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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.

Natural disasters often result in large-scale power outages. Real-time tracking of the extent, distribution, and timelines of electrical service loss and recovery can play an important role in minimizing disaster impacts. Using NASA's Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB), the extent and duration of disrupted electric utility infrastructure in the Florida Panhandle following Hurricane Michael were estimated. The percent loss of electrical service was downscaled to a neighborhood level using the 2013-2017 American Community Survey (ACS) data at the block group level. Two ordinary least square models were estimated to examine the association between socioeconomic characteristics and the extent and duration of the power outages as well as recovery rates. The study found that block groups with higher percent minorities, multi-family housing units, rural areas, and a higher percentage of households receiving public assistance were experiencing slower power restoration rates than urban and more affluent neighborhoods. The findings have implications for disaster preparedness and recovery planning. / Includes bibliography. / Thesis (MURP)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

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.

Master of Science / Department of Electrical and Computer Engineering / Sanjoy Das / Anil Pahwa / Outage management describes system utilized by electric distribution utilities to help restore power in event of an outage. The complexity of outage management system employed by different utilities to determine the location of fault could differ. First step of outage management is to know where the problem is. Utilities typically depend on customers to call and inform them of the problem by entering their addresses. After sufficient calls are received, the utility is able to pinpoint the location of the outage. This part of outage management is called trouble call analysis. In event of fault in a feeder of a radial distribution system, the upstream device or the device that serves to protect that particular zone activates and opens the circuit. This particular device is considered as the operated protective device. The knowledge of the activated protective device can help locate the fault. Repair crews could be sent to that particular location to carry out power restoration efforts. The main objective of this work is to study model of distribution system that could utilize the network topology and customer calls to predict the location of the operated protective device. Such prediction would be based on the knowledge of the least amount of variables i.e. network topology and customer calls. Radial distribution systems are modeled using the immune system algorithm and test cases with trouble calls are simulated in MATLAB to test the effectiveness of the proposed technique. Also, the proposed technique is tested on an actual feeder circuit with real call scenarios to verify against the known fault locations.

This dissertation studied new transmission line fault diagnosis approaches using new technologies and proposed a scheme to apply those techniques in preventing and mitigating cascading blackouts. The new fault diagnosis approaches are based on two time-domain techniques: neural network based, and synchronized sampling based. For a neural network based fault diagnosis approach, a specially designed fuzzy Adaptive Resonance Theory (ART) neural network algorithm was used. Several ap- plication issues were solved by coordinating multiple neural networks and improving the feature extraction method. A new boundary protection scheme was designed by using a wavelet transform and fuzzy ART neural network. By extracting the fault gen- erated high frequency signal, the new scheme can solve the difficulty of the traditional method to differentiate the internal faults from the external using one end transmis- sion line data only. The fault diagnosis based on synchronized sampling utilizes the Global Positioning System of satellites to synchronize data samples from the two ends of the transmission line. The effort has been made to extend the fault location scheme to a complete fault detection, classification and location scheme. Without an extra data requirement, the new approach enhances the functions of fault diagnosis and improves the performance. Two fault diagnosis techniques using neural network and synchronized sampling are combined as an integrated real time fault analysis tool to be used as a reference of traditional protective relay. They work with an event analysis tool based on event tree analysis (ETA) in a proposed local relay monitoring tool. An interactive monitoring and control scheme for preventing and mitigating cascading blackouts is proposed. The local relay monitoring tool was coordinated with the system-wide monitoring and control tool to enable a better understanding of the system disturbances. Case studies were presented to demonstrate the proposed scheme. An improved simulation software using MATLAB and EMTP/ATP was devel- oped to study the proposed fault diagnosis techniques. Comprehensive performance studies were implemented and the test results validated the enhanced performance of the proposed approaches over the traditional fault diagnosis performed by the transmission line distance relay.

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Made available in DSpace on 2014-10-09T12:48:53Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:01:14Z (GMT). No. of bitstreams: 1 09627.pdf: 6224254 bytes, checksum: 0192b8abd2aed7811607e803516e20a7 (MD5) / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP