Methods for leak detection of energy storage charging piles

Which fault detection method is best for electric vehicle charging pile diagnosis?

A fault detection method based on deep learning Convolutional Neural Networks and Long Short-Term Memory and the proposed CNN-LSTM method has the highest accuracy and exhibits the best performance in the electric vehicle charging pile diagnosis.

What is fault state detection method of DC charging pile?

However, the fault signal processing of the fault detection method is poor, resulting in low fault detection accuracy. Therefore, a fault state detection method of DC charging pile based on the least fourth moment adaptive filtering algorithm is proposed. This method is based on the electrical structure of DC charging pile.

Are public charging piles efficient?

Abstract: With electric cars, large-scale development, in order to make the electric vehicles charging more convenient and efficient, public charging piles began to be used on a large scale. However, traditional fault detection methods are still used in charging piles, which makes the detection efficiency low.

What is the error detection procedure of charging pile based on Elm?

This paper proposes an error detection procedure of charging pile founded on ELM method. Different from the traditional charging pile fault detection model, this method constructs data for common features of the charging pile and establishes a classification prediction frame work that relies on the Extreme Learning Machine (ELM) algorithm.

Can multiple concurrent faults be detected in DC charging pile charging module?

There may be multiple concurrent faults in the actual DC charging pile charging module fault state. Therefore, the fault detection performance of different methods is analyzed to verify whether the proposed method can accurately detect faults in the case of multiple concurrent faults in the context of this actual problem.

Can Ana-LSTM neural network predict charging pile battery life?

In this study, the improved anti-noise adaptive Long Short-term memory (ANA-LSTM) neural network was used to extract fault characteristics, thus achieving the life prediction of charging pile batteries and providing reference for the status detection of charging piles. However, the signal data was not effectively processed by this method.