Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks Algorithm

Abstract

Icing on power transmission lines is a serious threat to the security and stability of the power grid, and it is necessary to establish a forecasting model to make accurate predictions of icing thickness. In order to improve the forecasting accuracy with regard to icing thickness, this paper proposes a combination model based on a wavelet support vector machine (w-SVM) and a quantum fireworks algorithm (QFA) for prediction. First, this paper uses the wavelet kernel function to replace the Gaussian wavelet kernel function and improve the nonlinear mapping ability of the SVM. Second, the regular fireworks algorithm is improved by combining it with a quantum optimization algorithm to strengthen optimization performance. Lastly, the parameters of w-SVM are optimized using the QFA model, and the QFA-w-SVM icing thickness forecasting model is established. Through verification using real-world examples, the results show that the proposed method has a higher forecasting accuracy and the model is effective and feasible.

Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

“…In addition, this paper and the published paper (reference [12]) seem to be slightly similar, in that both of the papers are used for the icing forecasting of transmission lines. However, their models have many differences, mainly as follows: (1) The regression models used for icing forecasting of the two papers are different.…”

“…Paper [11] proposes a brand new hybrid method which is based on the weighted support vector machine regression (WSVR) model to forecast the icing thickness and particle swarm and ant colony (PSO-ACO) to optimize the parameters of WSVR. Paper [12] presents a combination model based on a wavelet support vector machine (w-SVM) and a quantum fireworks algorithm (QFA) for icing prediction, and several real-world cases have been applied to verify the effectiveness and feasibility of the established QFA-w-SVM model. Additionally, paper [13] proposes using the enhanced fireworks algorithm for support vector machine parameters optimization, and the experiment results show that the enhanced fireworks algorithm has been proved to be very successful for support vector machine parameter optimization and is also superior to other swarm intelligence algorithms.…”

Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search

“…SVM has the advantages of repeated training, fast convergence speed and it can solve real problems with the characteristics of small samples, non-linearity and local extrema. SVM is suitable for ice cover prediction, which is influenced by climate [19][20][21][22][23]. To improve the prediction accuracy, the wavelet method [19], particle swarm optimization algorithm [20], and other algorithms are used to optimize the parameters of SVM.…”

“…SVM is suitable for ice cover prediction, which is influenced by climate [19][20][21][22][23]. To improve the prediction accuracy, the wavelet method [19], particle swarm optimization algorithm [20], and other algorithms are used to optimize the parameters of SVM. The prediction results are improved to a certain degree.…”

“…Intelligent models are popular in the study of ice cover prediction [14][15][16][17][18][19][20][21][22][23]. Due to their nonlinear approximation ability, neural networks are widely used in icing prediction [14][15][16][17].…”