Innovative Projects Realized

Monitoring of turbine runner blade strains from indirect measurements using AI

Hydro-Québec has data acquisition systems for a multitude of sensors, some of which have been installed since almost 20 years in its electrical generation equipment (turbine-generator units – TGU). The collected data is primarily used to ensure that the information is adequate in the event of an equipment breakdown or for specific behavioral studies. Data from monitoring systems are little used in routine maintenance management activities, often due to lack of time and adequate and/or effective analysis methods.
Equipment maintenance is an important part of Hydro-Québec’s equipment management activities. The creation and maintenance of a surveillance system is a major investment for the company. With the development of machine learning analysis approaches, the goal is to provide operators with a clearer view of real-time asset status and predictions about their potential for use.

Short term Electrical Load Forecasting

Load forecasting is an essential activity for a company like Hydro-Québec. It is necessary for objectives as varied as the management of production or the management and maintenance of the electricity network. Any significant forecasting error can result in reliability issues, loss of opportunity, or additional costs to the business. On the other hand, a good prediction would allow Hydro-Québec to generate additional sales in neighbouring markets. With the deployment of its Advanced Measurement Infrastructure (AMI), Hydro-Québec now has a significant amount of new consumption data. This data can be used to improve demand forecasting, increasing reliability, decreasing expenses, and potentially generating new revenue.

Macroeconomic changes such as the decline of heavy industry, the recent changes in society (teleworking, variable rates, etc.) and in the future (transport electrification, behind the meter production, storage, smart grids, active role of the consumer, etc.) are current and incoming challenges for the parametric forecasting models such as those developed and currently used by HQ, since the load is more and more difficult to modelize with no clear physical phenomena and measures to explain it.

Active learning for visual detection on inspection robots

Robotics vehicles deployed at Hydro-Québec up to now are still mainly manually operated and human intervention is continuously required. The project aims to equip Hydro-Québec’s current and future fleet of inspection robots with autonomous inspection capabilities. The intern will leverage breakthroughs in artificial intelligence to enable robotic vehicles to realize real-time automated visual inspection of the company’s infrastructure and use a simply and securely deployable robotic vehicle to perform the company’s first fully autonomous power line components inspection mission. The large-scale deployment of autonomous inspection robots will have a major impact to help Hydro-Québec in asset management and gain in operational efficiency.

Solar Radiation Forecasting

The main duty of Hydro-Quebec is to respond efficiently to the energy demands of customers, in a safe way while remaining competitive in the markets as well. In a changing energy context, the production of solar photovoltaic energy represents a new challenge for Hydro-Quebec, which will have to integrate and to balance this intermittent resource to guarantee the reliability of the electricity grid. The objective of this project is to support Hydro-Quebec in the development of a future-oriented energy system by proposing innovative technological solutions notably for solar radiation forecasting. The project will focus on developing a state-of-the-art system using artificial intelligence algorithms to predict solar radiations 24 hours in the future from satellite images and numerical weather data.

GROKVIDEO

By combining the information contained in the visual, audio and text content of videos, it is possible to extract complex information about their content. It’s then possible to analyse a query from a search engine to find the video segments that best matches this query. During this project, the intern will be using state-of-the-art deep learning models to extract the best possible information from multi-source data and participate in the integration of these models in the Grokvideo search engine application. Increasing the quality of the information and the accuracy of the search engine will directly benefit the company as extracting the best possible information from video content is the core value of Grokvideo.

Brain Lesion Detection

Brain MRI scans are a critical component in the diagnosis of neurodegenerative disorders and their use will only increase in the following years. However, there is a wide diversity in terms of the image quality and resolution obtained across different sites and there is a need for robust methods that can handle such diversity. The goal of this project is to develop and validate the performance of state-of-the-art lesion detection methods for 3D brain MRIs.

Work Improvement and Data Analytics for Industrial Steel Fabrication

Work improvement is critical for performance increase in business environments. It is used to identify bottlenecks and inefficiencies in the manufacturing and other production processes, and to improve work performance by removing non-value-added activities. To conduct work improvement, the Lean Manufacturing concept is often used along with the Value Stream Mapping (VSM), a tool for visualizing the production processes and productivity metrics. In this research project, Ocean Steel & Construction Ltd., out of Saint John, New Brunswick has partnered with the research team at the University of New Brunswick’s Off-site Construction Research Centre to study their production facility and improve their productivity. In this proposed study, four sub-objectives are defined: (1) use process mapping to document the steel fabrication processes and collect productivity data at each process (e.g. workstation); (2) conduct Exploratory Data Analysis (EDA) to summarize the productivity metrics for each process; (3) use regression modeling to develop predictive models for processing time, and correlation analysis to identify the impact factors of productivity; and (4) develop Value Stream Mapping (VSM) to improve the production processes and incorporate predictive models into the VSM approach. The intern will have a chance to interact with professionals and gain exposure to the construction industry.

