Power Line Inspection Research Articles

UAV power line inspection strategy based on SAC algorithm

Power line sag monitoring is critical for ensuring transmission system reliability and optimizing grid capacity utilization. Traditional sag detection methods rely on hyperbolic cosine models that assume ideal catenary behavior under uniform loading conditions. However, these models impose restrictive assumptions about weight distribution and suspension conditions that limit accuracy under real-world scenarios involving wind loading, ice accumulation, and non-uniform environmental forces. This study introduces a novel Bézier curve-based mathematical framework for transmission line sag detection and monitoring. Unlike traditional hyperbolic cosine approaches, the proposed methodology eliminates idealized assumptions and provides enhanced flexibility for modeling actual conductor behavior under variable environmental conditions. The Bézier curve approach offers enhanced precision and computational efficiency through intuitive control point manipulation, making it well suited for Dynamic Line Rating (DLR) applications. Experimental validation was performed using a controlled laboratory setup with a 1:100 scaled transmission line model. Results demonstrate improvement in sag measurement accuracy, achieving an average error of 1.1% compared to 6.15% with traditional hyperbolic cosine methods—representing an 82% improvement in measurement precision. Statistical analysis over 30 independent experiments confirms measurement consistency with a 95% confidence interval of [0.93%, 1.27%]. The framework also demonstrates a 1.5 to 2 times increase in computational efficiency improvement over conventional template matching approaches. This mathematical framework establishes a robust foundation for advanced transmission line monitoring systems, with demonstrated advantages for power grid applications where traditional catenary models fail due to non-ideal environmental conditions. The enhanced accuracy and efficiency support improved Dynamic Line Rating implementations and grid modernization efforts.

Abstract The increasing complexity of power line infrastructure necessitates efficient inspection methods to ensure grid safety, with Unmanned Aerial Vehicles (UAVs) emerging as a key solution for automating image capture and fault detection. However, UAV-based systems face challenges such as limited onboard computational resources and high task execution latency, particularly when relying on centralized cloud processing. To address these issues, we propose a cloud-edge-end collaboration framework for power line inspection, where UAVs dynamically offload image processing tasks to edge or cloud servers based on real-time workload conditions. The offloading decisions are optimized using the Proximal Policy Optimization (PPO) algorithm, aiming to minimize task execution time while balancing computational demands across the architecture. Experimental results demonstrate that our approach reduces task execution time by up to 22% compared to traditional methods, offering a scalable and adaptive solution for efficient UAV-assisted power line inspections in smart grid applications. Beyond performance gains, this work contributes a generalizable framework for intelligent task scheduling in UAV systems, opening new possibilities for real-time, learning-based edge-cloud collaboration in smart infrastructure applications.

ABSTRACT To overcome cloud computing's limitations—high latency and costly data transfers that hinder rapid UAV detection—plus the challenges of spotting tiny targets against complex backgrounds in wide‐field aerial views, an edge computing solution with its specialized lightweight network: EfficientDet‐EdgeUAV is proposed. The network employs a structurally optimized EfficientNet backbone through lightweight architecture modifications, integrating a Squeeze‐and‐Excitation attention mechanism to mitigate interference from complex backgrounds in target detection. The network architecture enhances small object detection by incorporating large‐scale feature layers into the pyramid structure of the neck and applying lightweight architectural optimization to the neck module. The architecture further enhances detection robustness by implementing multi‐scale feature fusion in the neck module, which strategically combines shallow‐layer spatial details and deep‐layer semantic representations to improve discernment of small objects with blurred boundaries. Through extensive experiments that comprehensively evaluate and validate the effectiveness of the proposed method, the experimental results demonstrate superior detection accuracy and efficiency on the VisDrone dataset compared to baseline and state‐of‐the‐art methods. This demonstrates that the proposed method achieves exceptional effectiveness in real‐time UAV imaging scenarios, providing critical technical references for civilian applications of drone technology in power line inspection, geological exploration, and search and rescue operations.

