Challenge of Vegetation Management Near Power Lines
ELES faced a significant challenge in monitoring and predicting vegetation growth near power transmission lines to ensure the safe, reliable, and uninterrupted flow of electricity. Vegetation that grows too close to transmission lines can cause power outages, fires, and system failures, making it crucial to maintain clearances. However, tracking vegetation growth over vast and diverse terrains was difficult. The need for accurate and up-to-date data to feed into predictive models also complicated the process, as traditional methods struggled with this complexity.
Overview of ELES: Slovenia’s Grid Operator
ELES is Slovenia's combined electricity transmission and distribution system operator, responsible for ensuring the reliable and secure operation of the national electricity grid. The company manages and maintains high-voltage transmission lines and infrastructure, connecting Slovenia to neighboring countries and facilitating efficient energy distribution. ELES is dedicated to enhancing energy sustainability, grid modernization, and the integration of renewable energy sources.
Limitations of Previous Vegetation Management Solutions
ELES experimented with several tools, but none were sufficiently flexible or accurate for their specific requirements:
Terrain Adaptability: The power grid spans diverse landscapes, including steep and rugged terrain, where many tools failed to adapt. Standard vegetation management systems couldn't handle these unique challenges.
Ease of Use: Existing solutions were difficult to integrate into existing workflows, requiring extensive manual adjustments or expertise to function properly.
Performance Issues: While some tools provided basic functionality, they lacked the precision and efficiency needed for real-time vegetation analysis. In contrast, the aerial FlaiNet AI model demonstrated superior adaptability and ease of use, making it an ideal fit.
Automated LiDAR and AI-Driven Vegetation Monitoring
ELES's workflow has been streamlined with a high degree of automation. Flai's AI platform processes LiDAR data from drones with minimal human intervention, classifying points representing vegetation, power lines, and towers.
A preconfigured template specific to their use case identifies and separates low, medium, and high vegetation based on an advanced height-above-ground algorithm. This granularity allows for detailed growth prediction, tailored to different types of vegetation.
The final product is an accurately classified point cloud, giving ELES the insights they need to manage vegetation more efficiently. By automating both data capture via drones and processing, ELES significantly reduces the time and effort involved, while enhancing precision and reliability.
Early Success with AI-Enhanced Vegetation Control
By integrating Flai's AI-powered LiDAR data processing solution and combining it with regular drone or UAV monitoring, ELES achieved impressive early results. The drones allow for frequent and efficient data collection across vast areas, while Flai’s AI-driven processing transforms the data into actionable insights. The system provided precise vegetation classifications and accurate growth predictions, enabling the company to plan preventive maintenance more effectively. This resulted in fewer emergency interventions and reduced operational risks.
By using predefined processing templates, I can streamline my workflow to consistently apply the same settings and achieve uniform results automatically. The more steps I can automate, the more efficient my process becomes—and Flai's solution makes this possible. Aleš Zupančič, Technical system expert, ELES
Long-Term Benefits of Predictive Vegetation Management
The long-term value of the Flai-ELES partnership lies in the ability to predict and prevent vegetation-related incidents well before they occur. ELES’s decision to use a combination of drones for regular infrastructure monitoring and Flai's advanced AI models ensures continuous, high-quality data analysis.
Flai’s automated, accurate vegetation classification seamlessly integrates into our workflow, providing the precise data we need to make informed maintenance decisions for our power network. Aleš Zupančič, Technical system expert, ELES
Additionally, the growing potential of Beyond Visual Line of Sight (BVLOS) drones offers significant advantages for power network monitoring, enabling even more extensive coverage without manual intervention. Using LiDAR data captured by drones and processed by Flai's automatic artificial intelligence classification models and the VegeLine application, which is the result of ELES' latest know-how, ELES can now:
Predict Vegetation Growth (VegeLine): With continuous data input from drone flights and advanced prediction models, ELES can foresee potential risks and schedule maintenance before vegetation reaches dangerous levels.
Optimized Decision-Making: By incorporating multiple data sources, ELES can prioritize areas requiring immediate attention and allocate resources more effectively, leading to better workforce management and cost savings.
Improved Safety and Reliability: Proactive vegetation management, supported by UAV surveillance, ensures the safe and uninterrupted transmission of electricity, minimizing the chances of outages and infrastructure damage.
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