Operation Management Optimization of Hydropower Stations Based on Big Data Technology: A Case Study of X Hydropower Station Group

Abstract

In the operation management of hydropower stations, uneven scheduling often leads to issues such as resource wastage and unequal energy distribution; big data technology offers a new approach for optimizing the scheduling of hydropower stations in the information era. Taking the X Hydropower Station Group as a case study, this paper explores data acquisition, cleaning, clustering analysis, and the formulation of seasonal scheduling strategies to enhance the efficient utilization of hydropower resources and ensure the stable operation of the power grid. K-means clustering analysis is applied to explore typical output curves of cascaded hydropower stations, revealing the relationships between water levels, inflow rates, and load rates. Furthermore, a grey prediction model is developed to forecast future load rates, providing robust data support for short-term operational scheduling plans. The research not only improves monitoring and decision-support capabilities but also enhances the adaptability and response speed to seasonal changes, ensuring the stability and reliability of the power supply.

Keywords

Hydropower station operation management / Big data / Clustering analysis / Seasonal scheduling / Grey prediction model / Intelligent maintenance

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the authors used ERNIE Bot 4.5 Turbo in order to enhance English expression and Nano Banana Pro for figure beautification. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

Author Contributions

Conceptualization, Q.J.; methodology, Q.J.; software, Q.J.; validation, Q.J.; formal analysis, Q.J.; investigation, Q.J.; resources, Q.J.; data curation, Q.J.; writing—original draft preparation, D.D.; writing—review and editing, D.D.; visualization, D.D.; supervision, D.D.; project administration, Q.J.; funding acquisition, D.D. All authors have read and agreed to the published version of the manuscript.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to Sichuan Huaneng Baoxing River Hydropower Co., Ltd.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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