Open-circuit Fault Research Articles

An online open circuit faults diagnosis method for converter using the lightweight two-channel deep network

An online open circuit faults diagnosis method for converter using the lightweight two-channel deep network

Robust current residual vector-based multi-tube open-circuit fault diagnosis of inverters

Robust current residual vector-based multi-tube open-circuit fault diagnosis of inverters

Current Regulation in Multiphase Open-end Winding Machines under Switch Open Circuit Fault

Current Regulation in Multiphase Open-end Winding Machines under Switch Open Circuit Fault

Fault-Tolerant Performance Analysis of a Modified Neutral-Point-Clamped Asymmetric Half-Bridge Converter for an In-Wheel Switched Reluctance Motor

Reliability is an essential factor for the operation of the Switched Reluctance Motor (SRM) drive. Electric vehicles operate in harsh environments, which may degrade the operation of power converters. These failure modes include transistor open- and short-circuits, freewheeling diode open- and short-circuits, and DC-link capacitor failures. This work presents a performance analysis of an in-wheel SRM for an electric vehicle under short-circuit (SC) and open-circuit (OC) faults of a modified Neutral-Point-Clamped Asymmetric Half-Bridge (NPC-AHB) Converter. The SRM is modeled as an in-wheel electric vehicle. A separate vehicle model attached to the motor is also developed for validation and performance of the NPC-AHB under different faulty scenarios. The performance of the modified NPC-AHB is also compared with that of a conventional AHB under faulty conditions for an in-wheel 8/6 SRM. The performance indicators such as torque, speed, current, and flux are presented from MATLAB/Simulink 2023b numerical simulations.

Direct torque control of a dual star induction generator based on a modified space vector PWM under fault conditions

Recent studies have shown that electrical systems in wind power conversion are subject to a 67 % failure rate, and conventional three-phase generators are considered to be the most sensitive to these faults. Induction generator current harmonics are a major source of torque ripples causing acoustic noise and vibrations. These ripples can progressively increase under faulty operating conditions. In six-phase machines, there is appearance of high harmonic currents in the air gap as space harmonics. Power electronic converters are also subject to faults, the most common being Short-Circuit (SC) and Open-Circuit (OC) faults. SC faults cause large peaks in the line currents, which trigger the protection devices, resulting in a complete shutdown of the production system. OC faults, on the other hand, cause imbalances in the phases, but the system remains in operation.Direct Torque Control (DTC) based on Space Vector Modulation (SVM) at constant switching frequency is an effective solution to tackle induction generator current harmonics. This paper proposes a DTC combined with a Proportional Integral Fuzzy Logic Controller (PIFLC) optimized by Particle Swarm Optimization (PSO) in order to overcome the problems of torque ripples. Furthermore, using Vector Space Decomposition (VSD) and the Modified Space Vector Pulse Width Modulation (MSVPWM) strategy, a significant reduction of harmonics in the air-gap can be achieved.Simulations were carried out under MATLAB/Simulink, and the results obtained demonstrate the superiority of the proposed DTC-PIFLC-PSO controller in terms of robustness against wind speed variations and under faulty switch conditions in the power converter of the Dual Star Induction Generator (DSIG) generator. In addition, a comparative study with the classical PI controller is presented to show the effectiveness of the DTC-PIFLC-PSO controller against faults with a significant reduction in the Total Harmonic Distortion (THD) during both faulty and non-faulty conditions.

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.

Robust Open Circuit Fault Diagnosis Method for Converter Using Automatic Feature Extraction and Random Forests Considering Nonstationary Influence

Robust Open Circuit Fault Diagnosis Method for Converter Using Automatic Feature Extraction and Random Forests Considering Nonstationary Influence

An Integrated Approach for Current Balancing and Open-Circuit Fault Diagnosis for Interleaved Boost Converter

An Integrated Approach for Current Balancing and Open-Circuit Fault Diagnosis for Interleaved Boost Converter

Modeling and simulation of a photovoltaic generator for analyzing the impact of faults on the I-V curve

The rapid expansion of the solar industry has underscored the importance of photovoltaic installations in the ongoing transition to sustainable energy. With this growth comes the crucial task of effectively monitoring and controlling the power generated. Photovoltaic systems are particularly vulnerable to defects due to their exposure to challenging environmental conditions, which can lead to reduced power output and an increased risk of fire. Therefore, a thorough analysis of any faults is essential in order to mitigate potential damage to the system. The present study proposes a comprehensive analysis of the behavior of a photovoltaic generator comprising four modules. MATLAB/Simulink software is used to model the generator in healthy operation. Subsequently, a simulation of the generator in faulty conditions is conducted, considering four fault cases: partial shading (PS), open circuit fault (OCF), bypass diode disconnected (PSBD), and twinned fault bypass diode disconnected plus open circuit (PSBDOC). A detailed examination of the simulation results for the faults above reveals that the twinned fault results in a substantial reduction in the output current, as well as an elimination of the open circuit voltage of the photovoltaic generator. This contrasts the behavior observed in a system comprising two modules, wherein the open circuit voltage remains unaltered. This particular fault offers a compelling rationale for the monitoring of photovoltaic installations, to enhance overall productivity while avoiding any potential damage to the system.

