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A real-time control platform progressed for the power quality (PQ) change in a conveyance framework has been displayed in this paper. A DSP-based control platform is demonstrated for voltage sag compensation for dynamic voltage restorer in conjunction with a voltage source inverter (VSI) to enhance PQ. The output of VSI is controlled by activating IGBTs by SPWM control technique. A driver circuit is developed along with a filter circuit to compensate load voltage when sag occurs in a distribution system supplying a sensitive load of single-phase induction motor. The developed control platform has been asserted with a DSP processor TMS320F28027F, and its effectiveness has been shown by carrying a few experiments. [ABSTRACT FROM AUTHOR]

This paper proposes a sliding mode control method to be used in the dynamic voltage restorer (DVR) system for regulating voltages of three-phase loads. The voltages produced by the DVR’s inverter are provided by three sliding mode controllers. Each of them operates on a error resulting from the difference between the phase reference and the measured single-phase load voltage. The reference synchronization is carried out by a recursive least-square (RLS) algorithm. Moreover, a flag procedure is applied to assure that the DVR operates only on periods of time where system is in steady state. This is suitable for saving energy. The technique has been evaluated in Simulink for different sag conditions. Finally, an experimental setup of a DVR has been used to evaluate the method, and the results show the effectiveness of it. [ABSTRACT FROM AUTHOR]

• A fully connected neural network-based voltage compensation method is proposed for accurate reconstruction in brain electrical impedance tomography. • Under the condition of simulated dehydration of brain tissue and scalp, dehydration degree can be determined and voltage can be compensated. • The influence of the scalp dehydration is reduced and reconstruction quality is improved when the proposed compensation method is used. Acute cerebral ischemia is commonly accompanied by cerebral edema. With brain electrical impedance tomography (EIT), it is possible to monitor conductivity variation during dehydration treatment of the cerebral edema. However, the scalp is also dehydrated when dehydrating brain tissue which exerts a large impact on boundary measurement. Consequently, reconstruction quality of cerebral edema is seriously affected. To reduce the influence of scalp dehydration, a voltage data compensation method based on a fully connected neural network (FCNN) is proposed in this work. With this method, the voltage measured when brain tissue and scalp are simultaneously dehydrated can be compensated. To evaluate the performance of the proposed method, reconstruction of simulated cerebral ischemia in a three-layer head model is conducted. Images reconstructed with the compensated voltage data are compared with the results when there is no compensation. Besides, quantitative comparison is performed. The results indicate that it is almost impossible to identify the simulated cerebral ischemia without voltage compensation. In contrast, cerebral ischemia can be much more accurately reconstructed when the measured voltage is compensated with the proposed method. [ABSTRACT FROM AUTHOR]