Our client is one of the world’s leading embedded technology service providers
Business Challenge and Objectives
- High dust deposition on insulators/transformers leads to a rise in insulator temperature, causing grid failure.
- The grid failure in-turn results in revenue losses for the organization.
- The client currently uses state-of-the-art automation systems to capture the images of insulator/transformers at the substations to monitor the dust deposition on them.
- The client wanted to fetch and manage video feed data from 0.3 million towers.
- The client also required an analytics ecosystem to trigger preventive cleaning alerts on-time to avoid grid failure.
The TekLink provided a holistic solution considering the complexity of the factors involved in the grid functioning and other business needs.
- Pre-processing (image scaling, background noise removal, and others) the real-time data and images, and offline data (manual, image feed, video streams).
- Migration of the pre-processed data to the Data Warehouse.
- Using predictive models for data training.
- Building deep learning algorithms like convolution learning (CNN), using TensorFlow to categorize the clean and dirty images of the insulators.
- Implementing a smart predictive system to provide regular updates and critical warnings for preventive maintenance.
- Efficient processing and categorizing the insulator image data feed.
- Minimizing the consequential losses due to power supply downtime.
- Improve preventive maintenance with the clearing alerts generated from analytical models.
- Leverage AI in data processing to reduce error probabilities eliminating human intervention.