Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning
The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. The proposed technique consists of distribution learning of stochastic power injection, a Monte Carlo technique for the training of a deep neural network for state estimation, and a Bayesian bad-data detection and filtering algorithm. Structural characteristics of the deep neural networks are investigated. Simulations illustrate the accuracy of Bayesian state estimation for unobservable systems and demonstrate the benefit of employing a deep neural network. Numerical results show the robustness of Bayesian state estimation against modeling and estimation errors and the presence of bad and missing data. Comparing with pseudo-measurement techniques, direct Bayesian state estimation via deep learning neural network outperforms existing benchmarks.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceDeep LearningState EstimationSimilar Papers 제목 키워드 기반
BAYESIAN COMPRESSED DEEP LEARNING FOR STATE ESTIMATION OF UNOBSERVABLE POWER SYSTEMS
In recent years, state-of-the-art Deep Learning (DL)-based modeling has been applied to the problem of state estimation of unobservable electrical distribution systems, with promising results. Unfortunately, the definiti…
Deep LearningState EstimationDecentralized Coordinated State Estimation in Integrated Transmission and Distribution Systems
Current transmission and distribution system states are mostly unobservable to each other, and state estimation is separately conducted in the two systems owing to the differences in network structures and analytical mod…
State EstimationNonparametric Regression with Dyadic Data
This paper studies the identification and estimation of a nonparametric nonseparable dyadic model where the structural function and the distribution of the unobservable random terms are assumed to be unknown. The identif…
regressionState and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks
Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approac…
State EstimationUnobservable Systems: No Problem for Noise Identification
This paper deals with the noise identification of a linear time-varying stochastic dynamic system described by the state-space model. In particular, the stress is laid on the design of the correlation measurement differe…