Doctor of Philosophy

dissertationThree major catastrophic failures in photovoltaic (PV) arrays are ground-faults, line-to-line faults, and arc faults. Although the number of such failures is few, recent fire events on April 5, 2009, in Bakersfield, California, and April 16, 2011, in Mount Holly, North Carolina suggest the need for improvements in present fault detection and mitigation techniques, as well as amendments to existing codes and standards to avoid such accidents. A fault prediction and detection technique for PV arrays based on spread spectrum time domain reflectometry (SSTDR) has been proposed and was successfully implemented. Unlike other conventional techniques, SSTDR does not depend on the amplitude of the fault-current. Therefore, SSTDR can be used in the absence of solar irradiation as well. However, wide variation in impedance throughout different materials and interconnections makes fault locating more challenging than prediction/detection of faults. Another application of SSTDR in PV systems is the measurement of characteristic impedance of power components for condition monitoring purposes. Any characteristic variations in one component will simultaneously alter the operating conditions of other components in a closed-loop system, resulting in a shift in overall reliability profile. This interdependence makes the reliability of a converter a complex function of time and operating conditions. Details of this failure mode, mechanism, and effect analysis (FMMEA) have been developed. By knowing the present state of health and the remaining useful life (RUL) of a power converter, it is possible to reduce the maintenance cost for expensive high-power converters by facilitating a reliability centered maintenance (RCM) scheme. This research is a step forward toward power converter reliability analysis since the cumulative effect of multiple degraded components has been considered here for the first time in order to estimate reliability of a power converter

Robust Distributed Parameter Estimation in Wireless Sensor Networks

abstract: Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities. Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense. Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis. Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Novel Designs for Photovoltaic Arrays to Reduce Partial Shading Losses and to Ease Series Arc Fault Detection

A mismatch in a photovoltaic array implies differences in the I-V characteristics of the modules forming the array which can lead to significant energy losses known as mismatch losses. The sources of mismatch losses could be easy- or difficult-to-predict sources. This thesis proposes novel designs for photovoltaic arrays to reduce mismatch losses. The mismatch from easy-to-predict sources and its resulting losses can be reduced by altering the interconnection of the array. Therefore, this thesis proposes an optimal total-cross-tied interconnection, based on a thorough mathematical formulation, which can significantly reduce mismatch losses from easy-to-predict sources. Application examples of the operation of the optimal total-cross-tied interconnection under partial shading are presented. The effect of partial shading caused by easy- or difficult-to-predict sources can be considerably reduced by photovoltaic array reconfiguration. This thesis proposes a novel mathematical formulation for the optimal reconfiguration of photovoltaic arrays to minimize partial shading losses. The thesis formulates the reconfiguration problem as a mixed integer quadratic programming problem and finds the optimal solution using branch-and-bound algorithm. The proposed formulation can be used for equal or non-equal number of modules per row. Moreover, it can be used for fully reconfigurable or partially-reconfigurable arrays. Application examples of the operation of the reconfigurable photovoltaic array under partial shading are presented. Finally, the recently updated American National Electric Code requires the presence of a series arc fault detector in any Photovoltaic installation operating at a voltage greater than or equal to 80V. However, the Photovoltaic market nowadays lacks the presence of an accurate series arc fault detector that can detect series arc faults and discriminate between them and partial shading. The work in this thesis proposes an algorithm that can detect series arc faults and discriminate between them and partial shading in total-cross-tied arrays. This algorithm is based on the measurement of instantaneous row voltages.1 yea