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

在今天的电力系统中，从集中式化石燃料发电到可再生分布式发电源（即风能和太阳能）的能源转型正在取得广泛进展。然而，这一新的发展受到分销系统中许多现有挑战的限制，包括老化的基础设施和政策问题。因此，资本支出的大部分都用于更新项目，因此很少或根本没有空间投资于智能电网计划。配电系统上采用的老化基础设施需要对老化资产的更新和维护进行重大资本投资，以确保配电系统继续以更高的安全性和可靠性水平运行。这些资本投资主要是由干预者和能源监管机构加强公众监督推动的。在任何配电系统中，木电杆都是资产总重置价值的主要部分，因此木电杆的状态评估具有重要意义。迄今为止，木质杆的状况评估是通过检查每个杆手动执行的，这是劳动密集型的检查数据收集过程。这个项目的主要目标是应用机器学习技术和谷歌街景来估计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性，同时确保向社区提供可靠的电力。

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.