Intelligent trend analysis for a solar thermal energy collector field
|Author:||Juuso, Esko K.1|
1Control Engineering, Faculty of Technology, 90014 University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202201249876
|Publish Date:|| 2022-01-24
Solar thermal power plants collect available solar energy in a usable form at a temperature range which is adapted to the irradiation levels and seasonal variations. Solar energy can be collected only when the irradiation is high enough to produce the required temperatures. During the operation, a trade-off of the temperature and the flow is needed to achieve a good level for the collected power. The scaling approach brings temporal analysis to all measurements and features: trend indices are calculated by comparing the averages in the long and short time windows, a weighted sum of the trend index and its derivative detects the trend episodes and severity of the trend is estimated by including also the variable level in the sum. The trend index, trend episodes and especially, the deviation index reveal early evolving changes in the operating conditions, including cloudiness and load disturbances. The solution is highly compact: all variables, features and indices are transformed to the range [-2, 2] and represented in natural language which is important in integrating data-driven solutions with domain expertise. The special situations detected during the test campaigns are explained well.
IOP conference series. Earth and environmental science
2017 6th International Conference on Power Science and Engineering (ICPSE 2017) 2–4 December 2017, St. Petersburg, Russian Federation
6th International Conference on Power Science and Engineering (ICPSE 2017)
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
222 Other engineering and technologies
119 Other natural sciences
Experiments were carried out within the project "Intelligent control and optimisation of solar collection with linguistic equations (ICOSLE)" as a part of the project "Solar Facilities for the European Research Area (SFERA)" supported by the 7th Framework Programme of the EU (SFERA Grant Agreement 228296).
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