With the development of industry 4.0 technology, existing information can be harnessed to grow machine-learning solutions, which deliver real value, enhance decision making, increase elasticity, and attract top talent.
FREMONT, CA: As power-generation is shifting to the next normal, employing up-to-date digital and advanced analytics tools has become decisive. Many power firms began their digital transformations with technological solutions like data models that help enhance set points, permit better dispatch decisions, and support maintenance approaches and operating-mode selection. However, forward-thinking businesses have lately started employing visualization tools to manage real-time generation performance and digital control software to relay predictive data to control rooms. Yet the reforms are grounded in noticeably improving plant processes' outcomes and are thus only part of a digitally powered, next-generation power plant.
Next-generation Tools Quick Adoption and its Significant Value
Today power plants are incredibly sensorized with a tremendous amount of information continuously collected and stored. However, according to research, a measly 20 to 30 percent of the data gathered is
used to inform decision-making directly, and that the information collected from sensors can be better augmented.
For instance, one power plant gathered and stored over 30,000 different tags or exclusive types of plant data for over a decade, yet the data went entirely unnoticed by the management. Such statistics is often challenging to infuse, and links between the data set and its economic effect are not always noticeable. Therefore, many operators may perceive such information as a black box, without a knowledge of internal workings, unless they have been involved in the collection process all along.
The next generation of value will be assembled on the informational foundation. Operators can implement an analytics-backed tactic to find unique data predictors of plant performance. Consequently, expanding on the findings with operational insights and first-principles engineering can enhance previously unknown value drivers. For example, at the moment, fast machine-learning algorithms can recognize optimal parameters to surge combined-cycle gas turbine plant outputs and heat rates. Cutting-edge pattern-recognition methods can categorize and predict the need for maintenances and proactively recommend intensive preventive maintenance.
Optimization models for unit flexibility, efficiency, and operability can further help operators run their plants to the theoretical limits of performance. The outcomes can compromise their bottom lines by cumulative power availability and dipping fuel consumption to diminish carbon emissions.