Fault Diagnosis Of Inverter Research Articles

Short-Circuit Fault Diagnosis Based on Rough Sets Theory for a Single-Phase Inverter

The short-circuit (SC) fault diagnosis in inverters is an important procedure for the continuity of the performance and the extension of its useful life. The methods of diagnosis of SC failures produce good results, however, they present unfavorable aspects: they detect only one of the faults of SC, that is to say, the hard switch fault (HSF) or the fault under load (FUL); depend on switch parameters; and use artificial intelligence (AI) techniques in their algorithms, which are executed simultaneously with the inverter operation. This article presents a method of diagnosing SC faults performed with a digital circuit. The proposed method identifies short-circuit faults: HSF and FUL; can be used with any switch, regardless of its parameters; and does not use AI algorithms and techniques concurrently with inverter operation. The digital diagnostic circuit is obtained with the use of rough sets theory (RST), which optimizes and defines a minimum set of variables necessary to diagnose faults. Applying RST to the variables obtains a set of diagnostic rules. These rules are performed with basic logic functions and, for this reason, a digital diagnostic circuit is obtained. The diagnostic variables are the command signals and the voltage source inverter switches currents. The simulation and experimental results validate the shown diagnostic method.

A new approach for on-line open-circuit fault diagnosis of inverters based on current trajectory

Security and reliability of inverter are an indispensable part in power electronic system. Faults of inverter are usually caused by switch elements’ operating fault. Taking the inverter with hysteresis current control as the research object, a universal open-circuit fault location method which can be applied to multiple control strategies is proposed in the paper. If the switch open-circuit fault happens in inverter, the output phase current will inevitably change, which can be used as a characteristic for diagnosis, combined with the comparison of phase-current direction before and after the fault occurrence, to diagnose and locate the open-circuit fault in a half cycle. Moreover, this method requires neither system control signals nor sensor. The validity, reliability and limitation of the fault location method in the paper are verified and analyzed through dSPACE-based experiment platform.

Transistor open-circuit fault diagnosis in two-level three-phase inverter based on similarity measurement

Two-level three-phase inverters are widely applied to industrial automation, utility systems, transportation and aerospace as motor drives and energy conversion for their high efficiency and outstanding control performance. Their reliability such as open-circuit fault had attracted considerable attention in the past decades because of performance reduce and high maintenance cost, however, no universal theory and methods had been proposed. Considering that the topology is extremely symmetrical and it is composed of the same semiconductor devices, such as semiconductor or diodes. In healthy condition with no open-circuit transistor in the topology, the topology is symmetric and three phase currents are similar, in faulty condition, the symmetry is broken and three phase currents are not similar again, similarity measurement algorithm based on residuals of current ordered-sequence Mannharden distance are proposed to detect and locate the open-circuit faults. Experimental results show high efficiency and merit of the proposed fault diagnosis method.

Analysis of Different Meta Heuristics Method in Intelligent Fault Detection of Multilevel Inverter with Photovoltaic Power Generation Source

<p>Meta Heuristic methods have made a deep impact in the area of optimization in different streams of engineering. The performance of these algorithms is of importance because the hardware implementation of these algorithms is to be carried out for different engineering applications. As an important application in High Voltage DC (HVDC) transmission and Industrial Drives the multilevel inverter fault diagnosis is carried out using the different meta-heuristic methods with Neural Network as the decision making algorithm. The optimization of the weight and the bias values in the neural network diagnosis system is carried out in order to analyze the performance by means of comparing the Mean Square Error (MSE) while the Neural Network is getting trained for different fault conditions in the multilevel inverter. Matlab based implementation is carried out and the results are tabulated and inferred for a Multilevel Inverter fed from the Photovoltaic power generation system. In order to increase the robustness of the fault detection, with renewable energy based power generation system as the source for the Multilevel Inverter, the feature extracted from the multilevel inverter are positive, negative and zero sequence voltage along with the THD of the output voltage. The optimization algorithm used is Particle Swarm Optimization (PSO), Cuckoo Search Algorithm(CSA), Genetic Algorithm(GA) and Tabu Search Algorithm (TSA).</p>

Robust Fault Diagnosis and Adaptive Parameter Identification for Single Phase Transformerless Inverters

The paper presents the theoretical analysis and simulation verification of robust fault diagnosis and adaptive parameter identification for single phase transformerless inverters. The fault diagnosis is composed of two parts, fault detection and fault identification. In the fault detection part, a Luenberger observer is designed to realize the detection of faults. Then, we apply a bank of observers to identify the location of faults. Meanwhile, the fault identification observers based estimation along with a gradient descent algorithm are also used in the parameter identification to estimate the actual values of components in a single phase transformerless inverter. Not only we develop the design methodology for the robust fault diagnosis and adaptive parameter identifier but also we present simulation results. The simulation results show the effectiveness of fault diagnosis and the accurate tracking of changes in component parameters for a wide range.