Lightweight shuffle–SimAM network-based open-circuit fault diagnosis of grid-connected cascaded H-bridge inverters

Lightweight shuffle–SimAM network-based open-circuit fault diagnosis of grid-connected cascaded H-bridge inverters

A new structural reconfiguration for multilevel inverters with fault tolerance capability

Keeping electrical systems running is critical in industries especially in those that rely on static converters, for example in the case of inverters, continuous and safe of operation must be ensured even if one of the inverter’s static switch modules fails. This paper presents a method for fault detection in static converters. The study presents a 3-phase 3-level NPC (Neutral Point Clamped) inverter with IGBTs as static switches where open-circuit or closed-circuit faults are considered. In addition, leg failure and structural reconfiguration are also considered in this fault-tolerant inverter by adding a fourth 3-level leg.

A Novel Strategy for Multitype Fault Diagnosis in Photovoltaic Systems Using Multiple Regression Analysis and Support Vector Machines

This study focuses on analyzing common fault types in photovoltaic (PV) modules, employing fault diagnosis methods based on machine learning technology to enhance the accuracy and efficiency of diagnosing faults in solar power systems. Initially, we collected relevant data from the solar power system and used data analysis techniques to identify system faults, designing a human-machine monitoring interface for practical application. Furthermore, the experimental results proved that the system could accurately identify eight major types of faults, including solar panel output circuits, energy storage batteries, maximum power point tracking (MPPT) controllers, inverters, dust accumulation, loosening of mounting rack screws, damage to the mounting rack foundation, and deformation of the mounting rack structure. Particularly in the detection of dust accumulation, we developed a new method of estimating power generation from multiple regression analysis (MRA), which closely aligns the estimated power output with the actual power output, highlighting the significant impact of dust accumulation on the efficiency of solar power systems. Next, by integrating voltmeters and support vector machines (SVM) into the solar PV array modules, we are able to quickly and accurately measure and locate short-circuit and open-circuit faults in bypass diodes. Ultimately, the proposed PV fault diagnosis strategy includes diagnostics for dust accumulation and mounting frame faults, making it particularly suitable for areas with severe air pollution and frequent earthquakes, providing a comprehensive fault diagnosis solution.

Machine learning based fault detection technique for hybrid multi level inverter topology

AbstractMultilevel inverters (MLIs) have a significant contribution in many industrial sectors due to their improved power quality and lesser voltage stress, over the conventional three‐level inverters. However, the implementation of MLIs with an increased device count creates the scope of development in MLIs topologies. In this regard, a hybrid MLI topology is studied in this paper whose architecture is based on conventional two‐level inverters. This topology has lesser device count characteristics when compared to conventional and most of the recently presented configurations for nine‐level output voltage generation. The major issue of capacitor voltage balancing is resolved by employing an appropriate switching strategy. However, the semiconductor switches are the most vulnerable components and causes the open circuit faults frequently that creates issues in real time operation. Hence, it is important to detect the open circuit fault in switches in the least possible time. A new approach to open circuit fault detection technique based on the analysis of load voltage waveform is proposed in this paper. The wavelet transform technique has been implemented for feature extraction of load voltage. Later, the classification of the fault has been achieved by training an artificial neural network (ANN). The proposed work has been studied in MATLAB/simulation and the obtained results are verified experimentally.

Improved fault detection and classification in PV arrays using stockwell transform and data mining techniques

The growing integration of photovoltaic (PV) systems into the power grid necessitates reliable fault detection and classification mechanisms to ensure operational efficiency and safety. Fault detection in photovoltaic (PV) arrays is crucial for maintaining optimal system performance and ensuring the reliability of solar power generation. This paper proposes a novel approach for fault detection in PV arrays by employing the Stockwell transform in combination with various data mining techniques. The Stockwell transform is an advanced time-frequency analysis tool that allows for enhanced feature extraction from time-series data. By applying the Stockwell transform to the PV array's operational data, valuable frequency-domain information is extracted, enabling the identification of subtle fault signatures. To effectively detect and classify various faults, different data mining techniques, such as support vector machines, decision trees, random forests, and k-nearest neighbours, are applied to the transformed data. Each technique's effectiveness in identifying faults is evaluated and compared, enabling the selection of the most suitable algorithm for the specific application. Experimental results demonstrate the effectiveness of the proposed fault detection approach, exhibiting high accuracy, sensitivity, and specificity in identifying various types of faults in PV arrays. Extensive simulations and experimental validations were conducted on various fault conditions, including partial shading, open-circuit faults, and degradation. The results demonstrate the proposed method's superior performance, achieving an accuracy of 99.61 %, precision of 99.75 % and F1 score of 98.73 %. These metrics significantly surpass traditional fault detection techniques, highlighting the method's potential for real-world deployment. The approach not only enhances the reliability of PV systems but also contributes to reducing maintenance costs and improving system efficiency. The combination of the Stockwell transforms with data mining techniques proposed here provides a robust and efficient framework for early detection of faults, enabling timely maintenance and minimizing energy losses.

