The Application of Smart, Connected Power Plant Assets for Enhanced Condition Monitoring and Improving Equipment Reliability

2016 ◽  
Author(s):  
Michael Reid ◽  
Bernie Cook
Keyword(s):  
Power Plant ◽  
Data Storage ◽  
Condition Monitoring ◽  
Asset Management ◽  
Performance Monitoring ◽  
Significant Proportion ◽  
Electric Utility ◽  
Sensor Technology ◽  
Steam Turbines ◽  
Equipment Reliability

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.

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

2017 ◽  
Author(s):  
Michael Reid ◽  
Tony File
Keyword(s):  
Power Plant ◽  
Resource Constraints ◽  
Electric Utility ◽  
Handheld Devices ◽  
Steam Turbines ◽  
Contributing Factors ◽  
Equipment Reliability ◽  
Balance Of Plant ◽  
Aging Infrastructure ◽  
Substantial Investment

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.

The Value of Enterprise Data Integration and Trending Analysis Results for an Effective Equipment Reliability Program

2005 ◽  
Author(s):  
Evan Niemkiewicz ◽  
Walter Walejeski
Keyword(s):  
Data Integration ◽  
Condition Monitoring ◽  
Performance Monitoring ◽  
Power Industry ◽  
Diagnostic Tools ◽  
Web Based ◽  
Diagnostic Parameters ◽  
Equipment Reliability ◽  
Maintenance Personnel ◽  
Diagnostic Technologies

In the power industry, many companies are trying to improve Equipment Reliability by focusing on Performance Monitoring and the application of diagnostic technologies. Software tools are focused on performing detailed technical analysis and trending of diagnostic parameters. The diagnostic data is often stored in archival databases that can track this data for many years. However, the analysis results generated by engineering and maintenance personnel are often stored outside the diagnostic tools in various databases, spreadsheets, and word documents. This valuable information is then difficult to track, trend, and then recall when a similar event occurs in the future. The paper will focus on developed and implemented web-based tools that facilitate tracking, trending, and sharing these analysis results across sites and enterprises. This paper further discusses implementation details and demonstrates the value of incorporating this information seamlessly into existing as well as developing condition monitoring programs.

Specified Maintenance of Steam Turbines in Geothermal Power Plants

2013 ◽  
Author(s):  
Almar Gunnarsson ◽  
Ari Elisson ◽  
Magnus Jonsson ◽  
Runar Unnthorsson
Keyword(s):  
Power Plant ◽  
Condition Monitoring ◽  
Power Plants ◽  
Maintenance Management ◽  
Working Fluid ◽  
Steam Turbines ◽  
Effect Analysis ◽  
Geothermal Power Plant ◽  
Management Procedures ◽  
Geothermal Power

In a geothermal power plant the working fluid used to produce electricity is often wet steam composed of corrosives chemicals. In this situation, more frequent maintenance of the equipment is required. By constructing an overview for maintenance in geothermal power plants and how it can be done with minimum power outages and cost, the geothermal energy can be made more competitive in comparison to other energy resources. This work is constructed as a part of a project, which has the aim of mapping the maintenance management system at the Hellisheiði geothermal power plant in Iceland. The object of the project is to establish Reliability Centered Maintenance (RCM) program for Hellisheiði power plant that can be utilized to establish efficient maintenance management procedures. The focus of this paper is to examine the steam turbines, which have been defined as one of the main subsystems of the power plant at Hellisheiði. A close look will be taken at the maintenance needed for the steam turbines by studying for example which parts break down and how frequently they fail. The local ability of the staff to repair or construct turbine parts on-site is explored. The paper explores how the maintenance and condition monitoring is carried out today and what can be improved in order to reduce cost. The data collected is analyzed using Failure Mode and Effect Analysis (FMEA) in order to get an overview of the system and to help organizing maintenance and condition monitoring of the power plant in the future. Furthermore, the paper presents an overview of currently employed maintenance methods at Hellisheiði power plant, the domestic ability for maintaining and repairing steam turbines and the power plant’s need for repairs. The results show that the need for maintenance of the geothermal steam turbines at Hellisheiði power plant is high and that on-site maintenance and repairs can decrease the cost.

scholarly journals Intelligent Performance Analysis with a Natural Language Interface

2017 ◽  
Vol 25 (3) ◽  
pp. 168-175 ◽  
Author(s):  
Esko K. Juuso
Keyword(s):  
Natural Language ◽  
Condition Monitoring ◽  
Asset Management ◽  
Performance Monitoring ◽  
Expert Knowledge ◽  
Temporal Analysis ◽  
Monitoring Data ◽  
Statistical Process ◽  
Domain Expertise ◽  
Condition Indices

Abstract Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.

Brisa's model for life cycle performance monitoring and decision- making process

2019 ◽  
Author(s):  
Carlos Biscaia de Oliveira
Keyword(s):  
Decision Making ◽  
Asset Management ◽  
Performance Indicators ◽  
Performance Monitoring ◽  
Decision Support Techniques ◽  
Cycle Performance ◽  
Decision Making Process ◽  
Risk Levels ◽  
Asset Portfolio ◽  
Asset Monitoring

<p>The asset management model must allow for visibility across the asset portfolio, enabling a more coherent and informed decision-making process. This topic addresses how the need to improve analytic capabilities and decision support techniques leads to the guidelines of Brisa’s Information System Dashboard, covering asset’s availability and condition indexes (Asset Monitoring), risk levels and relevant costs key performance indicators.</p>

Sign in / Sign up

Export Citation Format

Share Document