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Variational Bayesian parameter learning method based harmonic current detection algorithm for electric vehicle charging station

A variational Bayesian, harmonic current technology, applied in the direction of measuring current/voltage, computing, measuring devices, etc.

Active Publication Date: 2019-03-08
YANCHENG POWER SUPPLY CO STATE GRID JIANGSU ELECTRIC POWER CO +2
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Problems solved by technology

[0005] Aiming at the superposition detection problem of harmonic current generated by random access of large-scale charging piles in electric vehicle charging stations to the power grid, the present invention proposes a harmonic current detection algorithm for electric vehicle charging stations based on a variational Bayesian parameter learning method. It is used to solve the problem of superimposed detection of harmonic current generated by random access of charging piles to the distribution network, to ensure the accuracy of superimposed detection of harmonic current, and to provide an effective basis for adopting accurate harmonic control schemes

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  • Variational Bayesian parameter learning method based harmonic current detection algorithm for electric vehicle charging station
  • Variational Bayesian parameter learning method based harmonic current detection algorithm for electric vehicle charging station
  • Variational Bayesian parameter learning method based harmonic current detection algorithm for electric vehicle charging station

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[0016] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0017] see figure 1 As shown, the equivalent circuit model diagram of the charging pile in the electric vehicle charging station randomly connected to the grid. Among them, the nonlinear load is used as the equivalent mathematical model of the charging pile, and a random number algorithm is added to control whether the nonlinear load is connected to the system, forming an equivalent model in which the charging pile in the electric vehicle charging station is randomly connected to the grid.

[0018] The equivalent mathematical model of the random access to the power grid of the charging pile in the charging station includes the following three parts:

[0019] (1) Taking the nonlinear load as the equivalent mathematical model of the charging pile, the load in the charging pile model is The output power model is

[0020] (2) Use the random number algorithm...

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Abstract

The invention discloses a variational Bayesian parameter learning method based harmonic current detection algorithm for an electric vehicle charging station. The algorithm includes establishing a charger equivalent circuit model, and judging whether a charging pile can be randomly accessed into a power distribution network system by utilizing a random number algorithm and a comparison module; performing ideal superposition calculation on the harmonic current meeting gaussian normal distribution so that an ideal harmonic superposition coefficient calculation method can be obtained; and performing sampling on harmonic phase to form two sets of random set of state space and measurement space, obtaining model parameters through the lower bound of a logarithmic edge likelihood function by usinga variational Bayesian parameter learning method, continuously maximizing the lower bound of the likelihood function, and iteratively updating variational phase parameters until approximate distribution approaches the true posterior distribution of the parameters so that harmonic phase superposition detection can be realized. Actual harmonic superposition coefficients can be obtained by substituting harmonic phase distribution into the harmonic superposition coefficient calculation method, so that accurate detection of multi-harmonic current superposition at same time in a charging station can be realized.

Description

technical field [0001] The invention relates to the field of superposition prediction and evaluation of same-order multi-harmonic current sources in electric vehicle charging stations, in particular to a harmonic current detection algorithm for electric vehicle charging stations based on a variational Bayesian parameter learning method. Background technique [0002] With the depletion of fossil energy, climate change and environmental pollution are becoming more and more serious, which have seriously threatened the survival of human beings and the sustainable development of society. As a new generation of environmentally friendly means of transportation, electric vehicles (EVs) have incomparable advantages over traditional vehicles in terms of energy saving and emission reduction, slowing down the greenhouse effect, reducing human dependence on traditional fossil energy, and ensuring the security of oil supply. Supporting large-scale charging stations are also developing rap...

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Application Information

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IPC IPC(8): G01R19/00G06Q10/06G06Q50/06
CPCG06Q10/067G06Q50/06G01R19/0023
Inventor 陈文黄永红周杰胥峥
Owner YANCHENG POWER SUPPLY CO STATE GRID JIANGSU ELECTRIC POWER CO
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