Hybrid Fault Research Articles

An intelligent hybrid technique for fault detection and condition monitoring of a thermal power plant

• A Hybrid fault detection approach based on intelligent classifiers was proposed. • Experimental data during emergency shut-downs were employed. • Decision fusion technique was applied to combine the outlet of classifiers. • Fault detection prevented from undesired system trips. This study presents an application of intelligent fault detection system for recognizing abnormal conditions during transient operation of a steam generator unit. Unobserved dynamics of evaporator section have been caused multiple false alarms and boiler emergency shut-downs. In order to detect faulty conditions, four different classifier agents were employed in parallel. The experimental data from real system performances during system trips were collected to train and validate the intelligent classifiers. The outlet results of all classifiers were combined using Yager's rule of fusion in order to improve the reliability and accuracy of fault detection process. The performances of the proposed fault detection system were evaluated during the unit's load variations and at different scenarios as one or two classifier(s) failed to detect the correct situations. The obtained results indicated the capability and feasibility of the proposed technique in preventing from raising false alarms by early detection of abnormal conditions.

Uncertain fault diagnosis problem using neuro-fuzzy approach and probabilistic model for manufacturing systems

This paper is concerned with fault detection and diagnosis problem in manufacturing systems. In such industrial environment, production systems are subject to several faults caused by a number of factors including the environment, the accumulated wearing, usage, etc. However, due to the lack of accuracy or fluctuation of data, it is oftentimes impossible to evaluate precisely the correct classification rate of faults. In order to classify each type of fault, neural networks and fuzzy logic are two different intelligent diagnosis methods that are more applied now, and each has its own advantages and disadvantages. A new hybrid fault diagnosis approach is introduced in this paper that considers the combined learning algorithm and knowledge base (Fuzzy rules) to handle ambiguous and even erroneous information. Therefore, to enhance the classification accuracy, three perceptron models including: linear perceptron (LP), multilayer perceptron (MLP) and fuzzy perceptron (FP) have been respectively established and compared. The conditional risk function “PDF” that measures the expectation of loss when taking an action is presented at the same time. We evaluate the proposed hybrid approach “Variable Learning Rate Gradient Descent with Bayes’ Maximum Likelihood formula” VLRGD-BML on dataset of milk pasteurization process and compare our approach with other similar published works for fault diagnosis in the literature. Comparative results indicate the higher efficiency and effectiveness of the proposed approach with fuzzy perceptron for uncertain fault diagnosis problem.

Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model

The contribution of renewable energies to the reduction of the impact of fossil fuels sources and especially energy supply in remote areas has occupied a role more and more important during last decades. The estimation of renewable power plants performances by means of deterministic models is usually limited by the innate variability of the energy resources. The accuracy of energy production forecasting results may be inadequate. An accurate feasibility analysis requires taking into account the randomness of the primary resource operations and the effect of component failures in the energy production process. This paper treats a novel approach to the estimation of energy production in a real photovoltaic power plant by means of dynamic reliability analysis based on Stochastic Hybrid Fault Tree Automaton (SHyFTA). The comparison between real data, deterministic model and SHyFTA model confirm how the latter better estimate energy production than deterministic model.

Design and Implementation of Fault Current Limiters in Distribution System Using Internet of Things

One of the essential execution of a wireless sensor network is observing hardware. Wireless Sensor Network are capable for taken a toll effective checking over gigantic geo location. Development of keen network depends on the Internet of Things (IoT). Nowadays the fault current levels are increased because of the increasing power demand. Fault Current Limiter(FCL) is used as a protective device to reduce the fault current in distribution system. This paper presents Solid State Fault Current Limiter (SSFCL) and Hybrid Fault Current Limiter (HFCL) to limit the fault current levels for different fault conditions. Also this paper presents the comparison between these two devices with fault current limiting ratio. The performance of the two devices is carried over in IEEE 6-bus system. The simulation and experimental results validate the performance of these two devices to reduce the fault current. The proposed model is executed with SSFCL and HFCL for 60 W three-phase load under various fault conditions. All the fault conditions are observed and controlled through IoT cloud framework.

