Online open‐switch fault diagnosis method in single‐phase PWM rectifiers

Ge, Xinglai; Pu, Junkai; Liu, Yong-Chao

doi:10.1049/el.2015.1194

Cited by 26 publications

(5 citation statements)

References 4 publications

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“…With the rapid development of high-speed railroads, the number of electric multiple units (EMUs) operating on high-speed railway lines, along with their operational density, is increasing [1,2]. Ensuring the safe operation of EMUs, each of which utilizes a singlephase AC power supply and the AC/DC/AC traction drive system, is crucial [3][4][5][6][7][8][9][10]. The vehicle-mounted high-voltage cable, a key component for power transmission, directly impacts the safe operation of EMUs [11,12].…”

Section: Introductionmentioning

confidence: 99%

Classification of Partial Discharge in Vehicle-Mounted Cable Termination of High-Speed Electric Multiple Unit: A Machine Learning-Based Approach

Yang,

Li,

Chen

et al. 2024

Electronics

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This paper presents a machine learning-based approach to identify and separate partial discharge (PD) and two typical pulse interference (PI) signals in the vehicle-mounted cable terminations of high-speed electric multiple units (EMUs). First, a test platform was established to capture PD and two typical PI signals in these terminations. The acquired signals were then processed using the square envelope method to extract feature parameters, such as the rise time proportion, the left–right symmetry, and the upper–lower symmetry. PD signal classification was carried out on these signals, utilizing waveform parameters derived from a hierarchical clustering algorithm. The results validate that the extracted feature components effectively classify and separate PD and two typical PI signals in the vehicle-mounted cable terminations of high-speed EMUs.

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Section: Introductionmentioning

confidence: 99%

Classification of Partial Discharge in Vehicle-Mounted Cable Termination of High-Speed Electric Multiple Unit: A Machine Learning-Based Approach

Yang,

Li,

Chen

et al. 2024

Electronics

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show abstract

“…To detect the voltages aberrations for diagnostics, an adaptable system is established [21]. For the purpose of identifying faults, a dynamic model using heterogeneous logic is created to predict the discrepancies in between motors currents that are measured and those that are predicted [22]. Both [23,24] describe fault diagnostic techniques based on alternating current (AC) dynamic analysis and current processing with an adaptable neuro-fuzzy inference process, respectively.…”

Section: Introductionmentioning

confidence: 99%

Fault Recognition of Multilevel Inverter Using Artificial Neural Network Approach

Athimoolam¹,

Balasubramanian²

2023

Intelligent Automation &Amp; Soft Computing

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This paper focuses on the development of a diagnostic tool for detecting insulated gate bipolar transistor power electronic switch flaws caused by both open and short circuit faults in multi-level inverter time-frequency output voltage specifications. High-resolution laboratory virtual instrument engineering workbench software testing tool with a sample rate data collection system, as well as specialized signal processing and soft computing technologies, are used in this proposed method. On a single-phase cascaded H-bridge multilevel inverter, simulation and experimental investigations of both open and short issues of the insulated gate bipolar transistor components are performed out. In all conceivable switch issues, the output voltage signals are evaluated for different modulation index values. Fast fourier transform and discrete wavelet transform methods are used to investigate the frequency domain properties of output voltage signals.In the artificial neural network, the back propagation training technique was employed, and the generated neural parameter values were used in the Laboratory Virtual Instrumentation Engineering Workbench real-time fault diagnosis model.

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“…Various fault detection techniques have been employed for insulated gate bipolar transistor (IGBT) systems, including mixed logical dynamic systems in [20], where a signal injection technique is used to excite the system and extract diagnostic features [21] presents an online fault diagnosis method that uses a modified phase-locked loop to estimate the rectifier output voltage and detect open-switch faults in the IGBTs of the rectifier. The proposed method is computationally efficient and can diagnose faults in real time.…”

Section: Introductionmentioning

confidence: 99%

Robust Sensor Fault Detection for a Single-Phase Pulse Width Modulation Rectifier

Ndabarushimana,

2023

Electronics

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Maintaining safe and efficient operation in a single-phase pulse width modulation (PWM) rectifier that employs current sensors relies heavily on accurate sensor readings. However, several factors such as environmental conditions, aging, or damage can lead to sensor faults. Therefore, it is imperative to implement robust fault detection methods to ensure reliable system operation. The use of unknown input observer techniques is one such method that involves analyzing the differences between actual and estimated states to detect and identify faults in the system. This paper presents the development of a fault detection method that employs an unknown input observer with high sensitivity to faults and disturbance rejection to achieve robust fault detection. The method involves modeling the system as a state-space model and designing an observer to estimate the system’s state variables based on input and output measurements. The deviations between the actual and estimated states are then analyzed to detect and identify sensor faults, without the need for additional hardware, making it a cost-effective solution. Hardware-in-the-loop tests confirm the effectiveness of the proposed method.

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Online open‐switch fault diagnosis method in single‐phase PWM rectifiers

Cited by 26 publications

References 4 publications

Classification of Partial Discharge in Vehicle-Mounted Cable Termination of High-Speed Electric Multiple Unit: A Machine Learning-Based Approach

Classification of Partial Discharge in Vehicle-Mounted Cable Termination of High-Speed Electric Multiple Unit: A Machine Learning-Based Approach

Fault Recognition of Multilevel Inverter Using Artificial Neural Network Approach

Robust Sensor Fault Detection for a Single-Phase Pulse Width Modulation Rectifier

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