Data-Driven Priors for Robust PSSE via Gauss-Newton Unrolled Neural Networks
Data-Driven Priors for Robust PSSE via Gauss-Newton Unrolled Neural Networks
复制标题
通过高斯-牛顿展开神经网络实现稳健 PSSE 的数据驱动先验
DOI：
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发表时间：
2022
影响因子：
4.6
通讯作者：
Gang Wang
中科院分区：
文献类型：
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作者：
Qiuling Yang;A. Sadeghi;Gang Wang
可再生能源、弹性负荷和有目的的仪表读数操纵对当今电力系统（PS）的监测和控制提出了挑战。在这种情况下，快速和健壮的状态估计（SE）是及时的，并且对于实时维护系统的全面视图非常重要。传统的PSSE求解方法通常需要最小化非线性和非凸最小二乘代价，例如使用高斯-牛顿方法。然而，这些迭代解对初始化很敏感，并且可能收敛到局部最小值。为了克服这些障碍，本文借鉴了图像去噪的最新进展，提出了一种新的PSSE公式，该公式使用数据驱动的正则化项捕获深度神经网络（DNN）先验。对于由此产生的正则化PSSE目标，首先建立了一个“高斯-牛顿型”交替最小化求解器。为了适应实时监控，随后通过展开所提出的交替最小化求解器构建了一个新的端到端深度神经网络。深度PSSE架构可以通过基于先验的图神经网络（GNN）进一步解释电网拓扑。为了进一步增强基于物理的深度神经网络对不良数据的鲁棒性，提出了一种对抗性深度神经网络训练方法。在IEEE 118总线基准系统上使用真实负载数据进行的数值测试表明，与几种最先进的替代方案相比，该方案的估计性能和鲁棒性有所提高。
Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintaining a comprehensive view of the system in real-time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization and may converge to local minima. To overcome these hurdles, the present paper draws recent advances on image denoising to put forth a novel PSSE formulation with a data-driven regularization term capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a “Gauss-Newton-type” alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives.