This manuscript addresses the critical challenge of fault classification and localization within smart distribution networks, exacerbated by the complex integration of distributed energy resources and the dynamic nature of modern power systems. Traditional methods fall short in accurately and efficiently managing these tasks due to their reliance on …
Combined with the fault degree, maintenance experience, and expert analysis of the charging pile, the state classification strategy is given. Each indicator of the charging pile is standardized according to the threshold level of the operating state.
Fault characteristic diagnosis of the charging pile is essentially fault diagnosis of the power electronic circuits, and the current fault diagnosis methods can be divided into two types : diagnostic methods based on analytical models or methods based on process data. The analytical-model-based approach is by building a mathematical model.
CS-LR is first used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data. The feasibility of the proposed model is illustrated through the case study on fault prediction of real-world smart charging piles.
The research purpose of this paper is to make better and faster diagnosis of the fault of the charging pile using technology based on deep learning. Compared with the traditional machine learning algorithm, this paper does not need to calibrate the fault characteristics manually.
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.
During the operation of DC charging pile, faults are easy to occur, mainly including communication faults, charging gun faults, charging module faults, etc. Among the possible faults of the DC charging post, the charging module failure rate is extremely high.
This manuscript addresses the critical challenge of fault classification and localization within smart distribution networks, exacerbated by the complex integration of distributed energy resources and the dynamic nature of modern power systems. Traditional methods fall short in accurately and efficiently managing these tasks due to their reliance on …
Charging Pile Instructions-V1.3.0 Rapid Installation Guidance Installation and Commission Flowchart Tasks Check if the installation base and the installation hole of the charging pile match Input cable wiring Charging trial run and commissioning Fault alarm and treatment methods (Page 39) Man-machine interactive operation (Page 19)
This paper aims to fill this gap and consider 8 types of fault data for diagnosing, at least including physical installation error fault, charging-pile mechanical fault,...
Aiming at the problems that convolutional neural networks (CNN) are easy to overfit and the low localization accuracy in fault diagnosis of V2G charging piles, an improved fault classification model based on convolutional neural networks (CNN-SVM) is proposed.
Many different types of electric vehicle (EV) charging technologies are described in literature and implemented in practical applications. This paper presents an overview of the existing and proposed EV charging technologies in terms of converter topologies, power levels, power flow directions and charging control strategies. An overview of the main charging …
Aiming at the problems that convolutional neural networks (CNN) are easy to overfit and the low localization accuracy in fault diagnosis of V2G charging piles, an improved fault...
With the continuous development of electric vehicles (EV), large-scale distributed charging piles have been deployed in the wild. Therefore, it is extremely essential to evaluate the risk state of ...
This paper aims to fill this gap and consider 8 types of fault data for diagnosing, at least including physical installation error fault, charging-pile mechanical fault, charging-pile program fault, user personal fault, signal fault (offline), pile compatibility fault, charging platform fault, and other faults. We aim to find out how to combine ...
In this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) …
In short, you must choose a charging pile that is not less than the power of the on-board charger and is compatible. Note that charging piles above 7kw require a 380V meter. [2] Safety protection. Current mainstream brands of AC …
By introducing the particle swarm optimization algorithm with mutation operator, a comprehensive analysis of opportunity service age factor and safety failure probability factor was conducted to establish an indicator …
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.
This paper aims to fill this gap and consider 8 types of fault data for diagnosing, at least including physical installation error fault, charging-pile mechanical fault,...
5 · In order to improve the situation that the fault data set of electric vehicle charging pile has unbalanced data distribution under each fault and the small amount of data leads to the inconspicuous data features, this paper …
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 ...
To the best of our knowledge, there are not many papers dealing with the provision of a summary view of faults and failures types in SGs. We collected the major previous studies in Table 1.With the ongoing interest for SG cybersecurity, several studies providing a list of main attack types against SGs including countermeasures were published, such as Mathas …
In this article, a real-time fault prediction method combining cost-sensitive logistic regression (CS-LR) and cost-sensitive support vector machine classification (CS-SVM) is proposed. CS-LR is first used to classify the fault data of smart charging piles, then the CS-SVM is adopted to predict the faults based on the classified data. The ...
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 …
The simulation results of this paper show that: (1) Enough output power can be provided to meet the design and use requirements of the energy-storage charging pile; (2) the control guidance ...
Aiming at the problems that convolutional neural networks (CNN) are easy to overfit and the low localization accuracy in fault diagnosis of V2G charging piles, an improved fault...
Wang et al. Cybersecurity Page 2 of 13 occurrence of charging-pile work orders may be due to a mechanical fault or cyber security. We can imagine a sce-nario of mechanical fault: (a) a customer ...
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 …
This paper aims to measure the capability of supervised and semi-supervised machine learning techniques in assessing the risk state of EV charging piles. We investigate 8 algorithms, including Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Self-Training based on SVM (ST ...
By introducing the particle swarm optimization algorithm with mutation operator, a comprehensive analysis of opportunity service age factor and safety failure probability factor was conducted to establish an indicator system for the operation status of charging piles, and a potential fault identification model was constructed.
Aiming at the problems that convolutional neural networks (CNN) are easy to overfit and the low localization accuracy in fault diagnosis of V2G charging piles, an improved fault classification model based on convolutional …
DOI: 10.3390/pr11051561 Corpus ID: 258811493; Energy Storage Charging Pile Management Based on Internet of Things Technology for Electric Vehicles @article{Li2023EnergySC, title={Energy Storage Charging Pile Management Based on Internet of Things Technology for Electric Vehicles}, author={Zhaiyan Li and Xuliang Wu and Shen Zhang …
5 · In order to improve the situation that the fault data set of electric vehicle charging pile has unbalanced data distribution under each fault and the small amount of data leads to the inconspicuous data features, this paper proposes a method of SAE-MLP model for fault diagnosis of charging pile fault data. This paper firstly utilizes AE to ...
This paper aims to measure the capability of supervised and semi-supervised machine learning techniques in assessing the risk state of EV charging piles. We investigate 8 …
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