Next-Generation Approaches to Vegetation Management in Smart Cities with Digital Intelligence

Written by G. Pradeep Reddy and Y. V. Pavan Kumar

The rapid urbanization of smart cities has led to increased difficulty managing vegetation near electrical lines. This poses potential risks such as power outages, equipment damage, and safety hazards. However, relying solely on manual labor for vegetation management also poses certain difficulties, as it can be time-consuming and costly. This article details a new approach to vegetation management that aims to address these issues with current methods in smart cities. This approach—using digital intelligence-- involves  employing drones equipped with onboard computers to identify and monitor potential vegetation-related problems near electrical lines. These drones are trained using vast amounts of data. They can run real-time models to detect potential hazards  and alert relevant municipal officers or corresponding personnel responsible for vegetation control.

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Introduction

Urban areas are experiencing increasing demands for electricity, leading to the expansion and densification of power distribution networks. This growth introduces challenges related to vegetation interference with electrical lines. Trees near power lines risk the infrastructure, cause power outages, and pose potential hazards to public safety. The traditional approach to vegetation management is often labor-intensive, time-consuming, and inefficient. Therefore, effective vegetation management in urban environments is vital for creating sustainable and resilient cities, as uncontrolled growth can disrupt critical infrastructure, including power systems.

The Northeast blackout, which occurred on 14 August 2003, serves as a clear illustration of how vegetation growth can significantly impact the reliability of power supply [1]. It affected a wide region spanning across multiple U.S. states, including New York and Ohio, and extended into parts of Canada. Approximately 50 million people were affected by the outage, which resulted in severe disruptions to transportation, communication, and other essential services. While the blackout was triggered by a combination of factors, including high electricity demand and equipment failures, vegetation played a crucial role in initiating the sequence of events leading to the widespread outage. Overgrown trees and vegetation near transmission lines and power distribution equipment came into contact with power lines, causing faults and thus tripping protective systems. This contact triggered a chain reaction of failures that eventually led to an extensive power outage. The Northeast Blackout of 2003 highlighted the significance of effective vegetation management practices.

In response to this event, utility companies and power system operators have begun to focus on vegetation management practices such as regular inspections, tree trimming programs, and the utilization of digital technologies. These have become  integral parts to identifying and mitigating risks associated with vegetation on power lines. By implementing these measures, utility companies aim to minimize the likelihood of major power outages caused by vegetation-related incidents [2]. In recent years, significant advancements in digital intelligence technologies, such as machine learning, computer vision, and remote sensing, have revolutionized various industries [3]-[7]. These breakthroughs have created new opportunities to enhance conventional vegetation management methods, facilitating more precise and prompt decision-making processes. By leveraging the power of digital intelligence, researchers and practitioners can now employ new techniques to monitor, analyze, and control vegetation growth, thereby improving the ecological sustainability and resilience of urban environments.

 

Implementation

The integration of intelligence into unmanned aerial vehicles offers a transformative solution for vegetation management. Drones equipped with advanced sensors, onboard computers, and trained machine-learning models can autonomously detect, classify, and monitor vegetation growth near electrical lines. The simplified representation of the entire flow pertaining to intelligence is depicted in Fig. 1.

 

 Figure 1: An overview of the intelligence execution flow.

 

This state-of-the-art technology includes high-resolution cameras, Light Detection and Ranging (LiDAR) sensors, and thermal imaging, enabling drones to capture precise data about vegetation density, proximity, and potential risks. By analyzing this data in real time, the drones can identify areas of concern and promptly alert municipal officers or corresponding personnel responsible for vegetation maintenance. The following are the key steps involved:

