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Current harmonic analysis method based on trigonometric function neural network

A technology of neural network and trigonometric function, applied in the field of current harmonic analysis based on trigonometric function neural network, to achieve the effect of a simple and feasible solution

Active Publication Date: 2018-10-16
杭州市电力设计院有限公司
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Problems solved by technology

[0004] The invention aims at the problem that the fault or abnormality of the electric vehicle charger itself affects the power quality of the power system, and proposes a current harmonic analysis method based on a trigonometric function neural network, which can quickly realize the harmonic analysis of the electric vehicle charging current. Analysis of current harmonic parameters

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  • Current harmonic analysis method based on trigonometric function neural network
  • Current harmonic analysis method based on trigonometric function neural network
  • Current harmonic analysis method based on trigonometric function neural network

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Embodiment Construction

[0033] The current harmonic analysis method based on the trigonometric function neural network: mainly converts the charging current of the electric vehicle into a trigonometric function represented by weight, constructs a trigonometric function neural network, and obtains the harmonics containing each harmonic through the forward recursion of the trigonometric function neural network. The output current of the component; compare the output current with the input current, and use the negative gradient descent method to obtain the optimal weight of the trigonometric neural network through the reverse iteration of the trigonometric neural network. Using the optimal weight can quickly and Accurately estimate system harmonic components and related parameters, with good robustness, and can quickly realize the analysis of current harmonic parameters.

[0034] The electric vehicle charging current can be expressed as the sum of each harmonic component in terms of amplitude and phase: ...

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Abstract

The invention relates to a current harmonic analysis method based on a trigonometric function neural network. Harmonic analysis of the electric vehicle charging current is achieved by aiming at the harmonic waves generated by an electric power system when charging an electric vehicle. The electric vehicle charging current is converted into a trigonometric function expressed by weight, a trigonometric function neural network is constructed, and forward recursion is carried out through the trigonometric function neural network. The output current containing all the harmonic components is obtained; the output current is compared with the input current, and a negative gradient descent method is adopted for carrying out reverse iteration of the trigonometric function neural network on the difference value of the output current and the input current to obtain the optimal weight of the trigonometric function neural network, so that accurate estimation of the charging current harmonic parameters of the electric vehicle is obtained. The method has higher convergence characteristic and better noise tolerance.

Description

technical field [0001] The invention relates to a power detection technology, in particular to a current harmonic analysis method based on a trigonometric function neural network. Background technique [0002] Traditional chargers have the disadvantages of low order, low power factor, high input current harmonics with uncontrollable state of charge, and limited control over battery current. Moreover, high input current harmonics with low frequency and low power factor do not comply with IEC1000-3-2 or IEEE519 harmonic standards, and the uncontrollable state of charge will shorten battery life. [0003] To mitigate harmonic pollution, accurate monitoring and analysis of harmonic components is paramount, and compensation techniques are applied to correct waveforms, or terminate power transmission. Therefore, estimation of harmonic parameters such as magnitude and phase is especially important in order to enhance harmonic monitoring. So far, great progress has been made in th...

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Application Information

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IPC IPC(8): G01R23/16
CPCG01R23/16
Inventor 俞荣江罗进圣陈忠华王育飞胡晨刚陈攀薛花许秀珍陈炳汪欣玥张帆金娇朱怡佳沈国恒
Owner 杭州市电力设计院有限公司
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