AE-MCDD: Attention-enhanced multiple component defects detection for UAV-assisted powerline inspection

Active learning is an interactive machine learning paradigm in which the model selectively queries an oracle (e.g. a human annotator) to label the most informative unlabeled examples. This enables building accurate models using far fewer labeled data than traditional supervised learning. In this paper, we define active learning and contrast it with conventional “passive” learning. We review common active learning architectures (pool-based, stream-based, membership query synthesis), label selection strategies (uncertainty-based, diversity-based) and describe in detail how to implement an active learning loop using modern tools (e.g. scikit-learn, PyTorch, modAL). We compare the results of applying active learning to the Iris Dataset[11] and Titanic Dataset[12] for classification using various label selection strategies. We then present a real-world–inspired case study of using active learning on drone-collected power-line inspection imagery. In this scenario, a convolutional vision model (e.g. YOLOv8) is iteratively refined by selectively querying a few ambiguous frames for expert annotation, dramatically reducing labelling effort. We report on potential savings: for example, an AWS-case active learning pipeline achieved ~90% reduction in labelling cost and cut annotation turnaround from weeks to hours [1]. We also discuss limitations and pitfalls of active learning (e.g. human-in-the-loop cost, computational overhead, class imbalance issues [2][3]). In summary, active learning can greatly improve data efficiency and agility of AI model development, but it requires careful design of query strategies and system integration.

UAV-based power line inspections offer a safer, more efficient alternative to traditional methods, but insulator detection presents key challenges: multiscale object detection and intra-class variance. Insulators vary in size due to UAV altitude and perspective changes, while their visual similarities across types (e.g., glass, porcelain, composite) complicate classification. To address these issues, we introduce APF-YOLO, an enhanced YOLOv8-based model integrating the Adaptive Path Fusion (APF) neck and the Adaptive Feature Alignment Module (AFAM). AFAM balances fine-grained detail extraction for small objects with semantic context for larger ones through local and global pathways by integrating advanced attention mechanisms. This work also introduces the Merged Public Insulator Dataset (MPID), a comprehensive dataset designed for insulator detection, representing diverse real-world conditions such as occlusions, varying scales, and environmental challenges. Evaluations on MPID demonstrate that APF-YOLO surpasses state-of-the-art models with different neck configurations, achieving at least a +2.71% improvement in mAP@0.5:0.9 and a +1.24% increase in recall, while maintaining real-time performance in server-grade environments. Although APF-YOLO adds computational requirements, these remain within acceptable limits for real-world applications. Future work will optimize APF-YOLO for edge devices through techniques such as model pruning and lightweight feature extractors, enhancing its adaptability and efficiency. Combined with MPID, APF-YOLO establishes a strong foundation for advancing UAV-based insulator detection, contributing to safer and more effective power line monitoring.

To address the challenges of high missed detection rates for minute transmission line defects, strong complex background interference, and limited computational power on edge devices in UAV-assisted power line inspection, this paper proposes a lightweight improved YOLOv12 real-time detection model. First, a Bidirectional Weighted Feature Fusion Network (BiFPN) is introduced to enhance bidirectional interaction between shallow localization information and deep semantic features through learnable feature layer weighting, thereby improving detection sensitivity for line defects. Second, a Cross-stage Channel-Position Collaborative Attention (CPCA) module is embedded in the BiFPN’s cross-stage connections, jointly modeling channel feature significance and spatial contextual relationships to effectively suppress complex background noise from vegetation occlusion and metal reflections while enhancing defect feature representation. Furthermore, the backbone network is reconstructed using ShuffleNetV2’s channel rearrangement and grouped convolution strategies to reduce model complexity. Experimental results demonstrate that the improved model achieved 98.7% mAP@0.5 on our custom transmission line defect dataset, representing a 3.0% improvement over the baseline YOLOv12, with parameters compressed to 2.31M (8.3% reduction) and real-time detection speed reaching 142.7 FPS. This method effectively balances detection accuracy and inference efficiency, providing reliable technical support for unmanned intelligent inspection of transmission lines.

Overhead power lines are critical infrastructures to ensure a reliable energy supply, and failures in the grid can lead to significant service disruptions. Locating these faults quickly is crucial but often challenging, especially in hard-to-reach areas such as mountainous regions. This paper presents an integrated solution for the long-range visual inspection of overhead power lines in minimum time using teams of Unmanned Aerial Vehicles (UAVs). The solution, designed for effective field operation while meeting end-user requirements, comprises route planning, autonomous execution, and monitoring of the inspection mission. Concerning route planning, a capacitated min-max multi-depot vehicle routing problem has been formulated to compute feasible routes that cover the entire grid in minimum mission time. The method can be applied to heterogeneous multi-UAV teams in terms of inspection speed and battery consumption, which helps maximise the utilisation of available robots. Moreover, the planning method is complemented by an accurate battery-consumption model based on energy principles that captures the effect of parameters often overlooked such as UAV mass, inspection speed, and weather conditions. The model has shown estimates with relative errors not exceeding 1.34% compared to real measurements. The proposed solution has been experimentally validated under real-world conditions, enabling the autonomous multi-UAV inspection of more than 10 kilometres of real power lines in 13 minutes, which represents a time reduction of up to 67.21% compared to the state of the art. The resulting videos enabled the identification of a simulated power outage and its exact location.

