subtle drift typically goes unnoticed when assessing asset health through simple maximum and minimum alarm thresholds or preventative maintenance measures.
Unlike conventional threshold alarming systems, which alert when a set point is passed, the higher sensitivity of these ECM and Predictive Maintenance solutions is able to identify a fault while the equipment is still well within the normal operating range.
As Figures 1 and 2 illustrate, by leveraging the invaluable information currently invisible to existing threshold-based monitoring approaches, early warning systems can deliver $1 million in annual benefit to a 600 MW power plant. This is made possible by: (1) validating sensor data and detecting performance degradation early so planned maintenance can address potential problems, and (2) reducing forced outages, planned downtime, and spending on maintenance and diagnostic services.
In summary, a new class of early warning solutions have emerged allowing power operations executives to increase ROA through early warning of equipment failure. In the increasing competitive power generation business, this capability can provide operators with a significant advantage.
For the first time, companies can greatly reduce unplanned downtime and the potential for catastrophic failure by implementing Predictive Maintenance and ECM technologies. Developing problems can be addressed as part of a coordinated and efficient condition-based maintenance strategy. As a result, power generators can significantly reduce the costs associated with downtime, maintenance, and diagnostic services. With a highly favorable cost/benefit ratio and a payback period typically less than one year, these solutions are a viable solution to maximizing asset availability.
About the Author
Brad True is a general manager for power, process and energy industries with Chicago-based SmartSignal Corporation. Mr. True has extensive background in advanced condition monitoring and predictive maintenance solutions. True has a bachelor of science in mechanical engineering from the Georgia Institute of Technology (btrue@smartsignal.com).
