Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation

A relatively new method of addressing different hydrological problems is the use of artificial neural networks (ANN). In groundwater management ANNs are usually used to predict the hydraulic head at a well location. ANNs can prove to be very useful because, unlike numerical groundwater models, they are very easy to implement in karstic regions without the need of explicit knowledge of the exact flow conduit geometry and they avoid the creation of extremely complex models in the rare cases when all the necessary information is available. With hydrological parameters like rainfall and temperature, as well as with hydrogeological parameters like pumping rates from nearby wells as input, the ANN applies a black box approach and yields the simulated hydraulic head. During the calibration process the network is trained using a set of available field data and its performance is evaluated with a different set. Available measured data from Edward??s aquifer in Texas, USA are used in this work to train and evaluate the proposed ANN. The Edwards Aquifer is a unique groundwater system and one of the most prolific artesian aquifers in the world. The present work focuses on simulation of hydraulic head change at an observation well in the area. The adopted ANN is a classic fully connected multilayer perceptron, with two hidden layers. All input parameters are directly or indirectly connected to the aquatic equilibrium and the ANN is treated as a sophisticated analogue to empirical models of the past. A correlation analysis of the measured data is used to determine the time lag between the current day and the day used for input of the measured rainfall levels. After the calibration process the testing data were used in order to check the ability of the ANN to interpolate or extrapolate in other regions, not used in the training procedure. The results show that there is a need for exact knowledge of pumping from each well in karstic aquifers as it is difficult to simulate the sudden drops and rises, which in this case can be more than 6 ft (approx. 2 m). That aside, the ANN is still a useful way to simulate karstic aquifers that are difficult to be simulated by numerical groundwater models.     

Predicting the technical impacts of high levels of small-scale embedded generators on low-voltage networks

The anticipated high penetrations of small-scale embedded generators (SSEGs) on public low-voltage (LV) distribution networks are likely to present distribution network operators (DNOs) with a number of technical impacts relating to power quality, distribution system efficiency and potential equipment overloads. Impact studies need to be performed using suitable case study networks in order to evaluate the effects of SSEGs on LV distribution networks and quantify allowable SSEG penetration levels. The aim is to propose a methodology for predicting the technical impacts of SSEGs on LV networks without the need for developing a detailed computerbased model of the power system and simulating a range of operating scenarios. This methodology is drawn from an analysis of the key electrical characteristics that determine the response of LV networks to the addition of SSEGs, focusing on the following technical aspects: (i) voltage regulation, (ii) voltage rise, (iii) voltage unbalance, (iv) cable and transformer thermal limits and (v) network losses. The analysis is carried out on a UK generic and a European generic LV network and simulation results for both networks are presented and discussed. The proposed methodology is then applied to an existing public UK LV network operated by E.ON UK Central Networks, indicating a good agreement between predicted and simulation results.