Transmission lines are primarily deployed overhead, and the transmission tower, acting as the fulcrum, can be affected by the unbalanced force of the wire and extreme weather, resulting in the transmission tower tilt, deformation, or collapse. This can jeopardize the safe operation of the power grid and even cause widespread failures, resulting in significant economic losses. Given the limitations of current tower tilt detection methods, this paper proposes a tower tilt detection and analysis method based on monocular vision images. The monocular camera collects the profile and contour features of the tower, and the tower tilt model is combined to realize the calculation and analysis of the tower tilt. Through this improved monocular visual monitoring method, the perception accuracy of the tower tilt is improved by 7.5%, and the axial eccentricity is accurate to ±2 mm. The method provides real-time reliability and simple operation for detecting tower inclination, significantly reducing staff inspection intensity and ensuring the power system operates safely and efficiently.
In this paper, a fault detection technology to large generating units was investigated. It proposed a fault diagnosis approach for large generator based on information fusion technology. This method monitors fault for large generating units by multi-sensor, realizes feature extraction of fault and obtains independent evidence with each other, so as to achieve the accurate diagnosis to the fault of large generating units. The simulation results indicated that using fault diagnosis approach based on information fusion effectively improved the diagnostic accurate of the fault of large generating units, and increased the confidence of the results of fault diagnosis.
Hyperspectral imaging technology has been applied to the status monitoring of power transmission line and equipment as its continuous and narrow-band induction characteristics, which provides a solution for the operation monitoring of lossless large-area power equipment. However, the optical and electronic equipment is complex, and the related products are large and expensive on the market. This paper describes a preparation method of a linear gradient filter that is cost performance and miniaturization, which can be used to realize the prototype hyper-spectral imaging camera. The high precision preparation process and principle make the camera show good spectral performance, which can scan with precision step length of 2nm between 400nm and 1000nm. The feature extraction and classification algorithm can be used to determine the health conditions of power equipment, such as partial discharge, with an accuracy of about 77%. The equipment collocation algorithm can also be used to identify defects in power equipment, which has been proved to be able to distinguish between aging insulators, icing of conductors and heating of wire clips. This method are promising for an entry-level, low-cost hyper-spectral imaging solution for power detection applications.
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