智能电网状态评估中基于多维协方差检验的海量流PMU数据建模与分析
Massive Streaming PMU Data Modeling and Analytics in Smart Grid State Evaluation Based on Multiple High-Dimensional Covariance Tests
摘要：
相量测量单元（PMU）的大量部署、数据量的增加以及电力市场的开放，都要求对大规模电力系统进行鲁棒状态评估。由于求解高维潮流方程的复杂性，实现基于模型的估计器是不切实际的。在本文中，我们首先表示为大随机矩阵流的大规模流PMU数据。通过利用大量流PMU数据的协方差阵的变化，提出了一种基于多个高维协方差阵测试的电力状态评估算法。建议的检验统计量是灵活的和非参数的，它假设没有特定的参数分布或维度结构的PMU数据。此外，它可以联合揭示系统事件的相对大小、持续时间和位置。为了便于实际应用，我们通过主成分计算和冗余计算消除，将所提出的检验统计量的计算量从$O(\varepsilon n_g^4)$减少到$O(ηn_g^2)$。利用IEEE 30，118节点系统，波兰2383节点系统，和一个真正的34-PMU系统的新算法进行了数值评估。实例分析验证了所提出的状态评价指标的优越性。
英文摘要：
The analogous deployment of phase measurement units (PMUs), the increase of data quantum and the deregulation of energy market, all call for the robust state evaluation in large scale power systems. Implementing model based estimators is impractical because of the complexity scale of solving the high dimension power flow equations. In this paper, we first represent massive streaming PMU data as big random matrix flow. By exploiting the variations in the covariance matrix of the massive streaming PMU data, a novel power state evaluation algorithm is then developed based on the multiple high dimensional covariance matrix tests. The proposed test statistic is flexible and nonparametric, which assumes no specific parameter distribution or dimension structure for the PMU data. Besides, it can jointly reveal the relative magnitude, duration and location of a system event. For the sake of practical application, we reduce the computation of the proposed test statistic from $O(\varepsilon n_g^4)$ to $O(ηn_g^2)$ by principal component calculation and redundant computation elimination. The novel algorithm is numerically evaluated utilizing the IEEE 30-, 118-bus system, a Polish 2383-bus system, and a real 34-PMU system. The case studies illustrate and verify the superiority of proposed state evaluation indicator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
