Open-switch Fault Research Articles

Optimized Fault-Tolerant Control of Dual Three-Phase PMSM Under Open-Switch Faults

In this article, an optimized fault-tolerant control (FTC) method without current judgement is proposed for open-switch faults (OSFs) in dual three-phase permanent magnet synchronous motor (DTPMSM) drives. The reason for the torque ripple under OSFs has been investigated. The theoretical analysis reveals a significant increase in torque ripple under OSFs. Then, an optimized FTC method is proposed for a DTPMSM with two isolated neutral points. The proposed method maintains the original control scheme, enabling the smooth transitions of current and torque between faulty operation and FTC without introducing noticeable torque ripples. In addition, the universality and robustness are enhanced by eliminating the need for current judgement, thereby avoiding misjudgments due to sinusoidal current zero crossings, sudden load, or speed changes. The experimental results are presented to validate the effectiveness of the proposed FTC strategy under OSFs on a laboratory DTPMSM.

Robust Open-Switch Fault Diagnosis of Bidirectional DC/DC Converters Based on Extended Kalman Filter With Multiple Corrections

Robust Open-Switch Fault Diagnosis of Bidirectional DC/DC Converters Based on Extended Kalman Filter With Multiple Corrections

Adaptive Takagi–Sugeno (T–S) fuzzy observer based fault tolerant control for DC–DC converters with robustness against uncertainties

AbstractThis paper proposes a Takagi–Sugeno fuzzy system model based fault tolerant control scheme for DC–DC converters, which is robust against parameter uncertainties and achieves the output voltage of an ideal converter. The control involves estimating the duty cycle change in the form of a fault parameter required to track the output voltage, in the presence of several uncertain conditions including converter losses, variation in input voltage, and unknown and changing output load. An adaptive law is designed to estimate the fault parameter that guarantees state and parameter error convergence. The adaptive law is derived using the Lyapunov stability theorem and the required parameters are evaluated by solving a linear matrix inequalities optimization problem. The load resistance is estimated in parallel by using a Kalman filter and fed to the fault parameter estimation scheme. Furthermore, a fast and robust method to detect short and open circuit switch faults is also presented. The proposed technique offers a simple, yet effective method to regulate the output voltage under several faulty and uncertain conditions. The proposed technique is tested on a DC–DC boost converter simulation model and the demonstrated MATLAB/Simulink results show the effectiveness of the proposed algorithm.

Open switch fault diagnosis of cascaded H-bridge 5-level inverter using deep learning

Open switch fault diagnosis of cascaded H-bridge 5-level inverter using deep learning

Voltage Error Estimation-Based Fault Diagnosis Method for Three- phase PWM-inverter in Grid-connected Photovoltaic Power System Without any Over-rating

Inverter is an essential component of a grid connected PV system. In fact, an open-switch fault in this power converter could result an important system malfunction and consequently leads to system disconnection. In this context, this paper focuses on the fault-tolerant control of PWM-inverter with redundancy leg for grid-connected PV system. In addition, a fast method of fault detection and compensation is used to maintain the continuous operation of the converter when there is a half-leg open-circuit fault (IGBT+ anti-parallel diode) on one of PWM-inverter legs. Therefore, it can detect this type of fault and compensate it in less than 50 μs by using a time criterion instead of voltage criterion. Also, the fuzzy logic technique is employed to control the active and reactive power injected into the network without exceeding the whole system power capacity limits. Note that, in this study, the active and reactive power references of the chosen operating point (the reactive-to-active power ratio: tgφ = Q/P = 0.19) are based on experimental tests. Simulation results show the feasibility and the effectiveness of the proposed fault diagnosis method in terms of fault detection and service continuity in presence of a half-leg open-circuit fault in PWM-inverter without any over-rating of the PV system components or instability.

