This dissertation is a systematic study of artificial neural networks (ANN) applications in power system restoration (PSR). PSR is based on available generation and load to be restored analysis. A literature review showed that the conventional PSR methods, i.e. the pre-established guidelines, the expert systems method, the mathematical programming method and the petri-net method have limitations such as the necessary time to obtain the PSR plan. ANN may help to solve this problem presenting a reliable PSR plan in a smaller time. Based on actual and past experiences, a PSR engine based on ANN was proposed and developed. Data from the Iowa 162 bus power system was used in the implementation of the technique. Reactive and real power balance, fault location, phase angles across breakers and intentional islanding were taken into account in the implementation of the technique. Constraints in PSR as thermal limits of transmission lines (TL), stability issues, number of TL used in the restoration plan and lockout breakers were used to create feasible PSR plans. To compare the time necessary to achieve the PSR plan with another technique a PSR method based on a breadth-search algorithm was implemented. This algorithm was also used to create training and validation patterns for the ANN used in the scheme. An algorithm to determine the switching sequence of the breakers was also implemented. In order to determine the switching sequence of the breakers the algorithm takes into account the most priority loads and the final system configuration generated by the ANN. The PSR technique implemented is composed by several pairs of ANN, each one assigned to an individual island of the system. The restoration of the system is done in parallel in each island. After each island is restored the tie lines are closed. The results encountered shows that ANN based schemes can be used in PSR helping the operators restore the system under the stressful conditions following a blackout. / Ph. D.

Musculoskeletal pain and injury continue to be prevalent sources of disability for thousands of workers in the U.S. every year. Proactive approaches to the reduction of this incidence attempt to prevent the injury by effecting task design so that human capabilities and limitations are driving factors in the task design and analysis process. Knowledge about the posture and kinematics that might be employed by an individual in performing a task is an important element of these proactive approaches to task design and analysis, especially for manual materials handling (i.e., lifting) exertions. In turn, accurate models that predict posture and kinematics can reduce the need for empirical postural and kinematic data in this task development process. Artificial neural networks were used in this investigation to achieve these predictions. As input, these networks received information about lift characteristics (e.g. target location, movement duration) and returned a predicted set of joint angles. Two types of networks were created, one to predict static posture based on target position, the second to predict the time histories of several joint angles (i.e., kinematics) as an object is lifted or lowered. Initial networks were created for sagittally symmetric lifts (two dimensions), but the final set of networks was expanded to make predictions for symmetric and asymmetric lifts in three dimensions. Networks were trained and verified with an empirical set of non-cyclic lifting motions. Notably, the within-subject variability in these motions was similar in magnitude to the associated between-subjects variability. In general, the networks were able to assimilate the data relatively well, especially in predicting kinematics, where root mean square errors were typically smaller than 20 degrees. These errors were similar in magnitude to the levels of within-subject variability observed in the dataset. Network performance also compared favorably to other existing models, typically resulting in smaller prediction errors than these other approaches. In addition, the internal connections of trained networks were examined to infer hypothetical motor control strategies. Results of this examination showed that feedback was an important component in providing kinematic predictions, whereas posture prediction benefited greatly from knowledge about individual anthropometry. Finally, potential improvements to increase prediction accuracy are discussed. Overall, these results support the use of artificial neural network models to predict posture and kinematics for lifting tasks. / Ph. D.

