This post is co-written with Andreas Astrom from Northpower.
Northpower provides reliable and affordable electricity and fiber internet services to customers in the Northland region of New Zealand. As an electricity distributor, Northpower aims to improve access, opportunity, and prosperity for its communities by investing in infrastructure, developing new products and services, and giving back to shareholders. Additionally, Northpower is one of New Zealand’s largest infrastructure contractors, serving clients in transmission, distribution, generation, and telecommunications. With over 1,400 staff working across 14 locations, Northpower plays a crucial role in maintaining essential services for customers driven by a purpose of connecting communities and building futures for Northland.
The energy industry is at a critical turning point. There is a strong push from policymakers and the public to decarbonize the industry, while at the same time balancing energy resilience with health, safety, and environmental risk. Recent events including Tropical Cyclone Gabrielle have highlighted the susceptibility of the grid to extreme weather and emphasized the need for climate adaptation with resilient infrastructure. Electricity Distribution Businesses (EDBs) are also facing new demands with the integration of decentralized energy resources like rooftop solar as well as larger-scale renewable energy projects like solar and wind farms. These changes call for innovative solutions to ensure operational efficiency and continued resilience.
In this post, we share how Northpower has worked with their technology partner Sculpt to reduce the effort and carbon required to identify and remediate public safety risks. Specifically, we cover the computer vision and artificial intelligence (AI) techniques used to combine datasets into a list of prioritized tasks for field teams to investigate and mitigate. The resulting dashboard highlighted that 141 power pole assets required action, out of a network of 57,230 poles.
Northpower challenge
Utility poles have stay wires that anchor the pole to the ground for extra stability. These stay wires are meant to have an inline insulator to avoid the situation of the stay wire becoming live, which would create a safety risk for person or animal in the area.
Northpower faced a significant challenge in determining how many of their 57,230 power poles have stay wires without insulators. Without reliable historical data, manual inspections of such a vast and predominantly rural network is labor-intensive and costly. Alternatives like helicopter surveys or field technicians require access to private properties for safety inspections, and are expensive. Moreover, the travel requirement for technicians to physically visit each pole across such a large network posed a considerable logistical challenge, emphasizing the need for a more efficient solution.
Thankfully, some asset datasets were available in digital format, and historical paper-based inspection reports, dating back 20 years, were available in scanned format. This archive, along with 765,933 varied-quality inspection photographs, some over 15 years old, presented a significant data processing challenge. Processing these images and scanned documents is not a cost- or time-efficient task for humans, and requires highly performant infrastructure that can reduce the time to value.
Solution overview
Amazon SageMaker is a fully managed service that helps developers and data scientists build, train, and deploy machine learning (ML) models. In this solution, the team used Amazon SageMaker Studio to launch an object detection model available in Amazon SageMaker JumpStart using the PyTorch framework.
The following diagram illustrates the high-level workflow.
Northpower chose SageMaker for a number of reasons:
- SageMaker Studio is a managed service with ready-to-go development environments, saving time otherwise used for setting up environments manually
- SageMaker JumpStart took care of the setup and deployed the required ML jobs involved in the project with minimal configuration, further saving development time
- The integrated labeling solution with Amazon SageMaker Ground Truth was suitable for large-scale image annotations and simplified the collaboration with a Northpower labeling workforce
In the following sections, we discuss the key components of the solution as illustrated in the preceding diagram.
Data preparation
SageMaker Ground Truth employs a human workforce made up of Northpower volunteers to annotate a set of 10,000 images. The workforce created a bounding box around stay wires and insulators and the output was subsequently used to train an ML model.
Model training, validation, and storage
This component uses the following services:
- SageMaker Studio is used to access and deploy a pre-trained object detection model and develop code on managed Jupyter notebooks. The model was then fine-tuned with training data from the data preparation stage. For a step-by-step guide to set up SageMaker Studio, refer to Amazon SageMaker simplifies the Amazon SageMaker Studio setup for individual users.
- SageMaker Studio runs custom Python code to augment the training data and transform the metadata output from SageMaker Ground Truth into a format supported by the computer vision model training job. The model is then trained using a fully managed infrastructure, validated, and published to the Amazon SageMaker Model Registry.
- Amazon Simple Storage Service (Amazon S3) stores the model artifacts and creates a data lake to host the inference output, document analysis output, and other datasets in CSV format.
Model deployment and inference
In this step, SageMaker hosts the ML model on an endpoint used to run inferences.
A SageMaker Studio notebook was used again post-inference to run custom Python code to simplify the datasets and render bounding boxes on objects based on criteria. This step also applied a custom scoring system that was also rendered onto the final image, and this allowed for an additional human QA step for low confidence images.
Data analytics and visualization
This component includes the following services:
- An AWS Glue crawler is used to understand the dataset structures stored in the data lake so that it can be queried by Amazon Athena
- Athena allows the use of SQL to combine the inference output and asset datasets to find highest risk items
- Amazon QuickSight was used as the tool for both the human QA process and for determining which assets needed a field technician to be sent for physical inspection
Document understanding
In the final step, Amazon Textract digitizes historical paper-based asset assessments and stores the output in CSV format.
Results
The trained PyTorch object detection model enabled the detection of stay wires and insulators on utility poles, and a SageMaker postprocessing job calculated a risk score using an m5.24xlarge Amazon Elastic Compute Cloud (EC2) instance with 200 concurrent Python threads. This instance was also responsible for rendering the score information along with an object bounding box onto an output image, as shown in the following example.
Writing the confidence scores into the S3 data lake alongside the historical inspection results allowed Northpower to run analytics using Athena to understand each classification of image. The sunburst graph below is a visualization of this classification.
Northpower categorized 1,853 poles as high priority risks, 3,922 as medium priority, 36,260 as low priority, and 15,195 as the lowest priority. These were viewable in the QuickSight dashboard and used as an input for humans to review the highest risk assets first.
At the conclusion of the analysis, Northpower found that 31 poles needed stay wire insulators installed and a further 110 poles needed investigation in the field. This significantly reduced the cost and carbon usage involved in manually checking every asset.
Conclusion
Remote asset inspecting remains a challenge for regional EDBs, but using computer vision and AI to uncover new value from data that was previously unused was key to Northpower’s success in this project. SageMaker JumpStart provided deployable models that could be trained for object detection use cases with minimal data science knowledge and overhead.
Discover the publicly available foundation models offered by SageMaker JumpStart and fast-track your own ML project with the following step-by-step tutorial.
About the authors
Scott Patterson is a Senior Solutions Architect at AWS.
Andreas Astrom is the Head of Technology and Innovation at Northpower
