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<p>Energy facilities are increasingly recognized as primary targets for UAV threats, both in peacetime and during armed conflict. In response to the growing need for comprehensive protection, there is a paradigm shift from traditional two-dimensional ground-based security frameworks to three-dimensional airspace defense systems. This study presents an empirical case of the Counter-Drone (C-Drone) system implemented at a Korea Electric Power Corporation (KEPCO) substation, focusing on the detection and identification equipment. The research details a systematic approach to system deployment and performance evaluation, providing key insights into strategic optimization for critical infrastructure protection.</p><p>First, the impact of newly introduced equipment on existing systems was quantitatively analyzed. In particular, an evaluation of potential electromagnetic interference (EMI) from the newly deployed radar to existing UHF sensors revealed a received power level of − 98.83 dBm at a distance of 80&#xa0;m. This result indicates that the likelihood of electromagnetic interference with existing infrastructure, such as UHF sensors, is extremely low under real-world conditions.</p><p>Second, the study investigated the practical impact of pre-existing environmental conditions on the performance of newly deployed radar systems. Empirical experiments demonstrated up to a 20.4% point variation in radar detection success rates across installation sites, attributed to environmental factors such as transmission towers, mountainous terrain, and metallic ground structures. For RF scanners, adequate separation from devices operating in similar frequency bands was found to be essential, and the effectiveness of this approach was validated through long-term data analysis.</p><p>Third, to advance the evaluation of system performance, this study proposes eight standardized and quantitative indicators. In particular, the False Negative Rate (FNR) was validated using empirical test data, proving its practical effectiveness. Finally, an AI-based threat assessment algorithm was developed by integrating data from radar, RF scanners, and EO/IR sensors. The model was trained using one year of accumulated empirical data, achieving a high prediction accuracy with an R² score of 0.9993.</p><p>The C-Drone system represents the first systematically structured counter-drone solution implemented in the energy sector and is currently in the early stages. Nonetheless, findings from real-world empirical studies offer a valuable technical foundation for strengthening national drone defense capabilities. Furthermore, these results are anticipated to support the proactive mitigation of potential system limitations associated with future advancements in UAV technologies.</p>

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Research on the Process of Establishing a Counter-Drone System for Critical Energy Facilities Through Empirical Result Analysis: A Case Study of a Substation

  • 期刊论文

  • 发表日期：

    2025年6月12日

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  • Taewon Kim,
  • Byung-Sung Ko,
  • Ji-Won Lee,
  • Jung-Wook Woo,
  • Jae-Kyung Lee

摘要

Energy facilities are increasingly recognized as primary targets for UAV threats, both in peacetime and during armed conflict. In response to the growing need for comprehensive protection, there is a paradigm shift from traditional two-dimensional ground-based security frameworks to three-dimensional airspace defense systems. This study presents an empirical case of the Counter-Drone (C-Drone) system implemented at a Korea Electric Power Corporation (KEPCO) substation, focusing on the detection and identification equipment. The research details a systematic approach to system deployment and performance evaluation, providing key insights into strategic optimization for critical infrastructure protection.

First, the impact of newly introduced equipment on existing systems was quantitatively analyzed. In particular, an evaluation of potential electromagnetic interference (EMI) from the newly deployed radar to existing UHF sensors revealed a received power level of − 98.83 dBm at a distance of 80 m. This result indicates that the likelihood of electromagnetic interference with existing infrastructure, such as UHF sensors, is extremely low under real-world conditions.

Second, the study investigated the practical impact of pre-existing environmental conditions on the performance of newly deployed radar systems. Empirical experiments demonstrated up to a 20.4% point variation in radar detection success rates across installation sites, attributed to environmental factors such as transmission towers, mountainous terrain, and metallic ground structures. For RF scanners, adequate separation from devices operating in similar frequency bands was found to be essential, and the effectiveness of this approach was validated through long-term data analysis.

Third, to advance the evaluation of system performance, this study proposes eight standardized and quantitative indicators. In particular, the False Negative Rate (FNR) was validated using empirical test data, proving its practical effectiveness. Finally, an AI-based threat assessment algorithm was developed by integrating data from radar, RF scanners, and EO/IR sensors. The model was trained using one year of accumulated empirical data, achieving a high prediction accuracy with an R² score of 0.9993.

The C-Drone system represents the first systematically structured counter-drone solution implemented in the energy sector and is currently in the early stages. Nonetheless, findings from real-world empirical studies offer a valuable technical foundation for strengthening national drone defense capabilities. Furthermore, these results are anticipated to support the proactive mitigation of potential system limitations associated with future advancements in UAV technologies.