考虑实际实现挑战的时间同步全系统状态估计
Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges
AI总结：
针对低电压等级缺乏PMU观测导致全系统时间同步状态估计困难的问题，提出基于深度神经网络的状态估计器DeNSE，利用贝叶斯框架融合SCADA与PMU数据实现亚秒级态势感知，并在多场景下验证了其优越性与可扩展性。
摘要：
由于相量测量单元（PMU）配置问题涉及成本效益权衡，更多PMU被配置在较高电压母线上。然而，这导致大电力系统的许多较低电压等级无法被PMU观测到。这种可见性的缺乏使得全系统的时间同步状态估计成为一个具有挑战性的问题。我们提出了一种基于深度神经网络的状态估计器（DeNSE）来克服这一问题。DeNSE采用贝叶斯框架，间接地将从慢时间尺度但分布广泛的监控与数据采集（SCADA）数据得出的推断，与快时间尺度但选择性的PMU数据相结合，以实现对整个系统的亚秒级态势感知。通过考虑拓扑变化、非高斯测量噪声以及不良数据的检测与校正，展示了所提方法的实际效用。使用IEEE 118节点系统获得的结果表明，从技术经济可行性的角度来看，DeNSE优于纯SCADA状态估计器和仅使用PMU的线性状态估计器。最后，通过估计大型且真实的2000节点Synthetic Texas系统的状态，证明了DeNSE的可扩展性。
英文摘要：
As the phasor measurement unit (PMU) placement problem involves a cost-benefit trade-off, more PMUs get placed on the higher voltage buses. However, this causes many of the lower voltage levels of the bulk power system to not be observed by PMUs. This lack of visibility then makes time-synchronized state estimation of the full system a challenging problem. We propose a Deep Neural network-based State Estimator (DeNSE) to overcome this problem. The DeNSE employs a Bayesian framework to indirectly combine inferences drawn from slow timescale but widespread supervisory control and data acquisition (SCADA) data with fast timescale but select PMU data to attain sub-second situational awareness of the entire system. The practical utility of the proposed approach is demonstrated by considering topology changes, non-Gaussian measurement noise, and bad data detection and correction. The results obtained using the IEEE 118-bus system show the superiority of the DeNSE over a purely SCADA state estimator and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus Synthetic Texas system.
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。
