Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks Algorithm
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 propose...
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doaj-eeeb795732194ed0b79524a5d803d5402020-11-25T01:01:31ZengMDPI AGApplied Sciences2076-34172016-02-01625410.3390/app6020054app6020054Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks AlgorithmTiannan Ma0Dongxiao Niu1Ming Fu2Departemnt of Economics and Management, North China Electric Power University, Beijing 102206, ChinaDepartemnt of Economics and Management, North China Electric Power University, Beijing 102206, ChinaDepartemnt of Economics and Management, North China Electric Power University, Beijing 102206, ChinaIcing 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.http://www.mdpi.com/2076-3417/6/2/54icing forecastingsupport vector machinefireworks algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tiannan Ma Dongxiao Niu Ming Fu |
spellingShingle |
Tiannan Ma Dongxiao Niu Ming Fu Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks Algorithm Applied Sciences icing forecasting support vector machine fireworks algorithm |
author_facet |
Tiannan Ma Dongxiao Niu Ming Fu |
author_sort |
Tiannan Ma |
title |
Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks Algorithm |
title_short |
Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks Algorithm |
title_full |
Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks Algorithm |
title_fullStr |
Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks Algorithm |
title_full_unstemmed |
Icing Forecasting for Power Transmission Lines Based on a Wavelet Support Vector Machine Optimized by a Quantum Fireworks Algorithm |
title_sort |
icing forecasting for power transmission lines based on a wavelet support vector machine optimized by a quantum fireworks algorithm |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2016-02-01 |
description |
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. |
topic |
icing forecasting support vector machine fireworks algorithm |
url |
http://www.mdpi.com/2076-3417/6/2/54 |
work_keys_str_mv |
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