Bayesian Deep Learning for Dynamic Power System State Prediction Considering Renewable Energy Uncertainty

作     者：Shiyao Zhang James J.Q.Yu Shiyao Zhang;James J.Q.Yu

作者机构：Academy for Advanced Interdisciplinary StudiesSouthern University of Science and TechnologyShenzhen 518055China Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2022年第10卷第4期

页      面：913-922页

核心收录：

学科分类：0710[理学-生物学] 0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the General Program of Guangdong Basic and Applied Basic Research Foundation(No.2019A1515011032) the Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation(No.2020B121201001)。 

主　　题：Bayesian deep learning data analytics NewtonRaphson power flow renewable energy source system state 

摘      要：Modern power systems are incorporated with distributed energy sources to be environmental-friendly and costeffective.However,due to the uncertainties of the system integrated with renewable energy sources,effective strategies need to be adopted to stabilize the entire power systems.Hence,the system operators need accurate prediction tools to forecast the dynamic system states effectively.In this paper,we propose a Bayesian deep learning approach to predict the dynamic system state in a general power system.First,the input system dataset with multiple system features requires the data pre-processing stage.Second,we obtain the dynamic state matrix of a general power system through the Newton-Raphson power flow model.Third,by incorporating the state matrix with the system features,we propose a Bayesian long short-term memory(BLSTM)network to predict the dynamic system state variables accurately.Simulation results show that the accurate prediction can be achieved at different scales of power systems through the proposed Bayesian deep learning approach.