Doctor of Philosophy / Department of Electrical and Computer Engineering / Anil Pahwa, Sanjoy Das / Occurrence of outages in overhead distribution systems is a significant factor in determining distribution system reliability. Analysis of animal-related outages has practical value since animals cause a large number of outages in overhead distribution systems. This dissertation presents several different methods to investigate the impact of weather and time of the year on the animal-related outage rate. The animal-related outages from year 1998 to year 2007 for different cities in Kansas are provided by Westar Energy. From examinations of the historical data, two factors which influence the animal-related outages, the month type and the number of fair weather days are taken as inputs along with historical outage data for prediction models. Poisson regression model, neural network model, wavelet based neural network model and Bayesian model combined with Monte Carlo simulations are applied to the weekly data of different cites. Even though Poisson regression models, Bayesian models and neural network models are able to recognize the changing pattern of outage rates under different weather conditions, they are limited in their ability to follow the high peaks in the time series of weekly animal-related outages. The introduction of wavelet transform techniques overcomes this problem. Simulation results indicate that the wavelet based neural network models are able to capture the pattern of fast fluctuations in the weekly outages of different cities in Kansas of various sizes. A hyperpermutation method inspired by artificial immune system algorithm is used to solve the overtraining problem in the application of neural networks. Finally, Monte Carlo simulations based on conditional probability tables from Bayesian models are used to find out the confidence intervals of the predictions. We aggregate the weekly data and carry out the analysis on a monthly and yearly basis too. Simulation results indicate that the models are able to capture the pattern as at least 90% of the observed values are within the upper limits of 95% confidence in the predictions for weekly, monthly and yearly animal-related outages of different cities in Kansas. The results obtained from Monte Carlo simulations are compared with the wavelet based neural network model to indentify years with more than expected level of outages.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Sanjoy Das and Anil Pahwa / Because a majority of day-to-day activities rely on electricity, it plays an important role in daily life. In this digital world, most of the people’s life depends on electricity. Without electricity, the flip of a switch would no longer produce instant light, television or refrigerators would be nonexistent, and hundreds of conveniences often taken for granted would be impossible. Electricity has become a basic necessity, and so any interruption in service due to disturbances in power lines causes a great inconvenience to customers. Customers and utility commissions expect a high level of reliability. Power distribution systems are geographically dispersed and exposure to environment makes them highly vulnerable part of power systems with respect to failures and interruption of service to customers. Following the restructuring and increased competition in the electric utility industry, distribution system reliability has acquired larger significance. Better understanding of causes and consequences of distribution interruptions is helpful in maintaining distribution systems, designing reliable systems, installing protection devices, and environmental issues. Various events, such as equipment failure, animal activity, tree fall, wind, and lightning, can negatively affect power distribution systems. Weather is one of the primary causes affecting distribution system reliability. Unfortunately, as weather-related outages are highly random, predicting their occurrence is an arduous task. To study the impact of weather on overhead distribution system several models, such as linear and exponential regression models, neural network model, and ensemble methods are presented in this dissertation. The models were extended to study the impact of animal activity on outages in overhead distribution system. Outage, lightning, and weather data for four different cities in Kansas of various sizes from 2005 to 2011 were provided by Westar Energy, Topeka, and state climate office at Kansas State University weather services. Models developed are applied to estimate daily outages. Performance tests shows that regression and neural network models are able to estimate outages well but failed to estimate well in lower and upper range of observed values. The introduction of committee machines inspired by the ‘divide & conquer” principle overcomes this problem. Simulation results shows that mixture of experts model is more effective followed by AdaBoost model in estimating daily outages. Similar results on performance of these models were found for animal-caused outages.