Fault‐tolerant delay cell for ring oscillator application in 65 nm CMOS technology

A novel fault tolerant delay cell for ring oscillator (RO) is proposed. As RO is one of the crucial blocks in phased locked loop, delayed locked loo and clock data recovery, it should be tolerated against single event transient (SET) and stuck at faults for harsh environment. Their proposed hybrid fault tolerant topology is combination of triple and quad transistors redundancy, which is applied to the delay cell structure based on the sensitivity role of each transistor. The simulation results with Cadence software show that the proposed fault-tolerant delay cell dissipates 34.34 μW power, while it occupies 127.2 μm 2 chip area. The proposed topology not only has lower power dissipation in comparison with existing fault tolerant delay cells but also is more reliable against stuck at single and multiple faults and also SETs. By using the proposed reliable delay cell in the RO, the achieved power dissipation and phase noise are about 249 μW and -96 dBc/Hz, respectively, while higher reliability is achieved in comparison with non-redundant RO s.

Software-Defined Next-Generation Satellite Networks: Architecture, Challenges, and Solutions

Traditional satellite networks depend on the closed and planned architecture. Thus, there are many challenges such as configuration update, new communication and networking technologies introduction, truly-differentiated services provision, satellite network device interoperability, and the integration of satellite and terrestrial networks. Software-defined networking (SDN) has the features of flexibility, programmability, and logical centralization, which increases network resource utilization, simplifies network management, reduces operating cost, and promotes the evolution and innovation. In this paper, a new software-defined architecture for next-generation satellite networks, called SoftSpace, is presented. The concepts of network function virtualization, network virtualization, and software-defined radio are exploited in the SoftSpace to facilitate the incorporation of new applications, services, and satellite communication technologies. This can not only reduce the capital expenditures and operational expenditures but also integrate satellite networks with terrestrial networks seamlessly, as well as can improve the interoperability of satellite network devices. In addition, we discuss the challenges and solutions for network management. The necessary network management instruments including multi-layer controller architecture, cooperative traffic classification, and utility-optimal network virtualization are presented. Finally, we discuss the challenges and solutions for space networking. The software-defined space networking solutions including quality of experience-aware space routing, SDN-enabled hybrid fault recovery mechanism, and software-defined space mobility management are developed.

Phase Current Reconstruction for the Grid-Side Converter With Four-Switch Three-Phase Topology in a DFIG-WT

To increase the reliability of a doubly fed induction generator-based wind turbine (DFIG-WT), this paper proposes a fault-tolerant four-switch three-phase (FSTP) grid-side converter (GSC) with a phase current reconstruction strategy. The proposed strategy is effective when the semiconductor open-circuit fault and phase current sensor failure occur simultaneously, which is a kind of hybrid fault. Only one current sensor is applied for measuring the currents flowing between the ac and dc sides of the GSC, and the phase current information can be derived by combining this information with the switching states of FSTP GSC. In the proposed current reconstruction strategy, only two scenarios of duty ratio allocation are needed to be considered. In addition, the space vector pulsewidth modulation (SVPWM) technique is simplified by obtaining the unified expressions for the duty ratios for the remaining four switches in GSC, and the sector identification is removed. Besides, the voltage balancing on the dc bus is achieved. Furthermore, the limitation of the proposed current reconstruction strategy is presented, and the dead zones for SVPWM techniques in both six-switch three-phase and FSTP converters are analyzed. Simulations are carried out in MATLAB/Simulink to verify the proposed hybrid fault-tolerant strategy for the GSC in a 1.5-MW DFIG-WT.

Current limiting methods for VSC-based DC distribution systems

The lack of mature and reliable fault isolation technologies constrains the development and application of DC distribution systems. In this paper, the protection differences between AC distribution systems and voltage source converter (VSC)-based DC distribution systems are analyzed. The indispensable role of the fault current limiter for the isolation of a DC fault is proposed. Based on the analysis of DC fault, the current limiting effects of resistance and inductance are analyzed. Analyses and corresponding simulation indicate that the resistance and inductance hybrid fault current limiter can protect the of AC source and diodes from overcurrent and limit the rising speed of fault current simultaneously.

