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Digitalization: Redefining Power Generation Services

Digitalization is transforming the energy industry, providing new insights into power plant operations and uncovering hidden opportunities to boost performance and availability.

By Akshay Patwal, Strategic Business Manager; and Timot Veer, Analytics Platform Lead; Controls and Digitalization, Siemens Power Generation Services

Akshay Patwal
Timot Veer

Digitalization is transforming the energy industry, providing new insights into power plant operations and uncovering hidden opportunities to boost performance and availability. Traditionally, much of the data generated within power plants was limited to remote diagnostic and monitoring services, primarily analyzing data for monitoring turbine operations. By adding new data sources and analytical capabilities, particularly in service and operations of power plants, new avenues are opening to greater understanding of customers’ business needs and the behavior of power assets.

Tremendous new possibilities for data utilization have lately emerged, providing novel business models to customers, tailored to meet their specific needs for operational flexibility and maintenance optimization. A solid approach goes beyond simply collecting the data or providing a standalone software program. It integrates valuable, insight-driven analytics with field service data, global fleet performance data, and other diverse data sources to optimize plant performance. Siemens’ digital services offerings are already yielding concrete results and paving the way for power plant owners and operators to obtain a more informed and judicious decision-making framework to support plant operations. This results in higher availability and improved predictability of asset performance for power plant owners/operators, with the ultimate goal being a positive impact on the bottom line.

Digital Transformation

Digitalization is about more than introducing advanced sensors, collecting big data, and developing powerful software. Digitalization changes how we interact and do business in a holistic way. The most important thing in this transformation is to foster a change in culture and mindset, enabling both the vendor and the customer to evolve in progressive thinking about ‘digital services’.

Siemens’ portfolio of data-driven services is called Digital Services for Energy, powered by Sinalytics. Traditional servicing of plant equipment still exists. However, now effective management of data and context-based analytics, coupled with domain expertise, is supplementing traditional services. The business insights and revelations gained in this effort enable a prescriptive approach to offer customers more operational flexibility and enhanced risk mitigation.

Sinalytics Platform Architecture

To implement an effective data management and analytics framework for digital services, a scalable, industrial-strength analytics platform architecture is necessary to leverage data across a company. The Sinalytics platform architecture is a hybrid infrastructure comprising both cloud-based and on-premise instances to provide value to customers by exchanging and interpreting data, reports, and other information, as well as to support internal process improvements and knowledge engineering. Access to applications, data, and various visualizations or reporting becomes streamlined, and complex information for decision-support is provided at one’s fingertips.

Data transfer to Sinalytics relies on highly secure and scalable data acquisition, and transfer solutions used across multiple industries. The Sinalytics platform architecture is designed to work with structured and unstructured data, including machine-operating data, performance data, field service and repair data, and various third-party data sources. One core feature is the generalized and comprehensive data-model, as well as internal interfaces allowing business users scalable and standardized access to develop and utilize analytical applications.

Data analytics plays an important role in empowering customers by providing the right decision-making tools for the present, and a forward-thinking vision for operations and maintenance scenarios. Advanced statistical algorithms and pioneering machine-learning capabilities are used to generate predictive maintenance and performance optimization business applications. The progression from descriptive to predictive or prescriptive, complemented by extensive domain expertise and fleet operations experience, offers competencies to optimize system operations, improve flexibility on performing maintenance, and positively impactthe bottom line.

Advanced data analytics offers the capability to render a ‘digital twin’ of all assets, laying the foundation for improved operations optimization, higher plant performance, and an improved revenue model at the plant and fleet level.

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