Editorial - VGB PowerTech Journal 4/2016
With digitalisation power plant operators will see ‘service’ in a new light
Digitalisation has transformed our lives. At our fingertips, we now have access to previously unimaginable quantities of information – from vast libraries of books, music, films, and news from across the globe, to the precise location of our child’s school bus or our home’s electricity consumption.
Certainly, digitalisation is impacting our industry too. Today’s power plants generate volumes of data that we are using in remote diagnostic and monitoring services. But bigger, more dramatic changes are coming to market, particularly in the service and operation of these plants.
However, tapping this potential is not just about advanced sensors, big data, and powerful software. To truly unlock the vast potential of digital services, those of us on both sides of the service contract – original equipment manufacturers and owners/operators – will have to evolve our thinking about what constitutes “service.”
“Service” will still include performing on-site routine maintenance, an outage, or an upgrade. But now, with digital services, we are supplementing this traditional approach with an advanced performance-focused service and maintenance model. With insights gained from data analytics, we can offer plant owners more options and less risk, with a flexible approach to managing their assets in a way that works best for them.
Whether it’s a large-scale power plant, a wind farm, or a cogeneration plant for an industrial application, owners/operators are seeking new ways to optimise performance in order to compete in an increasingly challenging environment.
The portfolio of data-driven services at Siemens is called Digital Services for Energy, powered by Sinalytics. These “intelligent knowledge systems” are enabled by advanced algorithms, sophisticated data analytics and pioneering machine-learning to create predictive maintenance models that are continuously fed by as-operated and as-maintained fleet and unit-specific data. We draw on more than a century of technology and domain expertise, and combine that with new powerful data analytics capabilities. Our secure, scalable, industrial-strength analytics platform can integrate huge volumes of complex, machine-generated data and combine it with proprietary field service data and global fleet performance data, as well as data from other diverse sources.
As a result, the insights gained provide operators with more flexibility on when to perform outages, more information to help them optimise revenues and reduce costs in merchant market environments, and hidden opportunities to boost output, efficiency and availability.
Another powerful tool is the ability to render a digital twin of almost any asset, from a single gas turbine to a detailed thermodynamic simulation of an entire combined-cycle power plant.
This helps operators understand how to run their power plants more efficiently by benchmarking against a relevant asset optimization scenario. We can work with them to test how a digital twin reacts to various changes to inputs or conditions in terms of output and efficiency, without the complexity, cost, or impact of testing these changes in the real world. The result is better asset performance, higher plant efficiency, and improved revenue.
By combining specific turbine outage, use, and environmental data, and comparing it against global fleet metrics, fixed outage scopes and schedules can be optimised to fit individual operator needs. The data analytics can allow for more flexibility in terms of the scope of an outage, the interval between outages and the performance guarantees we provide on the equipment.
Through these advanced data analytics, we get a better understanding of machine behaviour under changing operational conditions. At times, this can enable pushing the envelope beyond the standard operational boundaries of the equipment, while maintaining our performance guarantees.
Sharing advanced operational performance insights with plant operators in a merchant market is a good example. As they look at bid prices for the next trading window, they can know what costs would be in terms of wear and tear if, for example, they decide to over fire certain turbines during a high-revenue period.
Data-driven, digital services are collecting, managing, interpreting and analysing data in quantities never before available and in ways not traditionally seen. The insights generated help owners/operators manage their power generation assets, based on condition and business objectives, with the ultimate goal being a positive impact on the bottom line.
Accepting and embracing this data-supported approach to power plant operations and maintenance represents a fundamental shift in thinking about how services will be managed in the future.