Influences of Metal-Organic Polyhedron (MOP) Geometry on the Morphology of Porous Supramolecular Polymers

For our research project, we propose to explore how the geometry of the porous cage molecule, the metal-organic polyhedron (MOP), can affect its self-assembly into large gels, and how it can affect their resulting shapes and properties. We anticipate that the potential to use these porous molecules with differing geometries and pore sizes can offer an extra degree of control in the materials properties of these structures, including their mechanical behaviour and gas sorption properties. This can intern open doors to the diversity of the applications of these porous, solid materials in areas such as mass transport, separation and storage, and catalysis.

Enabling maximum penetration of distributed generations withoutviolating protection system coordination

Driven by economic, technical and environmental reasons, the energy sector is moving into an era
where Distributed Generation (DG) will meet a large segment of increasing electrical energy demand.
Local distribution companies are required to accept a given percentage of customer-owned renewable
DG units in their system. Generally, DG introduces new possibilities such as shaving of peak loads
and reliability enhancement. On the other hand, DG causes a number of protection problems in the
distribution networks, such as loss of coordination, de-sensitization, nuisance fuse blowing, relay
bidirectional operation, and overvoltage. In this work, general guidelines about maximum allowable
DG penetration that will not ignite these adverse effects will be identified for typical feeders in Ontario.
Moreover, a recommendation about suitable connection of the interfacing transformer to avoid
overvoltage during ground faults will be given. Furthermore, general recommendations will be given at
the end of this internship about possible methods that can be used to overcome any protection
coordination problem if it…..TOBECONT’D

Développement de modèles de détection d’anomalies pour les parcs éoliens et solaires

Power Factors offre une plateforme de suivi de performance de parcs éoliens et solaire. Cette plateforme collecte les données opérationnelles des parcs, les archive, puis les traite (calcul d’indicateurs de performance) pour ensuite les transmettre aux exploitants et propriétaires de ces parcs. Power Factors utilise déjà quelques modèles de détection d’anomalies à travers sa plateforme d’analytiques avancées. Ces modèles ont pour but d’identifier des anomalies et des tendances alarmantes telles que la détection précoce d’un bris ou d’une sous-performance et par la suite, aider les exploitants de ces parcs dans leurs opérations de planification de la maintenance.L’objectif du projet est de développer de nouveaux modèles de détection d’anomalie pour les parcs éoliens et solaires, ce qui permettrait à Power Factors de garder un avantage concurrentiel considérable.

Work, Family, Life of Police Offices and Their Families During and After a Pandemic – Examination of an Extreme Case

The COVID-19 pandemic is an on-going health crisis which is having a dramatic impact on how people in Canada and around the world live their lives. In response to the rapid spread of the virus, provincial and federal governments have enacted a number of ‘physical distancing’ measures, including closures of publicly-funded schools and all non-essential businesses. This has resulted in unprecedent work/life situations for thousands of Canadians. Police, as an essential frontline service, face particular challenges during this pandemic. They must continue their work on the frontline and risk exposure for themselves and their family to the novel coronavirus. They must balance the requirements of a stressful job (demands which have themselves changed because of the pandemic) with the needs of their children (who are now at home), their partner (who may now be unemployed or working from home) and worries about their elderly family members. We propose to undertake a longitudinal multimethod research study that will examine how police, and their spouses/partners are coping with work, family, and life during the pandemic.

Temperature dependent performance of high load multirotational bridge bearings

Bridge bearings are used to support the weight of the bridge superstructure on the bridge piers. These bearings allow for the bridge to expand and contract during thermal cycles and accommodate the varying forces on the bridge throughout its life. Elastomers within the bearing are flexible enough to allow for these rotation and translation demands while still effectively transferring forces. This study investigates the effects of combined axial forces and rotation demands on two types of bridge bearings for use within Canada. The bearings will also be subjected to extreme cold temperatures that reflect the design demands for Canadian bridges. The experimental and computer modeling results of this study will allow Canam Bridges, the partner organization, to specify safe and efficient bearing designs for Canadian bridges.