Case Study

How AiDash transformed Vegetation Management for a Fortune 500 company

9th Mar, 20

Managing vegetation effectively to maintain system reliability continues to be a major challenge for power utilities, especially when you are a Fortune 500 utility company with distribution lines spanning over 50,000 miles.

So, when our client was seeking to transform the way they conducted vegetation management, we employed cutting-edge technology and innovation powered by Artificial Intelligence combined with strong business competencies to work for them. We customized our product, AiDash Intelligent Vegetation Management System (IVMS), to meet their specific needs, helping them minimize costs and improve reliability in the process.

Key challenges

Vegetation management has never been an easy task to accomplish. It is often one of the largest operational expense items for power utilities, which can result in them spending over $100 million per year. Let’s understand the challenges our customer faced in optimizing their vegetation management budgets and carrying out the tasks in a timely and efficient manner.

  1. Legacy system, traditional approach - At a time when technology is empowering businesses, power utilities still rely on their legacy system and traditional approach for vegetation management. Historically, vegetation management has been driven by:
    - Scheduled trimming cycles every 4-6 years
    - Trees outside of Right of Way dealt with reactively
    - Manual processes of data collection 
    - Minimal predictive capabilities to prevent future outages, damages and costs
    - Difficult and time-consuming ways of information exchange 
     
  2. Lack of visibility - When your transmission system and distributed assets are spread across over 10,000 circuit miles across multiple states, it is impossible to manage vegetation efficiently via a manual approach. From data collection to data analysis, everything was being done on an ad-hoc basis.
     
  3. Very high costs - Vegetation management is often the single largest preventive maintenance expense in annual operating budgets, exceeding $100 million annually in many larger utilities. The lack of visibility with respect to urgent situations and hazards, inability to identify the exact point of failure or even prioritize tasks optimally resulted in reactive and ad-hoc maintenance that is primarily expensive.
     
  4. Increasing losses and risks of liabilities - As assets age and are impacted by weather and surroundings, power outages are a major risk for power utilities. Outages cost an average of about $33 billion per year in the United States alone. In addition to this, increasing scrutiny from regulators, legislators, activists, media and customers has caused utilities to understand the increasing risks of liabilities.

Our solution

We embarked on a mission to transform the way our Fortune 500 customer conducted vegetation management. For us, technology runs the show. And when we say technology, we’re talking Artificial Intelligence and Machine Learning. Our solution evolved in two phases:

The first phase: We deployed a centralized and robust AI-first platform and started developing a Deep Neural Network (DNN) model that could offer end-to-end visibility of the customer’s assets. The plan was to enable them to predict, plan and prioritize vegetation management tasks on-demand anywhere, anytime. To help our customer with an end-to-end vegetation management product, we started building an AI workbench, along with a user-friendly web app and mobile app. 

Our model identified and tracked the historic growth rate of species, weather, soil, and used the customer count on the feeder and past outage data. This data was used to make prioritized predictions on what trees need to be trimmed and when, keeping in mind the annual budget and reliability targets. While the AI workbench would work as the centralized control panel for operators, the mobile app could simplify the procedure and enable the field force to report and execute from the field. While the first phase gave us over 10% saving in cost and reliability, the AI model was only 75% accurate and predictions were made at feeder level. For more accurate predictions, we moved on to the second phase. 

The second phase: It was in this phase that we developed the new satellite-powered AI model that used 50 cm high-resolution multispectral satellite imagery from leading satellite constellations. Using these imageries and a deep neural network model, the system was able to learn and then predict the growth rate of the species in each feeder at a span level, i.e. power lines between two consecutive poles. The span level predictions were then clustered into a more practical plan -- at section or sub-feeder and feeder level -- and a 3-year to 5-year trim plan for the entire network was prepared with an accuracy of over 85%. The trim plans were then moved into work orders that can be assigned to contractors. The post-trim satellite image analysis can be used to audit the compliance of the clearance without the need for a supervisor to go on the field.

Take a look at the screenshots of our application to understand how IVMS simplifies the procedure of vegetation management for all stakeholders:

AiDash Mobile AppAiDash Web Dashboard

Business Benefits

Our plug-and-play AI model for vegetation management enabled our customer to:

  • Predict the growth rate of different tree species along power lines
  • Real-time identification of risks
  • Zoomed-in actions for individual trees
  • Plan Vegetation management  ops years in advance
  • Engage and enable the field force
  • Allowed a seamless, app-based contractor management
Outcome


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