This paper proposes a new state estimation (SE) approach for power systems called spanning tree maximum exponential absolute value (ST-MEAV). The novel state estimator is developed based on the combination of the MEAV and a low stretch spanning tree preconditioner. An overall algorithm is presented to show the process. A modified ST-MEAV called ST0-MEAV is also proposed to improve the computational efficiency. Furthermore, the SE of the two FACTS devices called IPC and UIPC is addressed. The new formulations minimize the number of additional variables needed for the SE to reduce the computational load and to simplify implementation compared to previous methods presented in the literature for similar FACTS devices to UPFC or IPFC. The SE approach incorporates detailed steady-state models of the devices including IPC and UIPC constrains. The ST-MEAV algorithm is modified based on the new formulations for IPC and UIPC. Two modified IEEE test systems are used to verify the performance of ST-MEAV in the presence of IPC and UIPC. Application tests of ST-MEAV on two power grids are also presented to evaluate the state estimator performance in large-scale power grids. The simulation results of the proposed method compare favorably with those for WLS-LNR and MEAV-RCE.

This paper presents a new control approach for nonlinear network-induced time delay systems by combining online reset control, neural networks, and dynamic Bayesian networks. We use feedback linearization to construct a nominal control for the system then use reset control and a neural network to compensate for errors due to the time delay. Finally, we obtain a stochastic model of the Networked Control System (NCS) using a Dynamic Bayesian Network (DBN) and use it to design a predictive control. We apply our control methodology to a nonlinear inverted pendulum and evaluate its performance through numerical simulations. We also test our approach with real-time experiments on a dc motor-load NCS with wireless communication implemented using a Ubiquitous Sensor Network (USN). Both the simulation and experimental results demonstrate the efficacy of our control methodology.

Electro-tactile based rehabilitation systems must be capable of self-tuning to suit the tactile preference of different users. However, tactile preference is difficult to assess in practice. We propose a Takagi-Sugeno-Kang (TSK) fuzzy logic modeling and control approach for the on-line assessment of tactile preference. The method relies on real-time measurements of voltage and power absorbed by the fingertip. Our results show that the fuzzy logic approach successfully models user tactile preference. We are currently developing an electro-tactile based Braille display (E-Braille) for assisting the blind and visually impaired (BVI) that exploits our fuzzy model.