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Opportunities for Data Analytics in Power Generation

Data science is defined as machine learning. One use is in Data Analytics which has four steps 1. Descriptive analytics ( what happened) 2. Diagnotic analytics (why did it happen) 3. Predictive Analytics ( when will it happen) 4. Prescriptive Analytics ( what should I do about it). Prognostics ( a combination of diagnostic and predictive analytics can be used to predict the time at which a system or a component will no longer perform its intended function . This is a challenge since most failures are random and not age relaed. It is important because of the cost of failure. Bearing Failures in Rotating Equipment cause $240B in downtime and repair costs. A combination of physics and data based models can be beneficially used for calculations of remaining useful life ( RUL) . The conclusions of the presentation are 1.Data analytics can help operators manage and improve reliability of generation assets. 2 Prognostics can be used to determine a RUL with a time element and confidence level. 3 Operators can use the RUL to actively manage maintenance schedule and operating conditions in order to maintain reliability. Data Analytics is a also applicable to efficiency ( fuel cost, capacity output) , emissions ( compliance and optimization ) , flexibility ( operational and economic).

Revision Date:  7/18/2016

Tags:  XMPL Energy, Pumps, Software, Valves, Optimization, Emissions Reduction, Combustion Optimization, Gas Turbine Protection

Improving Power Plant Efficiency and Power Generation Webinar - Hot Topic Hour August 8, 2013

 

Revision Date:  8/8/2013

Tags:  221112 - Fossil Fuel 化石燃料, Storm Technologies, Great River Energy, PROMECON USA, Zolo Technologies, Low Nox Burner, In Furnace, Back Pass, Manual, In Situ, Extractive, Coal Dryer, Air Flow, Air Inleakage, Air/fuel ratio, Fuel Fineness, Fuel Blending, Heat Exchange, Fuel Blending, Combustion Optimization, Coal Beneficiation, Heat Exchange, Air Pollution Control