Analytical solutions such as the Theis solution have historically been utilized to forecast changes in aquifer water levels resulting from human-driven withdrawals using pumping wells. This method, however, suffers from a number of disadvantages, such as long data acquisition times, model uncertainty, and trial-and-error calibrations. This study illustrated the effectiveness of alternate forecasting methods that utilized machine learning principles. The groundwater level dynamics of two sites located at the Virginia Eastern Shore were predicted using historical groundwater level below land surface (GWLBLS) data as the endogenous variable and local pumping data as the exogenous variable. Predicting the local pumping data from the GWLBLS values was also implemented, to not only enforce reliability of the model, but also to highlight the capability of verifying and enforcing permitted pumping data. The machine learning methods chosen for this study were the Random Forest and SARIMAX models. Historical datasets were divided into training/calibration and testing/validation sets, and the respective models were fit to the data. These calibrated models were then compared to the performance of the Theis solution. Across both study sites, the Random Forest performed best at forecasting groundwater level over time given the pumping data as an exogenous variable, with SARIMAX performing similarly to the Theis solution. The Theis solution, however, did perform well in terms of generalization ability (GA). / Master of Science / Groundwater is a vital resource for drinking, agriculture, and industry. In order to ensure aquifer health for future use, it is crucial to be able to forecast well water level in the midst of groundwater pumping. Currently, analytical solutions such as the Theis solution are utilized to predict water level over time, but data acquisition is time-consuming and many of the calibrations have to be based on trial-and-error. In this thesis, machine learning methods were explored as alternatives to the current analytical methods. The groundwater level dynamics of the two study sites, Oyster, Virginia and Temperanceville, Virginia, were used to calibrate corresponding machine learning models, called the Random Forest (RF) and SARIMAX models. While the Theis showed that it was the most adaptable model, the RF performed the best overall in terms of root mean square error and R2 scores, which were used as reliability metrics. This study provides a range of substitutes for the Theis solution that have the capability to perform better when calibrated on a site-by-site basis.

This thesis describes an automatic CT image interpretation approach that can be used to detect hardwood defects. The goal of this research has been to develop several automatic image interpretation systems for different types of wood, with lower-level processing performed by feed forward artificial neural networks. In the course of this work, five single-species classifiers and seven multiple-species classifiers have been developed for 2-D and 3-D analysis. These classifiers were trained with back-propagation, using training samples of three species of hardwood: cherry, red oak and yellow poplar. These classifiers recognize six classes: heartwood (clear wood), sapwood, knots, bark, split s and decay. This demonstrates the feasibility of developing general classifiers that can be used with different types of hardwood logs. This will help sawmill and veneer mill operators to improve the quality of products and preserve natural resources. / Master of Science

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Doctor of Philosophy / Department of Civil Engineering / Yacoub M. Najjar / This study explored the potential use of ANNs for profiling and characterization of various environmental sites. A static ANN with back-propagation algorithm was used to model the environmental containment at a hypothetical data-rich contaminated site. The performance of the ANN profiling model was then compared with eight known profiling methods. The comparison showed that the ANN-based models proved to yield the lowest error values in the 2-D and 3-D comparison cases. The ANN-based profiling models also produced the best contaminant distribution contour maps when compared to the actual maps. Along with the fact that ANN is the only profiling methodology that allows for efficient 3-D profiling, this study clearly demonstrates that ANN-based methodology, when properly used, has the potential to provide the most accurate predictions and site profiling contour maps for a contaminated site. ANN with a back-propagation learning algorithm was utilized in the site characterization of contaminants at the Kansas City landfill. The use of ANN profiling models made it possible to obtain reliable predictions about the location and concentration of lead and copper contamination at the associated Kansas City landfill site. The resulting profiles can be used to determine additional sampling locations, if needed, for both groundwater and soil in any contaminated zones. Back-propagation networks were also used to characterize the MMR Demo 1 site. The purpose of the developed ANN models was to predict the concentrations of perchlorate at the MMR from appropriate input parameters. To determine the most-appropriate input parameters for this model, three different cases were investigated using nine potential input parameters. The ANN modeling used in this case demonstrates the neural network’s ability to accurately predict perchlorate contamination using multiple variables. When comparing the trends observed using the ANN-generated data and the actual trends identified in the MMR 2006 System Performance Monitoring Report, both agree that perchlorate levels are decreasing due to the use of the Extraction, Treatment, and Recharge (ETR) systems. This research demonstrates the advantages of ANN site characterization modeling in contrast with traditional modeling schemes. Accordingly, characterization task-related uncertainties of site contaminations were curtailed by the use of ANN-based models.

