Open-circuit Fault Diagnosis Research Articles

Skewness Driven DFIG's DC Link Bus Short and Open Circuit Faults Diagnosis Using Rotor and Stator Currents

Skewness Driven DFIG's DC Link Bus Short and Open Circuit Faults Diagnosis Using Rotor and Stator Currents

An Open-Circuit Fault Diagnosis System Based on Neural Networks in the Inverter of Three-Phase Permanent Magnet Synchronous Motor (PMSM)

Three-phase motors find extensive applications in various industries. Open-circuit faults are a common occurrence in inverters, and the open-circuit fault diagnosis system plays a crucial role in identifying and addressing these faults to enhance the safety of motor operations. Nevertheless, the current open-circuit fault diagnosis system faces challenges in precisely detecting specific faulty switches. The proposed work presents a neural network-based open-circuit fault diagnosis system for identifying faulty power switches in inverter-driven motor systems. The system leverages trained phase-to-phase voltage data from the motor to recognize the type and location of faults in each phase with high accuracy. Employing separate neural networks for each of the three phases in a three-phase permanent magnet synchronous motor, the system achieves an outstanding overall fault detection accuracy of approximately 99.8%, with CNN and CNN-LSTM architectures demonstrating superior performance. This work makes two key contributions: (1) implementing neural networks to significantly improve the accuracy of locating faulty switches in open-circuit fault scenarios, and (2) identifying the optimal neural network architecture for effective fault diagnosis within the proposed system.

Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm

Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm’s effectiveness and robustness.

Open-Circuit Fault Diagnosis of T-Type Three-Level Inverter Based on Knowledge Reduction

Compared with traditional two-level inverters, multilevel inverters have many solid-state switches and complex composition methods. Therefore, diagnosing and treating inverter faults is a prerequisite for the reliable and efficient operation of the inverter. Based on the idea of intelligent complementary fusion, this paper combines the genetic algorithm–binary granulation matrix knowledge-reduction method with the extreme learning machine network to propose a fault-diagnosis method for multi-tube open-circuit faults in T-type three-level inverters. First, the fault characteristics of power devices at different locations of T-type three-level inverters are analyzed, and the inverter output power and its harmonic components are extracted as the basis for power device fault diagnosis. Second, the genetic algorithm–binary granularity matrix knowledge-reduction method is used for optimization to obtain the minimum attribute set required to distinguish the state transitions in various fault cases. Finally, the kernel attribute set is utilized to construct extreme learning machine subclassifiers with corresponding granularity. The experimental results show that the classification accuracy after attribute reduction is higher than that of all subclassifiers under different attribute sets, reflecting the advantages of attribute reduction and the complementarity of different intelligent diagnosis methods, which have stronger fault-diagnosis accuracy and generalization ability compared with the existing methods and provides a new way for hybrid intelligent diagnosis.

Robust Diagnosis of Multiple Open-Circuit Faults in DFIG Wind Turbines in Both Sub- and Super-Synchronous Operating Modes

Robust Diagnosis of Multiple Open-Circuit Faults in DFIG Wind Turbines in Both Sub- and Super-Synchronous Operating Modes

A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM

The open-circuit fault in electric vehicle charging stations not only impacts the power quality of the electrical grid but also poses a threat to charging safety. Therefore, it is of great significance to study open-circuit fault diagnosis for ensuring the safe and stable operation of power grids and reducing the maintenance cost of charging stations. This paper addresses the multidimensional characteristics of open-circuit fault signals in charging stations and proposes a fault diagnosis method based on an improved S-transform and LightGBM. The method first utilizes improved incomplete S-transform and principal component analysis (PCA) to extract features of front- and back-stage faults separately. Subsequently, LightGBM is employed to classify the extracted features, ultimately achieving fault diagnosis. Simulation results demonstrate the method’s effectiveness in feature extraction, achieving an average diagnostic accuracy of 97.04% on the test dataset, along with notable noise resistance and real-time performance. Additionally, we designed an experimental platform for diagnosing open-circuit faults in DC charging station and collected experimental fault data. The results further validate the effectiveness of the proposed method.

Open-circuit fault diagnosis in three-phase induction motor using model-based technique

Open-circuit fault diagnosis in three-phase induction motor using model-based technique

Open-Circuit Fault Diagnosis and Analysis for Integrated Charging System Based on Bidirectional Gated Recurrent Unit and Attention Mechanism

Open-Circuit Fault Diagnosis and Analysis for Integrated Charging System Based on Bidirectional Gated Recurrent Unit and Attention Mechanism

Research on Open Circuit Fault Diagnosis Strategy for DFPMM Drive System Based on Grey Prediction Theory With the Introduction of Euler Algorithm

Research on Open Circuit Fault Diagnosis Strategy for DFPMM Drive System Based on Grey Prediction Theory With the Introduction of Euler Algorithm

Deep Learning-Based Multi-feature Fusion Model for Accurate Open Circuit Fault Diagnosis in Electric Vehicle DC Charging Piles

Deep Learning-Based Multi-feature Fusion Model for Accurate Open Circuit Fault Diagnosis in Electric Vehicle DC Charging Piles

Open-Circuit Fault Diagnosis Method for Inverter in PMSM Drives Considering Closed-Loop Regulation

Open-Circuit Fault Diagnosis Method for Inverter in PMSM Drives Considering Closed-Loop Regulation

