Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems
Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems
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DOI：
10.1109/tpwrs.2022.3160384
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发表时间：
2021-04
影响因子：
6.6
通讯作者：
Yifan Zhou;Peng Zhang
中科院分区：
文献类型：
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作者：
Yifan Zhou;Peng Zhang
暂态稳定评估（TSA）是当今互联电网弹性运行的基石。本文是量子计算、数据科学和机器学习的融合，可能解决电力系统TSA问题。我们设计了一种量子TSA（QTSA）方法，以实现高效的数据驱动的大容量电力系统的暂态稳定预测，这是第一次尝试用量子计算来解决TSA问题。我们的贡献有三个方面：1）设计了一个高表达性、低深度的量子电路（HELD），以实现准确和抗噪声的TSA; 2）开发了一种量子自然梯度下降算法，以实现高效的HELD训练; 3）对QTSA在各种量子因素下的性能进行了系统的分析。QTSA为支持量子机器学习的电网稳定性分析奠定了基础。它使棘手的TSA简单和毫不费力的希尔伯特空间中，因此提供电力系统运行的稳定性信息。在量子模拟器和真实的量子计算机上的大量实验验证了QTSA的准确性、抗噪性、可扩展性和通用性。
Transient stability assessment (TSA) is a cornerstone for resilient operations of today’s interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. We devise a quantum TSA (QTSA) method to enable efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth quantum circuit (HELD) is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD training and 3) A systematical analysis on QTSA’s performance under various quantum factors is performed. QTSA underpins a foundation of quantum machine learning-enabled power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.