Control and fault diagnosis for two-interleaved boost converter associated with to PEMFC

This paper presents an enhanced control and fast fault detection method, localisation and isolisation for a two-phase interleaved boost DC/DC converter specifically designed for fuel cell applications. Fuel cells are known for their rapidly changing output voltage and current due to fluctuating load demands. Conventional converters might not be able to respond fast enough to maintain stable operation under such conditions.The two-phase interleaved design offers significant advantages. such as reduced ripple current delivered by dividing the Input current, the converter significantly lowers input and current ripple compared to single-phase designs. Each phase handles a smaller portion (1/N) of the total current, leading to reduced stress on individual components and enhanced reliability and operating margins. Additionally, Interleaving minimizes conduction losses by dividing the current among multiple converter stages. This work presents a model of a two-phase interleaved boost DC/DC converter and implements a PI controller for its output voltage regulation. This control method guarantees good performance even under varying reference voltages and load conditions. The proposed algorithm addresses the challenge of open circuit switch failures by utilizing current slopes for swift fault detection and rapid corrective action. This ensures accurate reference tracking, desired dynamic response, and timely fault detection and localization. The entire system was rigorously tested using MATLAB/Simulink software under various conditions, including reference voltage and load variations, as well as open circuit switch faults. The results showcase the proposed system's superior performance in terms of: Dynamic performance: The system exhibits rapid response to changes in operating conditions. Fast fault detection, localization and isolation.

A novel algorithm for open switch fault detection and fault tolerant control of interleaved DC‐DC boost converters

AbstractThis paper presents a novel algorithm for fault diagnosis and fault tolerant control of interleaved boost converters (IBCs) in the presence of single or double simultaneous open‐circuit faults (OCFs) in power switches. An innovative diagnosis signal (recorded by a cheap current sensor) will be introduced whose waveform mainly depends on the healthy or faulty condition of the converter. Since the diagnosis signal is periodic in the steady‐state operation of the converters, its Fourier Series coefficients, together with the duty cycle of the converter are used as distinguishing features for fault diagnosis of the OCFs in the converter. The well‐known Kalman filter is utilized for robust estimation of the Fourier‐based features. Finally, the modulation of the remaining healthy phases is rearranged in a way that the pre‐fault performance recovers. The proposed algorithm is verified in a laboratory four‐phase IBC setup in which the experimental results show its satisfactory performance. Also, the structure of the proposed method is straightforward and can be implemented in the same microcontroller which is used for voltage and current regulation of the converter.

Open-switch fault diagnosis and fault-tolerant control for three-phase VSI-fed induction motor drive

Open-switch fault diagnosis and fault-tolerant control for three-phase VSI-fed induction motor drive

Detection and Localization of Open Switch Faults for Level-Shifted PWM Cascaded H-Bridge Inverter

Detection and Localization of Open Switch Faults for Level-Shifted PWM Cascaded H-Bridge Inverter

Open-Switch Fault Diagnosis in Back-to-Back NPC Converters of PMSG-Based WTS via Zero Range Value of Phase Currents

Open-Switch Fault Diagnosis in Back-to-Back NPC Converters of PMSG-Based WTS via Zero Range Value of Phase Currents

Advanced Fault Diagnosis in Power Electronics: Switch Open Faults in DC-Link Shunt Sensor-Less Drives

Advanced Fault Diagnosis in Power Electronics: Switch Open Faults in DC-Link Shunt Sensor-Less Drives

Machine Learning Based Open Switch Fault Detection and Localization of Inverters

The location and detection of switch faults is a crucial step in improving the dependability of inverters, which is a requirement for critical applications. This paper focuses on a machine learning-based approach for the open-switch fault detection system for cascaded H-bridge (CHB) multilevel inverters (MLI) used in high- and medium-power applications. Each switch failure is taken into account in this analysis, and the problem is used to model logically different machine learning algorithms, namely Gaussian Naive Bayes (GNB), support vector machines (SVM) and multinomial logistic regression (MLR) models for fault identification and localization based on multi-class classification (MCC) methods. This work includes the simulation and hardware-based data collection from five-level CHB-MLI and correlation-based selection of prominent features in the final dataset. The results of three MCC models for all possible combinations of predictors (features) prove that the mean voltage of bridges is the dominant feature for fault detection in the CHB inverter. Other major findings are machine learning-assisted fault identification and diagnosis is undoubtedly accurate and significantly more promising in condition monitoring and fault diagnosis in CHB-MLIs. The results reveal 100% accuracy in fault diagnosis. Therefore, the presented work confirms switch fault detection in CHB-MLI with mean value as dominant fault feature along with machine learning techniques simple, robust and accurate fault detection.

