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

Abstract

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.

Multihorizons transfer strategy for continuous online prediction of time‐series data in complex systems

“…The Lorenz model is also often used to generate chaotic time-series data of the threedimensional dynamic system, and its mathematical form is shown in formula (19).…”

“…where a b c , , are the dimensionless parameters. Here, we set a b c = 10, = 28, = 8 3 , and construct a three-dimensional chaotic time series by using the formula (19), each time series contains 1000 sample points. We selected two data sets, namely, the Lorenz (X − Y) data set and the Lorenz (X − Z) data set, taking the time-series data in the X-dimension as the source domain data, and the data in the remaining dimensions as the target domain data.…”

“…Then the least‐square model is established for online prediction of data. Chen et al 19 used an accurate support vector regression algorithm to dynamically adjust the weighting coefficient of samples, so as to realize the online update of the prediction model. However, there are several problems in the above research.…”

Short-term wind speed prediction based on CEEMDAN-SE-improved PIO-GRNN model

“…Hence, it is necessary to judge the merits and demerits for each method according to several error evaluation criteria. Mean squared error (MSE), 31 mean absolute error (MAE) 32 and weighted mean absolute percentage error (WMAPE) 33 are three different indicators, which can be applied to estimate the prediction outcomes. The tinier the values of the three indicators are, the more precise the prediction accuracy of the model will be.…”

Dynamic Simulation of Power Systems Considering Transmission Lines Icing and Insulators Flashover in Extreme Weather

“…A transmission line ice coating prediction model based on ensemble empirical mode decomposition feature extraction was proposed in [8]. A field-data-driven online prediction model for icing loads on transmission lines was proposed in [9]. Some studies have been conducted based on the relevant models.…”