Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques

To ensure the power system operates safely and reliably, it is essential to monitor and evaluate the health condition of power equipment on-line or off-line. This study reviews the research status in condition monitoring and diagnosis of power equipment, including transformer, gas insulated switchgear, cable, external insulation, generator, and power capacitor in recent years. Although much progress has been made in technologies of condition monitoring and fault diagnosis such as test accuracy, fast and accurate fault localisation and recognition of fault types, there are still many deficiencies which needs further research work, including the reliability of signal collection from sensors, the accuracy of data treatment and analysis, anti-interference performance of test equipment, appropriate models used for condition evaluation. The prospective of condition monitoring and diagnosis technologies of power equipment are also presented in this study. It is proposed that the application of big data, internet of things and cloud computing should be expected and given special attention in the near future.

Condition monitoring and diagnosis of power equipment: review and prospective

Power transmission line inspection robots: A review, trends and challenges for future research

Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power lines using UAV images. This method, known as the power line automatic measurement method based on epipolar constraints (PLAMEC), acquires the spatial position of the power lines. Then, the semi patch matching based on epipolar constraints (SPMEC) dense matching method is applied to automatically extract dense point clouds within the power line corridor. Obstacles can then be automatically detected by calculating the spatial distance between a power line and the point cloud representing the ground. Experimental results show that the PLAMEC automatically measures power lines effectively with a measurement accuracy consistent with that of manual stereo measurements. The height root mean square (RMS) error of the point cloud was 0.233 m, and the RMS error of the power line was 0.205 m. In addition, we verified the detected obstacles in the field and measured the distance between the canopy and power line using a laser range finder. The results show that the difference of these two distances was within ±0.5 m.

https://www.mdpi.com/2072-4292/9/8/824/pdf

Automatic Power Line Inspection Using UAV Images

Point cloud filtering on UAV based point cloud

When the distance between an obstacle and a power line is less than the discharge distance, a discharge arc can be generated, resulting in the interruption of power supplies. Therefore, regular safety inspections are necessary to ensure the safe operation of power grids. Tall vegetation and buildings are the key factors threatening the safe operation of extra high voltage transmission lines within a power line corridor. Manual or laser intensity direction and ranging (LiDAR) based inspections are time consuming and expensive. To make safety inspections more efficient and flexible, a low-altitude unmanned aerial vehicle (UAV) remote-sensing platform, equipped with an optical digital camera, was used to inspect power line corridors. We propose a semi-patch matching algorithm based on epipolar constraints, using both the correlation coefficient (CC) and the shape of its curve to extract three dimensional (3D) point clouds for a power line corridor. We use a stereo image pair from inter-strip to improve power line measurement accuracy by transforming the power line direction to an approximately perpendicular to epipolar line. The distance between the power lines and the 3D point cloud is taken as a criterion for locating obstacles within the power line corridor automatically. Experimental results show that our proposed method is a reliable, cost effective, and applicable way for practical power line inspection and can locate obstacles within the power line corridor with accuracy better than ±0.5 m.

https://www.mdpi.com/2220-9964/6/1/14/pdf

UAV Low Altitude Photogrammetry for Power Line Inspection

When the distance between an obstacle and a power line is less than the discharge distance, a discharge arc can be generated, resulting in interruption of power supplies. Therefore, regular safety inspections are necessary to ensure safe operations of power grids. Tall vegetation and buildings are the key factors threatening the safe operation of extra high voltage transmission lines within a power line corridor. Manual or LiDAR based-inspections are time consuming and expensive. To make safety inspections more efficient and flexible, a low-altitude unmanned aerial vehicle remote-sensing platform equipped with optical digital camera was used to inspect power line corridors. We propose a semi-patch matching algorithm based on epipolar constraints using both correlation coefficient and the shape of its curve to extract three dimensional (3D) point clouds for a power line corridor. Virtual photography was used to transform the power line direction from approximately parallel to the epipolar line to approximately perpendicular to epipolar line to improve power line measurement accuracy. The distance between the power lines and the 3D point cloud is taken as a criterion for locating obstacles within the power line corridor automatically. Experimental results show that our proposed method is a reliable, cost effective and applicable way for practical power line inspection, and can locate obstacles within the power line corridor with measurement accuracies better than ±0.5 m.

https://www.preprints.org/manuscript/201608.0048/v1/download

Yi Fang

Papers

UAV Low Altitude Photogrammetry for Power Line Inspection

UAV Low Altitude Photogrammetry for Power Line Inspection