Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA data
<p>This paper introduces a novel model for predicting wind turbine power output in a wind farm at a high temporal resolution of 30 s. The wind farm is represented as a graph, with graph neural networks (GNNs) used to aggregate selected input features from neighboring turbines. A temporal compo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-06-01
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| Series: | Wind Energy Science |
| Online Access: | https://wes.copernicus.org/articles/10/1137/2025/wes-10-1137-2025.pdf |
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| Summary: | <p>This paper introduces a novel model for predicting wind turbine power output in a wind farm at a high temporal resolution of 30 s. The wind farm is represented as a graph, with graph neural networks (GNNs) used to aggregate selected input features from neighboring turbines. A temporal component is introduced by feeding a time series of input features into the graph, processed through a long short-term memory (LSTM) network before being passed to the GNN. Our model is integrated into a normal behavior model (NBM) framework for analyzing power loss events in wind farms. The results show that both the spatial and the spatio-temporal GNN models outperform traditional data-driven power curve methods, achieving reductions in the mean absolute error (MAE) of approximately 22.6 % and 30.3 %, respectively, and in the mean absolute percentage error (MAPE) of around 20.7 % and 30.5 %, respectively. Notably, the spatio-temporal GNN demonstrates superior performance, attributed to its ability to effectively capture both spatial and temporal dynamics. Additionally, the model achieves remarkable agreement with SCADA-derived energy ratios across the full range of wind directions, with a weighted average error of 0.0373, an improvement of approximately 57.4 % compared to the power curve binning method. This advantage is especially pronounced under waked conditions, where traditional methods such as the power curve and multilayer perceptron (MLP) models exhibit significantly higher error rates. Beyond power prediction, we illustrate the model's effectiveness in detecting and analyzing instances of reduced performance and its ability to identify various types of abnormal events beyond what is recorded in standard status logs. Compared to the power curve method, the spatio-temporal GNN reduces the rate of undetected power loss events from 12.6 % to just 0.02 %, demonstrating a substantial improvement in capturing abnormal events.</p> |
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| ISSN: | 2366-7443 2366-7451 |