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Bayesian Deep Learning for Dynamic Power system state Prediction Considering Renewable Energy Uncertainty

Journal of Modern Power systems and Clean Energy 2022年第4期10卷 913-922页

作者： Shiyao Zhang James J.Q.Yu Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China

Modern power systems are incorporated with distributed energy sources to be environmental-friendly and ***,due to the uncertainties of the system integrated with renewable energy sources,effective strategies need to be adopted to stabilize the entire power ***,the system operators need accurate prediction tools to forecast the dynamic system states *** this paper,we propose a Bayesian deep learning approach to predict the dynamic system state in a general power ***,the input system dataset with multiple system features requires the data pre-processing ***,we obtain the dynamic state matrix of a general power system through the Newton-Raphson power flow ***,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 *** results show that the accurate prediction can be achieved at different scales of power systems through the proposed Bayesian deep learning approach.

关键词： Bayesian deep learning data analytics NewtonRaphson power flow renewable energy source system state

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Chaos Behavior and Estimation of the Unknown Parameters of Stochastic Lattice Gas for Prey-Predator Model with Pair-Approximation

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Applied Mathematics 2016年第15期7卷 1765-1779页

作者： Saba Mohammed Alwan Department of Mathematics and Computer Science Faculty of Science Ibb University Ibb Yemen

In this paper, the problem of chaos, stability and estimation of unknown parameters of the stochastic lattice gas for prey-predator model with pair-approximation is studied. The result shows that this dynamical system exhibits an oscillatory behavior of the population densities of prey and predator. Using Liapunov stability technique, the estimators of the unknown probabilities are derived, and also the updating rules for stability around its steady states are derived. Furthermore the feedback control law has been as non-linear functions of the population densities. Numerical simulation study is presented graphically.

关键词： Stochastic Lattice Gas Model Prey-Predator Updating Rules Estimation system state

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