• Drone Selection: Choose a drone that suits the specific requirements, considering factors such as flight time, payload capacity, stability, and camera quality. Ensure it has a compatible interface for connecting the single-board computer.
• Single-Board Computer Setup: Set up the single-board computer (e.g., Raspberry Pi, NVIDIA Jetson) on the drone, ensuring it has sufficient processing power to run AI algorithms. Install the necessary operating system and software libraries for AI development.
• Data Collection: Equip the drone with appropriate sensors, such as RGB or multispectral cameras, LiDAR, or thermal imaging cameras, to capture relevant data about the vegetation. Ensure the sensors are compatible with the single-board computer and are capable of providing high-quality data for analysis.
• AI Model Development: Develop an AI model for vegetation analysis and management. Machine learning techniques such as image classification, object detection, or semantic segmentation can be used to identify and classify vegetation types, detect areas that require maintenance, or locate invasive species.
• Flight Planning and Navigation: Develop a flight planning system that enables the drone to autonomously navigate the area of interest. This system should include obstacle avoidance algorithms to ensure the drone can operate safely and efficiently while collecting data.
• Data Processing: Set up a data processing pipeline on the single-board computer to process the captured data in real time. This involves running the AI model on the collected data to perform vegetation analysis and generate actionable insights.
• Decision Making: Based on the analysis results, algorithms can make decisions regarding vegetation management tasks. For example, the drone could identify areas with excessive weed growth and provide recommendations for targeted applications.
• Communication and Reporting: Implement a communication module on the single-board computer to transmit data and analysis results to a central system or human operator. This enables remote monitoring of the drone's findings. Generate comprehensive reports and visualizations to aid in vegetation management decision-making.
• Iterative Improvement: Continuously evaluate and improve the AI model's performance by collecting feedback from field operations. Incorporate the feedback into the model to enhance its accuracy and efficiency.

 

Conclusion

By leveraging the power of digital intelligence and integrating it into vegetation management practices, cities can enhance the resilience of their power systems, minimize the risks associated with vegetation, and ensure uninterrupted electricity supply for urban communities. Through these advancements, we can create greener and more resilient cities where vegetation is effectively managed to support sustainable development and safeguard the well-being of urban populations. However, privacy concerns related to aerial surveillance and image capturing are significant considerations when implementing the use of drones. High winds, heavy rain, and other adverse weather conditions are also potential concerns, as these factors can interfere with the operation of drones. Consideration of these practical issues is vital to developing the proposed digital intelligence-based solutions for vegetation management in cities.

 

References

1. H. Gugel, S. Ekisheva, M. Lauby, and F. Tafreshi, “Vegetation-Related Outages on Transmission Lines in North America,”  2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR: IEEE, Aug. 2018, pp. 1–5. doi: https://doi.org/10.1109/PESGM.2018.8586462.
2. R. Wilson, R. Wickramasuriya, and D. Marchiori, “An Empirical Modelling and Simulation Framework for Fire Events Initiated by Vegetation and Electricity Network Interactions,” Fire, vol. 6, no. 2, p. 61, Feb. 2023, doi: https://doi.org/10.3390/fire6020061.
3. M. Doostan, R. Sohrabi, and B. Chowdhury, “A data‐driven approach for predicting vegetation‐related outages in power distribution systems,” International Transactions on Electrical Energy Systems, vol. 30, no. 1, Jan. 2020, doi: https://doi.org/10.1002/2050-7038.12154.
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5. B. Prasanth et al., “Maximizing Regenerative Braking Energy Harnessing in Electric Vehicles Using Machine Learning Techniques,” Electronics, vol. 12, no. 5, p. 1119, Feb. 2023, doi: https://doi.org/10.3390/electronics12051119.
6. S. N. V. B. Rao et al., “Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods,” Energies, vol. 15, no. 17, p. 6124, Aug. 2022, doi: https://doi.org/10.3390/en15176124.
7. P. P. Kasaraneni, Y. Venkata Pavan Kumar, G. L. K. Moganti, and R. Kannan, “Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data,” Sensors, vol. 22, no. 23, p. 9323, Nov. 2022, doi: https://doi.org/10.3390/s22239323.

 

This article was edited by Melkior Ornik.

To view all articles in this issue, please go to July 2023 eNewsletter. For a downloadable copy, please visit the IEEE Smart Cities Resource Center.