Transmission Line Icing Thickness Research Articles

GMSA-Net: A Transmission Line Ice Thickness Identification Network Based on Global Micro Strip Awareness.

Ice-covered transmission lines seriously affect the normal operation of the power transmission system. Resonance deicing based on different ice thicknesses is an effective method to solve the issue of ice-covered transmission lines. In order to obtain accurate ice thickness of transmission lines, this paper designs an ice thickness of transmission line recognition model based on Global Micro Strip Awareness Net (GMSA-Net) and proposes a Mixed Strip Convolution Module (MSCM) and a global micro awareness module (GMAM). The MSCM adapts to the shape of ice-covered transmission lines by using strip convolutions with different receptive fields, improving the encoder's ability to extract ice-covered features; the GMAM perceives through both global and micro parts, mining the connections between semantic information. Finally, the ice thickness of the generated segmented image is calculated using the method of regional pixel statistics. Experiments are conducted on the dataset of ice-covered transmission lines. The mean Intersection over Union (mIoU) of image segmentation reaches 96.4%, the balanced F-Score (F1-Score) is 98.1%, and the identification error of ice thickness is within 3.8%. Experimental results prove that this method can accurately identify the ice thickness of transmission lines, providing a control basis for the application of resonant deicing engineering.

Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters

Aiming to improve on the low accuracy of current transmission line icing prediction models and ignoring the objective law of icing of transmission lines, a transmission line icing prediction model considering the effect of transmission line tension on the bundle of icing thickness is proposed, based on a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU). Firstly, the finite element calculation model of the conductor and insulator system was established, and the change rule between transmission line tension and icing thickness was studied. Then, the convolutional neural network and bidirectional gated recurrent unit were used to construct a transmission line icing thickness prediction model The model incorporated a weighted fusion of soft−dynamic time warping (Soft−DTW) and the icing change rule as the loss function. Optimal weights were determined through the utilization of the grid search algorithm and cross−validation, contributing to an enhancement of the model’s generalization capabilities and a reduction in prediction errors. The results indicate that the proposed prediction model can consider the impact of line operating parameters, avoiding the shortcomings of prediction results conflicting with actual physical laws. Compared with traditional non−mechanical models, the proposed model showed reductions in root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 0.26–0.51%, 0.24–0.44%, and 5.77–13.33%, respectively, while the coefficient of determination (R2) increased by 0.07–0.13.

Transmission line ice disaster early warning method based on ice thickness prediction using GM(1, 6) model

The operation of the electricity system faces the difficulty of minimizing ice damage to transmission lines. Due to the uncertainty of transmission line icing thickness, accurate prediction of icing thickness is very important to guide transmission line planning and power grid anti-icing design effectively. Scientific and reasonable early warning assessment of conductor icing is also helpful to make timely and accurate response measures to the possible freezing disaster risk, so as to effectively ensure the safe and stable operation of power grid and reduce the occurrence of the freezing disaster. Based on this, this paper proposes a multi-factor prediction model based on GM (1, 6) grey theory, which considers the transmission line conductor icing model under the influence of multiple meteorological factors. Based on the conductor icing model, the conductor icing degree can be predicted in real time according to meteorological parameters, so as to realize the purpose of transmission line conductor icing disaster risk early warning. It is worth noting that the paper improves the traditional grey model by adding random data functions, which makes the model solve the problem of inaccurate prediction of small samples. Finally, a case study is carried out, and the icing disaster risk is divided into five levels. It is found that the maximal prediction error of icing thickness based on GM (1, 6) grey theory multi-factor prediction model is 10.06%(The average value is only 4.22%), and the accuracy of transmission line conductor icing disaster risk early warning is 88.9%. In addition, a certain safety margin value is added to the predicted value near the critical value of icing thickness, reducing the probability of judging the high-risk level as low-risk. Applying risk early warning method in ice areas can guide the anti-ice work of transmission line.

Transmission line icing thickness prediction model based on ISSA-CNN-LSTM

The prediction of transmission line ice cover thickness can effectively guide the scientific operation and maintenance of the power sector. An improved sparrow search algorithm, convolutional neural network and long short-term memory fusion ice cover prediction model is proposed in this paper. Environmental temperature, humidity, wind speed and other data are firstly normalised, CNN is used to both learn the data signature as well as input the results into LSTM, and then ISSA is used to optimise the parameters of LSTM such as the numeration of the neuron, the initial learning rate, and the regularisation coefficient, etc., and then the final output is the predicted value of the icing thickness of the transmission line cover. The simulation results show that the MSE and MAE of the ISSA-CNN-LSTM model are 0.23 and 0.37, respectively, which are much better than the prediction results of the SSA and CNN models.

Detection Method for Equivalent Ice Thickness of 500-kV Overhead Lines Based on Axial Tension Measurement and Its Application

Overhead line icing may threaten the operational stability of power grid. In this paper, a detection method for equivalent ice thickness of 500 kV overhead lines based on axial tension measurement was proposed. Firstly, according to the tension of insulator string when the transmission lines are not covered with ice, the horizontal distance from the lowest point of the transmission line to the tower on the side where the tension sensor is installed is calculated. Secondly, the state equation of the transmission line in the whole tension section is established. Then, the state equation is solved by iterative method. The initial ice thickness is taken as 0 mm. The ice thickness when the iteration value of tension <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> is equal to the measured value of tension <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> is taken as the equivalent ice thickness of transmission lines. The actual cases of single span transmission line and multi-span transmission line were used for verification and application effect analysis of the proposed method. The results show that the detected results of this method were consistent with the artificial ice observation values of typical 500 kV single span and multi-span transmission line cases. The accuracy of the proposed method can be further verified through finite element simulation analysis. The variation trends of the detected ice thickness is consistent with the icing conditions created by the meteorological factors during the same period, and can be verified by the icing photos captured on site.

