District heating system is an essential energy infrastructure. Due to the harsh working environment, sensors in district heating substations are prone to faults, which will mislead the control strategies of substations. We propose an online fault detection and diagnosis (FDD) method for sensors in substations. The proposed method establishes a Sequence-to-Sequence Prediction Model based on Long Short-Term Memory network with Spatial-Temporal Attention (abbreviated as STALSTM-seq2seq model) for each target sensor. The model can learn normal and evolving data patterns from historical time series of sensors and predict current sensor observation. Then, an adaptive threshold selection algorithm is used to autonomously determine current anomaly threshold based on the prediction error vector of the model. If the prediction error exceeds the anomaly threshold, the sensor malfunctions. Otherwise, the sensor operates normally. Through a comprehensive case study, we demonstrate that the adaptive threshold selection algorithm can fully utilize the forecasting ability of the STALSTM-seq2seq model, and thereby ensure the accuracy of sensor FDD. Overall, the online sensor FDD method achieves an average score of 0.8473. The research shows that the proposed method provides an effective and robust solution to timely and accurately identify sensor faults for district heating substations.

中文翻译：

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基于长短时记忆网络和自适应阈值选择算法的集中供热变电站传感器在线故障检测与诊断方法

区域供热系统是重要的能源基础设施。由于工作环境恶劣，集中供热变电站的传感器容易出现故障，从而误导变电站的控制策略。我们提出了一种变电站传感器在线故障检测和诊断（FDD）方法。该方法为每个目标传感器建立基于具有时空注意力的长短期记忆网络的序列到序列预测模型（简称STALSTM-seq2seq模型）。该模型可以从传感器的历史时间序列中学习正常和不断变化的数据模式，并预测当前的传感器观测结果。然后，使用自适应阈值选择算法根据模型的预测误差向量自主确定当前异常阈值。如果预测误差超过异常阈值，传感器就会发生故障。否则，传感器正常工作。通过综合案例研究，我们证明自适应阈值选择算法可以充分利用STALSTM-seq2seq模型的预测能力，从而保证传感器FDD的准确性。总体而言，在线传感器FDD方法的平均得分为0.8473。研究表明，该方法为及时、准确地识别区域供热变电站的传感器故障提供了有效且鲁棒的解决方案。