Switch fault detection and diagnosis in space vector modulated cascaded H-bridge multilevel inverter

ABSTRACTThis article investigates the development and online implementation of the power switch open-circuit fault detection and diagnosis in symmetric cascaded H-bridge multilevel inverter. A mathematical modelling technique is presented to understand the effect of the fault on the bridge voltages and output voltage. The modelled values of the output voltage, simulation results and experimental results indicate that the fault diagnostic methods based on the output voltage as the diagnostic feature have certain ambiguity in identifying the fault switch, since the output voltage waveform and its features remain the same for a group of switches under the fault condition. In order to overcome this, fault detection and diagnosis method based on the mean values of the bridge voltages is proposed in this article, which identifies the faulty switch pair and H-bridge in which the fault has occurred. Further, this method has been experimentally validated on a five-level space vector modulated symmetric cascaded H-bridge multilevel inverter.

Open-circuit Fault Diagnosis of Three-phase Inverter for Grid-connected Distributed Generation System

To solve the increase cost of acquisition of multivariate information and the impact of the rapidity of diagnosis system in processing multivariate data, we proposed a novel fault diagnosis method for three-phase inverter based on wavelet packet transform and the probabilistic neural network, which can further improve the accuracy of open-circuit fault diagnosis. The wavelet packet transform is used to extract the feature vector of the output voltage of three-phase inverter. The probabilistic neural network has strong tolerance and can be employed to classify the feature vector. The simulation results show that the method has better diagnosis accuracy, which can diagnose the open-circuit fault effectively.

A Nonintrusive Diagnostic Method for Open-Circuit Faults of Locomotive Inverters Based on Output Current Trajectory

Open-circuit (OC) fault diagnosis of voltage source inverters on trains still has two problems to be improved. First, it is usually forbidden to change the system structures of trains or to add any extra sensor. Therefore, diagnostic methods which need pulse width modulation control signals or extra output voltage sensors are no longer suitable for trains. Second, loads on the train vary in a wide range and have significant influence on output currents. In order to solve these problems and increase the maintenance efficiency, an online diagnostic method for single switch OC (SSOC) faults and double switches OC (DSOC) faults is proposed. Supposing that three phases are balanced, it is proved that the trajectory of two output currents is an ellipse in the Cartesian coordination, and each OC fault has a unique trajectory. SSOC faults and DSOC faults can be isolated by detecting abnormal slopes and directions of several consecutive points on the trajectory. The fastest diagnostic process takes less than half a period. Load variation has impact on the size and shape of the trajectory but does not affect the diagnostic method. Experimental tests are carried out on dSPACE platform. Results show the accuracy of the mathematical model and the effectiveness of the online diagnostic method proposed in this paper.

Multiple Open-Circuit Fault Diagnosis Based on Multistate Data Processing and Subsection Fluctuation Analysis for Photovoltaic Inverter

In this paper, a practical fault diagnosis algorithm is presented to realize multiple open-circuit fault diagnosis for photovoltaic (PV) inverters. By the feature analysis for the output currents of normal and fault states, a fault diagnosis algorithm is developed, which is composed of multistate data processing (MSDP) block, subsection fluctuation analysis (SSFA) block, and the artificial neural network (ANN) block. First, the MSDP block is used to distinguish the different feature data and to adopt the data processing scheme, which improves the smoothness of the main data, retains the main fault features, and removes the influence of load change. Second, the SSFA block is used to extract the data feature, which can be used to accurately distinguish the different states of any switch. Finally, ANN is used by combining with the proposed MSDP and SSFA to implement intelligent classification, in which the dependency and the number of thresholds can be reduced. Comparing with the existing fault classification algorithms, the proposed algorithm is simple and stable to realize the multiple switches fault diagnosis for PV inverters. It does not require additional hardware equipment, and it can reduce the complexity of design and realization. Finally, the effectiveness of the fault diagnosis algorithm is verified by the experimental results.