Open-Circuit Fault Diagnosis for Dual Three-Phase Permanent Magnet Machine Considering Nonideal Condition

Open-Circuit Fault Diagnosis for Dual Three-Phase Permanent Magnet Machine Considering Nonideal Condition

Open-Circuit Fault Diagnosis for the Grid-Tied T-Type Inverter Based Only on Three-Phase Currents

Open-Circuit Fault Diagnosis for the Grid-Tied T-Type Inverter Based Only on Three-Phase Currents

A new fault-tolerant converter for renewable energy applications with improved reliability

With power converters consisting of several components, the reliability of a power converter becomes a major concern. With a switched-capacitor (SC) integrated into multilevel inverter (MLI) topologies, the chances of faults is more due to a much larger number of components than the MLI topologies with multiple isolated dc voltage sources. In this article, several SC-based 5L-MLI topologies have been analyzed for their performance with open-circuit fault (OCF), and based on the analysis, three 5L fault-tolerant MLI topologies have been proposed. The analysis for the proposed topologies have been carried out in terms of power loss analysis using PLECS software and power quality analysis using MATLAB software for different fault case scenarios. Further, the proposed 5L topologies have been compared with a similar state-of-the-art. A hardware prototype has been developed, and results have been discussed for various operating conditions.

Development of a Low-Cost Data Acquisition System for Analyzing the Health of a Photovoltaic System

In recent years, fault diagnosis has become a major concern for ensuring the sustainability of photovoltaic systems. This article aims to develop a low-cost data acquisition device based on the Arduino card to collect data using sensors on two photovoltaic modules. Four defects were intentionally created on a photovoltaic panel and analyzed individually based on the evolution of current and voltage parameters over time. The results indicate that in the event of a fault, the current drops suddenly with a slight decrease in voltage. Under normal conditions, the maximum observed current was 3.8 A for an irradiation of 904 W/m2, compared to 2.2 A for 909 W/m2 and 32.78°C in the event of partial shading. This value further decreases to a maximum of 1.8 A for 867 W/m2 in an open circuit. The observation reveals that the partial shading fault with a disconnected bypass diode and open circuit has similar characteristics to the open circuit fault. However, it is important to note that this cannot be generalized as the fault only occurs in a configuration of two PV modules in parallel.

Capacitor voltage balancing, capacitance monitoring, and fast fault detection in a nested neutral point clamped (NNPC) converter with the reduced number of sensors

In multilevel converters (MCs), system components such as capacitors and semiconductor switches are very susceptible to damage. Furthermore, utilizing voltage sensors to balance capacitor voltages increases the system's cost and complexity while decreasing reliability. This paper presents methods for voltage balancing of capacitors, capacitance monitoring and open-circuit fault detection in nested neutral point-clamped (NNPC) converter with a reduced number of voltage and current sensors. In the proposed method, converter capacitors' voltage and current sensors are eliminated, and only the output sensors are employed to control the converter. In order to balance the voltage of capacitors, their voltages are estimated in three cases without using individual sensors. Comparing the capacitors' measured and estimated voltage change ratios is used to assess their health in the monitoring method. Thus, the capacitors' status can be checked by considering the system's health indicator during each period. Furthermore, the open-circuit fault detection method can identify defective switches within one processing cycle by comparing the converter's measured and standard output voltages in all switching states. The proposed methods have been evaluated in an experimental study of the system, and the results confirmed the effectiveness of the methods based on the new system configuration.© 2017 Elsevier Inc. All rights reserved.

Intermittent open-circuit fault diagnosis of inverters based on DC-link electromagnetic field signal

For the objective of improving the reliability of converters in electric drives, research on a method for early detection of intermittent open-circuit faults of power valves is reported in this article. Intermittent open circuit condition is the incipient form of power valve open-circuit fault in power converters. Prompt detection of this fault allows for timely remediation of permanent open circuit defects that is a commonly subsequent process. This study introduces an investigation of this fault, which occurs in the voltage source inverter of induction motor drives. Intermittent faults are created through interference with the control pulse of the power valve. Wavelet transform with the Mexican hat mother function is utilized for signal processing. Appropriate ranges of the scale are selected to obtain a high magnitude of the wavelet coefficient at faulty instants. The analysis for the direct current recorded at the DC-link in simulation and the electromagnetic signal measured at the DC-bus of the inverter can be effectively used for the fault diagnosis.