DC Interconnection Hubs

Conventional power systems are based upon ac voltages and currents. Connecting these systems is a simple matter and is done using transformers. Modern power systems wherein renewable energy sources are increasingly deployed often include dc voltages and currents. Connecting these systems is more challenging as conventional transformers will not be applicable. The proposed research is aimed at investigating and evaluating options for linking and interconnecting dc power systems. Power electronics is the enabling technology for achieving dc system interconnections.

Artificial Neural Net for He nano-bubble identification in structural materials for nuclear power applications

Nuclear power plants provide stable, carbon-free electricity to Canadians. In order to ensure their safe operations, materials in the reactors must be characterized on a regular basis. This project aims at developing an Artificial Intelligence—an artificial neural network—with the aim of automating the indentification of helium bubbles in Ni-based alloys currently in use in Canadian Nuclear power plants. These bubbles have a diameter of the order of nanometers, and can be observed using transmission electron microscopes. Currently, the analysis of the micrographs is done manually.

A set membership filtering approach to low-complexity state estimation from PMU measurements

The widespread use of phasor measurement units (PMUs) in power-grids can greatly enhance state-estimation (SE) by making use of accurate, GPS time-stamped synchronous phasor measurements. Unlike conventional SCADA measurements which are reported every 4 seconds, synchro-phasor measurements are typically available as frequently as 30-60 measurements per second. While the availability of more measurements can provide accurate state estimates in real-time, the sheer amount of data can overwhelm the computational capabilities of most data processing systems.

Modeling and Dynamic Performance Assessment of a Battery Energy Storage Systems

Bulk storage of energy is a relatively new concept in many power systems. Among various energy storage media, batteries have shown great promise as a suitable option for use in power systems. Integrating a battery energy storage system in a power grid is not a trivial task and requires extensive studies to ensure that the system is able to respond satisfactorily to its surrounding’s variable conditions and deliver what is expected of it.

Predictive Control Approach for Converted Multi Zone Residential Buildings with Central HVAC Systems

The current project aims to study a novel energy management system for residential heating ventilating and air conditioning (HVAC) system. Independently controlled wireless air damping vents will adjust the air flow in different zones of the building allowing independent control of the temperature which results in enhanced thermal comfort and energy savings. The intern will collaborate with the partner organization on studying a unique state-of-the-art predictive model to control the damping factor of the vents within fully closed to fully open range.

Improved Thermoplastic to Oil Pyrolysis Process Technology with Advanced Plasma Technology

The field of plastic waste management is essential for sustainable society that utilizes plastic waste for energy production. Land filing and incineration of plastic waste has large environmental impacts due to GHG emissions. Thus, pyrolysis is considered a low environmental impact process with high value end products. RF thermal plasma technology will help reduce operating cost, cleaner thermal source, shorten reaction time and provide high quality hydrocarbon gasoline and diesel.

Development of computationally efficient models for modular multilevel converters with integrated battery energy storage systems

The research project aims to develop new computer models for accurate representation of battery energy storage systems that are used in modern power systems. In particular state-of-the-art modular multi-level converters with integrated dc-dc converters will be considered. The models to be developed will provide high levels of accuracy and feature low computational intensity so that study of battery systems that are integrated into the grid using advanced converter systems becomes feasible on present-day computing systems.

A Deep Learning Approach to Soft Sensor Design and Process Optimization for an Industrial Nickel Extraction Process

The objective of this project is to use artificial intelligence (AI) approaches to solve complex industrial problems. The two biggest advantages of AI-based approaches are the ability to continuously learn and also learn adequately from historical data. Traditionally, many process information are unmeasurable during live operations because of instrumentation limitations. Also, plants are not sufficiently optimized to maximize production quality, while minimizing waste.

Hybrid Distributed Energy Resources for Net-Zero Energy Buildings

The main objective of the project is to develop hybrid distributed energy resource (HDER) systems to supply energy to net-zero energy commercial and residential buildings. This is expected to result in lower energy costs to consumers and utilities and in greater reliability of the grid. The HDERs will consist of solar panels, generators, and batteries. They will supply buildings with energy whenever possible and feed any excess energy to the grid. The grid will supply energy to the buildings whenever energy from the HDERs is insufficient.

Power transformers reliability modeling with data obtained in structure monitoring using fiber optics sensing

The intern will work on building power transformers’ aging models based on reliability theories and actual field test data collected using optic fiber based health monitoring technologies. This is part of a novel solution to handle the accident prevention and maintenance for major power and grid equipment. The novelty lies in that the model verification and execution will be realized with physical parameters directly obtained from transformers in real-time. The data used will be those collected on in-service transformers.

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