Showing papers on "Fault indicator published in 2017"

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

Abstract: 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.

Transient-Voltage-Based Protection Scheme for DC Line Faults in the Multiterminal VSC-HVDC System

Abstract: DC line faults are major issues for a multiterminal high-voltage direct current (HVDC) system based on voltage-source converter (VSC). The fault current increases quickly along with a large peak, and complete isolation of the faulted system is not a viable option. Therefore, protection with high selectivity and accuracy is essential. In this paper, a new protection scheme for dc line in multiterminal VSC-HVDC system is proposed, which consists of a main protection and a backup protection. Both the protection principles are based on the supplemental inductor placed at each end of the dc line. Fault identification can be achieved by calculating the ratio of the transient voltages (ROTV) at both sides of the inductor. The main protection is able to detect the fault quickly without communication, while the backup protection is a pilot method based on the ROTVs at both ends of dc line, which is employed to identify the high-resistance faults and offer a backup in case the former fails. Comparison with some previous protection methods shows that the performance of the proposed protection scheme is promising. Numerous simulation studies carried out in PSCAD/EMTDC and real-time digital simulator (RTDS) under various conditions have demonstrated that fault identification with high selectivity and strong robustness against fault resistance and disturbance can be achieved by employing the proposed protection scheme.

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Abstract: Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology.

A Unified Analysis of the Fault Tolerance Capability in Six-Phase Induction Motor Drives

Abstract: The fault tolerance of electric drives is highly appreciated at industry for security and economic reasons, and the inherent redundancy of six-phase machines provides the desired fault-tolerant capability with no extra hardware. For this reason some recent research efforts have been focused on the fault-tolerant design, modeling, and control of six-phase machines. Nevertheless, a unified and conclusive analysis of the postfault capability of six-phase machine is still missing. This paper provides a full picture of the postfault derating in generic six-phase machines and a specific analysis of the fault-tolerant capability of the three mainstream six-phase induction machines (asymmetrical, symmetrical, and dual three phase). Experimental results confirm the theoretical post fault current limits and allow concluding, which is the best six-phase machine for each fault scenario and neutral arrangement.

Fault location and detection techniques in power distribution systems with distributed generation: A review

Abstract: Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human errors. These phenomena affect the system reliability and results in expensive repairs, lost of productivity and power loss to customers. Since fault is unpredictable, a fast fault location and isolation is required to minimize the impact of fault in distribution systems. Therefore, many methods have been developed since the past to locate and detect faults in distribution systems with distributed generation. The methods can be divided into two categories, conventional and artificial intelligence techniques. Conventional techniques include travelling wave method and impedance based method while artificial intelligence techniques include Artificial Neural Network (ANN), Support Vector Machine (SVM), Fuzzy Logic, Genetic Algorithm (GA) and matching approach. However, fault location using intelligent methods are challenging since they require training data for processing and are time consuming. In this paper, most of the techniques that have been developed since the past and commonly used to locate and detect faults in distribution systems with distributed generation are reviewed. Research works in fault location area, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper. Hence, from this review, the opportunities in fault location research area in power distribution system can be explored further.

Fault Detection for Photovoltaic Systems Based on Multi-Resolution Signal Decomposition and Fuzzy Inference Systems

Abstract: This paper presents a detection scheme for DC side short-circuit faults of photovoltaic (PV) arrays that consist of multiple PV panels connected in a series/parallel configuration. Such faults are nearly undetectable under low irradiance conditions, particularly, when a maximum power point tracking algorithm is in-service. If remain undetected, these faults can considerably lower the output energy of solar systems, damage the panels, and potentially cause fire hazards. The proposed fault detection scheme is based on a pattern recognition approach that employs a multiresolution signal decomposition technique to extract the necessary features, based on which a fuzzy inference system determines if a fault has occurred. The presented case studies (both simulation and experimental) demonstrate the effective and reliable performance of the proposed method in detecting PV array faults.

Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multiresolution Signal Decomposition and Two-Stage Support Vector Machine

Abstract: Fault detection in photovoltaic (PV) arrays becomes difficult as the number of PV panels increases Particularly, under low irradiance conditions with an active maximum power point tracking algorithm, line-to-line (L-L) faults may remain undetected because of low fault currents, resulting in loss of energy and potential fire hazards This paper proposes a fault detection algorithm based on multiresolution signal decomposition for feature extraction, and two-stage support vector machine (SVM) classifiers for decision making This detection method only requires data of the total voltage and current from a PV array and a limited amount of labeled data for training the SVM Both simulation and experimental case studies verify the accuracy of the proposed method

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

Abstract: 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.

Model-Based Fault Detection and Identification for Switching Power Converters

Abstract: We present the analysis, design, and experimental validation of a model-based fault detection and identification (FDI) method for switching power converters using a model-based state estimator approach. The proposed FDI approach is general in that it can be used to detect and identify arbitrary faults in components and sensors in a broad class of switching power converters. The FDI approach is experimentally demonstrated on a nanogrid prototype with a 380-V dc distribution bus. The nanogrid consists of four different switching power converters, including a buck converter, an interleaved boost converter, a single-phase rectifier, and a three-phase inverter. We construct a library of fault signatures for possible component and sensor faults in all four converters. The FDI algorithm successfully achieves fault detection in under 400 $\mu$ s and fault identification in under 10 ms for faults in each converter. The proposed FDI approach enables a flexible and scalable solution for improving fault tolerance and awareness in power electronics systems.

High Impedance Fault Identification on Distribution Networks

Abstract: This paper presents a transient-based algorithm for high-impedance fault identification on distribution networks. It uses the discrete wavelet transform to monitor high- and low-frequency voltage components at several points of the power system, being able to indicate the most likely area within which the disturbance has occurred, without requiring data synchronization nor the knowledge of feeder or load parameters. The proposed algorithm is evaluated through electromagnetic transients program simulations of high-impedance faults in a 13.8 kV system modeled from actual Brazilian distribution grid data. Solid faults, capacitor bank switching, and feeder energization are also simulated, considering the system with and without distributed generation. Obtained results show that the algorithm significantly reduces the search field of the high-impedance fault, reliably distinguishing it from other disturbances.

Fault Detection and Faulted Line Identification in Active Distribution Networks Using Synchrophasors-based Real-Time State Estimation

Abstract: We intend to prove that phasor-measurement-unit (PMU)-based state estimation processes for active distribution networks exhibit unique time determinism and a refresh rate that makes them suitable to satisfy the time-critical requirements of protections as well as the accuracy requirements dictated by faulted line identification. In this respect, we propose a real-time fault detection and faulted line identification functionality obtained by computing parallel synchrophasor-based state estimators. Each state estimator is characterized by a different and augmented topology in order to include a floating fault bus. The selection of the state estimator providing the correct solution is performed by a metric that computes the sum of the weighted measurement residuals. The proposed process scheme is validated by means of a real-time simulation platform where an existing active distribution network is simulated together with a PMU-based monitoring system. The proposed process is shown to be suitable for active and passive networks, with solid-earthed and unearthed neutral, for low- and high-impedance faults of any kind (symmetric and asymmetric) occurring at different locations.

Arc Fault Detection Method Based on CZT Low-Frequency Harmonic Current Analysis

Abstract: This paper presents a method for the detection of series arc faults in electrical circuits, which has been developed starting from an experimental characterization of the arc fault phenomenon and an arcing current study in several test conditions. Starting from this, the authors have found that is it possible to suitably detect arc faults by means of a high-resolution low-frequency harmonic analysis of current signal, based on chirp zeta transform, and a proper set of indicators. The proposed method effectiveness is shown by means of experimental tests, which were carried in both arcing and nonarcing conditions and in the presence of different loads, chosen according to the UL 1699 standard requirements.

