Switch Fault Research Articles

Joint Fault Diagnosis of IGBT and Current Sensor in LLC Resonant Converter Module Based on Reduced Order Interval Sliding Mode Observer

LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter’s ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability. This article proposes a fault diagnosis strategy for LLC resonant converters utilizing a reduced-order interval sliding mode observer. Initially, an augmented generalized system for the LLC resonant converter is developed to convert current sensor faults into generalized state vectors. Next, the application of matrix transformations plays a critical role in decoupling open-circuit faults from the inverter system’s state and current sensor faults. To achieve accurate estimation of phase currents and detection of current sensor faults, a reduced-order interval sliding mode observer has been designed. Building upon the estimation results generated by this observer, a diagnostic algorithm featuring adaptive thresholds has been introduced. This innovative algorithm effectively differentiates between current sensor faults and open switch faults, enhancing fault detection accuracy. Furthermore, it is capable of localizing faulty power switches and estimating various types of current sensor faults, thereby providing valuable insights for maintenance and repair. The robustness and effectiveness of the proposed fault diagnosis algorithm have been validated through experimental results and comparisons with existing methods, confirming its practical applicability in real-world inverter systems.

Switch monitoring algorithm for 220 kV terminal substation startup process based on multi time scale graph model

The switch monitoring in the startup process of 220 kV terminal substation is a dynamic and changeable process, which has obvious characteristics of diversified scales in time, so as to solve the problem of switch monitoring under multivariable and multi-scale interference. The switch monitoring framework for the startup process of 220KV terminal substation is built. The process layer uses the graphic configuration software and combines the internal and external models of the substation to build the objective function for the generation of the substation graphic model. The particle swarm optimization algorithm is used to solve it to generate the optimal substation graphic model. Through the online monitoring device, the signals such as sensors, normally open and normally closed contacts are collected in real time, and the status of the switchgear is obtained through processing, the reduced half trapezoidal cloud model multivariable multi-scale sample entropy similarity tolerance criterion is used for softening treatment, and the multivariable multi-scale cloud sample entropy is determined to achieve the extraction of multiple time scale switch fault feature vectors, which are used as the input of the SE-DSCNN fault diagnosis model. Combined with the substation diagram, the switch fault identification during the startup process of the substation is realized, and the fault switch position is located. The experimental results show that the algorithm can accurately generate the substation model, which includes all the equipment in the substation, accurately describes the connection relationship and operation status of the equipment, and has high accuracy, integrity and aesthetics; The algorithm can effectively extract and analyze the MMCES entropy characteristics of different types of switch faults; The algorithm can realize the switch monitoring during the startup of substation C, and determine the fault switch and fault location.

Influence of fast switching fault current limiter on electromagnetic transient of extra-high voltage transmission line

Abstract The rapid advancement of power systems has led to an increase in short-circuit currents. The short-circuit current can be effectively limited by adding a fast-switching fault current limiter. However, incorporating a current-limiting reactor into ultra-high voltage lines may impact electromagnetic transients. This study utilizes PSCAD/EMTDC electromagnetic transient simulation software to develop a typical 330kV transmission line model for simulation analysis. The paper aims to study whether the current limiting reactor will affect the switching overvoltage in the automatic reclosing overvoltage. The results show that the input of the current-restricting transformer efficiently constrains the magnitude of short-circuit currents and exerts minimal influence on the automatic reclosing overvoltage in ultra-high voltage transmission lines.

Performance Analysis of a Reconfigurable Fault‐Tolerant Converter for Renewable Energy–Based Standalone System

ABSTRACTThis paper proposes a reconfigurable fault‐tolerant multiport converter for a renewable energy–based standalone system. The operation of the proposed converter for various single and multiple switch faults is discussed and verified with the help of a laboratory prototype. Experimental findings showcase the converter's ability to seamlessly connect renewable sources with a DC bus and an energy storage device, ensuring a consistent, regulated power supply despite single or multiple switch faults. Furthermore, a thorough examination of performance metrics including stress, loss, cost, sensitivity, and reliability affirms the effectiveness and resilience of the proposed approach. Comparative analysis solidifies the notion of the converter's superiority, emphasizing its fault tolerance, efficiency, and reliability when contrasted with alternative solutions.