Analysis on Relay Protection Transition Technology and Outer Pre-path Selection Based on PBT

To improve the performance of fault isolation and restoration in distribution grids, the feasibility of relay protection based on time-delay coordination in distribution systems is investigated. Design considerations of hybrid fault isolation and restoration combined by time-delay based relay protection and distribution automation system (DAS) are given. Firstly, the performance indexes of relay protection services are analyzed in four aspects: interface rate, time delay, bit error and channel. Secondly, based on the time delay formulas, the study is focused on the ending/receiving delay and the forwarding and switching delay about PBT transmitting relay protection services. Some commendations such as technology choice and routing configuration are given. Finally, how to choose linear protection switch is studied from the reliability standpoint.

Sensorless Control of Late-Stage Offshore DFIG-WT with FSTP Converters by Using EKF to Ride through Hybrid Faults

A hybrid fault scenario in a late-stage offshore doubly-fed induction generator (DFIG)-based wind turbine (DFIG-WT) with converter open-circuit fault and position sensor failure is investigated in this paper. An extended Kalman filter (EKF)-based sensorless control strategy is utilized to eliminate the encoder. Based on the detailed analysis of the seventh-order dynamic state space model of DFIG, along with the input voltage signals and measured current signals, the EKF algorithm for DFIG is designed to estimate the rotor speed and position. In addition, the bridge arm open circuit in the back-to-back (BTB) power converter of DFIG is taken as a commonly-encountered fault due to the fragility of semiconductor switches. Four-switch three-phase (FSTP) topology-based fault-tolerant converters are employed for post-fault operation by considering the minimization of switching losses and reducing the circuit complexity. Moreover, a simplified space vector pulse width modulation (SVPWM) technique is proposed to reduce the computational burden, and a voltage balancing scheme is put forward to increase the DC-bus voltage utilization rate. Simulation studies are carried out in MATLAB/Simulink2017a (MathWorks, Natick, MA, USA) to demonstrate the validity of the proposed hybrid fault-tolerant strategy for DFIG-WT, with the wind speed fluctuation, measurement noises and grid voltage sag taken into consideration.

Three-dimensional fault geometries and interactions within experimental models of multiphase extension

ABSTRACT We use experimental (analog) models to examine the three-dimensional (3-D) fault geometries and interactions that develop during two phases of noncoaxial extension. In the models, a homogeneous layer of wet clay undergoes two phases of extension whose directions differ by 45°. The resulting fault pattern varies significantly with depth. At shallow levels, second-phase normal faults accommodate most second-phase extension. At depth, both second-phase normal faults and reactivated, first-phase faults with oblique slip accommodate most second-phase extension. A variety of interactions occurs between first-phase and second-phase faults. One interaction involves the upward propagation of second-phase faults from tips of reactivated, blind, first-phase faults. These hybrid faults have deep segments that strike subperpendicular to the first-phase extension direction and shallow segments whose strike varies with depth, becoming increasingly subperpendicular to the second-phase extension direction at shallow levels. A second interaction involves the nucleation of second-phase normal faults on the surfaces of reactivated, first-phase faults. These splay faults propagate upward and laterally from their nucleation sites into the hanging walls of the first-phase faults. As they propagate, they commonly encounter and link with different first-phase faults. The resulting composite faults have zigzag geometries in both map and cross-sectional views. A third interaction involves either the termination of second-phase antithetic normal faults against or near first-phase faults or the offset of first-phase faults by second-phase antithetic normal faults. The 3-D fault patterns and interactions within our models closely resemble those within the Taranaki basin of offshore New Zealand and Milne Point on Alaska’s North Slope.

