The project aims to develop an advanced system that enables the detection of faults, disturbances, and other issues in the electrical infrastructure, thereby improving its safety and efficiency.
Project Objectives:
- Real-time monitoring of electrical infrastructure..
- Identification of potential threats, such as damages to transmission lines, vegetation encroachment on the lines, insulator damages, and contamination on the lines.
- Generating reports from power grid monitoring.
Project Progress:
1. Data Collection from Cameras:
Recorded hundreds of hours of video footage from drones, focusing on electrical transmission lines.
2. Data Labeling:
Annotators labeled the data, marking various areas for monitoring, including:
- Visible faults in transmission lines,
- Vegetation encroachment on the lines,
- Insulator damages,
- Contaminations on the lines.
3. Utilization of Pretrained AI Algorithms:
Pretrained models based on deep learning and Convolutional Neural Networks (CNN) were employed.
4. Algorithm Fine-Tuning:
Algorithms were fine-tuned for predicting disturbances in the electrical infrastructure through training on labeled data.
5. Offline Experiments on Historical Data:
Achieved an accuracy of 92% on historical data, serving as a starting point for further improvements.
6. Data Collection and Algorithm Validation:
Additional data was gathered and used to evaluate the algorithm, achieving an accuracy of 89.5%.
7. Iterations to Improve the Algorithm:
Ensemble techniques were applied, combining results from different models, resulting in a 94% accuracy.
8. Work on AI Model Size Reduction:
To enable deployment on target devices, the model was optimized while preserving its effectiveness.
9. Pilot - Algorithm Deployment in Two Test Locations:
The algorithm was tested in real-world conditions at two locations, allowing assessment and adaptation to different terrain conditions.
10. Generating Drone Flight Reports:
The algorithm was integrated with drones, enabling the generation of reports with monitoring results after each drone flight.
Investments and Resource Allocation:
The total project cost was 800,000 PLN, distributed as follows:
- Data collection, preparation, and labeling: 50,000 PLN.
- Initial model development (Proof of Concept): 150,000 PLN.
- Testing: 80,000 PLN.
- Revisions and final model development: 420,000 PLN.
- Deployment, documentation, report generation: 100,000 PLN.
Results and Benefits:
- Operational Cost Reduction: By applying AI algorithms in monitoring electrical infrastructure, operational costs were reduced by 15%, including field inspections and time-consuming analyses.
- Reduction in Failure Costs: Early detection of faults led to an 18% reduction in costs associated with failures, avoiding catastrophic damages and prolonged downtimes.
- Annual Financial Savings: Assuming a consistent reduction in operational costs and avoidance of failure-related costs, estimated annual savings are 2.5 million PLN.
- Shortened Incident Response Time: The average response time to electrical incidents decreased by 70%, leading to quicker issue resolution and minimized incident impacts.
