arXiv:2504.03649v1 Announce Type: new
Abstract: The French company EDF uses supervisory control and data acquisition systems in conjunction with a data management platform to monitor hydropower plant, allowing engineers and technicians to analyse the time-series collected. Depending on the strategic importance of the monitored hydropower plant, the number of time-series collected can vary greatly making it difficult to generate valuable information from the extracted data. In an attempt to provide an answer to this particular problem, a condition detection and diagnosis method combining clustering algorithms and autoencoder neural networks for pattern recognition has been developed and is presented in this paper. First, a dimension reduction algorithm is used to create a 2-or 3-dimensional projection that allows the users to identify unsuspected relationships between datapoints. Then, a collection of clustering algorithms regroups the datapoints into clusters. For each identified cluster, an autoencoder neural network is trained on the corresponding dataset. The aim is to measure the reconstruction error between each autoencoder model and the measured values, thus creating a proximity index for each state discovered during the clustering stage.
Expert Commentary: Monitoring and Analyzing Hydropower Plants with Supervisory Control and Data Acquisition Systems
Hydropower plants are complex systems that require constant monitoring to ensure efficiency and safety. EDF, a French company, has been utilizing supervisory control and data acquisition systems (SCADA) along with a data management platform to collect and analyze time-series data from their hydropower plants. However, the sheer volume of data collected from these plants can pose challenges in extracting valuable insights.
This article presents a novel approach to tackle this challenge by introducing a condition detection and diagnosis method that combines clustering algorithms and autoencoder neural networks for pattern recognition. The methodology comprises of two main steps:
- Dimension reduction: To aid in visualizing and identifying relationships between datapoints, a dimension reduction algorithm is employed. By reducing the data to a 2 or 3-dimensional projection, engineers and technicians can better understand the underlying structure of the data and uncover any unexpected relationships.
- Clustering and autoencoder neural networks: Once the dimension reduction is performed, a collection of clustering algorithms is used to group the datapoints into clusters. For each identified cluster, an autoencoder neural network is trained on the corresponding dataset. The aim is to measure the reconstruction error between each autoencoder model and the measured values, which then serves as a proximity index for each state discovered during the clustering stage.
This approach is inherently multi-disciplinary, combining concepts from data science, machine learning, and engineering. The use of clustering algorithms allows for unsupervised grouping of datapoints, enabling engineers to identify different states or conditions within the hydropower plant. The employment of autoencoder neural networks adds another layer of analysis, as these models can capture intricate patterns and anomalies in the data.
By leveraging this combined methodology, EDF can gain valuable insights into the condition and performance of their hydropower plants. The identified clusters and corresponding proximity indices can aid in proactive maintenance, anomaly detection, and fault diagnosis. It enables engineers and technicians to make data-driven decisions, optimize operational efficiency, and ensure the longevity of their hydropower assets.