A cascade intelligent fault diagnostic technique for nuclear power plants

ABSTRACT Safe operation of nuclear power plant is one of the most important tasks in nuclear power development. This justifies the variety of methods that have been proposed to support the operators in the task of plant condition monitoring, fault detection, and diagnosis. A number of hybrid fault detection and diagnosis methods have also been proposed, with their attendant weaknesses. This work proposes the hybrid of principal component analysis (PCA), signed directed graph (SDG), and Elman Neural Network (ENN) for fault detection, fault isolation, and severity estimation, respectively. The proposed hybrid method is verified with the data derived from Personal Computer Transient Analyzer (PCTRAN) simulation. The verification result shows that the PCA-based fault detection methodology realized timely detection of anomaly in the simulated nuclear power plants system, the SDG-based fault recognition method was able to isolate the system abnormality and identify the root causes, and the ENN-based fault severity estimation method presents the failure fraction of fault, representing the severity. With this integrated hybrid method, more fault information is provided for the operators, which serves as a good foundation for further decision-making and interventions.

On-line hybrid fault diagnosis method for high voltage circuit breaker

On-line hybrid fault diagnosis method for high voltage circuit breaker

Correlated EEMD and Effective Feature Extraction for Both Periodic and Irregular Faults Diagnosis in Rotating Machinery

Intelligent fault diagnosis of complex machinery is crucial for industries to reduce the maintenance cost and to improve fault prediction performance. Acoustic signal is an ideal source for diagnosis because of its inherent characteristics in terms of being non-directional and insensitive to structural resonances. However, there are also two main drawbacks of acoustic signal, one of which is the low signal to noise ratio (SNR) caused by its high sensitivity and the other one is the low computational efficiency caused by the huge data size. These would decrease the performance of the fault diagnosis system. Therefore, it is significant to develop a proper feature extraction method to improve computational efficiency and performance in both periodic and irregular fault diagnosis. To enhance SNR of the acquired acoustic signal, the correlation coefficient (CC) method is employed to eliminate the redundant intrinsic mode functions (IMF), which comes from the decomposition procedure of pre-processing known as ensemble empirical mode decomposition (EEMD), because the higher the correlated coefficient of an IMF is, the more significant fault signatures it would contain, and the redundant IMF would compromise both the SNR and the computational cost performance. Singular value decomposition (SVD) and sample Entropy (SampEn) are subsequently used to extract the fault feature, by exploiting their sensitivities to irregular and periodic fault signals, respectively. In addition, the proposed feature extraction method using sparse Bayesian based pairwise coupled extreme learning machine (PC-SBELM) outperforms the existing pairwise-coupling probabilistic neural network (PC-PNN) and pairwise-coupling relevance vector machine (PC-RVM) by 1.8% and 2%, respectively, to achieve an accuracy of 93.9%. The experiments conducted on the periodic and irregular faults in the gears and bearings have demonstrated that the proposed hybrid fault diagnosis system is effective.

Hybrid fault diagnosis algorithms for transmission lines

This paper presents a fault location method for transmission lines with the application of a mono-objective optimization technique using the ellipsoid algorithm with voltage and current data of both terminals. The fault detection is performed using the stationary wavelet transform and Parseval’s theorem, and the classification was conducted with the application of artificial neural networks. The minimization of the objective function defined for the short and long transmission line models provides not only the distance to the fault point, but also the fault resistance value. Many short-circuit situations simulated in the alternative transients program are tested with variations in the fault type, adjustments in the distance to the fault point, and fault resistance. The results of the algorithm applied to real faults in the electrical system of Brazil are also presented and compared to the values obtained with a classic fault location algorithm. According to the observations, the adopted formulation achieves the pre-established objectives, with mean errors of fault location for the real cases lower than 2% of the line length.

A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis

Fault diagnosis of mechanical components has been attracting increasing attention. Researches have been carried out to reduce unnecessary breakdowns of machinery. Signal processing approaches are the most commonly used techniques for fault diagnosis tasks. Ontology and semantic web technology have great potential in knowledge representing, organizing and utilizing. In this paper, a hybrid fault diagnosis method for mechanical components is proposed based on ontology and signal analysis (HOS-MCFD). The method is a systematic approach covering the whole process of fault diagnosis: feature extraction from raw data, fault phenomenon identification using continuous mixture Gaussian hidden Markov model and fault knowledge modeling and reasoning using ontology and semantic web technology. A semantic mapping approach is presented to relate signal analysis results to ontology elements. The hybrid method integrates the advantages of signal analysis and ontology. It can be applied to deal with fault diagnosis more accurately, systematically and intelligently. This method is assessed with vibration data of rolling bearings. The experimental results prove the proposed method effective.

