多模态数据融合下的电网防误诊断方法与应用

Power Grid Misoperation Diagnosis Method and Application under MultimodalData Fusion

摘要:

电网的误操作是设备损坏和系统故障的重要原因之一。为提高电网防误操作能力,文章提出了一种基于多模态数据融合的电网防误诊断方法。首先,通过双向门控循环单元(BiGRU)对采集的视频监控、传感器数据和操作日志进行多模态数据融合。并采用多交互注意机制来获取多模态数据的统一表示；其次,通过Bayesian神经网络更新长短期记忆网络(Long Short-Term Memory, LSTM)模型权重,结合Bayesian对不确定性估计和LSTM对时间序列数据处理的优势,将多模态数据的融合特征输入Bayesian-LSTM网络,实现电网防误诊断；最后,实验结果表明,所提方法可以有效提高电网误操作诊断的准确率,进一步提升电网运行的安全性与稳定性。

Abstract:

Misoperation of power grids is one of the main factors leading to equipment damage and system failure incidents. To enhance the grid"s ability to prevent misoperation, this paper proposes a multimodal data fusion-based power grid misoperation diagnosis method. First, video surveillance, sensor data, and operation logs are fused using a bidirectional gated recurrent unit (BiGRU). A multi-interaction attention mechanism is employed to obtain a unified representation of multimodal data. Then, a Bayesian neural network is used to update the weights of long short-term memory (LSTM) network. By combining the uncertainty estimation capability of Bayesian methods with the strength of LSTM in handling time-series data, the fused multimodal features are fed into a Bayesian-LSTM network to achieve grid misoperation diagnosis. Finally, simulation results show that the proposed method effectively improves the accuracy of misoperation diagnosis and further enhances the safety and stability of grid operations.