Open-Circuit Fault-Tolerant Control for Pentagon Winding Connected Five-Phase Current-Source Inverter Based PMSM Drives

Current-source inverter (CSI) is considered as a prospective solution for modern machine drives, for its higher reliability and superior harmonic suppression. The open-circuit fault-tolerant control for five-phase CSI-fed permanent magnet synchronous machine (PMSM) drive with pentagon winding connection is investigated. The switch open-circuit fault and winding open-circuit fault are studied. For the switch open fault, three cases are discussed including single-phase, adjacent two-phase, and nonadjacent two-phase faults. It is indicated that in pentagon circuit, despite the switch of fault phase is disabled, the corresponding phase winding is still connected in the end-to-end power circuit and contributes for torque generation. The new sets of coordinate transformation are proposed, and the mathematical modeling is presented. The fault-tolerant space vector modulation (SVM) techniques are proposed based on the reconstructed vector planes. In addition, the single winding open fault is studied, which is a specific fault case and breaks off the closed pentagon connection. An important conclusion is derived that the pentagon winding connected CSI (PWC-CSI) gains inherent fault-tolerant capability against the single winding open circuit. The control algorithm even does not need to be switched during this typical fault. Experiments are performed on verifying the proposed schemes.

A Square-Wave Voltage Injection Sensorless Control for Dual Three-Phase IPMs Robust to Open-Circuit Faults

Conventional sensorless control methods fail to estimate the accurate rotor position in case of open circuit faults (OCFs) in dual three-phase interior permanent-magnet machine (DT-IPM) drives, resulting in poor stability of the system. OCFs regularly contain open-phase faults (OPFs) and open-switch faults (OSFs). However, the current under OSFs can be considered as a combination of a one-half period of health status with a normal current and the other half period of OPF with zero current. Consequently, OSF is regarded as a generalized OPF. Based on the general solution to OCFs, a novel high-frequency (HF) square-wave voltage injection (HFSVI) sensorless control method is proposed for DT-IPMs at low speed to improve the fault-tolerance ability, which is robust to both OPFs and OSFs. With the help of vector space decomposition, the HFSVI is implemented in the decoupling space, where the effects of OCFs, e.g., varying amplitude and extra negative-sequence component of responded HF currents, can be compensated by HF currents in both αβ- and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xy</i> -subspaces in the rotor position estimation module. Compared with the non-compensation method, the proposed one is robust to parameter mismatches and only increases the computation burden slightly. Finally, experiments on a prototyped DT-IPM verify the proposed method.

Fault-Tolerant Control of Dual Three-Phase PMSM Based on Minimum Copper Loss Under Multi-Phase Open and Open-Switch Faults

Fault-Tolerant Control of Dual Three-Phase PMSM Based on Minimum Copper Loss Under Multi-Phase Open and Open-Switch Faults

Study of an open-switch fault detection algorithm for a three-phase interleaved DC–DC boost converter in a photovoltaic system

Study of an open-switch fault detection algorithm for a three-phase interleaved DC–DC boost converter in a photovoltaic system

Novel Fuzzy-based Open-Switch Fault Detection Scheme of Voltage Source Inverter Induction Motor Drive

Novel Fuzzy-based Open-Switch Fault Detection Scheme of Voltage Source Inverter Induction Motor Drive

Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter

Combined Wavelet and Ann-Based Open-Switch Fault Detection and Classification in PV-Fed Multilevel Inverter

An Online Multiple Open-Switch Fault Diagnosis Method for T-Type Three-Level Inverters Based on Multimodal Deep Residual Filter Network

With the development of the processing capacity of the embedded chip, it is possible to implement a machine learning algorithm in the embedded system. To achieve the fault status without poor portability, tricky threshold selection and complex rulemaking, this paper proposes a multi-modal deep residual filter network for online multiple open-switch fault diagnosis of T-type three-level inverter. It contains low-rank matrix fusion (LMF), deep residual filter network (DRFN), and cross transformer mechanism. The LMF fuses the voltage signal and the current signal for obtaining the unified representation. And then, the DRFN filters noise adaptively and extracts information effectively. Finally, the cross-transformer mechanism output the fault state of the T-type three-level inverter. The data sets consist of the dc-link voltage and load side current of the inverter control system. The data time window is selected as 20 ms. Through the real-time calculation of online monitored data, the experimental results show the effectiveness of the proposed fault diagnosis approach. Moreover, the accuracy of fault diagnosis obtains 99.18% and the average open-circuit fault diagnosis time is 21 ms.

A Global State Observer-Based Open-Switch Fault Diagnosis for Bidirectional DC–DC Converters in Hybrid Energy Source System

A Global State Observer-Based Open-Switch Fault Diagnosis for Bidirectional DC–DC Converters in Hybrid Energy Source System