Open-switch Fault Detection Research Articles

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.

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

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.

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

Rolling Average-Decision Tree-Based Fault Detection of Neutral Point Clamped Inverters

Multilevel inverters (MLI) with many switches, which are assets for EVs and other high and medium power applications are prone to switch faults. To ensure the reliable operation of MLI, simple, effective, and automatic fault monitoring and localization techniques are warranted. This paper focus on pole voltage rolling average (RA) based generalized open switch fault detection (FD) of neutral point clamped (NPC) MLI. The RA contributes advantage of smoothing the input, thus reducing the false alarm due to switching transients and standard deviation of averaged pole voltage serves as a good fault feature to distinguish between inner and outer switch faults for single switch fault case and the correct switch pair for double switch fault case. The proposed method based on pole voltage RA is fast and load-independent and uses decision tree tuned with random search as the classification tool for 12+1 and 78+1 classes, i.e., all possible chances of single and double switch fault combinations with training accuracy of 99% and 97.5% for three- level NPC. The paper also includes an analysis of how the DT classifier is affected by overfitting as the tree's depth increases.

Interleaved boost converter open switch fault detection and reconfiguration using harmonic selection calculation

SummaryIn this paper, a new technique for open‐circuit switch fault (OCF) detection and reconfiguration of an interleaved boost converter (IBC) is proposed. Harmonic selection calculation (HSC) is elaborated from the frequency domain analysis using the Fourier transformer of the input current. The symmetric phase shifting of the inductor currents has induced some characteristics in the input current that make the HSC an interesting tool for the fault detection process of an OCF. The HSC consists of selecting the adequate harmonic for detecting and isolating an OCF with a thresholding strategy. The proposed approach can handle different operation modes such as CCM, DCM, load change, and parameter change. Besides the short‐time detection (less than one cycle), this technique is based on the measurement of the only used inductor currents, which means that it does not require any additional hardware. The effectiveness of the proposed HSC technique is verified by simulation and experimental tests for three interleaved boost converters under different situations, such as load change, conduction mode change, and OCF mode. The obtained results show the validity of the proposed technique.

A Generalized Current Trajectory Based Fault Diagnostic Method for Three-Phase Two-Level Inverters

Inverters play a vital role in the distributed generated energy systems. Hence, the reliability of the entire distributed energy system depends on the consistent and continuous operation of the inverter. Consequently, to maintain a reliable operation, effective condition monitoring and fault diagnostic schemes have to be incorporated. In this paper, a method based on current trajectory is analyzed for the fault detection and diagnosis of open-switch faults in three-phase two-level voltage source inverters (VSI). The current trajectory-based method of fault diagnosis has been already presented literature for the identification and localization of open-switch fault in the three-phase two-level VSI. The main drawback of the existing current trajectory method is that the fault diagnosis and detection is dependent on the phase currents selected to plot the current trajectories. Therefore, in this paper, a generalization is proposed for the fault diagnosis and diagnosis based on the current trajectory, which make the method independent of the selection of the phase current used to plot the current trajectory. The validity and effectiveness of the proposed generalization is verified by simulation and experiments in a laboratory prototype of three-phase two-level VSI.

Open-Switch Fault Detection Based on Open-Winding Five-Phase Fault-Tolerant Permanent-Magnet Motor Drives

The difficulty of open-switch fault detection in an open-winding inverter is that the fault at the diagonally opposite position within the H-bridge power cell has the same fault characteristics. To solve this problem, this paper proposes an open-switch fault detection method based on open-winding (OW) five-phase fault-tolerant permanent-magnet (FPFTPM) motor drives. The detection method includes three steps: first, the drives open-switch fault is detected by monitoring the component of the third harmonic residual in the stationary reference frame; second, the least squares iterative method based on the forgetting factor is used to identify the relationship between the residual components, which determines the faulty phase and the faulty phase current type; third, an online current injection test method is proposed to accurately identify the open-switch within the H-bridge power cell. Based on the above steps, the open-switch fault at the diagonally opposite position within the H-bridge power cell could be accurately identified. The novelty of the proposed method lies in the possibility that the open-switch fault detection of any switches within the one H-bridge power cell could be accurately achieved by an online current injection test and does not require hardware changes. The experimental results demonstrate the effectiveness of the proposed detection method.

