Extracting the Operating Characteristics of Hydropower Plants Using a Clustering-based Efficient Methodology

Garbea, Razvan; Scarlatache, Florina; Grigoraș, Gheorghe; Neagu, Bogdan-Constantin

doi:10.1109/mps52805.2021.9492606

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Cited by 4 publications

References 6 publications

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Digital Twin-based Decision-Making Methodology for the Optimal Operation of Microgrids

Scarlatache,

Grigoraş,

Scarlatache

et al. 2023

2023 10th International Conference on Modern Power Systems (MPS)

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Digital Twin-based Decision-Making Methodology for the Optimal Operation of Microgrids

Scarlatache,

Grigoraş,

Scarlatache

et al. 2023

2023 10th International Conference on Modern Power Systems (MPS)

View full text Add to dashboard Cite

Autoencoder-based Fault Diagnosis for Hydropower Plants

Hajimohammadali,

Fontana,

Tucci

et al. 2023

2023 IEEE Belgrade PowerTech

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Toward Smart SCADA Systems in the Hydropower Plants through Integrating Data Mining-Based Knowledge Discovery Modules

Grigoras,

Gârbea,

Neagu

2024

Applied Sciences

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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 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 this 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.

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Extracting the Operating Characteristics of Hydropower Plants Using a Clustering-based Efficient Methodology

Cited by 4 publications

References 6 publications

Digital Twin-based Decision-Making Methodology for the Optimal Operation of Microgrids

Digital Twin-based Decision-Making Methodology for the Optimal Operation of Microgrids

Autoencoder-based Fault Diagnosis for Hydropower Plants

Toward Smart SCADA Systems in the Hydropower Plants through Integrating Data Mining-Based Knowledge Discovery Modules

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