Enhancement of an Equipment Reliability Program With Smart, Connected Power Plant Assets

Author(s):  
Michael Reid ◽  
Tony File

The U.S. electric utility industry continues to undergo dramatic and accelerating transformation. Reliability and resiliency are a key focus. A number of important issues including cyber and physical security challenges, aging infrastructure, and low natural gas prices continue to be of concern. Significant advances in technology, and prolonged regulatory uncertainty are also contributing factors. Electric utilities are now making substantial investment in renewable resources and other technologies needed for renewables integration. This means a reduction in investment in generation assets and an increase in the transmission and distribution grids. There is also increased investment in providing customers with solutions to lower their costs, reduce their carbon footprint and provide control over their energy management. The transformation ultimately demands significant increases in power plant generation operating capabilities and higher levels of equipment reliability while reducing O&M and capital budgets. Achieving higher levels of equipment reliability, with such tightening budget and resource constraints, requires a very disciplined approach to maintenance and an optimized mix of the following maintenance practices: • Preventative (time-based) • Predictive (condition-based) • Reactive (run-to-failure) • Proactive (combination of 1, 2 and 3 + root cause failure analysis) Preventive maintenance (PM) is planned maintenance actions taken to ensure equipment is capable of performing its required functions. PM tasks are generally time-based, depending on the availability of condition monitoring data through a predictive maintenance (PdM) program. Traditionally, PdM is largely performed by maintenance technicians in the field with handheld devices. Resource constraints usually mean that often weeks or even months elapsed between readings on the same piece of equipment. This approach has limitations with data volume, velocity, variety, and veracity. Significant recent advances in sensor and technology associated with the Industrial Internet of Things (IIoT) have enabled the transformation of critical power plant assets such as steam turbines, combustion turbines, generators, and large balance-of-plant equipment into smart, connected power plant assets. These enhanced assets, in conjunction with analysis and visualization software, provide a comprehensive on-line conditioning monitoring solution that enables both a reduction in time-based PM tasks and also automation of PdM tasks. This paper describes an approach by Duke Energy to apply smart, connected power plant assets to greatly enhance its fossil generation equipment reliability program and processes. It will outline the value that is currently being realized and will also examine additional opportunities.

Author(s):  
Michael Reid ◽  
Bernie Cook

The U.S. electric utility industry continues to undergo dramatic change due to a number of key trends and also prolonged uncertainty. These trends include: • Increasing environmental regulations uncertainty • Natural gas supply uncertainty and price • Economic / decoupling of electricity demand growth from GDP • Aging coal and nuclear generation fleet / coal retirements • Aging workforce • Increasing distributed energy resources • Increasing customer expectations The transformation ultimately demands significant increases in power plant generation operating capabilities (e.g. flexibility, operating envelop, ramp rates, turn-down etc.) and higher levels of equipment reliability, while reducing O&M and capital budgets. Achieving higher levels of equipment reliability and flexibility, with such tightening budget and resource constraints, requires a very disciplined approach to maintenance and an optimized mix of the following maintenance practices: • Reactive (run-to-failure) • Preventive (time-based) • Predictive (condition-based) • Proactive (combination of 1, 2 and 3 + root cause failure analysis) Many U.S. electric utilities with fossil generation have adopted and implemented elements of an equipment reliability process consistent with Institute of Nuclear Power Operations (INPO) AP-913. The Electric Power Research Institute has created a guideline modeled from the learnings of AP-913, that consists of six key sub-processes [1]: 1. Scoping and identification of critical components (identifying system and component criticality) 2. Continuing equipment reliability improvement (establishing and continuously improving system and component maintenance bases) 3. Preventive Maintenance (PM) implementation (implementing the PM program effectively) 4. Performance monitoring (monitoring system and component performance) 5. Corrective action 6. Life cycle management (long-term asset management) A significant proportion of Duke Energy’s coal fleet is of an age where individual components have reached their design intent end-of-life thereby creating an increased need for performance monitoring. Until recent times this was largely performed by maintenance technicians with handheld devices. This approach does not allow regular data collection for trending and optimization of maintenance practices across the fleet. Significant and recent advances in sensor technology, microprocessors, data acquisition, data storage, communication technology, and software have enabled the transformation of critical power plant assets such as steam turbines, combustion turbines, generators, transformers, and large balance-of-plant equipment into smart, connected power plant assets. These enhanced assets, in conjunction with visualization software, provide a comprehensive conditioning monitoring solution that continuously acquires sensory data and performs real time analysis to provide information and insight. This advanced condition monitoring capability has been successfully applied to obtain earlier detection of equipment issues and failures and is key to improving overall equipment reliability. This paper describes an approach by Duke Energy to create and apply smart, connected power plant assets to greatly enhance its fossil generation continuous condition monitoring capabilities. It will discuss the value that is currently being realized and also look at future possibilities to apply big data and analytics to enhance information, insight, and actionable intelligence.


