Improved power computation method for droop‐controlled single‐phase VSIs in standalone microgrid considering non‐linear loads

Computation of active and reactive powers is a crucial step in droop-controlled single-phase voltage source inverters (VSIs) in standalone microgrid since the performance and stability of the power-sharing strategy are strongly influenced by its speed and accuracy, especially in the case of non-linear loads. Here, an improved performance of power-sharing among single-phase droop-controlled VSIs in an islanded microgrid, considering DC component and nonlinear loads is presented. To achieve this goal, an enhanced power-sharing control scheme including a Multiple Enhanced Second-Order Generalized Integrator Frequency-Locked Loop (MESOGI-FLL) for power calculation is proposed. As a result, the proposed power computation technique provides high rejection capability of DC component and current harmonics, hence, perfect estimation of the fundamental component of the inverter output current and its 90◦ phase-shifted component. This strategy makes the power calculation method-based control scheme immune to disturbance effects of the DC component and the high current harmonics. Detailed analysis, mathematical modelling of MESOGI, as well as a comparison with recent methods, are also provided. Simulation and experimental tests were carried out and the obtained results have shown the effectiveness and robustness of the proposed power-sharing controller even under nonlinear load operating conditions

A neural computation to study the scaling capability of the undoped DG MOSFET

The DG MOSFET is one of the most promising candidates for further CMOS scaling beyond the year of 2010. It will be scaled down to various degrees upon a wide range of system/circuit requirements (such as high-performance, low standby power and low operating power). The key electrical parameter of the DG MOSFET is the subthreshold swing (S). In this paper, we present the applicability of the artificial neural network for the study of the scaling capability of the undoped DG MOSFET. The latter is based on the development of a semi-analytical model of the subthreshold swing (S) using the Finite Elements Method (FEM). Our results are discussed in order to draw some useful information about the ULSI technology

Design of a Selective Smart Gas Sensor Based on ANN-FL Hybrid Modeling

The selectivity is one of the main challenges to develop a gas sensor, the good chemical species detection in a gaseous mixture decreasing the missed detections. The present paper proposes a new solution for gas sensor selectivity based on artificial neural networks (ANNs) and fuzzy logic (FL) algorithm. We first use ANNs to develop a gas sensor model in order to accurately express its behavior. In a second step, the FL and Matlab environment are used to create a database for a selective model, where the response of this one only depends on one chemical species. Analytical models for the gas sensor and its selective model are implemented into a Performance Simulation Program with Integrated Circuit Emphasis (PSPICE) simulator as an electrical circuit in order to prove the similarity of the analytical model output with that of the MQ-9 gas sensor where the output of the selective model only depends on one gas. Our results indicate the capability of the ANN-FL hybrid modeling for an accurate sensing analysis