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Using Machine Learning and Google street View for Condition Assessment of Wood Poles

使用机器学习和谷歌街景进行木杆状况评估

基本信息

批准号：
544129-2019
负责人：
RezaeiZare, Afshin
金额：
$ 1.82万
依托单位：
York University
依托单位国家：
加拿大
项目类别：
Engage Grants Program
财政年份：
2019
资助国家：
加拿大
起止时间：
2019-01-01 至 2020-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681284
关键词：
Using Machine Learning Google street

项目摘要

In today power systems, extensive progress is being made in the energy transition from centralized fossilfuel generation to renewable distributed generation sources namely wind and solar. However, this new development is limited by numerous existing challenges in the distribution systems including the aging infrastructure and policy issues. As a result, the bulk share of capital spending is directed towards renewal project hence leaving little or no room to invest in smart grid initiatives. Aging infrastructure employed on power distribution systems requires major capital investments into renewal and maintenance of aged assets to ascertain that the distribution systems continue to operate with increased level of safety and reliability. These capital investments are primarily driven by increased public scrutiny from interveners and energy regulators. The wood poles represent major fraction of asset total replacement value in any distribution system hence the condition assessment of wood poles is of great significance. To date, condition assessment of wood poles is performed manually by inspecting each pole which is a labor-intensive inspection data gathering process. The key objective of this project is to apply machine learning technique and google street view to estimate the condition of wood poles used in power distribution systems. The proposed smart approach would increase safety for employees and end customers while ensuring reliable power delivery to the community.

在当今的电力系统中，从集中式化石燃料发电向可再生分布式发电(即风能和太阳能)的能源过渡正在取得广泛进展。然而，这一新的发展受到分配系统中许多现有挑战的限制，包括基础设施老化和政策问题。因此，资本支出的大部分用于更新项目，因此很少或根本没有投资于智能电网倡议的空间。配电系统上使用的老化基础设施需要大量资本投资更新和维护老化资产，以确保配电系统继续以更高的安全和可靠性水平运行。这些资本投资主要是由干预者和能源监管机构加强的公众监督推动的。在任何分配系统中，木杆都是资产重置价值的主要组成部分，因此对木杆的状态评估具有重要意义。到目前为止，木杆的状态评估是通过对每根杆子进行检查来手动进行的，这是一个劳动密集型的检查数据收集过程。该项目的主要目标是应用机器学习技术和谷歌街景来评估配电系统中使用的木杆的状况。拟议的智能方法将提高员工和最终客户的安全，同时确保向社区可靠地供电。