In September 2020, the Chinese government pledged at the 75th United Nations General Assembly to achieve peak carbon emissions before 2030 and carbon neutrality before 2060. As the nation advances its carbon peaking and carbon neutrality goals, the demand for technologies supporting green and low-carbon transitions has surged, particularly in high-energy-consuming sectors like power inspection. Traditional manual inspection methods are struggling to meet the operational and maintenance demands of modern smart grids due to limitations such as low efficiency, high operational risks, and incomplete inspection coverage. While unmanned aerial vehicle (UAV) inspection offers significant advantages, its limited flight time remains a major challenge to its widespread adoption. This paper focuses on analyzing and optimizing the endurance of UAVs in power line inspection based on wireless power transfer (WPT) technology. The aim is to provide a more efficient charging solution for UAVs by proposing strategies to maximize channel gain, thereby overcoming endurance limitations and promoting the extensive application of UAV technology in smart grid inspection to support the national strategy for green and low-carbon transition.

BackgroundUAV-based power line inspections offer a safer, more efficient alternative to traditional methods, but insulator detection presents key challenges: multiscale object detection and intra-class variance. Insulators vary in size due to UAV altitude and perspective changes, while their visual similarities across types (e.g., glass, porcelain, composite) complicate classification.MethodsTo address these issues, we introduce APF-YOLO, an enhanced YOLOv8-based model integrating the Adaptive Path Fusion (APF) neck and the Adaptive Feature Alignment Module (AFAM). AFAM balances fine-grained detail extraction for small objects with semantic context for larger ones through local and global pathways by integrating advanced attention mechanisms. This work also introduces the Merged Public Insulator Dataset (MPID), a comprehensive dataset designed for insulator detection, representing diverse real-world conditions such as occlusions, varying scales, and environmental challenges.ResultsEvaluations on MPID demonstrate that APF-YOLO surpasses state-of-the-art models with different neck configurations, achieving at least a +2.71% improvement in mAP@0.5:0.9 and a +1.24% increase in recall, while maintaining real-time performance in server-grade environments. Although APF-YOLO adds computational requirements, these remain within acceptable limits for real-world applications. Future work will optimize APF-YOLO for edge devices through techniques such as model pruning and lightweight feature extractors, enhancing its adaptability and efficiency.ConclusionCombined with MPID, APF-YOLO establishes a strong foundation for advancing UAV-based insulator detection, contributing to safer and more effective power line monitoring.

Abstract In recent years, UAV inspection technology has been widely applied in infrastructure monitoring and power line inspection. However, significant differences between infrared and visible light images pose challenges for high-precision image registration. To address these challenges, a novel UAV multispectral image registration method based on saliency-weighted edge features and multi-feature cascade matching has been proposed. During feature extraction and description, this method uses a saliency-weighted grayscale window for edge extraction, combined with a multi-scale potential Harris corner selection algorithm to extract significant feature points. These edge features are then described using an eight-direction equal-area sector descriptor. In the feature matching phase, a cascaded matching framework is employed. It comprises an adaptive NNDR pre-screening based on domain priors to filter initial matches, followed by multi-feature fusion matching for fine-grained screening. A final geometric consistency check using the FSC algorithm is applied to effectively reduce the probability of mismatches. Experimental results demonstrate that this algorithm achieves an average high-precision matching rate of 90% on 97 pairs of infrared and visible light power equipment images provided by FLIR, attaining sub-pixel level accuracy. This performance significantly surpasses that of classic algorithms such as SIFT and its derivatives, SURF and ORB.

Deep learning in automated power line inspection: A review

Efficient insulator-defect detection in transmission lines is crucial for ensuring the reliability and safety of power systems. This study introduces YOLOv8-IDX (You Only Look Once v8—Insulator Defect eXtensions), an enhanced DL (Deep Learning) based model designed specifically for detecting defects in transmission line insulators. The model builds upon the YOLOv8 framework, incorporating advanced modules, such as C3k2 in the backbone for enhanced feature extraction and C2fCIB in the neck for improved contextual understanding. These modifications aim to address the challenges of detecting small and complex defects under diverse environmental conditions. The results demonstrate that YOLOv8-IDX significantly outperforms the baseline YOLOv8 in terms of mean Average Precision (mAP) by 4.7% and 3.6% on the IDID and CPLID datasets, respectively, with F1 scores of 93.2 and 97.2 on the IDID and CPLID datasets, respectively. These findings underscore the model’s potential in automating power line inspections, reducing manual effort, and minimizing maintenance-related downtime. In conclusion, YOLOv8-IDX represents a step forward in leveraging DL and AI for smart grid applications, with implications for enhancing the reliability and efficiency of power transmission systems. Future work will focus on extending the model to multi-class defect detection and real-time deployment using UAV platforms.

