Advanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear relationship between travel time and the independent variables and this led to superior results. Because variability in travel time (both waiting and on-board) is extremely important for transit choices, it would also be useful to extend the model to provide not only estimates of travel time but also prediction intervals. With the ANN models, the prediction intervals of bus arrival time were calculated. Because the ANN models are non parametric models, conventional techniques for prediction intervals can not be used. Consequently, a newly developed computer-intensive method, the bootstrap technique was used to obtain prediction intervals of bus arrival time. On-time performance of a bus is very important to transit operators to provide quality service to transit passengers. To measure the on-time performance, the probability of a bus being on time is required. In addition to the prediction interval of bus arrival time, the probability that a given bus is on time was calculated. The probability density function of schedule adherence seemed to be the gamma distribution or the normal distribution. To determine which distribution is the best fit for the schedule adherence, a chi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well the schedule adherence. With the normal distribution, the probability of a bus being on time, being ahead schedule, and being behind schedule can be estimated.

Process monitoring in the chemical and other process industries has been of great practical importance. Early detection of faults is critical in avoiding product quality deterioration, equipment damage, and personal injury. The goal of this dissertation is to develop process monitoring schemes that can be applied to complex process systems. Neural networks have been a popular tool for modeling and pattern classification for monitoring of process systems. However, due to the prohibitive computational cost caused by high dimensionality and frequently changing operating conditions in batch processes, their applications have been difficult. The first part of this work tackles this problem by employing a polynomial-based data preprocessing step that greatly reduces the dimensionality of the neural network process model. The process measurements and manipulated variables go through a polynomial regression step and the polynomial coefficients, which are usually of far lower dimensionality than the original data, are used to build a neural network model to produce residuals for fault classification. Case studies show a significant reduction in neural model construction time and sometimes better classification results as well. The second part of this research investigates classification trees as a promising approach to fault detection and classification. It is found that the underlying principles of classification trees often result in complicated trees even for rather simple problems, and construction time can excessive for high dimensional problems. Fisher Discriminant Analysis (FDA), which features an optimal linear discrimination between different faults and projects original data on to perpendicular scores, is used as a dimensionality reduction tool. Classification trees use the scores to separate observations into different fault classes. A procedure identifies the order of FDA scores that results in a minimum tree cost as the optimal order. Comparisons to other popular multivariate statistical analysis based methods indicate that the new scheme exhibits better performance on a benchmarking problem.

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.

Inspection of LCD light-guide plate using digital image processing is proposed. Binary dot-pattern images from SEM observation are obtained by image segmentation. Pattern recognition for the images is then performed using moment invariants, Bayes classifier, and Neural network. The rotation independent classification for the recognition using only one descript shape factor are also proposed to reduce storage space. It is found the method has been applied successfully in inspection of different defects on the plate subject to any rotation angles and image scales.

The static synchronous series compensator (SSSC) is a series controller of Flexible AC Transmission Systems (FACTS). It can be controlled by Thyristors, it also has the ability of fast control adjustments and high frequency operation. Through impedance compensation, it is able to control the magnitude and directions of the real power flow in the transmission system. In order to achieve a fast and steady response for real power control in power systems, this thesis proposed a unified intelligent controller, which consists of RBFNN and GA for the SSSC to provide better control features for real power control in the dynamic operations of power systems. Finally, the simulation results of the proposed controllers is compared with the conventional proportional plus integral (PI) controllers to demonstrate the superiority and effectiveness of the unified intelligent controller.

PM10 and ozone are the dominant air pollutants in the Urban Kaohsiung. Ozone is a secondary pollutant generated in the troposphere from the precursors nitrogen dioxide and non-methane hydrocarbons. The trends of ozone concentrations first statistically are summarized utilizing the monitoring data during the period 1998¡Ð2007. All data are collected from four fixed-site air quality monitoring stations in Kaohsiung City. The results show that ozone concentration in Kaohsiung has one perennial peak concentration, occurring in October and March. The highest values occur in October and the secondary high value in March. The lowest values occur in the summer. The monitor data possess timeliness of data and the non-linear dynamic tendency. Artificial Neural Network ¡]ANN¡^, a system recognition, self-study function and ability of the solution to non-linearity dynamic system problem, was used as a tool to analyze these monitor data. This work utilizing neural networks develops a model to predict the trend of ozone situations in the Urban Kaohsiung. The network was trained using meteorological factor and air quality data when the ozone concentrations are the highest. The optimum set value of five parameters including date partition, hidden layer neurons, training function, leraning rate , and momentum coefficient were obtained based on trial and error methods. The simulated results of ozone concentration have a correlation coefficient within the range 0.865¡Ð0.899 and IOA within the range 0.927¡Ð0.934. The trend results of ozone concentration reflect strong relationships in all stations. The results of this study indicate that the artificial neural network (ANN) is a promising method for air pollution modeling.