Open-Circuit IGBT Fault Detection and Location Isolation for Cascaded Multilevel Converters

Abstract: An open-circuit insulated-gate bipolar transistor fault detection technique for cascaded H-bridge (CHB) multilevel converters is presented in this paper. This technique, designed to be implemented independently for each CHB leg, utilizes one current sensor and one voltage sensor to monitor a leg's current and output voltage. Measured voltages are compared to expected voltages, and deviations are used to determine open-circuit fault locations based on the deviation's magnitude and current flow direction. Once potential fault locations have been identified, the fault location is systematically isolated and then verified, reducing the possibility of unnecessary corrective actions due to fault misidentification, e.g., an intermittent gate-misfiring fault being classified as an open-circuit fault. The proposed technique can be implemented for any number of cells, is independent of the pulse width modulation strategy used, and can be applied to symmetric and asymmetric CHB converters regardless of the cell input dc-source magnitudes utilized, i.e., cell input voltages are not required to be equal or to exist in specific ratios. For a CHB leg with $M$ cells, the proposed technique identifies and isolates open-circuit switch faults in less than $2M$ measurement (sampling) cycles, and verification is completed in less than one full fundamental cycle. Experimentally obtained data demonstrate the efficacy of the proposed fault detection and isolation technique.

Data-driven fault diagnosis of a wind farm benchmark model

Abstract: The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances.

Reliable Modular Multilevel Converter Fault Detection With Redundant Voltage Sensor

Abstract: This paper presents a fault-tolerant configuration for the modular multilevel converter (MMC). The procedure is able to detect faults in voltage sensors and semiconductor switching devices, and it can reconfigure the system so that it can keep on operating. Both switch and sensor faults can be detected by comparing the output voltage of a set of submodules (SMs), which is measured by a so-called supervisory sensor, with two calculated reference voltages. Faults in the supervisory sensors are also considered. Sensor faults are overcome by using a measuring technique based on estimates that are periodically updated with the voltage measurements of the supervisory sensors. Additional SMs are included in the arms so that the MMC can bypass a faulty SM and continue operating without affecting the output voltage of the phase-leg. Experimental results obtained from a low-power MMC prototype are presented in order to demonstrate the effectiveness of the proposed techniques.

Quadrotor Actuator Fault Diagnosis and Accommodation Using Nonlinear Adaptive Estimators

Abstract: This brief presents the systematic design and real-time experimental results of a fault detection, isolation, and accommodation algorithm for quadrotor actuator faults using nonlinear adaptive estimation techniques. The fault diagnosis architecture consists of a nonlinear fault detection estimator and a bank of nonlinear adaptive fault isolation estimators designed based on the functional structures of the faults under consideration. Adaptive thresholds for fault detection and isolation are systematically designed to enhance the robustness and fault sensitivity of the diagnostic algorithm. After fault isolation, the fault parameter estimate generated by the matched isolation estimator is used for accommodating the fault effect. Using an indoor quadrotor test environment, real-time experimental results are shown to illustrate the effectiveness of the algorithms.

Fault-Tolerant Operation of Three-Phase Cascaded H-Bridge Converters Using an Auxiliary Module

Abstract: This paper presents a new fault-tolerant solution for cascaded H-bridge (CHB) converters, which generates equal output voltages in both prefault and single fault conditions. To generate a balanced three-phase voltage with the highest amplitude regardless of the fault location and its type, an auxiliary module is employed in series with the CHB converter. The auxiliary module is a two-level voltage-source inverter with a capacitive dc link. The module is brought to the circuit after fault detection and its capacitor is charged by a novel algorithm to the reference value. Then, using the space vector modulation, the inverter's reference space vector is synthesized and the voltage of auxiliary module is kept constant. The validation of the proposed method is confirmed by simulations and experiments on a three-phase five-level CHB converter.

Low Voltage Zones to Support Fault Location in Distribution Systems With Smart Meters

Abstract: This paper proposes to combine the voltage monitoring capability of smart meters with impedance-based fault location methods to provide an efficient fault location approach improving service restoration. The first step of the proposed methodology is to apply an impedance-based method to obtain a rough estimation of fault location. Since the result is an estimated distance to the fault, multiple branches can be indicated due to the typical distribution systems topologies. Therefore, the challenge is: how to recognize the actual fault location? To solve this problem, voltage measurements from smart meters are used to build the low voltage zones (LVZs). The main contributions of the proposed fault location technique are to decrease the multiple estimations associated with impedance-based methods, to propose a systematic approach to build the LVZs, and to explore the presence of smart meters for fault location. The proposed method was tested through intensive and extensive simulations in a real distribution system, proving its efficiency.

