Predictive Model for Equivalent Ice Thickness Load on Overhead Transmission Lines Based on Measured Insulator String Deviations

Jiang, Xingliang; Xiang, Ze; Zhang, Zhijin; Hu, Jianlin; Hu, Qin; Shu, Lichun

doi:10.1109/tpwrd.2014.2305980

Cited by 49 publications

(42 citation statements)

References 13 publications

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“…In heavy icing areas of China, thousands of icing monitoring systems and observation stations are used to observe the phenomenon of icing growth. Literatures have established many mechanical calculation models for ice thickness and classified the iced conductors into circular shape, elliptical shape, sector shape, and wavy shape [11,17,18]. In this paper, a typical elliptical iced conductor is taken as an example to analyze the critical ice-melting, and its cross-section is shown in fig.…”

Section: Experimental Apparatusmentioning

confidence: 99%

Thermodynamic model of critical ice-melting current on iced transmission lines

et al. 2019

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The thermal de-icing by Joule effect is a mostly valid way to prevent transmission-lines from the severe ice storm. A model was put forward to simulate the critical ice-melting current on iced conductor. Based on this model, the value of critical ice-melting current was calculated with various parameters, some of which were ignored in the earlier literatures, such as ice-layer heat conductivity, wind attack angle, and icing section shape. The results of the experiment and simulation show that the critical ice-melting current increase with wind speed, wind attack angle, and ice-layer heat conductivity, but decrease rapidly with ambient temperature and liquid water content. Moreover, the maximum difference between the results of simulation and experiment is about 9%, thus this model can be employed to estimate the engineering parameters in practical thermal de-icing projects.

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Section: Experimental Apparatusmentioning

confidence: 99%

Thermodynamic model of critical ice-melting current on iced transmission lines

et al. 2019

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“…Although composite insulators were initially considered to be maintenance-free, the performance of silicone rubber material on the surface of composite insulators deteriorated during the aging process in poor operating environments. With the increase of aging time, composite insulators lost their hydrophobicity and allowed cracks to appear in the surficial material, resulting in an increase of the probability of pollution and icing flashover accidents [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Research on Lifespan Prediction of Composite Insulators in a High Altitude Area Experimental Station

Zhang

Jiang

et al. 2019

Applied Sciences

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In this paper, composite insulators of the same batch from Factory A, aged for 1–12 years in a high altitude area experimental station of Hunan province, were sampled. In order to investigate the changing law of lifespan prediction parameters with aging time, widely accepted testing methods, such as the static contact angle (CA) and hardness, were employed for composite insulators in accordance with previous research. Based on test results, lifespan prediction parameters were concluded and some parameters significantly correlated with aging time were filtered by means of correlation calculation. On this basis, a prediction method which can be used to determine the aging time of composite insulators was proposed based on a back propagation (BP) neural network. Test results indicate that parameters, including the static contact angle (θav), the relative content of Si (XSi) and O (XO) elements, and salt-fog flashover voltage (Uf), have significant correlation with aging time, and that these parameters can be used to evaluate the aging degree of composite insulators. In addition, due to the high accuracy in experimental verification, the method proposed in this paper can be used to predict the aging time of composite insulators from Factory A in high altitude areas in future research.

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“…1. Mechanistic models: [8][9][10][11] Marcacci et al 8 conducted a computational analysis of the effects of air temperature, wind speed, and droplet diameter on the icing intensity and local collision rates during icing processes. On this basis, a computational module for computing ice accretion on transmission lines was developed, and the icing processes were numerically simulated.…”

Section: Introductionmentioning

confidence: 99%

Field data–driven online prediction model for icing load on power transmission lines

Chen

Ren

et al. 2019

Measurement and Control

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Methods for the accurate prediction of icing loads in overhead transmission lines have become an important research topic for electrical power systems as they are necessary for ensuring the safety and stability of power-grid operations. Current machine learning models for the prediction of icing loads on transmission lines are afflicted by the following issues: insufficient prediction accuracy, high randomity in the selection of kernel functions and model parameters, and a lack of generalizability. To address these issues, we propose a field data-driven online prediction model for icing loads on transmission lines. First, the effects of the type of kernel function used in the support vector regression algorithm on the prediction accuracy of the model were analyzed using micrometeorological data and icing data collected by on-site monitoring systems. The particle swarm optimization algorithm was then used to optimize and determine the model parameters such as penalty coefficients. An offline support vector regression prediction model was thus constructed. Using the accurate online support vector regression algorithm, the weighting coefficients of the samples were dynamically adjusted to satisfy the Karush-Kuhn-Tucker conditions, which allowed online updates to be made to the regression function and prediction model. Finally, a simulation analysis was performed using actual icing incidents that occurred in a transmission line of the Yunnan Power Grid, which demonstrated that our model can make online predictions for the icing load on transmission lines in actual applications. Our model proved to be superior to conventional icing-load prediction models with regard to the single-step and multi-step prediction accuracies and generalizability. Hence, our prediction model will improve the decision-making processes regarding the deicing and maintenance of power transmission and transformation systems.

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Predictive Model for Equivalent Ice Thickness Load on Overhead Transmission Lines Based on Measured Insulator String Deviations

Cited by 49 publications

References 13 publications

Thermodynamic model of critical ice-melting current on iced transmission lines

Thermodynamic model of critical ice-melting current on iced transmission lines

Research on Lifespan Prediction of Composite Insulators in a High Altitude Area Experimental Station

Field data–driven online prediction model for icing load on power transmission lines

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