With more data-driven applications introduced in wide-area monitoring systems (WAMS), data quality of phasor measurement units (PMUs) becomes one of the fundamental requirements for ensuring reliable WAMS applications. This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation, which can: ① identify bad data under both steady states and contingencies; ② achieve higher accuracy than conventional pre-filtering approaches; ③ reduce iteration burden for linear state estimation; ④ efficiently identify bad data in a parallelizable scheme. The proposed method consists of four key steps: ① preprocessing filter; ② online training of short-term deep neural network; ③ offline training of long-term deep neural network; ④ a decision merger. Through delicate design and comprehensive training, the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information. An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method. Multiple test scenarios are applied, which include steady states, three-phase-to-ground faults with (un)successful auto-reclosing, low-frequency oscillation, and low-frequency oscillation with simultaneous three-phase-to-ground faults. The proposed method demonstrates satisfactory performance during both the training session and the testing session.

中文翻译：

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线性状态估计中不良数据识别的双馈深度学习方法

随着在广域监视系统（WAMS）中引入更多数据驱动的应用程序，相量测量单元（PMU）的数据质量成为确保可靠的WAMS应用程序的基本要求之一。本文提出了一种用于线性状态估计中不良数据识别的双馈深度学习方法，该方法可以：①在稳态和突发情况下识别不良数据；②获得比传统的预过滤方法更高的精度；③减少线性状态估计的迭代负担；④通过可并行化方案有效地识别不良数据。所提出的方法包括四个关键步骤：①预处理过滤器；②短期深度神经网络在线培训；③长期深度神经网络的离线训练；④决定合并。通过精美的设计和全面的培训，该方法可以有效地将不良数据与事件数据区分开，而无需依赖实时拓扑信息。通过DSATools TSAT仿真的IEEE 39总线系统和收集了实际PMU数据的省级电力系统来验证该方法。应用了多种测试方案，其中包括稳态，自动重合闸不成功的三相接地故障，低频振荡以及同时发生三相接地故障的低频振荡。所提出的方法在训练和测试期间均表现出令人满意的性能。通过DSATools TSAT仿真的IEEE 39总线系统和收集了实际PMU数据的省级电力系统来验证该方法。应用了多种测试方案，其中包括稳态，自动重合闸不成功的三相接地故障，低频振荡以及同时发生三相接地故障的低频振荡。所提出的方法在训练和测试期间均表现出令人满意的性能。通过DSATools TSAT仿真的IEEE 39总线系统和收集了实际PMU数据的省级电力系统来验证该方法。应用了多种测试方案，其中包括稳态，自动重合闸不成功的三相接地故障，低频振荡以及同时发生三相接地故障的低频振荡。所提出的方法在训练和测试期间均表现出令人满意的性能。