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.
Highlights
In recent years, a variety of extreme weather phenomena have occurred on a global scale, causing overhead power transmission line icing disasters to happen frequently, accompanied by power outages and huge economic losses due to the destruction of a large number of fixed assets
Transmission line icing is affected by many factors, which mainly include wind direction, light
[ ́3%,+3%]; none of the forecasting points of the model are in MLR model are in the scope. These results demonstrate that the quantum fireworks algorithm (QFA)-wavelet support vector machine (w-support vector machines (SVM)) model has a better the scope
Summary
A variety of extreme weather phenomena have occurred on a global scale, causing overhead power transmission line icing disasters to happen frequently, accompanied by power outages and huge economic losses due to the destruction of a large number of fixed assets. Paper [7] presented an ice thickness prediction model based on fuzzy neural network, the tested result demonstrated its forecasting abilities of better learning and mapping. In transmission line icing forecasting, there are many influencing factors, so a large amount of input data will make the SVM algorithm become unfeasible using the traditional Gaussian kernel for such a large-scale training sample. In view of this problem, this paper will replace the Gaussian kernel function with the wavelet kernel function, and establishes the wavelet support vector machine (w-SVM) for icing forecasting. That combines the QFA and w-SVM models is established; In Section 3, several real-world cases are selected to verify the robustness and feasibility of QFA-w-SVM, and the computation, comparison and discussion of the numerical cases are discussed in detail; Section 4 concludes this paper
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