An Extended Model Predictive-Sliding Mode Control for Three-Level AC/DC Power Converters

Abstract- In the proposed approach, an Extended Model Predictive Sliding Mode Controller (EMPSMC) was designed to control three-level AC / DC power converters for better dynamic performance and better achievement. The traditional proportional integration (PI) controller is used in the model predictive PI controller (MPPIC) technique to generate active power reference. However, this technique results in a significant overshoot/undershirt and steady-state error. Instead of PI, sliding mode control (SMC) is used to address these shortcomings. The performance of EMPSMC and MPPIC is compared and analyzed without interruption. The results show that the introduction of SMC reduces the time lag of the system and reduces overshoot. The simulation results validate the performance of the designed model.

Spatio – Temporal Data Mining Framework for Natural Resource Exploration

Technology has allowed for a substantial increase in success rate of identifying the presence of energy sources such as oil and natural gas. Data mining, an emerging technology characterized by significantly advanced analytical tools, can contribute to this success rate as it has the potential to guide or at least assist opportunists in hydrocarbon prediction. To make a prediction about presence of hydrocarbon reserve beneath the surface of earth involves geological, geochemical, seismic and microbial prospecting. The test methods involved in the mentioned process need a great deal of cost and time. This project is aimed at developing decision support system to improve the process of hydrocarbon need evaluation and reserve detection by integrating the methods and tools from data mining and potential surface analysis to approach the problem from an interdisciplinary stance. In the thesis, the world countries are classified with respect to sustainable energy development with the underlying assumption that hydrocarbon is the major source of energy all over the world. The addressed question is whether the hydrocarbon reserves in the world comply with its consumption? As a result, two possibilities arose to ensure energy sustainability: (1). To provide an optimal framework for improvement in hydrocarbon exploration process. (2). To provide a framework for improvement in hydrocarbon consumption. The study is about the aforementioned. The presence of hydrocarbon reserves beneath earth‘s surface is predicted on the basis of either (a). Surface indicators or (b). Beneath surface parameters. The surface indicators which are considered in this project may consist of geological and microbial indicators. In state of the art geological and microbial methodologies, the cost and time involved is in affordable. The research attempts to replace geological predictions with intelligent remote sensing and microbial indications with microbe data mining. But the existing techniques of data mining cannot produce desired accuracy if applied to surface indicators database. Some data mining techniques for mining temporal spatial and non spatial data related to surface indicators of hydrocarbon reserves are proposed. The model includes the classification mechanism of world countries on the basis of sustainable hydrocarbon development and then extraction of useful patterns from surface indicators to predict hydrocarbon reserve in a time and cost effective manner. A series of empirical investigations have been made to evaluate the performance of proposed techniques using different and diverse databases that show the effectiveness of methodology.