A Simple and Efficient Strategy for Online Detection of Multiple Open-Switch Fault in Multiphase Machine Drive Systems

As the fault localization for multiphase machines is critical for fault isolation and active fault-tolerant strategies, reliable fault diagnosis strategy is essential to be proposed. After open-circuit fault occurs, harmonic current vectors will follow different trajectories according to different faulty phases. However, this event can only be evident in case of single phase fault occurrence. Thus, existing technique can only identify limited types of open-switch fault. This article proposes a simple and efficient open-switch fault detection strategy to extract fault features. Proposed strategy is able to locate any combinations of open-switch faults, which cannot be realized in the previous literature. The faulty phases are detected first to reduce computational burden and for avoidance of false alarms. Then the faulty switches are determined on the basis of the direction of current vectors in the harmonic plane. Taking account of open-switch faults in different phases, normal current vectors are reconstructed with newly defined auxiliary current variables, which can be utilized to detect the failure of multiple switches and result in enhanced robustness. Experimental results validate the effectiveness of the proposed multiple open-switch fault detection method in a seven-phase machine at 5.5 kW power level.

A New Technique for Online Open Switch Fault Detection and Location in Single-phase Pulse Width Modulation Rectifier

A New Technique for Online Open Switch Fault Detection and Location in Single-phase Pulse Width Modulation Rectifier

Open-Circuit Fault Detection and Isolation Method for Five-Level PUC Inverter Based on the Wavelet Packet Transform of the Radiated Magnetic Field

This article proposes a noninvasive open-switch fault detection method based on the analysis of the high-frequency signals. The faulty power switch is detected by metering the emitted near-field above a new topology using the wavelet packet transform and considering a complex control algorithm. The proposed method is tested on a single-phase five-level packed U-cell (PUC5) inverter. The latter is controlled using the finite-control set model predictive control (FCS-MPC) technique that generates the gate signals with a variable switching frequency. Moreover, unlike the conventional pulsewidth modulation strategy, the switching pattern provided by the FCS-MPC technique is not known in advance. Despite these complexities, the proposed fault detection method proves its high performance and effectiveness. Experimental studies are carried out on a single-phase PUC5 inverter using the real-time controller (OP4510) from OPAL-RT and prove the capability of this method to properly identify the faulty switch in this multilevel topology.

Detection and isolation of current-sensor and open-switch faults in electric traction drives

In this work, a strategy for detection and isolation of current-sensors and open-switch faults in electric traction drives is proposed. An induction motor drive with only two current sensors is considered for the design of the proposed strategy. The current estimations from a simple open-loop observer are used to perform the fault detection and isolation. In addition, the same observer estimates the rotor flux angle required in a vector control strategy. Simulation results analyze the sensitivity to parametric and operating point variations. Moreover, the performance of the proposed approach is validated in the event of different types of faults, considering the dynamic behavior of an electric vehicle. Performed tests include gain and disconnection sensor faults and open-switch faults in the inverter.

Open‐switch fault diagnosis in three‐level rectifiers based on selective calculation method for instant voltage deviation

This paper presents an open-switch fault detection, and identification method for a neutral-point clamped rectifier system. A new calculation method for phase-to-phase pole voltage deviations is proposed for reducing calculation errors. In this way, the more accurate diagnosis can be achieved. Both single-switch faults, and multiple-switch faults can be identified effectively by using fewer diagnostic variables, which makes the diagnostic method simpler, and more reliable. And all the variables can be simply calculated by signals which are available to the control system of the rectifier, avoiding the use of additional hardware. Moreover, different voltage thresholds are designed for different faulty feature sections to further reduce the diagnostic time. Additionally, a diagnostic result checking method is proposed to avoid the misdiagnosis when considering the effect of outer-switch faults. Finally, experiments are carried out, and the results show the robustness, and effectiveness of the proposed method.

