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Appl. Sci. 2016, 6(2), 54; doi:10.3390/app6020054

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

Departemnt of Economics and Management, North China Electric Power University, Beijing 102206, China
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Author to whom correspondence should be addressed.
Academic Editor: Fan-Gang Tseng
Received: 22 December 2015 / Revised: 23 January 2016 / Accepted: 4 February 2016 / Published: 17 February 2016
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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. View Full-Text
Keywords: icing forecasting; support vector machine; fireworks algorithm icing forecasting; support vector machine; fireworks algorithm
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ma, T.; Niu, D.; Fu, M. Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks Algorithm. Appl. Sci. 2016, 6, 54.

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