High Temperature Silicon Carbide Mixed-signal Circuits for Integrated Control and Data Acquisition

Wide bandgap semiconductor materials such as gallium nitride (GaN) and silicon carbide have grown in popularity as a substrate for power devices for high temperature and high voltage applications over the last two decades. Recent research has been focused on the design of integrated circuits for protection and control in these wide bandgap materials. The ICs developed in SiC and GaN can not only complement the power devices in high voltage and high frequency applications, but can also be used for standalone high temperature control and data acquisition circuitry. This dissertation work aims to explore the possibilities in high temperature and wide bandgap circuit design by developing a host of mixed-signal circuits that can be used for control and data acquisition. These include a family of current-mode signal processing circuits, general purpose amplifiers and comparators, and 8-bit data converters. The signal processing circuits along with amplifiers and comparators are then used to develop an integrated mixed-signal controller for a DC-DC flyback converter in a microinverter application. The 8-bit SAR ADC and the 8-bit R-2R ladder DAC open up the possibility of a remote data acquisition and control system in high temperature environments. The circuits and systems presented here offer a gateway to great opportunities in high temperature and power electronics ICs in SiC

A Distributed Dynamic State Estimator Using Cellular Computational Network

The proper operation of smart grid largely depends on the proper monitoring of the system. State estimation is a core computation process of the monitoring unit. To keep the privacy of the data and to avoid the unexpected events of the system, it needs to be made fast, distributed, and dynamic. The traditional Weighted Least Squares (WLS) estimator is neither scalable, nor distributed. Increase in the size of the system increases the computation time significantly. The estimator can be made faster in different ways. One of the major solutions can be its parallel implementation. As the WLS estimator is not completely parallelizable, the dishonest Gauss Newton method is analyzed in this dissertation. It is shown that the method is fully parallelizable that yields a very fast result. However, the convergence of the dishonest method is not analyzed in the literature. Therefore, the nature of convergence is analyzed geometrically for a single variable problem and it is found that the method can converge on a higher range with higher slopes. The effects of the slopes on multi-variable cases are demonstrated through simulation. On the other hand, a Cellular Computational Network (CCN) based framework is analyzed for making the system distributed and scalable. Through analysis, it is shown that the framework creates an independent method for state estimation. To increase the accuracy, some heuristic methods are tested and a Genetic Algorithm (GA) based solution is incorporated with the CCN based solution to build a hybrid estimator. However, the heuristic methods are time-consuming and they do not exploit the advantage of the dynamic nature of the states. With the high data-rate of phasor measurement units, it is possible to extract the dynamic natures of the states. As a result, it is also possible to make efficient predictions about them. Under this situation, a predictor can be incorporated with the estimation process to detect any unwanted changes in the system. Though it is not a part of the power system to date, it can be a tool that can enhance the reliability of the grid. To implement the predictors, a special type of neural network named Elman Recurrent Neural Network (ERNN) is used. In this dissertation, a distributed dynamic estimator is developed by integrating an ERNN based predictor with a dishonest method based estimator. The ERNN based predictor and the dishonest method based estimator are each implemented at the cell level of a CCN framework. The estimation is a weighted combination of the dishonest module and the predictor module. With this three-stage distributed computation system, it creates an efficient dynamic state estimator. The proposed distributed method keeps the privacy and speed of the estimation process and enhances the reliability of the system. It fulfills the requirements of the deregulated energy market. It is also expected to meet the future needs of the smart grid

An Impact of Foreign Remittance on the Profitability of EXIM (Export Import) Bank in Bangladesh

Remittance is an important and challenging issue in the banking industry nowadays. Remittance affects the health of banking sector and also the economy of a country. The aim of this paper is to identify the trend of remittance and the contribution of remittance to the profitability of EXIM Bank. The study based on secondary data collected from the different annual reports of the bank during its establishment year 1999 to 2016. The findings of this study show that remittance has the significant positive impact on the profitability. Keywords: Remittance, Profitability, EXIM BANK, Banglades

Convergence of the Fast State Estimation for Power Systems

Power system state estimation is a fundamental computational process that requires both speed and reliability. To meet the needs, some variants of the constant Jacobian methods have been used in the industry over the last several decades. The variants work very well under normal operating conditions with nominal values of the states. However, the convergence of the methods are not analysed mathematically and it may contain pitfalls. In this study, the convergence of the constant Jacobian methods are analysed and it is shown that the methods fail under high variations of the states. To increase the reliability of the processes, a multi-Jacobian method is proposed. Through simulation, a special case is shown for IEEE 68, and IEEE 118-bus systems where the Jacobian calculated with the nominal value fails, and the proposed multi-Jacobian method succeeds

Power System Distributed Dynamic State Prediction

The security of the power system can be enhanced with the prediction of the dynamic state variables. To increase the security, a distributed predictor is developed based on the Elman Recurrent Neural Network (ERNN) in this study. To develop a scalable distributed predictor, the whole network is divided in a number of ERNNs. They take the current and the previous actual states from its own and its neighbors to predict the near future values of the states. The concept is inspired from the Cellular Computational Network (CCN) framework. Each ERNN is a cell in the CCN framework. Through simulation, the effectiveness of the proposed network is shown for a single step and a multi-step predictor and their accuracies are analyzed for IEEE 68-bus system

ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting

Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature. These networks use parameter sharing by repeating a set of fixed architectures with fixed parameters over time or space. The result is that the overall architecture is time-invariant (shift-invariant in the spatial domain) or stationary. We argue that time-invariance can reduce the capacity to perform multi-step-ahead forecasting, where modelling the dynamics at a range of scales and resolutions is required. We propose ForecastNet which uses a deep feed-forward architecture to provide a time-variant model. An additional novelty of ForecastNet is interleaved outputs, which we show assist in mitigating vanishing gradients. ForecastNet is demonstrated to outperform statistical and deep learning benchmark models on several datasets

Feature weighting and retrieval methods for dynamic texture motion features

Feature weighing methods are commonly used to find the relative significance among a set of features that are effectively used by the retrieval methods to search image sequences efficiently from large databases. As evidenced in the current literature, dynamic textures (image sequences with regular motion patterns) can be effectively modelled by a set of spatial and temporal motion distribution features like motion co-occurrence matrix. The aim of this paper is to develop effective feature weighting and retrieval methods for a set of dynamic textures while characterized by motion co-occurrence matrices