Aeroderivative gas turbines are used all over the world for different applications as Combined Heat and Power (CHP), Oil and Gas, ship propulsion and others. They combine flexibility with high efficiencies, low weight and small footprint, making them attractive where power density is paramount as off shore Oil and Gas or ship propulsion. In Western Europe they are widely used in CHP small and medium applications thanks to their maintainability and efficiency. Reliability, Availability and Performance are key parameters when considering plant operation and maintenance. The accurate diagnose of Performance is fundamental for the plant economics and maintenance planning. There has been a lot of work around units like the LM2500® , a gas generator with an aerodynamically coupled gas turbine, but nothing has been found by the author for the LM6000® . Water wash, both on line or off line, is an important maintenance practice impacting Reliability, Availability and Performance. This Thesis aims to select and apply a suitable diagnostic technique to help establishing the schedule for off line water wash on a specific model of this engine type. After a revision of Diagnostic Methods Artificial Neural Network (ANN) has been chosen as diagnostic tool. There was no WebEngine model available of the unit under study so the first step of setting the tool has been creating it. The last step has been testing of ANN as a suitable diagnostic tool. Several have been configured, trained and tested and one has been chosen based on its slightly better response. Finally, conclusions are discussed and recommendations for further work laid out.

The thesis outlines the use of Orthogonal Arrays for the training of Artificial Neural Networks. Such arrays are popularly used in system optimisation and are known as Taguchi Methods. The chief advantage of the method is that the network can learn quickly. Fast training methods may be used in certain Control Systems and it has been suggested that they could find application in ‘disaster control,’ where a potentially dangerous system (for example, suffering a mechanical failure) needs to be controlled quickly. Previous work on the methods has shown that they suffer problems when used with multi-layer networks. The thesis discusses the reasons for these problems and reports on several successful techniques for overcoming them. These techniques are based on the consideration of the neuron, rather then the individual weight, as a factor to be optimised. The applications of technique and further work are also discussed.

In our country farmers have to face many difficulties because of the poor irrigation system. During flood situation, excessive waters will be staged in paddy field producing great loss and pain to farmers. So, proper Irrigation mechanism is an essential component of paddy production. Poor irrigation methods and crop management are rapidly depleting the country’s water table. Most small hold farmers cannot afford new wells or lawns and they are looking for alternative methods to reduce their water consumption. So proper irrigation mechanism not only leads to high crop production but also pave a way for water saving techniques. Automation of irrigation system has the potential to provide maximum water usage efficiency by monitoring soil moistures. The control unit based on Artificial Neural Network is the pivotal block of entire irrigation system. Using this control unit certain factors like temperature, kind of the soil and crops, air humidity, radiation in the ground were estimated and this will help to control the flow of water to acquire optimized results. / Water is an essential resource in the earth. It is also essential for irrigation, so irrigation technique is essential for agriculture. To irrigate large area of lands is a tedious process. In our country farmers are not following proper irrigation techniques. Currently, most of the irrigation scheduling systems and their corresponding automated tools are in fixed rate. Variable rate irrigation is very essential not only for the improvement of irrigation system but also to save water resource for future purpose. Most of the irrigation controllers are ON/OFF Model. These controllers cannot give optimal results for varying time delays and system parameters. Artificial Neural Network (ANN) based intelligent control system is used for effective irrigation scheduling in paddy fields. The input parameters like air, temperature, soil moisture, radiations and humidity are modeled. Using appropriate method, ecological conditions, Evapotranspiration, various growing stages of crops are considered and based on that the amount of water required for irrigation is estimated. Using this existing ANN based intelligent control system, the water saving procedure in paddy field can be achieved. This model will lead to avoid flood in paddy field during the rainy seasons and save that water for future purposes.