Open-switch fault detection and isolation of DC-AC static inverter feeding induction machine

In the aim to ensure system reliability and monitoring of latent faults that decrease the installation's life and damage them in the short-term, this paper presents an online open-circuit switch fault diagnosis of a DC-AC inverter. The fault detection and isolation (FDI) employed method is articulated on two techniques based practically on stator currents analysis. For the first technique, the developed decision algorithm is based on d-q Park's vector components. In the second technique, fault diagnostic is achieved using normalised quantities of residues from the currents phases in ABC frame. When the open circuit switch fault occurs, the residual current vector between the stator currents with and without fault is calculated. After that, this vector will be evaluated by a decision algorithm. The drive chain modelling (power inverter-machine) and the monitoring algorithms are developed and established under the MATLAB/Simulink environment, where the results are presented and analysed. The feasibility and the effectiveness of the proposed techniques have been validated by simulation under different operating conditions.

Open-switch fault detection and isolation of DC-AC static inverter feeding induction machine

In the aim to ensure system reliability and monitoring of latent faults that decrease the installation's life and damage them in the short-term, this paper presents an online open-circuit switch fault diagnosis of a DC-AC inverter. The fault detection and isolation (FDI) employed method is articulated on two techniques based practically on stator currents analysis. For the first technique, the developed decision algorithm is based on d-q Park's vector components. In the second technique, fault diagnostic is achieved using normalised quantities of residues from the currents phases in ABC frame. When the open circuit switch fault occurs, the residual current vector between the stator currents with and without fault is calculated. After that, this vector will be evaluated by a decision algorithm. The drive chain modelling (power inverter-machine) and the monitoring algorithms are developed and established under the MATLAB/Simulink environment, where the results are presented and analysed. The feasibility and the effectiveness of the proposed techniques have been validated by simulation under different operating conditions.

Open-switch fault detection scheme in wind energy conversion system based on rotor currents analysis

Open-switch fault detection scheme in wind energy conversion system based on rotor currents analysis

Open-switch fault detection scheme in wind energy conversion system based on rotor currents analysis

To deal with the performance and service continuity of doubly fed induction generator (DFIG) based wind energy conversion systems (WECSs) during open-switch Insulated Gate Bipolar Transistor (IGBT) fault, a fault-tolerant Voltage Source Inverter (VSI) is proposed. The fault-tolerant VSI is designed with redundancy leg to replace the faulty leg after open-switch fault occurrence. In this investigation, we propose a new topology of DC-Bus tied DFIG based on uncontrolled rectifier in stator side. A Field Oriented Control (FOC) scheme is used in this paper to control the DFIGURE The open-switch IGBT fault is studied and a fault detection scheme based on rotor currents analysing within healthy and faulty conditions is proposed. Case study results show that the proposed fault-tolerant VSI can ensure the service continuity of DFIG-DC system. Experimental results are presented in this paper to validate the proposed fault detection scheme.

MULTIPLE FAULT ANALYSIS USING A FUZZY LOGIC TECHNIQUE FOR AN INVERTER CONTROL DRIVE

This study evaluates electrical faults on an inverter control drive using simulations in MATLAB Simulink and recommends a fuzzy logic technique for analyzing electrical faults of such drives. Fault analysis of a drive is based on voltage, speed, current, and torque. This article presents soft computing methods for voltage fluctuations and fault detection in an inverter switch to an inverter control drive with the support of a MATLAB Simulink model. Accordingly, failure analysis techniques based on fuzzy logic are explained. The fuzzy system allows altered voltage and open-switch fault detection. Soft computing techniques offer excellent analysis of a faulty structure, even if precise models are not available. This study presents a fuzzy logic-based fault diagnosis scheme for detecting and identifying faults. The simulation outcomes reveal excellent performance of the method, proving its applicability for fault detection in squirrel-cage induction motors. All simulated results, voltage fluctuations, torques, currents, ripples, and harmonics in healthy and defective situations are analyzed.