Observer-based neural network dynamic surface control of single-phase LCL-filtered grid-connected inverters under parametric uncertainties and weak grid condition

This paper focuses on the current control of single-phase LCL-filtered grid-connected inverters in the presence of parametric uncertainties and weak grid condition. Therefore, a novel neural network dynamic surface control (NNDSC) method is proposed by overcoming the problem of ‘explosion of complexity’. In addition, radial basis function neural networks (RBFNNs) are employed to approximate the system parametric uncertainties. Furthermore, by considering practical considerations, a novel state observer (SO) is designed to estimate the inverter-side current and capacitor voltage. As a result, additional current and voltage sensors are not required, which makes the implementation of the proposed approach straightforward and reliable. The origin neighbourhood convergence of estimated and tracking errors is assured through Lyapunov stability theorem and Young’s inequality. The effectiveness and performance of the proposed NNDSC+SO approach is demonstrated through MATLAB/Simpower simulations in view of the reference current changes, LCL filter parametric uncertainties and weak grid condition.

How groundwater level can predict under the effect of climate change by using artificial neural networks of NARX

The phenomenon of climate change in recent years has led to significant changes in climatic elements and as a result the status of surface and groundwater resources, especially in arid and semi-arid regions, this issue has sometimes caused a significant decline in groundwater resources. In this paper, the effects of climate change on the status of groundwater resources in Marvdasht plain have been investigated. Water supply of different parts of this region is highly dependent on groundwater resources and therefore the study of groundwater changes in future periods is important in the development of this plain and the management of its water resources. In order to evaluate the effects of climate change, the output of atmospheric circulation models (GCM) has been used. Then, in order to adapt the output scale of these models to the scale required by local studies of climate change, precipitation and temperature data have been downscaled by LARS-WG model. Downscaled information was used to determine the amount of feed and drainage of the aquifer in future periods. To investigate changes in groundwater levels at different stages, a neural network dynamic model has been developed in MATLAB software environment. It is also possible to study and compare other points using other scenarios and mathematical modeling. The results of the study, assuming the current state of development in the region, indicate a downward trend in the volume of the aquifer due to climate change and its effects on resources and uses of the study area. The results also introduce Scenario A2 as the most critical scenario related to climate change, which also shows the largest aquifer decline in neural network modeling.