A framework for constrained static state estimation in unbalanced distribution networks
원문 초록
State estimation plays a key role in the transition from the passive to the active operation of distribution systems, as it allows to monitor these networks and, successively, to perform control actions. However, designing state estimators for distribution systems carries a significant amount of challenges. This is due to the physical complexity of the networks, e.g., phase unbalance, and limited measurements. Furthermore, the features of the distribution system present significant local variations, e.g., voltage level and number and type of customers, which makes it hard to design a "one-size-fits-all" state estimator. The present paper introduces a unifying framework that allows to easily implement and compare diverse unbalanced static state estimation models. This is achieved by formulating state estimation as a general constrained optimization problem. The advantages of this approach are described and supported by numerical illustration on a large set of distribution feeders. The framework is also implemented and made available open-source.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification
Benchmarking Spatiotemporal Reasoning in LLMs and Reasoning Models: Capabilities and Challenges
UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units
Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer
Secure Safety Filter Design for Sampled-data Nonlinear Systems under Sensor Spoofing Attacks
Edge-Enabled VIO with Long-Tracked Features for High-Accuracy Low-Altitude IoT Navigation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.