2019 ◽  
Vol 113 ◽  
pp. 01005
Author(s):  
Adrien Reveillere ◽  
Martin Longeon ◽  
Iacopo Rossi

System simulation is used in many fields to help design, control or troubleshoot various industrial systems. Within the PUMP-HEAT H2020 project, it is applied to a combined cycles power plant, with innovative layouts that include heat pumps and thermal storage to un-tap combined cycle potential flexibility through low-CAPEX balance of plant innovations. Simcenter Amesim software is used to create dynamic models of all subsystems and their interactions and validate them from real life data for various purpose. Simple models of the Gas Turbine (GT), the Steam loop, the Heat Recovery Steam Generator (HRSG), the Heat Pump and the Thermal Energy storage with Phase Change material are created for Pre-Design and concept validation and then scaled to more precise design. Control software and hardware is validated by interfacing them with detailed models of the virtual plant by Model in the Loop (MiL), Software in the Loop (SiL) and Hardware in the Loop (HiL) technologies. Unforeseen steady state and transient behaviours of the powerplant can be virtually captured, analysed, understood and solved. The purpose of this paper is to introduce the associated methodologies applied in the PUMP-HEAT H2020 project and their respective results.


2021 ◽  
Vol 17 (1) ◽  
pp. 31-47
Author(s):  
Meng Seng Wong ◽  
Stephen Jackson

This paper investigates the nature of expectations and its influence on attitudes towards government electronic services (e-services) in Malaysia. Based on a discussion of findings from in-depth focus group studies with government providers and users of e-services in Malaysia, a conceptual model is devised which explores both the extrinsic and intrinsic forces (in the form of e-government stimuli) influencing the articulation and actualization of stakeholder expectations, which can sway attitudes toward e-services. Key contributing factors (e.g., technological issues, managerial/institutional challenges, resource constraints, user needs), which have inhibited the extent of benefits realization when using e-services are explored. The model also introduces the concept of situational context—the importance of considering e-services in relation to its specific setting or circumstances at play.


1999 ◽  
Author(s):  
Alejandro Zaleta-Aguilar ◽  
Armando Gallegos-Muñoz ◽  
Antonio Valero ◽  
Javier Royo

Abstract This work builds on the previous work on “Exergoeconomics Fuel-Impact” developed by Torres (1991), Valero et. al. (1994), and compares it with respect to the Performance Test Code (PTC’s) actually applied in power plants (ASME/ANSI PTC-6, 1970). With the objective of proposing procedures for PTC’s in power plant’s based on an exergoeconomics point of view. It was necessary to validate the Fuel-Impact Theories, and improve the conceptual expression, in order to make it more applicable to the real conditions in the plant. By mean of a program using simulation and field data, it was possible to validate and compare the procedures. This work has analyzed an example of a 110 MW Power Plant, in which all the exergetic costs have been determined for the steam cycle, and a fuel-impact analysis has been developed for the steam turbines at the design and off-design conditions. The result of the fuel-impact analysis is compared with respect to a classical procedure related in ASME-PTC-6.


2014 ◽  
Vol 891-892 ◽  
pp. 273-277
Author(s):  
Josef Volák ◽  
Zbynek Bunda

This paper describes the fatigue properties of the steel P92. This material is widely used in the energy industry, especially for pipes and pipe bends of supercritical steam turbines. Steel P92 is alloyed with 2 % of tungsten compared to steel P91. This increases a creep strenght of the material. It is possible to reduce wall thickness of the P92 pipe up to about 20%. Fatigue tests were carried out on standard samples and compared with SFT samples (Small Fatigue Test). Using the device SSam 2 made by company Rolce Royce, it is possible to gently remove a samples from energy component without power plant shutdowns. Consider these correlations, i tis possible to determine mechanical properties of the material from small amount of removed experimental material.


Author(s):  
J A Hesketh ◽  
P J Walker

Courses in mechanical engineering usually introduce the theory of axial-flow turbo-machines in terms of simple velocity triangles representing the bulk flow of ideal compressible fluid through the blade passages. A distinctive practical difference, peculiar to steam turbines (ST), is the presence of liquid-water in the flow field. The steam wetness in such turbines is widely known to be doubly-damaging, leading to both loss of efficiency and to mechanical damage (erosion, etc.) of the machine components. Over recent decades, a whole new field of mechanical engineering science has evolved on the subject of wetness in steam turbines, and general practices have been established within the industry. This article reviews the general effects that are of major importance to the turbine designer/engineer, power plant operator, and especially to researchers in this field.


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