Written by Jan Weustink, Stefan Niebler, Tarun Dhall and Andy Sleeman

09:00 AM: Felix grabs a coffee in his office at home. He is the maintenance engineer for a fleet of dozens of “Power to X plants”, which generate e.g. hydrogen from solar/wind energy offshore and a few more localized Combined Cycle power plants. These plants are distributed all around the world, but Felix knows their setup and behavior by heart, from working with their “Digital Twins”. In another part of the world, Siemens Mindsphere hosts a virtual copy of each site and each power plant, each environment. It accurately simulates the site conditions and power plant behavior. Each virtual power plant is being continuously updated and optimized using data gathered from the real power plant. A “Digital Twin” continuously simulates the power plant and predicts the best settings for the control algorithms, automatically adjusting these in simulation of the actual control software which is also running in the cloud services.

09:15 AM: Felix had a good rest last night, without any disturbances and now the first site analysis reports pop up at his tablet. The Omnivise suite provides him with prioritized reports classified as “low priority” and “medium priority”, along with all the relevant data, such as generated load, environmental data, forecast for the day, but also with the findings of the previous days. The image analysis system detected a deviation in the temperature profile of a steam pipe insulation and also an increased temperature at one gearbox. These images, both full vision and thermal scans, plus sound files were recorded by flying drones and robots. These autonomous drones and robots frequently navigate through the power plant, on predefined routes, looking for anything out of the ordinary. Right after being streamed to the Mindsphere, the images are analyzed, together with the data collected from the site control system. In this instance, the site solution is a D3000 and part of a wider DCS. Felix is now free to work on the problems remotely. He sends a request to visit the cause of the first report to the site drone. Felix puts on his VR-goggles and is then able to have a look at the findings himself, with live images. The laser scanners of the drone provide photo enhanced 3D images, so Felix can even change the angle of sight without moving the drone. He sees that the insulation needs repairing, so dispatches a robot to the scene. He can now carry out the repair work by remotely controlling the robot. Felix can perform mechanical fixes, use manual tools, operate hand valves, all using his own hands, because the arms and hands of the robot are controlled by his joysticks or augmented gloves. These interfaces have been used already for many years by astronauts and surgeons. In rare cases, Felix orders spare parts and once they arrive, he sends local engineers to site. These local engineers are equipped with augmented reality goggles, so Felix can easily guide them to fix the issues. Felix can even assist them using one of the drones or robots. He can hold the torch, offer support with heavy tools and give a high five if the work was done well.

10:15 AM: One motor shows higher temperatures than expected and some vibration, which cannot be clearly identified by the Mindsphere analysis software. In more complicated cases, Felix can do a detailed device analysis. This motor was damaged thousands of times in the Digital Twin simulation and each time the sensor signals were recorded to a database. This data has been used for the damage predictions and FMEA analysis, but Felix wants to see more. He picks this motor in his augmented reality application and puts it on his desk. There he can see the simulation model fed with live data from the real plant. He can disassemble the motor, while the shaft is moving and see the temperature profile, which is extrapolated from the measurement data. Superman’s X-ray vision has become a reality!

10:43 AM: The fleet management system forecasts cloudy weather and medium winds in the evening in the districts around one of the combined cycle power plants, so it automatically triggers the prewarming process of the power plant. With the Omnivise portfolio, the P3000 plant optimization suite improved the load operation and the load range and with Digital Lifecycle Services (DLS), the alarms and manual interactions are further reduced. The whole power plant fleet is operated from the headquarters and no operators have been needed at site for many years. Local human interaction is only required for safety relevant tasks. The fleet monitoring system calculates which power plant should be started, which one should be in hot or in cold standby and triggers the required operation mode directly. Classical DCS systems have become obsolete and only a single server at site controls and sends commands to the EDGE-Devices, via ProfiNet. EDGE-Devices are smart devices like sensors or drives, which can run more software like control loops and they can connect directly and operate mainly independently from DCS systems. All subloop controls are operated by the EDGE-devices themselves, with only superior step sequences or unit control tasks being carried out by this server.

Since no local control room is required anymore, the application server is hosted in the cloud, which enables an easy worldwide plant operation. This makes it easy for Felix to roll out new features or updates to several sites in one operation.
Since Blockchain transactions has become more mature, the plants are operated and billed in real time and several power plant owners do not operate the power plants by themselves anymore. They pay SIEMENS for the operation of the plant and for all maintenance. The I&C and the DCS systems are no longer purchased by customers. All services are leased, and payments only occur when the load requests are fulfilled successfully. The power plant owner buys a basic package and the various features and optional enhanced services can be added on top, when they are required. Whenever power plant startups or load ramps are successful, payments are automatically collected. When the plant is not in operation, our customers will not have running costs, only standby costs.

With the lean DCS and DCS in the cloud approach, which is accompanied by the Omnivise Portfolio and Mindsphere, this service solution can also be applied to a Non-SIEMENS Fleet and it can be tailored to the needs of our customers to optimize their assets.

01:35 PM: Felix receives a notification on his watch from the machine learning AI, Siemens Autonomous Machine Learning (SAML), that there is an enhanced algorithm review required and to see his tablet for more information. SAML has been monitoring the start characteristics of multiple power plants and found that the hydraulic systems are far less efficient than the simulation models have predicted. SAML is suggesting some minor changes to the Start Sequence that will shorten start up time and save energy. The Start Sequence is at a security level which cannot be automatically modified by the AI system. SAML has raised a low priority request for Felix to simulate, and then live test, the new enhancements. Felix knows that he must carry out the live tests when demand on the system is at its lowest, so picks up his tablet and reviews the message from SAML. A suitable time slot for carrying out the tests at 4:00pm this evening has already been suggested by the Load Prediction AI. Felix can relax so decides it’s time for a cup of tea.

More about these topics and much more can be found here:
GP SCD GTE C&I: Mid & Long Term Innovation, Simulators and Digital Twins