A Fault-Injection Strategy for Traction Drive Control Systems

Abstract: A traction drive control system (TDCS) plays an important role in safety running of high-speed trains. This paper presents a new fault-injection strategy for safety testing and fault diagnosis verification in the TDCS. First, the fault scenarios on the signal level of each faulty component are analyzed. Then, the fault-injection method based on signal conditioning is proposed, and the injected signal, reflecting the fault scenario at a fault point, is generated to simulate the fault scenarios. Subsequently, the injected signal benchmark is constructed for all faults in traction converters, traction motors, sensors, and traction control units. Finally, a fault-injection benchmark platform is developed to simulate various fault scenarios in the TDCS. The simulation and comparison results show that the presented strategy is effective and easy to implement.

Nuclear Power Plant Thermocouple Sensor-Fault Detection and Classification Using Deep Learning and Generalized Likelihood Ratio Test

Abstract: In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.

An Early Stage Interturn Fault Diagnosis of PMSMs by Using Negative-Sequence Components

Abstract: This paper proposes an early stage interturn short-circuit fault (ISCF) diagnosis method for permanent magnet synchronous machines. A fault indicator is suggested based on a new theoretical analysis of the relationship between the fault current and the rotor speed. The fault indicator is shown to be robust to the rotor speed changes in slight ISCFs. It is calculated by introducing negative-sequence components (NSCs). It is shown that the fault indicator using NSCs can diagnose slighter ISCFs than that using zero-sequence components. Experimental results demonstrate the effectiveness of the proposed method for diagnosing early stage ISCFs with a small number of short-circuited turns and low fault current.

Fault-Tolerant Control for an Internet-Based Three-Tank System: Accommodation to Sensor Bias Faults

Abstract: This paper focuses on the fault-tolerant control problem for an Internet-based three-tank system in the presence of possible sensor bias faults. The Internet-based three-tank system is an experimental setup that can be regarded as a typical networked system for evaluating networked fault-diagnosis and fault-tolerant control methods. Packet dropout phenomenon in the sensor-to-controller link is considered in this paper, and the fault type we deal with is chosen as the sensor bias fault. Fault-diagnosis unit is designed toward an auxiliary system. Sensor bias faults can be detected by comparing the residual signal generated by the fault detection filter and a prescribed threshold. After that, the fault can be isolated by using the residual analysis approach. Once the fault is isolated, it can be estimated iteratively in the least-squares sense. A fault accommodation method is proposed, and a fault-tolerant control strategy is achieved based on the fault information provided by the fault-diagnosis unit. The approach brought forward in this paper is demonstrated via an experimental study on the practical Internet-based three-tank system. Results show the effectiveness and the applicability of the proposed techniques.

Open-Phase Fault Detection of a Five-Phase Permanent Magnet Assisted Synchronous Reluctance Motor Based on Symmetrical Components Theory

Abstract: This paper presents a novel approach for open-phase fault detection of a five-phase permanent magnet assisted synchronous reluctance motor (PMa-SynRM). Under faults, the five-phase PMa-SynRM is expected to run at fault-tolerant control (FTC) mode, otherwise it draws a large amount of current with a significant reduction in the reluctance torque. To successfully achieve FTC operation of five-phase PMa-SynRM, the accurate detection of a fault condition has to be preceded. With the best of these authors knowledge, the detection of faults has been limitedly studied for five-phase motors. The analysis of open-phase fault in five-phase machine involves complicated conditions including single-phase open fault, two-phase adjacent fault, and two-phase nonadjacent fault. To perform the timely fault-tolerant operation, those faults have to be accurately analyzed and detected. In this paper, a novel symmetrical components (SCs) analysis is utilized to extract the feature of those fault conditions. This analysis will provide the types of faults by logically analyzing the pattern of magnitude and phase angle changes of the fundamental signal in the SCs. The proposed method has been comprehensively analyzed through theoretical derivation, finite-element simulations, and experimental testing through a 5 hp PMa-SynRM controlled by TI-DSP F28335.

