Abstract
In order to improve the accuracy of theoretical energy loss calculation in low voltage distribution area (LV-area), this paper proposes a new prediction method based on variational mode decomposition (VMD) and particle swarm optimization (PSO) least squares support vector machine (LSSVM). Firstly, the main influencing factors of energy loss calculation in LV-area are determined by the grey correlation method, which reflects the data-driven characteristic of the method and ensures the objectivity of the prediction results and the generalization of the calculation model. Secondly, the trend component and fluctuation component are obtained by VMD of daily energy loss series in the LV-area. The variable set of main influencing factors of energy losses is used as the input variable of LSSVM, and the VMD result of the energy loss sequence is used as the output. The theoretical energy loss training and calculation model of LV-area is established. Compared with the traditional calculation model, this model has more accurate calculation accuracy by taking into account the frequency characteristics of energy losses in different frequency bands. PSO is used to optimize the parameters of LSSVM for the purpose of improving the accuracy of LSSVM. Finally, an example of 252 LV-area in a city in northern China is given to verify the validity of the proposed method. The results indicate that the proposed method generates more accurate results.
Highlights
In recent years, the development of intelligent distribution network has carried out a simulation of construction and energy saving simultaneously
The factors affecting the energy losses of low voltage distribution area (LV-area) are listed, and the main influencing factors of energy loss calculation of LV-area are determined by the grey correlation method. en, the daily LV-area energy loss series of LV-area is decomposed into trend components and fluctuation components with different frequency characteristics by variational mode decomposition (VMD), the main influencing factors of energy losses obtained by grey correlation method are used as input variables of least squares support vector machine (LSSVM), and the VMD score of energy loss series is calculated. e component obtained from the solution is taken as the output, and the theoretical energy loss calculation model of LV-area is established
An energy loss calculation method of LV-area based on variational mode decomposition and particle swarm optimization (PSO) optimized LSSVM is proposed
Summary
The development of intelligent distribution network has carried out a simulation of construction and energy saving simultaneously. Zhong and Chen [9] selected eleven energy loss features from LV-area sample data, constructed a relatively perfect electrical index evaluation system of the LVarea, took them as the input variable of the training model, and proposed a line loss calculation method based on deep learning. Some methods using the artificial intelligence algorithm achieve accurate distribution network energy loss calculation by using limited data. The paper proposes the energy loss calculation method based on variational mode decomposition and particle swarm optimization least squares support vector machine for LV-area. En, the daily LV-area energy loss series of LV-area is decomposed into trend components and fluctuation components with different frequency characteristics by VMD, the main influencing factors of energy losses obtained by grey correlation method are used as input variables of LSSVM, and the VMD score of energy loss series is calculated. E correlation coefficients of influencing factor series and daily energy loss rate series at different times can be expressed as follows [13]: Section 1 Section 2
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