A Noninvasive Sliding Mode Observer Based Approach for Detecting Open-Circuit Faults in HERIC Inverters

This article presents a new technique for detecting and localizing the open-circuit (OC) faults in the semiconductor switches of the Highly Efficient and Reliable Inverter Concept (HERIC) converter. It is a transformerless Photovoltaic (PV) inverter based on the full-bridge topology, with two extra switches on the AC side called ac bypass switches. The proposed method uses a sliding mode observer (SMO) to detect faults in the AC bypass switch and main switches of the full bridge inverter. To diagnose OC faults in the AC bypass leg, two parallel SMOs are created based on the converter’s state space model. Similarly, two SMOs are designed for the main switches. A residual is generated by combining the observed and measured grid currents to accurately locate the OC fault within the inverter system. To localize the faulty main switch, once the fault is detected the inverter is reconfigured and based on the grid current the faulty switch is identified. A major benefit of this method is that it does not rely on additional sensors for fault diagnosis, making the system more robust. The effectiveness and reliability of this approach are demonstrated through simulation studies. In addition, a laboratory prototype is developed to validate the practical applicability of the method.

Switch Fault Diagnosis of Grid-Connected Inverter Using Symmetrical Components

Switch Fault Diagnosis of Grid-Connected Inverter Using Symmetrical Components

Impact of Adding Fast Switching Fault Current Limiter (FSFCL) to the Neutral Point of 220 kV Transformer

Impact of Adding Fast Switching Fault Current Limiter (FSFCL) to the Neutral Point of 220 kV Transformer

Review on Short-Circuit Protection Methods for SiC MOSFETs

SiC MOSFETs have been a game-changer in the domain of power electronics, thanks to their exceptional electrical traits. They are endowed with a high breakdown voltage, reduced on-resistance, and superior thermal conductivity, which make them supremely suitable for high-power and resilient applications across aviation, automotive, and renewable energy sectors. Despite their intrinsic advantages, SiC MOSFETs also necessitate advanced safeguarding mechanisms to counteract the vulnerability to short-circuit conditions due to their lower short-circuit robustness. This review paper offers an in-depth analysis of the array of short-circuit protection (SCP) methods applied to SiC MOSFETs. This paper scrutinizes techniques such as desaturation detection, di/dt detection, gate charge characteristics monitoring, two-dimensional monitoring, Rogowski coil-based detection, and two-stage turn-off strategies. The paper meticulously explores the operational principles, merits, and limitations of each method, with an emphasis on their adaptability to various fault types, including hard switching faults and load-induced faults. This review acts as a thorough compendium, guiding the choice of pertinent SCP strategies, ensuring the secure and efficient functioning of SiC MOSFETs in demanding applications.

A Fast Diagnosis Method for Power Switch Faults in Inverter Based on DC Voltage Waveform

A Fast Diagnosis Method for Power Switch Faults in Inverter Based on DC Voltage Waveform

A Cascaded H-Bridge Multilevel Inverter with DC Cells Input Fault Tolerance Capability Based on PSC-PWM Control

Multilevel inverters have proven their efficiency in generating better AC voltage outputs. This functional quality is mainly due to the use of multi-source DC inputs. Despite the fact that this type of topology is generally reliable, switching faults caused by the complete loss of a switching component or DC input cell may still occur. Such incident may inflict heavy impacts to the conversion chain resulting in a permanent damage to the switching cells or the connected load. This article presents a dynamic switching control strategy capable of tolerating DC cells open-circuit input faults in basic symmetric Cascaded H-bridge multilevel inverter architectures. The proposed topology uses a control scheme to overcome the switching cells’ fault by adapting the configured level of conversion and bypassing the flawed unit with no additional switching components. Furthermore, the proposed control strategy is capable of reversing back the inverter configuration to its original functional state after the disappearance of the faut. This research article answers the need for a theorical study of a fault tolerant Cascaded H-Bridge multilevel inverter where the short circuit’s fault types are bypassed by Pulse width modulation alteration only.