A Novel Three-Phase Inverter Fault Diagnosis System Using Three-dimensional Feature Extraction and Neural Network

In general, fault diagnosis and classification is concerned about monitoring a system, identifying the fault occurrence and pinpointing the exact fault location. Fault diagnosis and fault-tolerant systems are crucial for modern electrical and electronic system safety. Inverters are most frequently used DC–AC power converters. DC–AC three-phase inverter faults are very common to occur and need an effective and robust fault diagnosis system for protection and isolation. A new fault detection and classification technique is introduced in this paper. Feature extraction system considers three-phase voltage pattern plot in 3D, and the neural network are used to pinpoint the fault location. The testing process is conducted under simulation and physical environment to confirm its expediency. Proposed technique and experimental results are discussed in detail. Simulation experiment followed by practical implementation on SPWM three-phase inverter is included with results to ensure the system accuracy and reliability.

Online diagnostic method of open‐switch faults in PWM voltage source rectifier based on instantaneous AC current distortion

The three-phase pulse-width modulating (PWM) voltage source rectifier (VSR) system is widely used because of its advantages in performance. Numerous studies show that power semiconductors are significant contributors to the overall failure rate of power converters. This study presents an online diagnosis method for a single open-switch fault of a three-phase PWM VSR, because existing research mainly focuses on fault diagnosis of inverters. The proposed method employs current distortion to diagnose faults in a convenient and simple way. It requires no extra hardware but only AC current signals, which are available in the control system and can be embedded into the existing driving software as a subroutine without excessive computational effort. The proposed method offers high diagnostic effectiveness and reliability. Experimental results show that the proposed method can diagnose the faulty switch at low currents as well as under abrupt load transient conditions without false alarms. Furthermore, the average diagnostic time of this method is only 1.8 ms.

Open-Circuit Fault Diagnosis of Neutral Point Clamped Three-Level Inverter Based on Sparse Representation

Decomposition of the signal on the orthogonal or nonorthogonal basis of the signal space is the traditional method for fault feature extraction in the field of inverter fault diagnosis. These signal analysis methods make the result of signal decomposition not sparse and they are not self-adaptive. In recent years, sparse representation has received considerable attention in signal processing because the method can overcome the shortcomings of traditional methods by decomposing the signal on an over-complete dictionary instead of on an orthogonal or nonorthogonal basis. This paper proposes a combination of sparse representation and support vector machine (SVM) for the fault diagnosis of neutral point clamped (NPC) three-level inverter. First, the three-phase phase voltage signals are sampled as the characteristic signals for analysis. Then, the K-SVD algorithm is used as the fault feature extraction technology to obtain an over-complete dictionary and the sparse representation coefficients of the characteristic signals. The latter are used as the feature information for the characteristic signals. Finally, the SVM with powerful generalization capability is used as the fault identification method to identify NPC three-level inverter fault types according to the extracted feature information and analyze the fault diagnosis effect. Simulation experiments show that the combination of sparse representation and the SVM for fault diagnosis of NPC three-level inverters has the advantage of high diagnostic accuracy. The fault diagnosis method proposed in this paper is compared with other methods to further verify its superiority in the fault diagnosis of NPC three-level inverters.

Hybrid Artificial Intelligence based Fault Diagnosis of SVPWM Voltage Source Inverters for Induction Motor

Induction motors are widely used in various industries for production processes due to their inherent characteristics like high reliability, robustness, low cost and ease of construction and operation. However, while in operation they face harsh and severe conditions imposed on them by the electrical, mechanical, thermal and environmental stresses which give rise to abnormalities in machine parameters such as voltages and currents. If the motor drive is allowed to continue to run under such situations, it will lead to catastrophic failure of the motor disrupting the production process and huge loss of revenue. Hence, an intelligent drive control method based on Hybrid Artificial Intelligence (AI) technique of Neuro-Genetic Algorithm is proposed in this paper for condition monitoring, fault diagnosis and evaluation of induction motor without any additional information. The hybrid architecture of Back Propagation Neural Network (BPN) to get the desired output fast and with Genetic Algorithm to overcome the computational complexity and time consuming process involved in BPN.

Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

ABSTRACTThe increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.

