The recent trends in energy technology and corresponding shift towards the renewable energy
has introduced new challenges for grid and power plant operators; although power generation
in Gas-Fired power plants is not a re-newable energy technology, it will play a crucial role
in the future.
As the portion of re-newable energy in grids is increasing, power fluctuations
due to solar and wind energy become even greater.The rapid availability of power in Gas-Fired
power plants is an advantageous characteristic that makes them suitable for variety of loads
in power grids to compensate for the power fluctuations and subsequently prevents power outages.
In addition, Gas-Fired power plants have the lowest CO2 emission among all fossil-fuel power plants.
Also, Hydrogen or Methane, generated from green electricity, can be stored in gas network
and, if necessary, converted back into electricity in Gas power plants.
To compensate for the power fluctuations, the Gas power plants need to be operated at partial
load or large variable load ranges. So, there is still great need for optimization with regards
to efficient and low-pollutant operation of the Gas power plant. Scuh optimizations would potentially
lead to efficient and economic operation of the power, reduced maintenance costs, longer machine life
and higher availability of the power plant.
In order to perform these optimizations, operational data from sensors in power plants are
collected and analysed using measurement and monitoring systems based on the desires of the
producers and the operators.
Huge amount of data are measured using the sensors in gas turbines and can potentially be used
for optimization of the Gas power plants. For instatnce, raw data from dynamic vibration
sensors with sampling frequency up to 25.6 kHz. Handling this huge amount of high-frequency
data requires storage, computation and analysis techniques and in TurbO, we try to address
this problem using Big Data.
More information about TurbO can be found in
Targets of TurbO
The aim of TurbO is to investigate the ways in which high-frequency sensor data, stored
decentrally in the power plant, can be used to improve and optimize data evaluations
by a central server:
It is desired to analyze each power plant based on the high-frequency data for large
periods of time. High-frequency data is targeted to to achieve better analysis and storage
techniques have to be developed to prevent data loss due to erasing raw data.
Data aggregation methods are to be improved to minimize the information loss due to the
Comparison of the data in a whole gas turbine fleet 5; this allows previously unseen
analyzes, allowing even more detailed insights into the processes of the gas turbines
Development of a flexible computer system on the power plant side as well as
on the side of the central server.
Development of a performance model to optimize the configurations of the computer systems in
power plants and central server