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

Switch fault identification scheme based on machine learning algorithms for PV-Fed three-phase neutral point clamped inverter

Any faults in power electronic switches must be detected and located promptly to certify the unchanging operation and reliability of multilevel inverter (MLI) systems. Machine learning (ML) techniques are increasingly being utilized to diagnose faults in MLIs due to their advantages, such as high precision, less calculation time, and reduced complexity. A faulty switch identification and phase identification method based on different ML classifiers is presented in this paper, explicitly targeting open-circuit faults in switches in a PV-fed 3-phase three-level Neutral Point Clamped (NPC) inverter. The NPC MLI output voltage and current signals are input signals to classify faults. The information or essential feature from the raw voltage and current data is extracted using a recursive discrete Fourier (RDF) followed by a discrete wavelet transform (DWT). Finally, the standard deviations of the approximate and detailed coefficients are used as input for ML algorithms. To do the comparative analysis, a variety of ML techniques are then applied to these features, including logistic regression (LR), random forest (RF), quadratic discriminate analysis (QDA), K-nearest neighbour (K-NN), and support vector machines (SVM). The experiment results indicate that the RF algorithms achieve the highest classification accuracy of 99.59 % for various switch fault conditions.

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.

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.

Diagnosis of Multicomponent Faults in SRM Drives Based on Auxiliary Current Reconstruction Under Soft-Switching Operation

Switched reluctance motors (SRMs) are considered a potential candidate for utilization in numerous safety-critical applications including electric vehicles (EVs) and more/all electric aircraft (MEA), etc., where the reliability of the drive system is critically important. An SRM drive system suffers from various electrical, mechanical, and sensor faults during operation. The existing fault detection schemes investigated each fragile component separately and proposed different approaches for each fault, which not only make the system complex but also less reliable for real-time implementation in safety-critical applications. Thus, a more sophisticated algorithm to detect and discriminate each of these faults in real time becomes attractive. A simple fault detection and isolation scheme for multiple faults in SRM drive systems is reported in this paper. The investigated faults include open- and short-circuit faults in power switches, open-phase winding, and zero-output current sensors faults. An auxiliary phase current for each phase was reconstructed from the dc-link and the faults are identified and discriminated effectively through the digital analysis of the fundamental current and the auxiliary current in real-time. The feasibility of the proposed scheme is verified by the extensive simulation and experiments conducted on a three-phase 12/8 SRM drive system.

Data-Driven Switch Fault Diagnosis for DC/DC Boost Converters in Photovoltaic Applications

Switch semiconductors are essential components in power electronics stages for photovoltaic (PV) systems, which are prone to failures caused mainly by thermal stress, thereby affecting PV energy production. Specifically, dc/dc boost power converters are commonly used as maximum power point tracking (MPPT) systems. Several works have been reported so far in the literature for switch fault detection and isolation (FDI) purposes in boost power converters. However, most of them require additional sensors and circuitry to those included in any PV system, which generate extra costs on the overall system. Hence, this work presents an efficient and low-cost switch fault diagnosis proposal, which requires only the most common measurements in PV systems, namely, PV current and voltage. This work departs from a data-driven FDI methodology for diagnosing switch open- and short-circuit faults in dc/dc boost converters. The experimental results have been evaluated against sudden irradiance changes and switch faults in a test bench of 350 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm {W}$</tex-math></inline-formula> operating under a closed-loop nonlinear control action for MPPT purposes by using the dSpace 1104 board.

Global Stabilization of Antipodal Points on $n$-Sphere With Application to Attitude Tracking

Existing approaches to robust global asymptotic stabilization of a pair of antipodal points on unit $n$-sphere $\mathbb{S}^n$ typically involve the non-centrally synergistic hybrid controllers for attitude tracking on unit quaternion space. However, when switching faults occur due to parameter errors, the non-centrally synergistic property can lead to the unwinding problem or in some cases, destabilize the desired set. In this work, a hybrid controller is first proposed based on a novel centrally synergistic family of potential functions on $\mathbb{S}^n$, which is generated from a basic potential function through angular warping. The synergistic parameter can be explicitly expressed if the warping angle has a positive lower bound at the undesired critical points of the family. Next, the proposed approach induces a new quaternion-based controller for global attitude tracking. It has three advantageous features over existing synergistic designs: 1) it is consistent, i.e., free from the ambiguity of unit quaternion representation; 2) it is switching-fault-tolerant, i.e., the desired closed-loop equilibria remain asymptotically stable even when the switching mechanism does not work; 3) it relaxes the assumption on the parameter of the basic potential function in literature. Comprehensive simulation confirms the high robustness of the proposed centrally synergistic approach compared with existing non-centrally synergistic approaches.