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首页> 外文期刊>IEEE transactions on industrial informatics >Intelligent Short-Term Voltage Stability Assessment via Spatial Attention Rectified RNN Learning
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Intelligent Short-Term Voltage Stability Assessment via Spatial Attention Rectified RNN Learning

机译：智能短期电压稳定性评估通过空间关注整流RNN学习

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摘要

Focusing on fully learning intrinsic spatial and temporal dependencies from smart grids' complicated transients in a computationally efficient way, this article develops an intelligent machine learning approach for online short-term voltage stability (SVS) assessment. Based on static network information and dynamic system responses, spatial correlations are first comprehensively described from both model-based and data-based viewpoints. Such correlations are further formulated as spatial attention factors, which are leveraged to carefully rectify multiple transient trajectories. Taking the rectified trajectories as inputs, the long short-term memory based deep recurrent neural network (RNN) algorithm is employed to learn sequential SVS features. In this way, the RNN learning procedure is comprehensively guided by both spatial and temporal information, thereby deriving a highly reliable and robust classification model for online SVS assessment. Extensive numerical tests on the Nordic test system and the realistic Guangdong Power Grid in South China illustrate the superior reliability, scalability, and applicability of the proposed approach over existing methods.
机译：本文以计算有效的方式从智能电网复杂瞬态完全学习从智能电网复杂瞬态的内在空间和时间依赖性，开发了用于在线短期电压稳定（SVS）评估的智能机器学习方法。基于静态网络信息和动态系统响应，首先从基于模型和基于数据的视点全面地描述空间相关性。这种相关性进一步配制成空间关注因子，其利用以仔细纠正多个瞬态轨迹。将整流的轨迹作为输入，基于长的短期内存的深度复发性神经网络（RNN）算法用于学习顺序SVS特征。以这种方式，通过空间和时间信息全面地引导RNN学习过程，从而导出用于在线SVS评估的高度可靠和坚固的分类模型。对北欧测试系统的广泛数值测试和华南地区的广东电网逼真地说明了所提出的方法对现有方法的卓越可靠性，可扩展性和适用性。

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