Version 1
: Received: 3 September 2024 / Approved: 3 September 2024 / Online: 3 September 2024 (23:31:41 CEST)
How to cite:
Grigoras, G.; Garbea, R.; Neagu, B.-C. Toward Smart SCADA Systems in the Hydropower Plants through Integrating Data Mining-based Knowledge Discovery Modules. Preprints2024, 2024090264. https://doi.org/10.20944/preprints202409.0264.v1
Grigoras, G.; Garbea, R.; Neagu, B.-C. Toward Smart SCADA Systems in the Hydropower Plants through Integrating Data Mining-based Knowledge Discovery Modules. Preprints 2024, 2024090264. https://doi.org/10.20944/preprints202409.0264.v1
Grigoras, G.; Garbea, R.; Neagu, B.-C. Toward Smart SCADA Systems in the Hydropower Plants through Integrating Data Mining-based Knowledge Discovery Modules. Preprints2024, 2024090264. https://doi.org/10.20944/preprints202409.0264.v1
APA Style
Grigoras, G., Garbea, R., & Neagu, B. C. (2024). Toward Smart SCADA Systems in the Hydropower Plants through Integrating Data Mining-based Knowledge Discovery Modules. Preprints. https://doi.org/10.20944/preprints202409.0264.v1
Chicago/Turabian Style
Grigoras, G., Razvan Garbea and Bogdan-Constantin Neagu. 2024 "Toward Smart SCADA Systems in the Hydropower Plants through Integrating Data Mining-based Knowledge Discovery Modules" Preprints. https://doi.org/10.20944/preprints202409.0264.v1
Abstract
The increasing importance of hydropower generation has led to the development of new smart technologies and the need for reliable and efficient equipment in this field. As long as hydropower power plants are more complex to build up than other power plants, the operation regimes and maintenance activities become essential for the hydropower companies to optimize their performance, such that including the data-driven approaches in the decision-making process represents a challenge. In the paper, a comprehensive and multi-task framework integrated into a Knowledge Discovery module based on Data Mining to support the decisions of the operators from the control rooms and facilitate the transition from the classical to smart Supervisory Control and Data Acquisition (SCADA) system in hydropower plants has been designed, developed, and tested. It integrates tasks related to detecting the outliers through advanced statistical procedures, identifying the operating regimes through the patterns associated with typical operating profiles, and developing strategies for loading the generation units that consider the number of operating hours and minimize the water amount used to satisfy the power required by the system. The proposed framework has been tested using the SCADA system's database of a hydropower plant belonging to the Romanian HydroPower Company. The framework can offer the operators from the control room comparative information for a time horizon longer than one year. The tests demonstrated the utility of a Knowledge Discovery module to ensure the transition toward smart SCADA systems that will help the decision-makers improve the management of the hydropower plants.
Keywords
smart SCADA; Knowledge Discovery; Data Mining; Clustering; Hydropower plants
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
