The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.
Linear Single- and Three-Phase Voltage Forecasting and Bayesian State Estimation with Limited Sensing
AI Now Institute at New York University（纽约大学AI研究所）；Alliander（阿利安德）；Grid Integration Group at Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室电网集成组）；Energy & Resources Group at UC Berkeley（加州大学伯克利分校能源与资源小组）；Department of Electrical Engineering & Computer Sciences, UC Berkeley（加州大学伯克利分校电气工程和计算机科学系）
Event Detection and Localization in Distribution Grids with Phasor Measurement Units
University of British Columbia（不列颠哥伦比亚大学）；Huazhong University of Science and Technology（华中科技大学）；University of California at Berkeley（加州大学伯克利分校）；California Institute of Technology（加州理工学院）