none

Stability of the power quality is one of the objectives that power companies always try to assure. With energy shortage and the increases of fuel cost over years, reduction of expenses in all areas is another effort of the power company. Dealing with the above problems, Taiwan Power Company sets up a standard voltage for secondary side of each primary substation. Standard voltage is a commitment of expected 69kV primary substation bus voltage. A proper setting of the standard voltage can reduce voltage variation, in the secondary substation, and reduce the operation frequencies of the on load tap changer. Besides, it can prolong the service life and the maintenance cycle, and it can also reduce maintenance cost of each main transformer. This study proposes a method to calculate the standard voltage to improve the shortcomings that the voltage used to be set up with experience rule. The load and voltage data were used to build a neural network model. Improved particle swarm optimizer was used to find the parameters of the radial basis function neural network in order to build an efficient network. This network uses improved particle swarm optimizer again to the standard voltage. The proposed approach has been verified by the comparison of winter and summer standard voltages on the Tainan primary substation of taipower with accurate results.

This dissertation is to design the operation strategy and protective scheme of micro-grid systems with dispersed generation (DG). The industrial power system with cogeneration units and the distribution feeder with wind power generators were selected as the study micro-grids for computer simulation. The mathematical models of cogeneration units and wind generators were included in the computer simulation by considering the operation control modes of DGs. The micro-grid systems and the nearby utility networks were constructed to solve the power flows of the micro-grids with various operation scenarios of power generation and load demand. For the severe external fault contingencies, the micro-grids have to be isolated from the utility power system in time to prevent the tripping of critical loads and DGs. By considering the fault ride through capability of cogenerators and voltage tolerance curves of critical loads, the critical tripping time (CTT) of tie circuit breaker of the micro-grids was determined according to the transient stability analysis. To maintain the stable operation of the micro-grids after tie line tripping, the load shedding scheme was designed by applying the under frequency and under voltage relays to disconnect the proper amount of non-critical loads according to the governor responses of cogeneration units. For the micro-grid of distribution feeder with wind power generator, the STATCOM was used to provide adaptive reactive power compensation for the mitigation of voltage fluctuation due to the variation of wind speed and feeder loading. The STATCOM can also be applied for the support of terminal voltage of wind generator (WG) to enhance the transient response of the micro-grid. The CTT of tie circuit breaker was determined by considering the low voltage ride through (LVRT) capability and the critical fault cleaning time of WG. To achieve more effective islanding operation of the micro-grids, the artificial neural network (ANN) was applied to determine the proper timing for tie line tripping and the proper amount of load shedding by using the wind speed, feeder loading and the voltage of micro-grid system as the input of ANN. To verify the effectiveness of the proposed tie line tripping and load shedding scheme, different fault contingencies of the external utility network have been simulated by using the computer program for the transient stability analysis. It is found that the critical and voltage sensitive loads of the micro-grid can be maintained when the tie circuit breaker is activated to isolate the external fault in time and followed by the execution of load shedding scheme.

A connectivity adjusting learning algorithm, Optimal Brain Growth (OBG) was proposed. Contrast to the conventional training methods for the Artificial Neural Network (ANN) which focus on the weight-only optimization, the OBG method trains both weights and connectivity of a network in a single training process. The standard Back-Propagation (BP) algorithm was extended to exploit the error gradient information of the latent connection whose current weight has zero value. Based on this, the OBG algorithm makes a rational decision between a further adjustment of an existing connection weight and a creation of a new connection having zero weight. The training efficiency of a growing network is maintained by freezing stabilized connections in the further optimization process. A stabilized computational unit is also decomposed into two units and a particular set of decomposition rules guarantees a seamless local re-initialization of a training trajectory. The OBG method was tested for the multiple canonical, regression and classification problems and for a surrogate modeling of the pressure distribution on transonic airfoils. The OBG method showed an improved learning capability in computationally efficient manner compared to the conventional weight-only training using connectivity-fixed Multilayer Perceptrons (MLPs).