A Novel Fault Location Method and Algorithm for DC Distribution Protection

Abstract: DC fault current is contributed by various distributed energy resources in dc distribution systems. The tightly coupled dc distribution systems have relatively low line impedance values. The fault current increases fast because of the low impedance. Some converters in dc distribution systems include fault current limiting function. The controlled fault currents at different locations are very close. Thus, it is important to design a reliable and fast fault detection and location method for dc distribution systems. This paper proposes a novel local measurement-based fault location algorithm for tightly coupled dc distribution systems. The proposed fault location algorithm can estimate the equivalent inductance between a protective device and a fault in less than 1 ms. The performance of the developed protection algorithm was validated by numerical simulation and hardware tests.

Fault Prognosis for Power Electronics Systems Using Adaptive Parameter Identification

Abstract: This paper presents the design, implementation, and experimental validation of a method for fault prognosis for power electronics systems using an adaptive parameter identification approach. The adaptive parameter identifier uses a generalized gradient descent algorithm to compute real-time estimates of system parameters (e.g., capacitance, inductance, parasitic resistance) in arbitrary switching power electronics systems. These estimates can be used to monitor the overall health of a power electronics system and to predict when faults are more likely to occur. Moreover, the estimates can be used to tune control loops that rely on the system parameter values. The parameter identification algorithm is general in that it can be applied to a broad class of systems based on switching power converters. We present a real-time experimental validation of the proposed fault prognosis method on a 3 kW solar photovoltaic interleaved boost dc–dc converter system for tracking changes in passive component values. The proposed fault prognosis method enables a flexible and scalable solution for condition monitoring and fault prediction in power electronics systems.

An Alternative Method for Locating Faults in Transmission Line Networks Based on Time Reversal

Abstract: We present an alternative method based on the time-reversal process to locate faults in transmission line networks. The proposed procedure considers different media for the forward and the backward propagation phases. Specifically, the transverse branch representing the fault is removed from the circuit in the backward propagation since its location represents the solution of the process and, therefore, is not known in advance. The advantage of the proposed method is twofold. First, the proposed backward model requires only one simulation for the time-reversed backward propagation phase, thus reducing significantly the computational burden. Second, we demonstrate that this modified backward propagation medium satisfies a property such that the fault location can be identified by computing, in the frequency domain, the argument of the voltage along the line. The theory is first formulated for the case of a lossless homogeneous single-phase transmission line; then, its applicability is extended to lossy inhomogeneous transmission line networks. A single-phase inhomogeneous transmission line and an inhomogeneous Y-shape network are specifically considered to support this claim. We show that the proposed procedure can provide high fault location accuracy (i.e., in the range of $\pm 1$ m), using only one observation point. Furthermore, we propose a criterion to link the bandwidth of the sampling system to the desired fault location accuracy.

Multi-Agent-Based Technique for Fault Location, Isolation, and Service Restoration

Abstract: This paper proposes a communication-assisted fault localization, isolation, and restoration method for microgrids based on a multi-agent system. The proposed system comprises distributed agents, located in the middle and at the two ends of a protection section, which will detect a fault through phase angle comparison of current signals at both sides of a given distribution line. The agents then send trips signal to corresponding circuit breakers accordingly. The importance of the proposed protection technique is twofold. First, it eliminates the use of voltage transformers and thus reduces costs. Second, it does not require transfer of data along long distances, which decreases the delay time for fault isolation. Power restoration processes following the fault clearance considering voltage, frequency, and power flow constraints in the microgrid under study were also performed. Simulation of the proposed protection methodology was presented followed by experimental verification. The experimental results showed excellent agreement with the simulated protection scheme.