A Novel DC Fault Ride-Through Scheme for MTDC Networks Connecting Large-Scale Wind Parks

This paper proposes a novel fault ride-through protection scheme for multiterminal dc (MTDC) networks connecting large-scale offshore wind parks. The proposed scheme introduces an MTDC network with a hybrid fault current limiter (FCL), which consists of a high-temperature superconducting FCL (HTS-FCL) and series braking resistor (SBR) integrated with low-loss mechanical dc circuit breaker (DCCB). The aim of HTS-FCL is to provide fast quenching, limit the dc fault current within the interrupting capacity of the DCCB, and reduce the dc fault current to certain levels such that converters can sustain prolonged periods of operation without blocking. Also, SBR allows automatic reclosing of DCCBs following a temporary fault, thereby enhancing the network security and reliability. Mechanical DCCBs with the proposed hybrid limiter are installed at the terminating points of each dc cable. The proposed protection scheme is based on differential current approaches with fault clearing time of few milliseconds, which gives enhanced network selectivity and reliability. A four-terminal meshed MTDC network with the proposed hybrid FCL is modeled in PSCAD/EMTDC. The obtained simulation results confirm successful dc fault isolation and network recovery for different dc fault scenarios.

English

English

Analysis and Verification of Differential Protect Operation of AC / DC Hybrid Power

In this paper, the characteristics of AC / DC hybrid faults are analyzed, and the influence of AC / DC hybrid power system on AC current differential protection is analyzed and discussed. When the DC commutation fails, the power frequency current injected into the AC power grid is ahead of its voltage. At this time, the phase angle between the power frequency current and the voltage will change, which will cause the action of the AC line fault. If the current decreases, the brake current will increase, as a result, differential protection refused to action. The PSCAD is used as the platform to simulate the differential protection operation. And the parameter identification based transmission line protection with shunt reactors is proposed. Simulation results show the correctness of the theoretical analysis.

A hybrid approach for improving unsupervised fault detection for robotic systems

The use of robots in our daily lives is increasing. As we rely more on robots, thus it becomes more important for us that the robots will continue on with their mission successfully. Unfortunately, these sophisticated, and sometimes very expensive, machines are susceptible to different kinds of faults. It becomes important to apply a Fault Detection (FD) mechanism which is suitable for the domain of robots. Two important requirements of such a mechanism are: high accuracy and low computational-load during operation (online). Supervised learning can potentially produce very accurate FD models, and if the learning takes place offline then the online computational-load can be reduced. Yet, the domain of robots is characterized with the absence of labeled data (e.g., “faulty”, “normal”) required by supervised approaches, and consequently, unsupervised approaches are being used. In this paper we propose a hybrid approach - an unsupervised approach can label a data set, with a low degree of inaccuracy, and then the labeled data set is used offline by a supervised approach to produce an online FD model. Now, we are faced with a choice – should we use the unsupervised or the hybrid fault detector? Seemingly, there is no way to validate the choice due to the absence of (a priori) labeled data. In this paper we give an insight to why, and a tool to predict when, the hybrid approach is more accurate. In particular, the main impacts of our work are (1) we theoretically analyze the conditions under which the hybrid approach is expected to be more accurate. (2) Our theoretical findings are backed with empirical analysis. We use data sets of three different robotic domains: a high fidelity flight simulator, a laboratory robot, and a commercial Unmanned Arial Vehicle (UAV). (3) We analyze how different unsupervised FD approaches are improved by the hybrid technique and (4) how well this improvement fits our prediction tool. The significance of the hybrid approach and the prediction tool is the potential benefit to expert and intelligent systems in which labeled data is absent or expensive to create.