Reference number : GC-2017-Q4-01333, amendment number : , agreement type : G, recipient legal name : Naik, Kshirasagar (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : An electrical power grid comprises of three basic elements: power generating stations, load centres, and_x000D_
transmission lines. A transmission line connects a generating station with a load centre, and the topology of a_x000D_
power grid evolves over time. Drawing a new transmission line from a generating station to a load centre is a_x000D_
complex decision process involving: (D1) satisfaction of feasibility constraints, policies, and environmental_x000D_
constraints, among others; (D2) cost-benefit analysis; and (D3) optimization of power flow. Before carrying out_x000D_
steps D2 and D3, a grid operator may want to evaluate and rank all possible instances of a new transmission_x000D_
line by considering D1. In this project, we will develop a framework to evaluate and rank the instances of a_x000D_
new transmission line in a given power grid. The inputs to the framework will be: (i) the topology of the power_x000D_
grid; (ii) a GPS (Global Positioning System) annotated geographic map of the physical space covered by the_x000D_
grid; (iii) important features of the geographic space, namely, cities, water bodies, mountains, and off-limit_x000D_
areas; and (iv) regulatory and environmental constraints. The output from the framework will be a ranked list_x000D_
of potential routes for a new transmission line. Each instance of a new transmission line will be described by_x000D_
means of a tuple: <source, destination, rank, sequence of GPS locations>. The <source, destination> pair will_x000D_
identify the two end points of a transmission line, the sequence of GPS locations will define the physical route,_x000D_
and the rank of the transmission line will indicate the degree of its satisfaction of all its input constraints. The_x000D_
ranking problem on hand is a challenging one because of two reasons: (i) there is a need to identify a physical_x000D_
route in the geographic context; and (ii) all possible routes need to be evaluated against the given constraints to_x000D_
produce a total ordering. We will achieve two milestones in this project: model the geographic space covered_x000D_
by the power grid as a multi-feature labeled graph; and design a machine-learning based algorithm to evaluate_x000D_
and rank the top routes. Results from this research will enable the industry partner, SNC-Lavalin, to consider_x000D_
developing a software system for the automation of selection of routes for new transmission lines., program purpose french : An electrical power grid comprises of three basic elements: power generating stations, load centres, and_x000D_
transmission lines. A transmission line connects a generating station with a load centre, and the topology of a_x000D_
power grid evolves over time. Drawing a new transmission line from a generating station to a load centre is a_x000D_
complex decision process involving: (D1) satisfaction of feasibility constraints, policies, and environmental_x000D_
constraints, among others; (D2) cost-benefit analysis; and (D3) optimization of power flow. Before carrying out_x000D_
steps D2 and D3, a grid operator may want to evaluate and rank all possible instances of a new transmission_x000D_
line by considering D1. In this project, we will develop a framework to evaluate and rank the instances of a_x000D_
new transmission line in a given power grid. The inputs to the framework will be: (i) the topology of the power_x000D_
grid; (ii) a GPS (Global Positioning System) annotated geographic map of the physical space covered by the_x000D_
grid; (iii) important features of the geographic space, namely, cities, water bodies, mountains, and off-limit_x000D_
areas; and (iv) regulatory and environmental constraints. The output from the framework will be a ranked list_x000D_
of potential routes for a new transmission line. Each instance of a new transmission line will be described by_x000D_
means of a tuple: <source, destination, rank, sequence of GPS locations>. The <source, destination> pair will_x000D_
identify the two end points of a transmission line, the sequence of GPS locations will define the physical route,_x000D_
and the rank of the transmission line will indicate the degree of its satisfaction of all its input constraints. The_x000D_
ranking problem on hand is a challenging one because of two reasons: (i) there is a need to identify a physical_x000D_
route in the geographic context; and (ii) all possible routes need to be evaluated against the given constraints to_x000D_
produce a total ordering. We will achieve two milestones in this project: model the geographic space covered_x000D_
by the power grid as a multi-feature labeled graph; and design a machine-learning based algorithm to evaluate_x000D_
and rank the top routes. Results from this research will enable the industry partner, SNC-Lavalin, to consider_x000D_
developing a software system for the automation of selection of routes for new transmission lines., agreement title english : Evaluation and Ranking of Electrical Transmission Reinforcement Options Using Machine Learning Techniques, agreement title french : Evaluation and Ranking of Electrical Transmission Reinforcement Options Using Machine Learning Techniques, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-02-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada