Short-term voltage stability of power systems is governed by load dynamics, especially the proportion of small induction motors prevalent in residential air-conditioners. It is essential to efficiently monitor short-term voltage stability in real-time by detailed data analytics on voltage measurements acquired from phasor measurement units (PMUs). It is likewise critical to identify the location of faults resulting in short-term voltage stability issues for effective remedial actions. This paper proposes a time-series deep learning framework using 1D-convolutional neural networks (1D-CNN) for real-time short-term voltage stability assessment (STVSA), which relies on a limited number of phasor measurement units (PMU) voltage samples. A two-stage STVSA application is proposed. The first stage comprises a 1D-CNN-based fast voltage collapse detector. The second stage comprises of 1D-CNN-based regressor to quantify the severity of the short-term voltage stability event. Two novel indices are presented, and their predicted future values are used to quantify the severity of short-term voltage stability events. This work also considers DB-SCAN clustering-based fault detection and physics-based fault localization for effective short-term voltage stability assessment and remedial actions by identifying the most critical PMUs. A bad data pre-processing technique is also included to mitigate the impact of missing data and outliers on short-term voltage stability assessment accuracy. The proposed framework is validated using the standard IEEE test systems and compared against other machine learning models to demonstrate the superiority of 1D-CNN-based time-series deep learning for short-term voltage stability assessment.

中文翻译：

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使用卷积神经网络进行数据驱动的短期电压稳定性评估，考虑数据异常和本地化


电力系统的短期电压稳定性取决于负载动态，尤其是住宅空调中普遍使用的小型感应电机的比例。通过对从相量测量单元 (PMU) 获取的电压测量值进行详细数据分析，有效实时监控短期电压稳定性至关重要。同样重要的是确定导致短期电压稳定性问题的故障位置，以便采取有效的补救措施。本文提出了一种使用一维卷积神经网络（1D-CNN）进行实时短期电压稳定性评估（STVSA）的时间序列深度学习框架，该框架依赖于有限数量的相量测量单元（PMU）电压样本。提出了两阶段 STVSA 应用。第一级包括基于 1D-CNN 的快速电压崩溃检测器。第二阶段包括基于 1D-CNN 的回归器，用于量化短期电压稳定性事件的严重性。提出了两个新颖的指数，它们的预测未来值用于量化短期电压稳定事件的严重性。这项工作还考虑了基于 DB-SCAN 集群的故障检测和基于物理的故障定位，通过识别最关键的 PMU 来进行有效的短期电压稳定性评估和补救措施。还包括不良数据预处理技术，以减轻丢失数据和异常值对短期电压稳定性评估准确性的影响。所提出的框架使用标准 IEEE 测试系统进行验证，并与其他机器学习模型进行比较，以证明基于 1D-CNN 的时间序列深度学习在短期电压稳定性评估方面的优越性。