Study on transmission line icing prediction based on micro-topographic correction

For the requirement of refinement prediction of transmission line icing thickness, this paper presents an icing thickness correction method which combines the prediction using low spatial resolution with the correction of micro-topography features. By analyzing the regional characteristics of micro-topography, the areas can be divided into three categories: plains, high-altitude mountains, and low-altitude mountains. For each type of terrain, the quantitative correction model for the icing thickness is established, which is dependent on altitude, relief amplitude, slope angle, slope aspect, water system, valley line, ridge line, and saddle point. In this paper, according to the numerical prediction on the 3 × 3 km2 grids, the icing thickness of the transmission line on the 300 × 300 m2 grids with micro-topography is obtained using the correction method in Hubei Power Grid. The results show that the correction method is efficient and convenient to improve the accuracy of icing thickness prediction of the transmission line.

Prediction of Transmission Line Icing Thickness Applying AMPSO-BP Neural Network Model

Prediction of Transmission Line Icing Thickness Applying AMPSO-BP Neural Network Model

Icing-EdgeNet: A Pruning Lightweight Edge Intelligent Method of Discriminative Driving Channel for Ice Thickness of Transmission Lines

The icing monitoring of transmission lines is of great significance to prevent freezing disasters of transmission lines and ensure the security and stability of power system. Measurement methods based on image recognition is gradually applied for icing monitoring but fails to distinguish the icing thickness under the extreme meteorological conditions. Therefore, this article built an ice monitoring system based on edge intelligence to achieve the ice thickness identification at the icing monitoring terminal and proposed a lightweight vision identification method based on discriminative-driven channel pruning for icing monitoring terminals with limited computing resources. In this method, the lightweight convolutional neural network (CNN) MobileNetV3 is utilized for feature extraction, and the multiscale target detection network SSD is used to extract the high-dimensional feature information for ice monitoring. Then, the channel pruning driven by discrimination is used to further compress model, so as to obtain a lightweight identification method suitable for ice monitoring terminals with limited resources. The effectiveness and superiority of the proposed method are verified in comparison with other CNN-based target detection methods and traditional ice monitoring methods. The experimental results showed that the proposed method has 74.5% recognition accuracy for the ice images collected under extreme meteorological conditions. Moreover, the model size is only 11 MB, which can be deployed in ice thickness monitoring terminals with limited computing resources.

An Analysis of the Reliability of a New Dataset of Transmission Line Icing Thickness in Southern China

AbstractBased on ERA-Interim data, gauge observations, transmission line icing observational data, and hindcasted predictors from a numerical forecast system of transmission line icing, a new transmission line icing thickness (TLIT) dataset was constructed to solve the problem of limited historical data. The reliability of the dataset was analyzed using case studies and climate data. The results showed that the descriptions of three icing events in southern China by the TLIT were consistent with the actual observational data, and the icing thickness differences were less than 2 mm. The spatial distribution of annual icing days and icing thickness calculated using meteorological observation station icing data (OIT) and the TLIT data had a similar pattern, with small differences in the numerical values. A rotated empirical orthogonal function (REOF) decomposition was conducted for 67 transmission line icing events. It was found that the spatial distributions of the first three characteristic vectors of the TLIT and OIT data were similar, and the correlation coefficients for the time coefficients of the first three characteristic vectors were 0.801, −0.443, and 0.576, respectively. Three key areas were identified based on the first three patterns of REOF, and the average icing thickness of 67 events in southern China and the three key areas was calculated. The correlation coefficients of icing thickness calculated by the TLIT and OIT data for these areas were 0.648, 0.384, 0.565, and 0.599, respectively. The results illustrate that the TLIT data can reflect the temporal and spatial variations of ice thickness in southern China.

Study on Prediction of Transmission Line Icing Thickness Based on AdaBoost and LS-WSVM

Study on Prediction of Transmission Line Icing Thickness Based on AdaBoost and LS-WSVM

A Study on Prediction of Transmission Line Icing Thickness Based on Ada-Boost and LS-WSVM

A Study on Prediction of Transmission Line Icing Thickness Based on Ada-Boost and LS-WSVM

Capacitance-Type Sensor System for Characterizations of Icing Behavior

Abstract An online monitoring and real-time simulation system for icing of transmission lines is designed using sensing technology with a coplanar multipoint capacitance. A sensor is used in the system to detect changes in the metal electrode capacitance at various positions surrounding transmission lines and verify the ice thickness on the analog lines. Using theoretical analysis and simulations of coplanar electrode fields and capacitors, as well as freezing tests of the system at low temperatures, we found that the system could be quite useful for monitoring the ice thickness of transmission lines.

Ice Thickness Sensor for Overhead Transmission Lines Based on Capacitance Sensing

Abstract A transmission line ice thickness real-time simulation and online monitoring system were designed by using the coplanar multi-point capacitance sensor technology. The capacitance sensing type ice thickness sensor is used in the system to detect the metal electrode capacitance change on various positions surrounding the analog transmission line, and verify the ice thickness on analog line. By theoretical analysis and simulation of a plurality of coplanar metal line electrode fields and capacitors, as well as the freezing test of the system in low temperature, the system is validated to have the feasibility of monitoring the ice thickness of transmission lines.