Digitalisation will greatly influence the plant and product lifecycle, and the related services value chain. Digitisation and consequently digitalisation of enterprises is impacting both the technical and business environments of organisations and enabling them to obtain tangible benefits. Companies now have the digital plant data, and can further use it to digitalise and automate manufacturing process and systems within organisations. From the perspective of the power industry, the benefits appear both comprehensive and viable. Digital transformation is happening at multiple levels – asset level, operations level, aftermarket services, and business transformation.
Improving asset utilisation
For power producers, digitalisation is facilitating improved asset utilisation right from field devices to the fleet level. It, therefore, affects the smallest production asset up to the largest, fixed or stationary. While there are numerous opportunities for improvement, when it comes to improving Asset Performance Management (APM) in asset-intensive industries such as power generation, it’s not always easy for owner-operators to identify the investment opportunities that will deliver the best ROI. The business case for a new concept or technology implementation (related to asset performance) would depend on the expected outcomes, such as improved efficiency, reliability, asset utilisation, asset longevity, cost control, and safety, to name a few. So, the next question is, ‘In what areas of power plant could the application of the latest digital and data technologies help to improve APM and ensure a clear return on investment?’
Before we proceed, it is important to consider the health of an asset with respect to the maintenance strategy and operations requirements. The availability and reliability of an asset cannot be separated from its performance/efficiency. When monitored together using the right tools and methods, users can ensure an optimal asset performance management. The use of machine/asset data and advanced analytics will go a long way in achieving these objectives.
Asset Performance Management (APM)
By transforming data into actionable intelligence to predict and prevent operational issues, reduce downtime and boost the overall performance and life of assets. APM would further include the following aspects—machine and equipment health monitoring, reliability monitoring, and maintenance optimisation.
Machine and equipment health monitoring: It would be aimed at increasing the value of existing power generation assets with historical forensic analysis, anomaly detection, asset diagnostics, and condition monitoring.
Reliability monitoring: This could be used to foresee potential operational performance issues across the entire plant with predictive data analysis, case management and diagnostic support.
Maintenance optimisation: It would help increase fleet reliability and utilisation with personalised, financially beneficial recommendations that prioritise maintenance activities based on risk tolerance and asset criticality.
To be able to clearly understand the dependencies/asset relationships and the impact on downstream activities, it is important to have a single dashboard view based on time series data. This would reduce a lot of time and effort for the plant personnel. Self-service dashboards could provide a view on key performance indicators, asset relationships, work orders as well as providing other crucial data analysis.
Digital thread across the asset lifecycle
To start with, it is important to have a clear digital roadmap. This comprises of smart sensors (prices for which are getting lower) and data acquisitions systems followed by a centralised data repository and historians, standardised database that can be accessed easily and consistently by users, secure transmission channels, and aggregated visualisation of KPIs up to the fleet level.
Digitalisation would also entail centralisation of asset information, which would then facilitate reliable and seamless information sharing and collaboration between stakeholders. This way, contextual information and predictive analytics can be leveraged to enhance power plant fleet management; one way of doing this is by monitoring the fleet from a central location. In future, production plants could be operated and maintained in a unified way, as a fleet, rather than separate production islands. This would enable power producers to respond quickly to market demands. This would also open opportunities for the OEMs, as they can add more value to their offerings for end users.
Activities such as remote monitoring including condition monitoring and performance monitoring, outage management, and heat rate improvements (heat energy input per unit of electrical energy output) can be outsourced in the form of connected services (accessing real-time sensor-based data from customer assets), as they offer good opportunities for improving asset as well as power plant performance.
When it comes to the relative benefits of digitalising old plants compared to new plants, many in the industry feel that digitalisation could provide more value to the existing plants, as the need to optimise processes, asset maintenance/performance, and operations is more acute in existing plants than in greenfield units. For this, a multi-pronged approach could be taken. One approach is to use using the existing and historical data gathered over the operating life of the assets, units, and power plants to provide high-value insights. Even technologies such as virtual sensors (analysing sensor readings using mathematical models), and coalescing data from multiple sources will bring more value from existing data.
The second approach is to retrofit the plant with new sensors to capture vital asset and operations data. Wireless sensor networking technology such as WirelessHART can be used to add new sensors at low cost and low risk for measuring parameters that were not monitored before. These sensors form the basis for condition and performance monitoring of assets, such as motor or pump health monitoring, that provides significant value to customers and could ensure quicker ROI.
Plant instrumentation is now getting more intelligent and is utilising different communication protocols such as fieldbus, HART and Profibus, to name a few. Analysis of data from these smart devices could provide crucial inputs to operations. Operational analytics can focus on a plant’s key performance indicators (KPI), which may vary depending on a plant’s requirements and circumstances. For instance, a utility would want to reduce its fuel costs by centrally monitoring the performance of its assets. Monitoring the KPIs would ensure various benefits: from leveraging unused or overlooked resources, optimising asset utilisation, reducing production costs to lowering emissions.
Going the digital way…
Understanding and capturing the operational aspects of production fleets would help in assessing the performance and condition of assets, both in existing and new plants. For new projects, it is best to have a digital strategy as part of the design package. With the ‘digital thread’ approach to track the product lifecycle from design to decommissioning, insights on operations, maintenance, and services from other similar units or projects can be used to make smart operational decisions. End users will benefit as the centralised fleet knowledge can be made available quickly to experts, and OEMs can refine the design process to adapt to new technical challenges. Both the changes in machine design and the new related behaviour reflected in the real process, should be adapted from one to the other continuously. This will be one of the key areas of collaboration between end customers and suppliers. Moreover, advanced software and analytics connecting machines, people, processes, and products is ushering a new era of data-driven digital enterprises.
The article is based on the panel discussion moderated by ARC Advisory Group, during NTPC’s Global Energy Technology Summit.