Abstract
The transmission cable and power conversion device need to be buried underground for dynamic wireless charging of an expressway, so cable insulation deterioration caused by aging and corrosion may occur. This paper presents an on-line insulation monitoring method based on BP neural network for dynamic wireless charging network. The sampling signal expression of the injection signal is derived, and the feasibility of this method is verified by experiments, which effectively overcomes the problem of large calculation error of insulation resistance when the cable capacitance to ground is large. The experimental results indicate that the error of the proposed method is less than 9%, which can meet the needs of insulation monitoring.
Highlights
The dynamic wireless charging road can continuously supply the electric energy for driving electric vehicles (EVs), so it has a broad development prospect
An insulation monitoring method of wireless charging cable based on BP neural network is proposed
The errors between the calculated values and the theoretical values of nine groups were less than ±9%. Because this method is realized by learning curve of BP neural network, the insulation resistance value can be calculated accurately in the case of large capacitance to ground, which effectively overcomes the problem of large error of direct calculation result in large capacitance
Summary
The dynamic wireless charging road can continuously supply the electric energy for driving electric vehicles (EVs), so it has a broad development prospect. The cable insulation monitoring in ITN system is mainly realized by injecting signal, which is divided into DC signal and AC signal [2]. The disadvantage is that when the value of the ground capacitor is large, the charging time will be very long, and the current insulation status cannot be analyzed quickly. Ground large, the calculation resultmethod, error is there are other methods, such as adaptive pulse injection, which uses superimposed adaplarge due to phase error. The disadvantage is that it is only for DC such as bridge balance method, signal tracing method and differential current detection power grid and the hardware implementation is complex. We creatively introduce deep learning deep learning method into the field of electric vehicle cable insulation monitoring. In method into the field of electric vehicle cable insulation monitoring.
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