An Intelligent Control Strategy for a Highly Reliable Microgrid in Island Mode

An intelligent control strategy based on a membership cloud model in a high reliable off-grid microgrid with a reconfigurable inverter is proposed in this paper. The operating principle of the off-grid microgrid with the reconfigurable inverter is provided, which contains four operating modes. An open-circuit fault diagnosis for the inverter is presented first. The polarities of the midpoint voltages defined in the paper are used to recognize the faulty power switch. The reconfigurable inverter allows the power switches of different bridges to be reconfigured, when there are power switches faulty, to let the inverter operate in faulty state. The working principle of the reconfigurable inverter is given. The membership cloud model with two output channels is built to obtain the virtual impedance to suppress the circulating currents between inverters when the reconfigurable inverter is in faulty state. A pulse resetting method is presented. The general intelligent control strategy for the reconfigurable inverter is formed as the droop-virtual impedance-voltage-current-pulses resetting control. The validity of the intelligent control strategy of the system is verified by simulation.

Analog Circuit Fault Diagnosis Using a Novel Variant of aConvolutional Neural Network

Analog circuits play an important role in modern electronic systems. Aiming to accurately diagnose the faults of analog circuits, this paper proposes a novel variant of a convolutional neural network, namely, a multi-scale convolutional neural network with a selective kernel (MSCNN-SK). In MSCNN-SK, a multi-scale average difference layer is developed to compute multi-scale average difference sequences, and then these sequences are taken as the input of the model, which enables it to mine potential fault characteristics. In addition, a dynamic convolution kernel selection mechanism is introduced to adaptively adjust the receptive field, so that the feature extraction ability of MSCNN-SK is enhanced. Based on two well-known fault diagnosis circuits, comparison experiments are conducted, and experimental results show that our proposed method achieves higher performance.

A New Fault Diagnosis Model for Circuits in Railway Vehicle Based on the Principal Component Analysis and the Belief Rule Base

Circuits are considered an important part of railway vehicles, and circuit fault diagnosis in the railway vehicle is also a research hotspot. In view of the nonlinearity and diversity of track circuit components, as well as the diversity and similarity of fault phenomena, in this paper, a new fault diagnosis model for circuits based on the principal component analysis (PCA) and the belief rule base (BRB) is proposed, which overcomes the shortcomings of the circuit fault diagnosis method based on data, model, and knowledge. In the proposed model, to simplify the model and improve the accuracy, PCA is used to reduce the dimension of the key fault features, and varimax rotation is used to deduce the fault features. BRB is used to combine qualitative knowledge and quantitative data effectively, and evidential reasoning (ER) algorithm is used to carry out the inference of knowledge. The initial parameters of the model are optimized, and the optimal precondition attributes, rule weights, and belief degree parameters are obtained to improve the accuracy. Through the training and testing of the model, the experimental results show that the method can accurately diagnose the fault of the driver controller potentiometer in the railway vehicle. Compared with other methods, the model shows high accuracy.

On-line Diagnosis Method of Power Electronics Fault Diagnosis

With the development of science and technology, the application of computer technology in electronic power fault diagnosis technology has become more and more extensive. This article mainly studies the detection methods of power electronics and the application of power electronics circuit fault diagnosis.

IGBT Open-Circuit Fault Diagnosis for MMC Submodules Based on Weighted-Amplitude Permutation Entropy and DS Evidence Fusion Theory

Modular multilevel converters (MMCs) have a complex structure and a large number of submodules (SMs). If there is a fault in one of the SMs, it will affect the reliable operation of the system. Therefore, rapid fault diagnosis and accurate fault positioning are crucial to ensuring the continuous operation of the system. However, the IGBT open-circuit faults in different submodules of MMCs have similar fault features, and the traditional fault feature extraction method cannot effectively extract the key features of the fault so as to accurately locate the faulty submodules. In response to this problem, this paper proposes a fault diagnosis method based on weighted-amplitude permutation entropy (WAPE) and DS evidence fusion theory. The simulation results show that WAPE has better feature extraction ability than basic permutation entropy, and the fused multiscale feature decision output has better diagnostic accuracy than the single-scale feature. Compared with traditional fault diagnosis methods, this method achieves the diagnosis of multiple fault types by collecting a single signal, which greatly reduces the number of samples and leads to higher diagnostic accuracy and faster diagnostic speed.