图神经网络用于电力和水力数据融合以增强抽水蓄能水电短期预测
Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricity
AI总结：
针对抽水蓄能电站动态状态预测，提出谱时图神经网络，融合电气与水力子系统数据，提升预测精度与泛化能力。
摘要：
抽水蓄能水电站（PSH）积极参与电网功率频率控制，因此经常在动态条件下运行，导致系统状态快速变化。预测这些动态变化的状态对于理解底层传感器和机器状况至关重要。这种理解有助于检测异常和故障，确保所连接电网的可靠运行，并识别故障和校准错误的传感器。PSH是复杂、高度互联的系统，包含电气和水力子系统，每个子系统都有各自的基础网络，可分别表示为图。为了利用这种关系归纳偏置，图神经网络（GNNs）已被分别应用于各个子系统的状态预测任务，但没有考虑它们之间的相互依赖性。然而，在PSH中，这些子系统依赖于相同的控制输入，使得它们的运行高度相互依赖和互联。因此，水力传感器和电气传感器数据应在PSH子系统之间进行融合，以提高状态预测的准确性。这种方法在GNN文献中尚未被探索，因为许多可用的PSH图仅限于各自的子系统边界，这使得该方法无法直接应用。在这项工作中，我们引入了谱时图神经网络的应用，该网络利用自注意力机制同时捕获和学习有意义的子系统相互依赖性以及电气和水力传感器中观察到的动态模式。我们的方法通过在一个统一的、系统范围的图上运行，直接从数据中学习，有效地融合了PSH子系统的数据。这种方法显著提高了状态预测性能并增强了泛化能力。
英文摘要：
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.
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