Air-core dry-type electrical reactors are integrated into power system infrastructures to limit current and regulate voltage in transmission lines. These reactors, are designed and built to facilitate customer specific requirements using an elementary noise prediction model, which was developed almost 30 years ago. With increasingly stricter noise emission guidelines set by the environmental regulatory bodies, the need to better predict and meet specific noise requirements has become more important to the design and manufacturing of the reactors.
The objective of this project is to develop an analytics tool for REALPAC to use to better classify buildings using the â20 x â15â dataset collected by REALPAC since 2009. Preliminary analysis has been conducted of this data in past years, but this has been limited to a simple retrospective analysis.
Rapid growth of renewable energy generation and DC loads as electric vehicles and consumer electronics results in the proliferation of DC microgrids. DC microgrids are small electrical grids where energy sources and loads are connected to a main energy distribution power line using power converters.
Two High Voltage DC (HVDC) transmission technologies, the mature Line Commutated Converter (LCC) and newer Voltage Source Converter (VSC) technologies have their own pros and cons. For a HVDC transmission system carrying power from a single generation center to multiple load centers, by using a multi-terminal LCC-VSC type hybrid HVDC configuration, advantages of both technologies can be exploited. There is also the possibility of tapping into existing point-to-point LCC transmission lines using this hybrid configuration to supply intermediate locations.
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.
In the wake of the Paris meeting on global climate change in December of 2015 (COP21), commitments to drive down greenhouse gas emissions have escalated around the world. Man-made carbon dioxide (CO2) emissions are accepted as the largest contributor to climate change. Promising next-generation technologies for decreasing CO2 emissions are being investigated at the lab scale. Unfortunately, the technology developers often lack next-step projects and connections with industrial end-users to allow the technology to advance and become commercialized.
Yukon Energy Corporation (YEC) began generating hydro-electricity at the Aishihik Hydro Facility, situated within Champagne and Aishihik First Nations (CAFN) Traditional Territory, Yukon, in 1975. Their continuing water use license will expire in 2019. Notwithstanding the Aishihik facilitys 41 years of energy production, CAFN has repeatedly expressed social and environmental concerns associated with the facility's operation.
During the proposed internships, smart-grid integrated adaptive corrosion protection system (ACPS) will be developed as a stand-alone unit to provide optimum corrosion protection along with the nanostructured local data storage and off-grid powering. This will allow the continuous monitoring of the corrosion status of the metal infrastructures (e.g. transmission towers) along with the power-grid monitoring data. The proposed system can be directly monitored from the centralized control-room.
Lithium ion batteries (LIBs) are considered the top candidates among electrochemical energy storage systems (ESS) due to their high energy density which has triggered the growth market of popular devices such as cell phones, computers, electric vehicles (EV) etc. As ESS, LIBs are continuously charged and discharged during their utility. The charge/discharge cycle is known to contribute towards their degradation depending on the charging protocol and operating conditions.
Degradation assessment for critical assets in power generation has great significance for power industry. Existing degradation assessment models failed either in combining the multimodal condition monitoring data or in removing time-varying working condition influences, resulting in inaccurate and/or unreliable degradation assessment results. In order to achieve robust and accurate degradation assessment for power generation critical assets, this project aims at developing new models based on both maintenance data and condition monitoring data from ENMAX.