  Pumped-storage hydropower plants (PSH) actively participate in grid
power-frequency control and therefore often operate under dynamic conditions,
which results in rapidly varying system states. Predicting these dynamically
changing states is essential for comprehending the underlying sensor and
machine conditions. This understanding aids in detecting anomalies and faults,
ensuring the reliable operation of the connected power grid, and in identifying
faulty and miscalibrated sensors. PSH are complex, highly interconnected
systems encompassing electrical and hydraulic subsystems, each characterized by
their respective underlying networks that can individually be represented as
graphs. To take advantage of this relational inductive bias, graph neural
networks (GNNs) have been separately applied to state forecasting tasks in the
individual subsystems, but without considering their interdependencies. In PSH,
however, these subsystems depend on the same control input, making their
operations highly interdependent and interconnected. Consequently, hydraulic
and electrical sensor data should be fused across PSH subsystems to improve
state forecasting accuracy. This approach has not been explored in GNN
literature yet because many available PSH graphs are limited to their
respective subsystem boundaries, which makes the method unsuitable to be
applied directly. In this work, we introduce the application of
spectral-temporal graph neural networks, which leverage self-attention
mechanisms to concurrently capture and learn meaningful subsystem
interdependencies and the dynamic patterns observed in electric and hydraulic
sensors. Our method effectively fuses data from the PSH's subsystems by
operating on a unified, system-wide graph, learned directly from the data, This
approach leads to demonstrably improved state forecasting performance and
enhanced generalizability.