Civil engineering is a field of knowledge in direct contact with the citizen, not only in the design and construction of infrastructure but also in its maintenance, conservation, monitoring, and management. The integration of new technologies, such as drones, is revolutionizing work methodologies, offering new possibilities for the execution and management of infrastructure and minimizing human intervention in these jobs, with the increase in occupational safety and cost reduction that this entails. This study presents a comprehensive review of the literature on UAV applications for the monitoring and management of civil infrastructure. The applicability of UAVs and their connection with the main existing sensors and technologies are analyzed, such as visible cameras (RGB), multispectral cameras, and hyperspectral cameras, in the most relevant areas of civil engineering, such as building inspection, bridge inspection, dams, power line inspection, photovoltaic plants, inspection, hydrological studies road inspection, slope supervision, and the maintenance and monitoring of landfill operation. The impact and scope of these technologies are addressed, as well as the benefits in terms of process automation, efficiency, safety, and cost reduction. The incorporation of drones promises to significantly transform the practice of civil engineering, improving the sustainability and resilience of infrastructures.

Unmanned aerial vehicles (UAVs) have gained widespread attention in recent years due to their expanding applications across various industrial sectors. Selecting the most suitable UAV for a given task is a critical decision-making challenge, which is typically modeled as a multi-criteria decision-making (MCDM) problem. However, expert assessments in such selection processes often involve considerable uncertainty and hesitation. To address this, this paper proposes a novel integrated MCDM framework that combines dual hesitant fuzzy sets (DHFSs), the best-worst method (BWM), and the MULTIMOORA method to evaluate and rank UAV alternatives. In the proposed method, DHFSs are employed to capture both membership and non-membership degrees of expert assessments under uncertainty, while expert weights are objectively determined based on the entropy of their assessments. Criteria weights are then calculated using an extended dual hesitant fuzzy BWM. Subsequently, the MULTIMOORA method is extended into the dual hesitant fuzzy environment, where UAV alternatives are evaluated from three perspectives: the ratio system, the extended reference point approach, and the full multiplicative form, and the evaluation results are aggregated to generate a comprehensive and reliable final ranking. To demonstrate the practicality and effectiveness of the proposed method, a case study on UAV selection for power line inspection is presented. The results show that the proposed approach effectively handles uncertainty, produces stable and consistent rankings, and offers reliable decision support under uncertain and fuzzy conditions. The proposed method provides a flexible and systematic decision-making tool that can assist decision-makers in solving UAV selection problems in complex, real-world scenarios.

A Machine Vision Assisted Automatic Docking System for Power Line Inspection

Unmanned aerial vehicles (UAVs) make power line inspections more safe, efficient, and cost-effective, replacing risky manual checks and expensive helicopter surveys while overcoming challenges like stability and regulations. The aim of this study is to conduct a systematic review of the application of UAVs for power line inspections. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is implemented to ensure a structured and comprehensive review process. The Scopus database is used to identify relevant publications, and after screening and applying eligibility criteria, 75 documents were selected for further analysis. The study results show a shift toward predictive maintenance, multi-UAV operations, and real-time data analysis. However, challenges remain, including UAV–grid connectivity, resilience to extreme weather, and large-scale automation. This work provides key insights into technological and algorithmic advancements and research trends on UAV-based power line inspections while pointing out gaps in the existing literature. Finally, future research directions to advance UAV-based power line inspections are suggested.

The monitoring and maintenance of grid equipment have become increasingly crucial due to the continual progress in smart grid technology. Efficient identification technology for grid equipment is crucial for enabling equipment status monitoring and fault diagnosis, directly influencing the operational stability of the grid concerning precision and timely functionality. Nevertheless, the reliance of current image recognition methods on intricate models and extensive computational resources poses implementation challenges in resource-limited field environments, thereby restricting their use in operations such as drone-based power line inspections. In response to this obstacle, the paper introduces a streamlined identification approach for grid equipment through model compression. This method aims to uphold recognition precision while minimizing the computational workload and storage demands of the model, making it well-suited for integration into drone-based power line inspections. Introducing a target recognition network, this method integrates tailored multi-scale information for grid equipment and embeds an attention mechanism within the network to enhance the model’s capacity for identifying crucial features. Expanding on this approach, model compression techniques are utilized to condense the trained model. This process maintains accuracy by removing redundant weights, thereby shrinking the model’s size and computational complexity, ultimately achieving a lightweight network.

Advanced drone-based powerline inspection using image segmentation and adaptive visual control