A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems

Permanent magnet synchronous motor and power electronics-based three-phase inverter are the major components in the modern industrial electric drive system, such as electrical actuators in an all-electric subsea Christmas tree. Inverters are the weakest components in the drive system, and power switches are the most vulnerable components in inverters. Fault detection and diagnosis of inverters are extremely necessary for improving drive system reliability. Motivated by solving the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters. Two output line-to-line voltages for different fault modes are measured, the signal features are extracted using fast Fourier transform, the dimensions of samples are reduced using principal component analysis, and the faults are detected and diagnosed using Bayesian networks. Simulated and experimental data are used to train the fault diagnosis model, as well as validate the proposed fault diagnosis methodology.

A Robust Observer-Based Method for IGBTs and Current Sensors Fault Diagnosis in Voltage-Source Inverters of PMSM Drives

Permanent magnet synchronous motors (PMSMs) drives using three-phase voltage-source inverters (VSIs) are currently used in many industrial applications. The reliability of VSIs is one of the most important factors to improve the reliability and availability levels of the drive. Accordingly, this paper presents a robust fault diagnostic method for multiple insulated gate bipolar transistors (IGBTs) open-circuit faults and current sensor faults in three-phase PMSM drives. The proposed observer-based algorithm relies on an adaptive threshold for fault diagnosis. Current sensor and open-circuit faults can be distinguished and the faulty sensors and/or power semiconductors are effectively isolated. The proposed technique is robust to machine parameters and load variations. Several simulation and experimental results using a vector-controlled PMSM drive are presented, showing the diagnostic algorithm robustness against false alarms and its effectiveness in both IGBTs and current sensors fault diagnosis.

Instantaneous power factor signature analysis for efficient fault diagnosis in inverter fed three phased induction motors

Majority of the electricity used in the industry is consumed by induction motors. Early detection of abnormal cases that occur at the electrical and mechanical parts of these motors is very important for the safe operation of industrial facilities and the decrease of economic losses. In this presented study, the detection of parallel, angular and mixed misalignment fault in inverter fed induction motors has been carried out via the harmonic analysis of the instantaneous power factor signal. Results obtained from experimental studies carried out under different speed and loads indicated that the detection of misalignment fault can be carried out successfully. The presented method can be used effectively without any additional cost since inverter fed motors have both voltage and current sensors. Also online monitoring of induction motors with the suggested method not only improves the motor's performance and longevity but also its efficiency. The main innovative parts of this study is that instantaneous power factor signal was used at the first time for detection of misalignment faults.

Open-circuit Fault Diagnosis for Grid-connected NPC Inverter based on Independent Component Analysis and Neural Network

Open-circuit Fault Diagnosis for Grid-connected NPC Inverter based on Independent Component Analysis and Neural Network

APPLICATION OF AN EXTENSION NEURAL NETWORK METHOD FOR FAULT DIAGNOSIS OF THREE-LEVEL INVERTERS USED IN MARINE VESSELS

This study proposes an extension neural network (ENN) algorithm to carry out the fault diagnosis of an inverter in a motor drive system for marine vessels. First, we use PSIM software to construct a three-level neutral point clamped (NPC) inverter, which simulates the occurrence of the fault of any power transistor in the NPC inverter. Simultaneously, we use fast Fourier transforms to transcribe the waveform of phase voltage in the time domain during the fault of any power transistor entering the spectrum in the frequency domain, looking for the corresponding characteristic spectrum during the fault of the power transistor. Next, we establish the relationship between the fault type and the specific characteristic spectrum as the learning basis of the extension neural network. By doing so, we build up a fault diagnosis system for the inverter. Finally, we use the simulation results to verify the feasibility of the proposed inverter fault diagnosis system.

Load‐current‐based current sensor fault diagnosis and tolerant control scheme for traction inverters

Based on analysing the load currents, a current sensor fault diagnosis method for three-phase voltage source inverters in high-speed railway electrical traction drive system is presented. Then, a fault-tolerant algorithm is applied to reconstruct the three-phase currents. This fault diagnosis and tolerant control approach is an extension of the normalised average currents method, which requires no additional hardware and permits much more robust diagnostics. Experimental results confirm the effectiveness and accuracy of the proposed algorithm. It is shown that such diagnosis method can locate the faulty sensor, identify the fault modes, and recover the drive system in a fundamental period which is important to avoid catastrophic consequences.