Evaluation and Ranking of Electrical Transmission Reinforcement Options Using Machine Learning Techniques

使用机器学习技术对电力传输加固方案进行评估和排名

基本信息

  • 批准号:
    520329-2017
  • 负责人:
  • 金额:
    $ 1.82万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Engage Grants Program
  • 财政年份:
    2017
  • 资助国家:
    加拿大
  • 起止时间:
    2017-01-01 至 2018-12-31
  • 项目状态:
    已结题

项目摘要

An electrical power grid comprises of three basic elements: power generating stations, load centres, andtransmission lines. A transmission line connects a generating station with a load centre, and the topology of apower grid evolves over time. Drawing a new transmission line from a generating station to a load centre is acomplex decision process involving: (D1) satisfaction of feasibility constraints, policies, and environmentalconstraints, among others; (D2) cost-benefit analysis; and (D3) optimization of power flow. Before carrying outsteps D2 and D3, a grid operator may want to evaluate and rank all possible instances of a new transmissionline by considering D1. In this project, we will develop a framework to evaluate and rank the instances of anew transmission line in a given power grid. The inputs to the framework will be: (i) the topology of the powergrid; (ii) a GPS (Global Positioning System) annotated geographic map of the physical space covered by thegrid; (iii) important features of the geographic space, namely, cities, water bodies, mountains, and off-limitareas; and (iv) regulatory and environmental constraints. The output from the framework will be a ranked listof potential routes for a new transmission line. Each instance of a new transmission line will be described bymeans of a tuple:
电网由三个基本要素组成:发电站、负荷中心和输电线路。输电线路将发电站与负荷中心连接起来,电网的拓扑结构随着时间的推移而变化。绘制一条从发电站到负荷中心的新输电线路是一个复杂的决策过程,涉及:(D1)满足可行性限制、政策和环境限制等;(二)成本效益分析;(D3)潮流优化。在超越D2和D3之前,电网运营商可能希望通过考虑D1来评估和排名所有可能的新传输在线实例。在这个项目中,我们将开发一个框架,对给定电网中新建输电线路的实例进行评估和排序。该框架的输入将是:(i)电网的拓扑结构;(ii)网格所覆盖的物理空间的GPS(全球定位系统)注释地理地图;(三)地理空间的重要特征,即城市、水体、山地、禁区;(四)监管和环境约束。该框架的输出将是一条新传输线的潜在路由的排序列表。新传输线的每个实例将通过元组来描述:

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Naik, Kshirasagar其他文献

Smartphone processor architecture, operations, and functions: current state-of-the-art and future outlook: energy performance trade-off Energy-performance trade-off for smartphone processors
  • DOI:
    10.1007/s11227-020-03312-z
  • 发表时间:
    2020-05-16
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Ginny;Kumar, Chiranjeev;Naik, Kshirasagar
  • 通讯作者:
    Naik, Kshirasagar
A Performance Comparison of Delay-Tolerant Network Routing Protocols
  • DOI:
    10.1109/mnet.2016.7437024
  • 发表时间:
    2016-03-01
  • 期刊:
  • 影响因子:
    9.3
  • 作者:
    Abdelkader, Tamer;Naik, Kshirasagar;Srivastava, Vineet
  • 通讯作者:
    Srivastava, Vineet
Vehicular Networks for a Greener Environment: A Survey
  • DOI:
    10.1109/surv.2012.101912.00184
  • 发表时间:
    2013-01-01
  • 期刊:
  • 影响因子:
    35.6
  • 作者:
    Alsabaan, Maazen;Alasmary, Waleed;Naik, Kshirasagar
  • 通讯作者:
    Naik, Kshirasagar
Optimization of Fuel Cost and Emissions Using V2V Communications
ID-CEPPA: Identity-based Computationally Efficient Privacy-Preserving Authentication scheme for vehicle-to-vehicle communications
  • DOI:
    10.1016/j.sysarc.2021.102387
  • 发表时间:
    2022-01-11
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    Bansal, Udit;Kar, Jayaprakash;Naik, Kshirasagar
  • 通讯作者:
    Naik, Kshirasagar

Naik, Kshirasagar的其他文献

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{{ truncateString('Naik, Kshirasagar', 18)}}的其他基金

Predicting Risks of Forest Fires using Federated Machine Learning Methods
使用联合机器学习方法预测森林火灾风险
  • 批准号:
    570503-2021
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Alliance Grants
An IoT security framework using deep/machine learning techniques for smart offices
使用深度/机器学习技术实现智能办公室的物联网安全框架
  • 批准号:
    563132-2021
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Alliance Grants
Mathematical Models of Mobile Computing Devices and Application Software
移动计算设备和应用软件的数学模型
  • 批准号:
    RGPIN-2017-04238
  • 财政年份:
    2021
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Sustainable wireless sensor networks for long term monitoring of corrosion of water pipes
用于长期监测水管腐蚀的可持续无线传感器网络
  • 批准号:
    528276-2018
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Mathematical Models of Mobile Computing Devices and Application Software
移动计算设备和应用软件的数学模型
  • 批准号:
    RGPIN-2017-04238
  • 财政年份:
    2020
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Mathematical Models of Mobile Computing Devices and Application Software
移动计算设备和应用软件的数学模型
  • 批准号:
    RGPIN-2017-04238
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Sustainable wireless sensor networks for long term monitoring of corrosion of water pipes
用于长期监测水管腐蚀的可持续无线传感器网络
  • 批准号:
    528276-2018
  • 财政年份:
    2019
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Sustainable wireless sensor networks for long term monitoring of corrosion of water pipes
用于长期监测水管腐蚀的可持续无线传感器网络
  • 批准号:
    528276-2018
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Collaborative Research and Development Grants
Mathematical Models of Mobile Computing Devices and Application Software
移动计算设备和应用软件的数学模型
  • 批准号:
    RGPIN-2017-04238
  • 财政年份:
    2018
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual
Mathematical Models of Mobile Computing Devices and Application Software
移动计算设备和应用软件的数学模型
  • 批准号:
    RGPIN-2017-04238
  • 财政年份:
    2017
  • 资助金额:
    $ 1.82万
  • 项目类别:
    Discovery Grants Program - Individual

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