Online Open-Circuit Fault Diagnosis for Neutral Point Clamped Inverter Based on an Improved Convolutional Neural Network and Sample Amplification Method Under Varying Operating Conditions

Online Open-Circuit Fault Diagnosis for Neutral Point Clamped Inverter Based on an Improved Convolutional Neural Network and Sample Amplification Method Under Varying Operating Conditions

An open-circuit fault diagnosis strategy for input series output parallel dual active bridge DC/DC converter based on the mean of AC voltages and currents

An open-circuit fault diagnosis strategy for input series output parallel dual active bridge DC/DC converter based on the mean of AC voltages and currents

Open-circuit Fault Diagnosis Method for Grid-tied T-type Converters based on Harr-like Features

Open-circuit Fault Diagnosis Method for Grid-tied T-type Converters based on Harr-like Features

A Robust Data-Driven Method for Open-Circuit Fault Diagnosis of Power Switches in Three-Phase Inverters With Low-Quality Data

A Robust Data-Driven Method for Open-Circuit Fault Diagnosis of Power Switches in Three-Phase Inverters With Low-Quality Data

Open circuit fault diagnosis of wind power converter based on VMD energy entropy and time domain feature analysis

AbstractAiming at the shortcomings of feature extraction and fault identification in fault diagnosis of wind power converters, a novel method for open circuit fault diagnosis of wind power converters based on variational mode decomposition (VMD) energy entropy (EE) and time domain feature analysis (TDFA) is proposed. Primarily, the three‐phase output current at the grid side of the wind power converter is collected as the original signal, and the VMD is used to decompose the original signal into a series of intrinsic mode functions (IMF). To reduce noise interference as much as possible, the Pearson correlation coefficient between each mode component and the original signal under different fault states is analyzed, and the IMF component containing the major failure features is selected to calculate the energy entropy of each component; afterward, according to the Pearson correlation coefficient results, the modal components of the first layer are selected for time domain feature analysis; finally, the feature matrix that combines energy entropy and time domain feature analysis is inputted into the long short‐term memory neural network for training and fault identification. The simulation and experimental results show that the open circuit fault diagnosis method proposed in this paper has high accuracy and robustness.

Open circuit fault diagnosis of three‐phase inverter based on SR‐WOA‐ELM

SummaryTo ensure smooth power exchange in a three‐phase inverter, it is important to accurately detect the faults of insulated gate bipolar transistor (IGBT) switches. However, the fault characteristics of power tubes are complex, the fault diagnosis results are affected by the load, and the recognition accuracy of some fault diagnosis models is low. To tackle this issue, this paper proposes an inverter open circuit fault diagnosis method named SR‐WOA‐ELM, which integrates signal reconstruction (SR), the whale optimization algorithm (WOA), and an extreme learning machine (ELM). First, the three‐phase output current is processed by the signal reconstruction method to eliminate the effect of load fluctuation, the average value of low frequency coefficients and wavelet entropy is extracted from the reconstructed three‐phase currents using the improved wavelet packet transform, and the fault feature vector is constructed by fusing the above two feature parameters. Second, the parameters of the ELM fault diagnosis model are optimized using WOA. Finally, the fault feature vector is added in the SR‐WOA‐ELM model to obtain the fault diagnosis results. Through simulation and experimental verification, the model can accurately identify the single‐tube and double‐tube faults of the inverter and is not affected by load fluctuations.

Fault Diagnosis for Modular Multilevel Converter (MMC) Based on Deep Learning: An Edge Implementation Using Binary Neural Network

Recently deep neural network (DNN) has been proposed for fault diagnosis of modular multilevel converter (MMC). The training of DNN is convenient due to its end-to-end feature, and DNN has a strong learning ability due to its deep structure. However, DNN is hard to be implemented at the edge (i.e., the MMC side) since it involves a large number of floating-point parameters and calculations. This paper proposes a methodology to deploy a DNN with fault diagnosis purpose at the edge. First, the floating-point DNN is converted to a binary neural network (BNN) version. BNN not only binarizes the floating-point weights as “1” or “–1” to save the storage footprint, but also replaces the floating-point multiply-accumulates (MACs) with bitwise operations to reduce the computation complexity. Second, the computation of different BNN layers is distributed to different embedded real-time controllers in MMC, so as to fully use the existing computing resources. With the proposed methodology, a typical DNN, namely one-dimensional convolutional neural network (1-D CNN) for open-circuit fault (OCF) diagnosis of MMC, which has 238.93kB parameter storage footprint and 91,752 floating-point MACs, is replaced by a BNN with only 7.48kB parameter storage footprint and bitwise operations. 96.87% storage footprint is saved and almost floating-point operations are replaced, and as a tradeoff, the diagnosis accuracy is only reduced by 4.33% compared with the 1-D CNN counterpart. This work shows the huge potential of edge intelligence for fault diagnosis of power electronics.

A novel multi-resolution network for the open-circuit faults diagnosis of automatic ramming drive system

The open-circuit fault is one of the most common faults of the automatic ramming drive system (ARDS), and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor (PMSM) and the open-circuit faults of Voltage Source Inverter (VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them. Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network (MrNet) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than 98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that MrNet has the capability of diagnosing the fault types accurately under the interference of noise signals (Laplace noise and Gaussian noise).

Open-Circuit Fault Diagnosis for Three-Level ANPC Inverter Based on Predictive Current Vector Residual

Open-Circuit Fault Diagnosis for Three-Level ANPC Inverter Based on Predictive Current Vector Residual