Automatic Surveillance and Analysis of Snow and Ice Coverage on Electrical Insulators of Power Transmission Lines

Gu, Irene Yu‐Hua; Sistiaga, Unai; Berlijn, Sonja; Fahlström, Anders

doi:10.1007/978-3-642-02345-3_36

Cited by 8 publications

(8 citation statements)

References 2 publications

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“…All the desired data were collected synchronously by the corresponding sensors at sampling period ∆ t = 20 minutes, transmitted to a GSM module through a Zigbee communication system, and then transmitted to the monitoring center after synchronization through GSM. It is important to note that all micrometeorology sensors were installed on the top of each power tower so as not to be influenced by Aeolian vibrations or the galloping of conductors . Furthermore, the measurements of these sensors are unavoidably corrupted by external noise and inter‐noise, more or less.…”

Section: Description Of Online Model Structure Micrometeorological Smentioning

confidence: 99%

“…It is important to note that all micrometeorology sensors were installed on the top of each power tower so as not to be influenced by Aeolian vibrations or the galloping of conductors. 11 Furthermore, the measurements of these sensors are unavoidably corrupted by external noise and inter-noise, more or less. Therefore, signal prefiltering is a necessary step in building a robust icing prediction model.…”

Section: Micrometeorological Signal Collectionmentioning

confidence: 99%

“…From (9), (10), and (11), if mapping function F τ can be obtained, icing accretion weight, τ time ahead at time t + mτ can be computed by using the mapping relation (11). Therefore, τ is also called the prediction time length in this paper.…”

Section: Multivariate Modeling Of the Icing Time Seriesmentioning

confidence: 99%

“…where e Q W is expressed in Equation (9), the predicting length is τ, and F τ is the univariate mapping function. Additionally, the multivariate predicting model is expressed in Equation (11). Figure 8 presents the comparison of the univariate model and multivariate model with respect to MAE and RMSE.…”

Section: Comparison Of the Performance Of The Multivariate Model Anmentioning

confidence: 99%

See 3 more Smart Citations

Online prediction method of icing of overhead power lines based on support vector regression

Miao

et al. 2018

Int Trans Electr Energ Syst

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Summary This paper proposes a novel online multivariate time series prediction method, using support vector regression, to build an icing alert system that can forecast short‐term icing accretion load on overhead power lines. Conventional icing alert methods either cannot predict future icing accretion or these methods suffer from inadequate predictive accuracy. To resolve these issues, historical and online micrometeorological data from local observations are used to build a multivariate prediction model. Moreover, data preprocessing based on wavelets is used to prefilter spiking noise within the obtained field signals. In addition, the phase‐space reconstruction theory is applied to find the minimal embedding dimensions of the contributing factors by which the computational complexity of the multivariate model is reduced. Finally, an online adaptive predictive model based on support vector regression is proposed and implemented to further improve the predictive accuracy and predictive length of the icing process of overhead power lines. Experimental results indicate that this method can predict the real‐time icing value on overhead power lines 5 hours in advance, with an acceptable error term.

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Section: Description Of Online Model Structure Micrometeorological Smentioning

confidence: 99%

Section: Micrometeorological Signal Collectionmentioning

confidence: 99%

Section: Multivariate Modeling Of the Icing Time Seriesmentioning

confidence: 99%

Section: Comparison Of the Performance Of The Multivariate Model Anmentioning

confidence: 99%

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Online prediction method of icing of overhead power lines based on support vector regression

Miao

et al. 2018

Int Trans Electr Energ Syst

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“…This method is unable to forecast the icing status; it can only display the real-time status of the icing process. The Glouba model [ 5 ] and the edge of image extraction model [ 6 ] succeed in practice; they can estimate the icing load at a low cost, but their precision is less than the mechanical model and they are also unable to forecast icing status.…”

Section: Introductionmentioning

confidence: 99%

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

Zhao

Zhou

et al. 2014

The Scientific World Journal

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The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters.

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Limited sliding network: Fine‐grained target detection on electrical infrastructure for power transmission line surveillance

Zhao

Zhang

Wang

et al. 2020

Circuit Theory & Apps

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Summary Because of its small size, low local contrast, and much interference, the field image of fine‐grained equipment taken from power transmission line surveillance is hard to be sustained by the traditional small target detection technique, which requires the manual extraction of features, making it difficult to accurately detect micro‐fine‐grained equipment. The deep learning‐based algorithms have prospective application but require abundant data to guarantee performance and tackle the problem of foreground–background imbalance. This paper develops an effective pipeline, i.e., limited sliding network (LSNet), to detect the small and fine‐grained defects on equipment in power transmission line infrastructure. The model firstly performs the regional analysis on the entire image to determine the potential target locations. The feature extraction and classification on the potential location image blocks are further performed by the VGG‐style model for the dense target locations, and the nonmaximum suppression method is finally applied to locate the target. On the other hand, a specific training method is also developed to better deal with a wide range imbalances of positive and negative samples. The proposed method achieves the detection mean average precision (mAP) rate of 98.66% on the real datasets, while limiting the computational overhead of hardware.

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Automatic Surveillance and Analysis of Snow and Ice Coverage on Electrical Insulators of Power Transmission Lines

Cited by 8 publications

References 2 publications

Online prediction method of icing of overhead power lines based on support vector regression

Online prediction method of icing of overhead power lines based on support vector regression

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

Limited sliding network: Fine‐grained target detection on electrical infrastructure for power transmission line surveillance

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