Distributed dynamic state estimation considering renewable generation and packet losses

The penetration of renewable distributed energy resources such as wind turbine has been dramatically integrated in distribution networks due to clean, sustainable, and economical green energy. Due to intermittent wind speed, the power generation patterns vary which can risk the distribution network operation. So, it is intrinsically required to monitor the wind turbines in a distributed way. This paper presents an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication link between the wind turbines and energy management system. Firstly, the wind turbine is represented by a state-space linear equation, with sensors deployed to obtain system state information. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining estimation results with weighting factors which are calculated by minimizing estimation error covariance based on semi-definite programming. Finally, the convergence analysis indicates that estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the turbine model. The research is valuable for green energy technologies, households and interconnected industrial information societies, and also provides the knowledge towards a smart and secure green energy future.